Category: Data

Navigating Regulatory Compliance With Data Governance Strategies

Posted on November 5, 2024 by Kopius
Navigating Regulatory Compliance With Data Governance Strategies

Nowadays, data is everything. It fuels your decisions, drives business growth, and improves customer relationships. Data governance and regulatory compliance are heavily intertwined aspects of managing and securing your organization’s data. A strong data governance policy sets the standard for how you collect, store, process, access, and use data throughout its life cycle.

Without a proper governance strategy, it becomes increasingly difficult to maintain compliance when handling and processing sensitive data, such as financial, personal, or health records. Failure to comply with these regulations can result in significant financial and reputational losses for your business. Understanding data governance and compliance is key to implementing robust policies and practices.

Understanding Data Governance and Regulatory Compliance

The terms “data governance” and “regulatory compliance” are often used interchangeably, but they differ. Before you can implement effective data governance, it’s important to know the definitions, objectives, and importance of each term.

What Is Data Governance?

Data governance refers to the processes, guidelines, and rules that outline how an organization manages its resources, including data. These guidelines exist to make sure data is accessible, accurate, consistent, and secure. The key components of data governance typically include:

  • Ensuring regulatory compliance.
  • Maintaining data quality.
  • Outlining roles and responsibilities.
  • Monitoring the use of resources.
  • Facilitating data integration and interoperability.
  • Scaling based on demand.
  • Securing sensitive data against unauthorized access and breaches.
  • Improving cost-effectiveness.

Data governance is essential for protecting and maintaining crucial data and confirming that it aligns with business objectives. Data governance also plays a pivotal role in helping organizations meet regulatory compliance requirements for data management, privacy, and security. As regulations continue to evolve, so does the need to meet them. Data governance supports organizations in this regard by establishing and enforcing policies for responsible data use.

What Is Regulatory Compliance?

Regulatory compliance refers to the regulations, laws, and standards that an organization must meet within its industry. Compliance standards vary by state and industry, but their primary purpose is to ensure organizations securely handle personal and sensitive data. Data protection and privacy laws are essential aspects of regulatory compliance. For instance, health care organizations are required to meet industry-specific regulations like the Health Insurance Portability and Accountability Act to protect patient privacy. 

The Fair Credit Reporting Act outlines protection measures for sensitive personal information regarding consumer credit report records. The Family Educational Rights and Privacy Act is another example of a data governance policy that protects access to students’ educational data. Compliance is essential for organizations because it enables them to build trust with their customers, improve their reputation, and avoid legal risks.

Data Governance vs. Compliance

Data governance refers to how organizations use, manage, and control their data internally, while regulatory compliance is about how they adhere to external regulations. Data governance guides decision-makers to be proactive, while compliance is often reactive.

Can an organization be compliant without data governance? The answer is yes. It’s possible for your organization to have data governance standards in place without being fully compliant if your policies do not meet industry or external regulations. Alternatively, your organization may be compliant by meeting the minimum regulatory standards without establishing an effective data governance framework.

While one is possible without the other, both data governance and compliance are crucial for a cohesive data management strategy. Governance builds the framework within which compliance operates to keep your business efficient. These two closely related aspects help your organization achieve business objectives, identify opportunities for strategic data utilization, and improve legal integrity.

The Role of Data Governance in Ensuring Compliance

The Role of Data Governance in Ensuring Compliance

Now that you know the distinctions between data governance and compliance, it’s time to examine the integral role of data governance in adhering to policy, regulatory, and legal requirements.

Data governance significantly supports compliance efforts by ensuring the enforcement of data procedures and their alignment with regulatory requirements. Additionally, having strong data governance standards in place can help organizations achieve data compliance by:

  • Simplifying the interpretation of compliance laws and regulations.
  • Proactively addressing compliance needs.
  • Establishing data stewards to create data governance consistency.
  • Identifying data governance risks and areas of noncompliance.
  • Reducing the complexity required to adhere to regulatory standards.
  • Maintaining well-documented data processes to facilitate streamlined audits.
  • Continuously monitoring data quality management practices.
  • Establishing the traceability of data processes.

Similarly, poor data quality can lead to compliance issues, which can result in fines, penalties, and legal complications. As a result, data governance procedures are necessary to verify that data is ethically and securely aligned with industry regulations. Safeguarding your organizational data’s integrity with data governance policies can also enhance your ability to demonstrate compliance with external standards — a benefit to all stakeholders.

Challenges in Meeting Regulatory Compliance

What stands in the way of compliance? In the digital age, organizations in all industries face obstacles due to ever-changing regulatory landscapes. Here are some of the most common challenges in working toward compliance:

1. Evolving Regulations

Laws and regulations constantly change, making it challenging for organizations to keep up. As lawmakers develop new policies for protecting consumer data, organizations must frequently update to meet diverse compliance demands. Following the continuous growth of data governance regulations can put additional strain on compliance teams as they strive to safeguard data integrity. 

2. Gaps and Overlaps

Alongside rapidly evolving laws is the challenge of balancing internal policies with external regulations. As new regulations arise to meet data privacy and security concerns, organizations must address existing gaps and overlaps to create consistency.

3. Monitoring Needs

Tracking data flow and usage is a key part of data governance. However, organizations that fail to properly monitor and audit data practices may struggle to adhere to compliance regulations. Some organizations may lack the staff or resources needed for continuous monitoring.

4. Vast Amounts of Data

It’s no secret that businesses are collecting, using, and storing more data than ever. Maintaining compliance becomes even more complex as more and more data flows in. Without proper data storage, managing these large volumes of data can be difficult.

5. Vulnerability of Legacy Systems

Relying on outdated technology to maintain compliance is nearly impossible due to the lack of security upgrades and other modern compliance essentials. Organizations that still use legacy systems will find it increasingly complex to meet today’s strict regulations.

6. Risk of Data Breaches

Data breaches increased by 20% in 2023, along with significant spikes in ransomware attacks and theft of personal data. However, as companies put more and more of their data into computerized systems, the risk of data breaches grows without proper configuration and security measures.

7. Lack of Expertise

As a result of increased data security concerns, there is a growing need for skilled personnel who can navigate the legal aspects of data compliance. Staff training is also required to keep employees up to date on changing regulations to ensure ongoing compliance.

8. Cost Concerns

Maintaining compliance can be costly, especially when factoring in hiring skilled personnel, training internal compliance staff, and upgrading technology. Maintaining ongoing compliance in an evolving landscape of regulations can lead to increased operating costs as continuous audits and assessments are needed.

Benefits of Implementing Data Governance Strategies for Compliance

Implementing a robust data governance framework is essential to creating a culture of data compliance. Here are some advantages you can expect with data governance policies:

1. Minimized Legal Risks

Minimized Legal Risks

Data governance procedures can help your organization identify and manage potential compliance risks. Adhering to data regulations can protect your organization from legal consequences, such as fines and penalties. Without an organized framework for every team member to follow, it can be challenging to know whether you’re meeting regulatory requirements.

Data governance allows you to meet standards that dictate how data should be managed and protected. Similarly, data governance guidelines can simplify compliance reporting and audits, which can also reduce the risk of fines and legal issues.

2. Enhanced Security

Robust data security measures can benefit businesses across all industries. Establishing data governance strategies can protect sensitive data from breaches and cyber threats. Data governance also prevents the unauthorized use or misuse of data, which is particularly important in the health care and finance industries. In today’s landscape of increasing cybersecurity hacks and threats, data governance allows for a proactive approach to organizational security.

3. Improved Decision-Making

Data governance is a powerful tool that decision-makers across your organization can utilize to drive your business forward. Data governance strategies can help your teams make well-informed decisions by gathering key insights on how data is being accessed, handled, and secured.

4. Increased Data Accessibility and Quality

Effective data governance strategies help your teams properly manage your data, meaning it will be organized and cataloged effectively. As a result, users can find the data they need when they need it and expect it to be accurate, up to date, and complete. Additionally, you and your teams won’t have to rely on poor-quality data to make important decisions.

Adhering to data regulations can lead to minimal errors and allow employees to quickly and easily access the information they need to do their jobs. Organizations that have multiple business partners or units can feel confident in data sharing, knowing their data is consistent and well-controlled.

5. Improved Compliance

Though the existence of data governance strategies does not make an organization inherently more compliant, it creates an environment that prioritizes compliance. Establishing data governance strategies demonstrates that organizations take data privacy seriously and will continue to update policies as needed to align with relevant regulations. Companies that use data governance procedures may also be more likely to meet regulations that govern the use and protection of data because they’re well-informed of the potential risks of noncompliance.

6. Strengthened Reputation

Transparency is key when it comes to building and maintaining customer relationships. Organizations that adhere to data regulations and strive to keep consumer data safe may enhance their reputation among stakeholders, customers, partners, and employees. They are more likely to foster trust among clients and consumers who want to know that their data is being handled responsibly.

7. Facilitate Room for Innovation

When it comes to data, organizations have to think three steps ahead. Data governance strategies ensure your data is well-managed and maintained, creating an environment conducive to business innovation. Employees can access high-quality data faster, enabling more time for innovative solutions and new ideas. What’s more, a robust data governance framework signifies to stakeholders that future innovation efforts are built on secure, dependable data governance practices.

8. Identify New Revenue Opportunities

Taking a proactive approach to data security with data governance allows you to identify potential risks and gaps in your current workflow. However, it can also help you identify opportunities for revenue growth.

Effective data governance means you can more easily view customer trends and market insights that enable you to develop new products and services to meet current demands. Data governance procedures turn your data into a strategic asset, allowing you to take advantage of opportunities to improve sales and customer satisfaction.

Implementing a Data Governance Framework for Compliance

Implementing a Data Governance Framework for Compliance

Every organization has unique needs for meeting compliance regulations by state or industry. However, there are some practical steps you can follow for effective data governance implementation:

  • Conduct an assessment: The first step is to identify your organization’s data needs. What are the current noncompliance risks you’re facing? Identify and catalog all data assets and determine how they should be handled moving forward. 
  • Choose a solution: If your organization has vast amounts of data or significant security issues, it’s time to choose a data storage solution or data security compliance service to help you address your data needs.
  • Establish a team: Create a data governance team or committee within your organization to help facilitate cross-department collaboration and oversee continuous auditing. This cross-functional team should include compliance, business, legal, and IT team members who routinely develop, improve, and enforce data governance policies.
  • Train and educate: Once you’ve developed and documented your data governance policies, it’s critical to make sure all employees understand their role in maintaining data integrity. Provide training on the importance of data governance and compliance to raise awareness of all new and existing policies.
  • Continuous auditing and improvement: As with any company-wide adjustments, it’s important to regularly review and update your data governance framework to align with current regulations and arising cybersecurity risks.

JumpStart Your Data Governance and Compliance

Data governance is nonnegotiable, especially when it comes to regulatory compliance. However, aligning data governance with compliance requires careful balance. At Kopius, we offer data security compliance services to help businesses meet their industry standards.

Our experts will manage your data collection and establish an infrastructure that makes compliance fulfillment more achievable. As a reliable data security compliance company, our top goal is to mitigate data security breaches without restricting your business growth. Contact us today to see how we can help you meet your data security obligations and learn about our JumpStart program.

JumpStart Your Data Governance and Compliance

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What Is a Modern Data Platform?

Posted on November 5, 2024 by Kopius
What Is a Modern Data Platform

Your business is constantly dealing with streams of data. With so much data needing processing, collecting, and organizing, modern companies need a way to manage it effectively.

Enter modern data platforms (MDPs). These platforms are reliable solutions for managing and leveraging all your data. MDPs make optimizing your operation easier than ever. Understanding data platform capabilities can help you unlock your data’s full potential.

What Is a Data Platform?

A data platform is a central space that holds and processes your data. A unified data platform takes all your data from each source and collects, manages, stores, and analyzes it. Traditionally, data platforms had limited data-handling abilities. They often had data silos — data stores that were disconnected from the rest of the data. Modern data platforms, however, are more advanced and convenient.

An MDP is a data platform designed to handle the data demands of the modern day. These data platforms are built to handle data from multiple sources. They can easily scale with your needs, processing data in real time and giving you the tools to analyze it effectively. Big data platforms are a version of MDPs that work with data on a vast scale. With a quality MDP, you can make more accurate decisions, adapt quickly to market changes, and maintain productivity.

Modern Data Platform Features

An MDP is a more advanced enterprise data platform (EDP) version. EDPs manage all your data in a central hub. At the same time, MDPs take this feature and add to it with data analysis, decision-making, and even machine learning (ML) or artificial intelligence (AI). You can break MDPs down into several key components that work together to maximize your data use:

  1. Data ingestion: This is the first step. Your MDP collects and imports data from databases, sensors, application programming interfaces, and more. Data flows into and through the MDP, collecting in a central space.
  2. Data storage: Once ingested, the MDP stores your data. Data warehouses and cloud-based data storage spaces can hold significant amounts of data. Storage is set up for easy organization and retrieval.
  3. Data processing: After ingestion and storage, data needs processing. Processing takes the data and turns it into an analyzable format. Data processing includes batch and real-time processing, allowing you to instantly receive information on your data. 
  4. Analytics: Next comes analytics. MDPs take your data and use various tools to find patterns and insights. These analytics give you an unmatched understanding of your data, letting you make more strategic decisions.
  5. Security and compliance: MDPs come with strong security measures to prevent data from becoming vulnerable to attacks and other incidents. Security is essential for protecting data and maintaining data regulation compliance.
  6. Orchestration: Orchestration involves getting everything where it needs to be when it needs to be there. It oversees two processes — moving data between components and automating workflows.

Modern Data Platform Applications Across Industries

Modern data platforms allow industries to manage their data more effectively. With the right MDP, your company can easily manage data and derive better insights. Here are some data platform examples in different industries:

  • Manufacturing: Predictive maintenance data lets manufacturing companies know when to send equipment for upkeep. Additionally, MDPs can improve quality control efforts by checking data.
  • Retail: The retail industry uses MDPs to analyze customer behavior and personalize shopping experiences.
  • Health care: MDPs in health care settings streamline operations and improve the patient experience. Health data needs secure protection and efficient management to meet compliance and improve care standards.
  • Financial: The financial sector relies on MDPs to detect fraud, personalize products, and assist with risk management.
Benefits of Modern Data Platforms

Benefits of Modern Data Platforms

If you’re looking to overhaul your business’s approach to data, MDPs can help. Consolidating data and improving its management has many benefits for your operation, including:

  • Improved decision-making: Better data processing and real-time analytics boost your decision-making capabilities. Teams can use accurate, up-to-date data to respond quickly and effectively to market changes, customer needs, and other challenges.
  • Enhanced performance: MDPs are designed to handle massive amounts of data while adjusting to your needs. MDPs scale with your data, efficiently managing everything without slowing down. 
  • Cost-efficiency: Traditional manual data handling is expensive to scale and maintain. MDPs let you only pay for what you use, ensuring you work within your budget and needs. 
  • Future-proofing: As technology changes and data needs grow, MDPs can evolve with them. Incorporate new tools, data sources, and technology into your MDP without overhauling your central infrastructure.

Potential Challenges in Implementing Data Platforms

While data platforms are excellent tools for handling data, getting the infrastructure in place can be challenging. Investing in the right partner is essential for ensuring you have the support you need for success. Some data platform challenges you might face are:

  • Integration complexities: Integrating your diverse data sources and systems can be challenging. Legacy systems often struggle to work with modern platforms. It takes a quality platform and expert support to make your data flow seamless.
  • Data quality and consistency: Data quality is key for strategic decision-making. However, integrating data from different sources can lead to duplicates, errors, and incomplete data. To ensure accurate data, you need processes for cleaning, standardizing, and validating data.
  • Security concerns: More centralized data can also mean more cyberattack threats. You need an MDP with strong security measures to protect your data from cyberattack threats.
  • Skill gaps and resource allocation: MDPs can require specialized skill sets in data analytics and engineering. Finding the talent to manage your MDPs can strain your current budget and resources.

The Future of Modern Data Platforms

As advanced as current MDPs are, they’re only going to become more powerful. AI and ML are changing how we approach data. Automating data processing allows these strategies to deliver faster, more accurate insights.

AI-driven platforms can spot patterns, predict trends, and make decisions independently. Using AI can also free up your human talent for more complex tasks. ML models improve with every piece of data they learn from. They can develop advanced predictive capabilities the longer you use them.

JumpStart Your Data Platform Journey

Your data is one of your most valuable assets. Fully harness your data and drive innovation with help from Kopius. We specialize in helping businesses leverage advanced data analytics, machine learning, data governance, and more to make smarter, data-driven decisions.

Whatever your challenges, our experts are here to help. We provide comprehensive data solutions tailored to your unique needs. With Kopius, you can create insightful dashboards, improve data security, and more.

Reach out to Kopius today and see how we can JumpStart your long-term success!

JumpStart Your Data Platform Journey

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Data Lake vs. Data Warehouse vs. Database

Posted on November 5, 2024 by Kopius
Data Lake vs Data Warehouse vs Database

From retail to aerospace industries, managing your data effectively and securely is critical to your overall business objectives. Data storage comes in many shapes and sizes, especially with the advancements in modern digital technology. To properly store large amounts of data, you need the right location. While a database on a computer might be enough to make data accessible for a small business, a large enterprise likely requires a data warehouse or data lake.

How do you find the ideal solution? The first step is to consider the type of data you need to store and how you will use it. No data strategy is the same, so it’s important to understand how data solutions can be tailored to meet your needs.

What Is a Database?

A database is a type of electronic storage location for data. Businesses use databases to access, manage, update, and secure information. Most commonly, these records or files hold financial, product, transaction, or customer information. Databases can also contain videos, images, numbers, and words. 

The term “database” can sometimes refer to “database management system” (DBMS), which enables users to modify, organize, and retrieve their data easily. However, a DBMS can also be another application or the database system itself.

There are many different types of databases. For example, you may consider a smartphone a database because it collects and organizes information, photos, and files. Businesses can use databases on an organizational-wide level to make informed business decisions that help them grow revenue and improve customer service. 

Some key characteristics of a database include:

  • Storing structured or semi-structured data
  • Security features to prevent unauthorized use
  • Search capabilities
  • Backup and restore capabilities
  • Efficient storage and retrieval of data
  • Support for query languages

Some common uses for databases include:

  • Streamlining and improving business processes
  • Simplifying data management
  • Fraud detection
  • Keeping track of customers
  • Storing personal data
  • Securing personal health information
  • Gaming and entertainment
  • Auditing data entry
  • Creating reports for financial data
  • Document management
  • Analyzing datasets
  • Customer relationship management
  • Online store inventory

What Is a Data Warehouse?

A data warehouse is a larger storage location than a database, suitable for mid- and large-size businesses. Companies that accumulate large amounts of data may require a data warehouse to keep everything structured. Data warehouses can store information and optimize it for analytics, enabling users to look for insights from one or more systems. Typically, businesses will use data warehouses to look for trends across the data to better understand consumer behavior and relationships.

These specialized systems consolidate large volumes of current and historical data from different sources to optimize other key processes like reporting and retrieval. Data warehouses also enable businesses to share content and data across teams and departments to improve efficiency and power data-driven decisions.

The four main characteristics of a data warehouse include:

  1. Subject-oriented: Data warehouses allow users to choose a single subject, such as sales, to exclude unwanted information from analysis and decision-making.
  2. Time-variant: A key component of a data warehouse is the capability to hold large volumes of data from all databases in an extensive time horizon. Users can perform analysis by looking at changes over a period of time.
  3. Integrated: Users can view data from various sources under one integrated platform. Data warehouses extract and transform the data from disparate sources to maintain consistency.
  4. Non-volatile: Data warehouses stabilize data and protect it from momentary changes. Important data cannot be altered, changed or erased.

A data warehouse can also have the following elements: 

  • Analysis and reporting capabilities
  • Relational database for storing and managing data
  • Extraction, loading, and transformation solutions for data analysis
  • Client analysis tools

Common use cases for data warehouses include:

  • Financial reporting and analysis
  • Marketing and sales campaign insights
  • Merging data from legacy systems
  • Team performance and feedback evaluations
  • Customer behavior analysis
  • Spending data report generation
  • Analyzing large stream data

What Is a Data Lake?

The next step up in data storage is a data lake. A data lake is the largest of the three repositories and acts as a centralized storage system for organizations that need to store vast amounts of raw data in their native format, including:

  • Structured
  • Semi-structured
  • Unstructured

As the name suggests, a data lake is a large virtual “pond” where data is stored in its natural state until it’s ready to be analyzed. Data lakes are also unique because they are flexible — they can store data in many different formats and types, enabling businesses to utilize them for real-time data processing, machine learning, and big data analytics.

Data lakes solve a common organizational challenge by providing a solution to managing and deriving insights from large, diverse datasets. They allow businesses to overcome the obstacles of traditional data storage and efficiently and cost-effectively analyze data from many sources. Data scientists and engineers can also use data lakes to hold a large amount of raw data until they need it in the future.

Several key characteristics of a data lake include:

  • Scalability as data volume grows
  • Data traceability
  • Comprehensive data management capabilities
  • Compatibility with diverse computing engines

Some use cases for data lakes include:

  • Ensuring data integrity and continuity
  • Backup solutions
  • Data exploration and research
  • Centralized data repository
  • Archiving operational data
  • Storing vast amounts of big data
  • Maintaining historical records
  • Internet of Things data storage and analysis
  • Real-time reporting
  • Providing the data needed for machine learning 

Core Differences Between Databases, Data Warehouses, and Data Lakes

The most noticeable difference between these three types of data solutions is their applications. For example, you would have much more storage for raw data in a data lake vs. a data warehouse.

Alternatively, databases are typically used for relatively small datasets, while data warehouses and data lakes are more suited to large volumes of raw data across a wide range of sources. However, other factors contribute to the distinction among these data storage options.

Structure and Schema

1. Structure and Schema

Databases work best with structured data from a single source because they have scaling limitations. They have relatively rigid, predefined schemas but can provide a bit of flexibility depending on the database type. Data warehouses can work with structured or semi-structured data from multiple sources and require a predefined or fixed schema when data flows in. Data lakes, however, can store structured, semi-structured, or unstructured data and do not require a schema definition for ingest.

2. Data Types and Formats

Databases are ideal for transactional data and applications that require frequent read-and-write operations. Data warehouses are suitable for read-heavy workloads, analytics, and reporting. Data lakes can store large amounts of raw, natural data in many formats. If comparing a data lake vs. a database, you’d have much more flexibility for different types of data in a data lake.

3. Performance and Scalability

Scalability is limited with databases, making them more suitable for small to medium-sized applications and moderate data volumes. It is challenging for databases to adapt to new types or formats of data without significant reengineering.

Data warehouses can provide a high level of scalability and optimized performance for large amounts of structured data. While they can accommodate changes in data structures and sources, it requires intentional planning. Data lakes offer the most flexibility and scalability for organizations, allowing them to store data in various formats and structures. Data lakes can also accommodate new data sources and analytical needs.

4. Cost Considerations

The cost of data storage plays an important role in deciding which solution is best for your needs. Databases offer cost-effectiveness for most small- to medium-sized applications and can scale up and down to meet changing needs.

Data warehouses provide more scalability and improved performance, but they often require significant investment in software and hardware. Data warehouses also tend to incur higher storage costs than databases. For this reason, when comparing a data lake vs. a data warehouse solution, you may get more for your investment in a data lake. Data lakes are the most cost-effective option for organizations looking to store vast amounts of raw data.

Advantages and Disadvantages of Each Solution

To further understand which data storage solution is right for your business, let’s take a look at the pros and cons of databases, data warehouses, and data lakes.

Advantages and Disadvantages of Each Solution

Databases

Databases can improve operational efficiency and data management processes for many small and mid-size businesses. Some key advantages of using databases include:

  • Removing duplicate or redundant data
  • Providing an integrated view of business operations
  • Creating centralized data to help streamline employee accessibility
  • Improving data-sharing capabilities 
  • Fostering better decision-making
  • Controlling who can access, add, and delete data

Using databases can also come with several drawbacks, such as:

  • Potential for more vulnerabilities
  • More significant disruptions or permanent data loss if one component fails
  • May require specialized skills to manage
  • Can lead to increased costs for software, hardware, and large memory storage needs

Data Warehouses

Data warehousing can help your organization make strategic business decisions by drawing valuable insights. Advantages of a data warehouse include:

  • High data throughput
  • Effective data analysis
  • Consolidated data in a single repository
  • Enhanced end-user access 
  • Data quality consistency
  • A sanitization process to remove poor-quality data from the repository
  • Storage of heterogeneous data
  • Additional functions such as coding, descriptions, and flagging
  • High-quality query performance
  • Data restructuring capabilities
  • Added value to operational business applications
  • Merging data to form a common data model

When working with a data warehouse, you may experience some disadvantages, including:

  • Reduced flexibility 
  • The potential for lost data
  • Data insecurity and copyright issues
  • Hidden maintenance problems
  • Increased number of reports
  • Increased use of resources

Data Lakes

Data lakes are capable of handling large amounts of raw data, which means they can be an attractive option for organizations that require scalability and advanced analytics. Other key advantages of data lakes include:

  • An expansive storage space that grows to your needs
  • Ability to handle enormous volumes of data
  • Easier collection and indefinite storage of all types of data
  • Flexibility for big data and machine learning applications
  • Capable of accommodating unstructured, semi-structured, or structured data
  • Ability to adapt and accept new forms of data from various sources without formatting
  • Eliminate the need for expensive on-site hardware
  • Reduced maintenance costs
  • Capability to integrate with powerful analytical tools

Some potential drawbacks of data lakes may include:

  • Complex management processes
  • Security concerns due to storing sensitive data
  • Potential for disorganization
  • More vulnerable to becoming data silos

Choosing the Right Data Storage Solution

Now that you know the difference between a data lake, a data warehouse, and a database, it’s time to find a solution that fits your organization’s needs. Here’s what to consider:

Choosing the Right Data Storage Solution
  • Your data requirements: Not all data storage solutions can support all types of data. For example, if your data is structured or semi-structured, you may prefer a data warehouse. However, a data lake supports all types of data, including structured, semi-structured, and unstructured.
  • Current storage setup: How do you store your organization’s data? Depending on where and how you store it, you may or may not have to move data to a new storage solution. For instance, a data lake may not require you to move any data if it’s already accessible, which means your organization can skip the process.
  • Industry-specific considerations: You’ll need to consider the primary users of the data. For example, will a data scientist or business analyst need access to the data? Do you need it for business insights and reporting? Understanding your unique needs can help you narrow down which storage solution is best.
  • Primary purpose: In addition to your industry-specific needs, consider the main function of your data storage solution. For instance, databases are often used for transactions and sales, while data warehouses are more ideal for in-depth analytics of historical trends and reporting. Because databases and data warehouses serve different purposes, some organizations choose to use both to address separate needs. Data lakes, alternatively, are suitable for large-scale analytics and big data applications. If your organization hosts large amounts of varied, unfiltered data, a data lake may be the best option.

Future Trends and Considerations

Modern data storage continues to advance and evolve. Data lake solutions, in particular, have become vital to many organizations for their unparalleled flexibility in data management. Looking to the future, organizations can expect the integration of data lakes to become more advanced with the help of digital technologies like artificial intelligence and machine learning. These emerging trends suggest promising enhancements in threat detection, data management and security, and predictive analytics. 

Adopting a data lake for your business can help instill a forward-thinking approach to data management and storage. Addressing common issues like poor scalability and the constraints of a fixed schema can help your organization shift to a more convenient way to manage diverse data types.

JumpStart Your Data Journey With Kopius

JumpStart Your Data Journey With Kopius

Data storage and organization are unique to every business. While a database or data warehouse may suit your needs for a while, there’s no telling what your needs will be in the future.

When you partner with Kopius, you benefit from data solutions that drive strategic outcomes from one accessible location. Gone are the days of struggling to keep up with the latest transformations to power growth. Today, setting up a data lake is easier than you think.

With data lake capabilities from Kopius, you can make decisions faster, yield actionable reports and store data in all types and formats. Our turnkey solutions are designed to meet your needs, whether you require robust access control or oversight and support for your data lake.

Learn more about our JumpStart program, where we’ll create a tailored approach for your data needs. You can also contact us to schedule a consultation with our data lake developers.

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To Get Started with Generative AI, You Need a Solid Data Foundation. Here’s What that Means.

Posted on September 16, 2024 by Matt Wright
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Generative AI (GenAI) adoption is surging. Sixty five percent of respondents to the McKinsey Global Survey on the State of AI in Early 2024 indicate their businesses are using generative AI in at least one functional area. Yet, more than half of individual GenAI adopters use unapproved tools at work, according to a Salesforce survey. Clearly, businesses want and need to implement the technology to meet their business goals, but in the absence of a clear path forward, employees are finding ways to adopt it anyway, perhaps putting sensitive data at risk. Organizations need to move fast, put a strategy in place, and implement pilot projects with impact.

But what’s the best way to get started?

We get this question often at Kopius. Maybe you have a problem you need to solve in mind or a general use case, or maybe that’s not yet clear. You might understand the possibilities but haven’t narrowed down an opportunity or area of impact. Regardless of which camp you’re in, when we peel back the onion, we find that most companies need to step back and address fundamental issues with their data foundation before they can begin to tackle GenAI.

At Kopius, we have a detailed framework for walking you through the things you need to take into consideration to identify a GenAI pilot project and build a data foundation to support. But asking—and answering questions like the ones below—is at the root of it.

  • What problem are you trying to solve? 

    In a survey of Chief Data Officers (CDOs) by Harvard Business Review, 80% of respondents believed GenAI would eventually transform their organization’s business environment, and 62% said their organizations intended to increase spending on it. But no company can afford to make investments that don’t deliver on outcomes. While there is value in just getting started, it’s both worthwhile and necessary to define an initial use case. Not only do you want your program to have impact, but the GenAI ecosystem is so broad that without some sort of use case, you will be unable to define what type of outputs need to be generated.

    Some companies will have a clear use case, while others will have a more general sense of where they’re headed. Still others are working with an “AI us” request from senior leadership to explore the landscape. Wherever you are in this process, our framework is designed to help you identify a meaningful pilot project.
  • What are your data sources? What do you need to capture?
    Next, you’ll need to take stock of your data sources, so you have a solid understanding of the full set of data you’re working with. What inputs do you have coming in and what inputs do you need to get to your end goal? Often, there is a project behind a project here. If you don’t have the data you need to solve the business challenge, then you’ll have to develop and implement a plan to get it. For instance, say you want to measure the impact of weather conditions on fleet performance, and you’re planning on using IoT data from your vehicles. You’ll also need to determine what weather data you need and put a solution in place to get it.
  • What is the state of your data? Is it relevant, quality, and properly housed and structured?

    With GenAI, your ability to get quality outputs that deliver on business outcomes depends on the quality of your inputs. That means data must be current, accurate, and appropriately stored and structured for your use case. For instance, if you’re developing a GenAI enabled chatbot that employees can query to get information about policies, procedures, and benefits, you’ll need to make sure that information is current and accurate.

    At this point, you’ll also need to consider where the data is being stored and what format it’s in. For instance, JSON documents sitting in non-relational database or tables sitting in a SQL database are not necessarily a model for GenAI success. You may have to put your raw data in a data lake, or if you already have a data lake, you may need to warehouse and structure your data so that it’s in the right format to efficiently deliver the output you want.
  • What governance and security measures do you need to take?
    Data governance is about putting the policies and procedures in place for collecting, handling, structuring, maintaining, and auditing your data so that it is accurate and reliable. All these things impact data quality, and without quality data, any outputs your GenAI solution delivers are meaningless. Another important aspect of data governance is ensuring you are compliant with HIPPA or any other regulatory mandates that are relevant to your organization.

    Data security, in this context, is a subset of data governance. It is about protecting your data from external threats and internal mishandling, including what user groups and/or individuals within your organization can access what. Do you have PPI in your system? Salary data? If so, who can modify it and who can read it? Your answers to these questions may inform what data platform is best for you and how your solution needs to be structured.
  • What is your endgame? What types of outputs are you looking for? 

    The problem you’re trying to solve is closely tied to the types of outputs you are looking for. It’s likely that exploration of the former will inform conversation of the latter. Are you building a chatbot that customers can interact with? Are you looking for predictive insights about maintaining a fleet or preventing accidents? Are you looking for dashboards and reporting? All this is relevant. This also gets into questions about your user profile—who will be using the solution, when and where will they be using it, what matters most to them, and what should the experience be like?

A Rapidly Evolving Data Platform Landscape Drives Complexity

Getting started with GenAI is further complicated by how complex the third-party GenAI, cloud, and data platform landscapes are and how quickly they are evolving. There are so many data warehouse and data lake solutions on the market—and GenAI foundational models—and they are advancing so rapidly that it would be difficult for any enterprise to sort through the options to determine what is best. Companies that already have data platforms must solve their business challenges using the tools they  have, and it’s not always straightforward. Wherever you land on the data maturity spectrum, Kopius’ framework is designed to help you find an effective path forward, one that will deliver critical business outcomes.

Do You Have the Right Data Foundation in Place for GenAI?

In the previously mentioned survey by Harvard Business Review, only 37% of respondents agreed that their organizations have the right data foundation for GenAI—and only 11% agreed strongly. But narrowing in on a business problem and the outcomes you want and defining a use case can be useful in guiding what steps you’ll need to take to put a solid data foundation in place.

One last thought—there are so many GenAI solutions and data platforms on the market. Don’t worry too much about what’s under the hood. There are plenty of ways to get there. By focusing on the business problem and outcomes you want, the answers will become clear.

JumpStart Your GenAI Initiative by Putting a Solid Data Foundation in Place

At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed a JumpStart program to prioritize digital transformation together.

Let’s connect!

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A Comprehensive Guide to Integrating Diverse Data Sources

Posted on September 9, 2024 by Kopius
A Comprehensive Guide to Integrating Diverse Data Sources

It’s impossible to understate the importance of data integration in digital transformation. Centralizing and standardizing your data enhances collaboration, boosts efficiency, reduces IT costs, and so much more. 

If you need a primer for developing your data integration strategy, this guide is for you.

What Is Data Integration?

Put simply, data integration is the process of pulling data from multiple different sources and combining it to create a single, comprehensive view of your organization. It typically requires you to invest in a centralized, web-based data storage and analytics solution such as Microsoft Azure Data Factory or Oracle Data Integrator.

The benefits of a successful data integration include but are not limited to:

  • More informed decisions: Unlocking access to all your organization’s data can help you generate more valuable, accurate insights for better business decision-making.
  • Greater agility: An integrated collection of data streamlines analysis and enables you to respond to situations as soon as they arise. You can pivot any time you encounter roadblocks like supply chain disruptions or lack of resource availability.
  • Increased visibility: When all your data is consolidated in one easily accessible location, you gain greater visibility into every area of your organization.
  • Cost savings: A unified data integration platform eliminates the need to maintain multiple data solutions, which can help you reduce your IT expenses and simplify compliance requirements. 
  • Operational efficiency: Integrating data from multiple sources creates a single source of truth for your entire organization, helping reduce waste and duplicate work for increased productivity. 
  • Improved customer satisfaction: Integrated data makes it easier to analyze and understand customers’ preferences, allowing you to create personalized products and experiences.
  • Competitive advantage: Easier access to better data makes it easier for teams to collaborate, especially across departments. 

Types of Data Integration

Types of Data Integration

Your integration strategy will determine exactly what kind of value you can expect to gain from your data, which is why taking the extra time to plan everything out at the beginning of the process can help you improve your overall results.

There are two main types of data integration strategies you might use:

Types of Data Integration
  • Batch data integration: Processing data in large batches is highly efficient for companies that do not require real-time access to new data. You can also schedule integration ahead of time to ensure predictable updates and optimize resource allocation.
  • Real-time data integration: For companies that need to provide real-time updates to clients or stay at the forefront of a rapidly changing industry, processing and integrating new data as soon as it’s available is far more profitable. Specialized software is usually required to achieve real-time integration.

Understanding Different Data Types and Sources

Becoming familiar with the kinds of data your organization handles in its everyday operations can help you determine how to integrate data from different sources in a way that best fits your company.

Data can fall under one or more of the following categories:

  • Structured: This type of data is machine-readable and adheres to a specific format that enables easy storage, querying, and analysis. Some examples include customer billing information, currency data, or product specifications.
  • Unstructured: This type of data is not machine-readable, which means it requires manual analysis and cataloging. Some examples include images, audio files, and product reviews.
  • Internal data: This data pertains to your organization’s everyday processes, such as historical customer interactions, transactional information, and email marketing metrics.
  • External data: This data comes from sources outside your organization and helps you predict how external factors might influence business. For example, collecting and analyzing weather patterns in your area of service can help you more accurately predict demand for your products or services.
  • Open data: Open-source data and software are free to use and open to anyone, making it a convenient resource for general analyses. It often comes from government and research organizations, such as the World Health Organization and the United States Bureau of Labor Statistics.

Depending on what type of data you’re using and where it comes from, you may need to perform additional formatting and transformation steps to make it suitable for integration.

Once your data is in the proper format, your organization might store it in one or more of the following ways: 

  • Data warehouses: Many organizations use data warehouses to hold their structured databases for easy access and analysis. While all raw data must be transformed to match the warehouse’s standards, a data warehouse is an efficient and organized way to store your integrated data.
  • Data marts: A data mart is a subset of a data warehouse that contains curated, structured datasets for specific use cases and users. For example, you might create a data mart for your marketing department that contains customer data, campaign metrics, and other relevant information.
  • Data lakes: Unlike data warehouses and marts, data lakes are broad, open repositories that house both structured and unstructured data. While it’s easier to begin new analyses with data lakes, these repositories are often more challenging to work with due to the lack of cohesion between formats.

Common Data Sources in Organizations

While each organization uses different methods for collecting the data they need, most use at least a few of the same sources.

Some of the data sources companies most frequently use include:

  • Customer relationship management platforms
  • External marketing tools
  • IT management platforms
  • Virtual meeting tools like Zoom and Microsoft Teams
  • Online chat software
  • Transaction histories
  • Physical forms and documents
  • Social media platforms and aggregate tools
  • Spreadsheets and other organization tools

The integration of data combines all of this information under one umbrella, creating a master dataset that serves as your organization’s single source of truth. This dataset is accurate and up to date, ensuring you have the necessary information to make effective data-driven decisions.

Data Integration Challenges

Even if you plan your integration from start to finish, you might run into roadblocks during the process. There are a few steps you can take to avoid these obstacles, but understanding how to solve them can help you keep moving forward if you encounter difficulties anyway.

Some of the most common challenges companies face when beginning their data integration journeys include:

  • Delays in delivery: Because so many of today’s data operations require data to become available in real time, even a short delay in integration can impact productivity. Investing in a data system that uses trigger events to manage issues as they arise can help you minimize delays and maintain business continuity.
  • Resource limitations: Building your own data integration process in-house requires more time and resources than many organizations can afford to spend. Automating data integration with a user-friendly platform enables your employees to monitor data integration without taking them away from their usual tasks.
  • Security: Organizations often collect and use sensitive data, including health records, personally identifiable information, and company finances. Your system must support various safeguards, such as encryption, data masking, and access controls, to both protect that data and comply with relevant data security standards and regulations.
  • Data quality: Making good decisions is a serious challenge without high-quality data to support them. Your team — or an automated data integration solution — must validate and inspect your data before fully integrating it into your system to ensure accuracy and quality. 
  • Usability issues: Your employees need to be able to efficiently use the data you collect after integration to make an impact. While best practices tend to vary between organizations, building a system tailored to your company’s unique requirements can help you shrink the learning curve and reduce delays.
Data Integration Challenges

Working with data integration experts can help you minimize the impact of these challenges, which can help you save valuable time and money in building and maintaining your data system. Plus, they can help you understand your limitations, which is important for effectively planning your strategy.

Data Integration Methods and Techniques

There are multiple ways you can approach the process of integrating your data, each with its own pros and cons. Some examples of data integration approaches you might use include:

  • Extract-transform-load (ETL): This traditional data integration method involves extracting the desired data from its sources, transforming it into the correct format and loading it into its destination system. Other important components of this process include data cleansing, filtering, and aggregation for easier analysis.
  • Extract-load-transform (ELT): This method is similar to ETL, but instead of transforming the raw data right away, your system first loads it into the destination data repository. It then transforms the data to meet the required format and standard.
  • Data virtualization: Virtualization is a more modern approach that creates virtual copies of your data, which makes it possible to query and analyze it without having to physically move any of it.
  • Data streaming: This approach involves creating a pipeline that enables the processing, ingestion, and integration of new data as it is generated in or near real time. Because it’s so fast, data streaming enables your teams to make data-driven decisions on the fly and adapt to new situations as they arise.

Your organization can also combine these types of data integration to create a more comprehensive system that works for all your data. For example, if you want to maintain historical databases and enable real-time availability, you could combine ETL with data streaming.

Data Integration Tools

Data integration platforms are an essential component in any data processing and analysis system, and they’re especially important if you plan to grow your business moving forward. Some of the most popular data integration solutions available today include: 

  • Microsoft SQL Server: This relational database management system uses Structured Query Language (SQL) to manage databases and quickly pull data in response to queries.
  • Oracle Data Integrator (ODI): ODI is capable of both ETL and ELT for high-volume batches and real-time integration. Its flexible architecture and strong support for big data processes enable streamlined integration between data warehouses, data lakes, external sources, and more.
  • Azure Data Factory: Microsoft’s Azure Data Factory enables you to integrate data from various sources into one centralized Azure hub, which makes your data easily accessible to all users. You can then connect it to Azure Synapse Analytics for streamlined processing and analysis.
  • AWS Kinesis: The Kinesis data streaming platform provides real-time collection and processing for large volumes of data, making it suitable for use in companies of various sizes and business structures.
  • Apache Kafka: This open-source data streaming platform can ingest and integrate large volumes of data for storage, analysis, and processing. It’s highly scalable and connects easily to various event sources, including JMS and AWS S3.

The right solution for your organization will depend on various factors, including:

  • Installation and maintenance costs
  • Data connector quality
  • Intelligent automation capabilities
  • Security and compliance requirements
  • Reliable support for users
  • Integration with other platforms in your tech stack
  • Ease of use

Best Practices for Data Integration

Having a clear plan and an understanding of the best practices for data integration are key requirements for successfully achieving your goals.

Best Practices for Data Integration

The following tips can help you ensure your data integration works as expected:

  1. Set clear goals: Before your company can begin integrating its data, you need to identify what you aim to achieve with this process. Whether you have one overarching goal or several specific ones, a clear vision will guide you through your integration.
  2. Factor in integration requirements: Consider the volume of data you need to process and the speed at which you need to do so to keep operations moving smoothly. This evaluation will help you determine how you generate and integrate data.
  3. Consider data complexity: Evaluate the complexity of the data coming from each source, including any variations in data structure, format, semantics, or any other factor that could impact processing speed.
  4. Invest in the right technology: Using a suitable data storage and analytics solution is essential for successful integration. For example, an automated data integration platform can help you minimize the risk of poor data quality by performing data validation and quality checks while your employees focus on their tasks.
  5. Monitoring and maintenance: As with any other major tech implementation, you’ll need to continuously monitor and maintain your data storage and analysis programs to ensure everything is working as needed. Depending on the software you choose, some of this responsibility may fall on your technology vendor.
  6. Work with an experienced consultant: If your organization lacks the expertise or resources to integrate data on its own, participating in an expert-led workshop program can help you decide where to start and what steps you need to take.
JumpStart Your Data Platform Transformation With Kopius

JumpStart Your Data Platform Transformation With Kopius

Whether your company is at the beginning of its digital transformation or you’re looking to enhance your existing data operations, the expert team at Kopius can help you create the best plan of action.

Our JumpStart program combines a user-centric approach with tech expertise and collaborative processes, driving innovation and data success. We can help your organization accelerate business growth with data integration solutions that keep operations moving in real time.

See how working with us can take your IT and business teams to the next level. Contact our team today to learn more about our JumpStart Program.

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Data Mesh: Understanding Its Applications, Opportunities, and Constraints 

Posted on December 22, 2023 by Brian Lamilla

Data has experienced a metamorphosis in its perceived value and management within the corporate sphere. Previously underestimated and frequently discarded, data was often relegated to basic reports or neglected due to a lack of understanding and governance. This limited vision, combined with emerging technologies, led to an overwhelming influx of data, and nowhere for it to go. There was little to no governance or understanding of what data they had, or how long they had it.  

In the early 2000s, enterprises primarily used siloed databases, isolated data sets with limited accessibility. The 2010s saw the rise of Data Warehouses, which brought together disparate datasets but often led to bottlenecks. Data Lakes emerged as a solution to store vast quantities of raw data and quickly became swamps without adequate governance. Monolithic IT and data engineering groups would struggle to document, catalog, and secure the growing stockpile of data. Product owners and teams that would want, or need access to data would have to request access and wait. Sometimes those requests would end up in a backlog and forgotten about.  

In this new dawn of data awareness, the Data Mesh emerges as a revolutionary concept, enabling organizations to efficiently manage, process, and gain insights from their data. As organizations realize data’s pivotal role in digital transformation, it becomes imperative to shift from legacy architectures to more adaptive solutions, making Data Mesh an attractive option.  

 

The Basics of a Data Mesh 

The importance of personalized customer experiences should not be understated. More than ever, consumers are faced with endless options. To stand out from competitors, businesses must use data and customer behavior insights to curate tailored and dynamic customer journeys that both delight and command their audience. Analyze purchasing history, demographics, web activity, and other data to understand your customer, as well as their likes and dislikes. Use these insights to design customized customer experiences that increase conversion, retention, and ultimately, satisfaction.  

When discussing data architecture concepts, the terms “legacy” or “traditional” imply centralized data management concepts, characterized by monolithic architectures developed and maintained by a data engineering organization within the company. Business units outside of IT would often feel left in the dark, waiting for the data team to address their specific needs and leading to inefficiencies. 

First coined in 2019, the Data Mesh paradigm is a decentralized, self-service approach to data architecture. There are four central principles that Data Mesh is based on: Domain ownership, treating data as a product, self-service infrastructure, and federated computational governance. 

With Data Mesh, teams (Domains) are empowered to own and manage their data (Product). This requires stewardship at the team level to effectively manage their own resources to ingest, persist and serve data to their end users. Data stewards are responsible for the quality, reliability, security, and accessibility of the data. Data stewards bridge the gap between decentralized teams and enterprise-level governance and oversight. 

While teams enjoy autonomy, chaos would ensue without a federated governance approach. This ensures standards, policies and best practices are followed across all product owners and data stewards.  

Implementing a Data Mesh requires significant investment in both infrastructure and enhancing teams with the resources and expertise required to manage their own resources. It requires a fundamental change in companies’ mindset of how they treat data.  

While a Lakehouse would aim to combine the best of Data Lakes and Data Warehouses, Data Mesh ventures further by decentralizing ownership and control of data. While Data Fabric focuses on seamless data access and integration across disparate sources, Data Mesh emphasizes domain-based ownership. On the other hand, event-driven architectures prioritize real-time data flow and reactions, which can be complementary to Data Mesh. 

data mesh decentralized architecture

When and Where to Implement Data Mesh 

  1. Large Organizations with Data Rich Domains: With large organizations, departments often deal with a deluge of data.  From Human Resources to Sales, each team has their own requirements for how their data is used, stored, and accessed. As teams consume more data, time to market and development efficiency suffer in centralized architectures. External resources and time constraints are often the biggest issue. By implementing Data Mesh, teams can work independently and take control of their data, increasing efficiency and quality. As a result, teams can optimize and enrich their product offering and cut costs by streamlining ELT/ETL processes and workflows. 

With direct control over their data, teams can tune and tailor their data solutions to better meet customer needs.  

  1. Complex Ecosystem: Organizations, especially those operating in dynamic environments with intricate interdependencies, often face challenges in centralized data structures. In such architectures, there’s limited control over resource allocation, utilization, and management, which can hinder teams from maximizing the potential of their data. Centralized approaches can curtail innovation due to rigid schemas, inflexible data pipelines, and lack of domain-specific customization. Data Mesh offers organizations the flexibility to adapt to evolving data needs and utilize domain-specific expertise to curate, process, and consume data tailored to their unique requirements. 
  1. Rapidly growing data environments: Today’s digital age sees organizations collecting data at an unprecedented scale. The sheer volume of data can be overwhelming with the influx of IoT devices, vendor integrations, user interactions, and digital transactions. Centralized teams often grapple with scaling issues, processing delays, and the challenge of timely data delivery. Data Mesh addresses this by distributing the data responsibility across different domains or teams. Multiple decentralized units handle the influx as data inflow increases, ensuring timely processing and reducing system downtime. The result is a more resilient data infrastructure ready to meet both current demands and future needs. 

When Not to Implement Data Mesh 

  1. Small to Medium-sized Enterprises (SMEs): While Data Mesh presents numerous advantages, it may not be suitable for all organizations or projects. Smaller organizations typically handle lower data volumes and may not possess the resources needed to manage their data independently. In these cases, a centralized data architecture would be more suitable to minimize complications in design and maintenance with fewer resources to manage them. 
  1. Mature and Stable Centralized Architectures: Organizations usually only turn to new solutions when they are experiencing problems. If a well-established centralized architecture is performing and fitting the needs of the company, there isn’t a need necessarily for Data Mesh adoption. Introducing a fundamental change in how data is managed is an expensive and disruptive undertaking. Building new infrastructure and expanding team capabilities changing organizational culture takes time.  
  1. Short-term Projects: Implementing a Data Mesh requires significant time and resource investment. The benefits of a Data Mesh won’t be seen when building or designing a limited lifespan project or proof of concept. If a project’s duration doesn’t justify the investment of a Data Mesh or the scope doesn’t require domain-specific data solutions, then the benefits of a Data Mesh aren’t utilized. Traditional data architectures are usually more appropriate for these applications and don’t need the oversight/governance that a Data Mesh requires.

  

Opportunities Offered by Data Mesh 

  1. Scalability: Data Mesh enables organizations to scale their data processing capabilities more effectively by enabling teams to control how and when their data is processed, optimizing resource use and costs, and ensuring they remain agile amidst expanding data sources and consumer bases.  
  1. Enhanced Data Ownership: Treating data as a product rather than a byproduct or a secondary asset is revolutionary. By doing so, Data Mesh promotes a culture with a clear sense of ownership and accountability. Domains or teams that “own” their data are more inclined to ensure its quality, accuracy, and relevance. This fosters an environment where data isn’t just accumulated but is curated, refined, and optimized for its intended purpose. Over time, this leads to more prosperous, more valuable data sets that genuinely serve the organization’s needs. 
  1. Speed and Innovation: Decentralization is synonymous with autonomy. When teams have the tools and the mandate to manage their data, they are not bogged down by cross-team dependencies or bureaucratic delays. They can innovate, experiment, and iterate at a faster pace, resulting in expanded data collection and richer data sets. This agility accelerates data product development, enabling organizations to adapt to changing needs quickly, capitalize on new opportunities, and stay ahead of the curve in the competitive market.  
  1. Improved Alignment with Modern Architectures: Decentralization isn’t just a trend in data management; it’s a broader shift seen in modern organizational architectures, especially with the rise of microservices. Data Mesh naturally aligns with these contemporary structures, creating a cohesive environment where data and services coexist harmoniously. This alignment reduces friction, simplifies integrations, and ensures that the entire organizational machinery, services, and data operate in a unified, streamlined manner. 
  1. Enhanced Collaboration: As domains take ownership of their data, there’s an inclination to collaborate with other domains. This cross-functional collaboration fosters knowledge sharing, best practices, and a unified approach to data challenges, driving more holistic insights.

Constraints and Challenges 

  1. Cultural Shift: Teams may not want to own their own data or have the experience to take on the responsibility. Training initiatives, workshops, and even hiring external experts might be necessary to bridge these skill gaps. 
  1. Increased Complexity: Developing an environment that supports a Data Mesh architecture is not without its challenges. As the Data Mesh model expands, managing the growing number of interconnected resources and solving integration issues to ensure smooth communication between various domains can be a considerable obstacle. Planning appropriately to support teams with access, training and management of a Data Mesh is critical to its evolution and success. This includes well defined requirements for APIs, data exchange, and interface protocols. 
  1. Cost Implications: Transitioning to a Data Mesh could entail substantial upfront costs, including hiring additional resources, training personnel, investing in new infrastructure, and possibly overhauling existing systems. 
  1. Governance: Data Governance has become a hot topic as data architectures grow and mature. Ensuring a consistent view of data across all domains can be challenging, especially when multiple teams update or alter their datasets independently. Tools to manage integrity, security and compliance are a requirement in a Data Mesh architecture. The need for teams to have autonomy in a decentralized environment is balanced with a flexible but controlled governance model that is the foundation for federated governance. This can be a challenge when initially designing the model based on team requirements, but it’s an important step to take as early as possible when building a data platform.  

Skillset: Evolving with the Data Mesh Paradigm

With an evolved mindset, the Data Mesh paradigm demands expertise that may not have previously been cultivated within traditional data teams. This transition from central data lakes to domain-oriented data products introduces complexities requiring a deep understanding of the data and the specific use cases it serves, both internally and externally. Skills such as collaboration, domain-specific knowledge translation, and data stewardship become vital. As data responsibility becomes decentralized, each team member’s role becomes more critical in ensuring data integrity, relevance, and security. As data solutions evolve, teams must adopt a mindset of perpetual learning, keeping pace with the latest methodologies, tools, and best practices related to managing their data effectively. 

Embracing the Data Mesh

In the evolving landscape of data management, the Data Mesh presents a promising alternative to traditional architectures. It’s a journey of empowerment, efficiency, and decentralization. The burgeoning community support for Data Mesh, evident from the increasing number of case studies, forums, and tools developed around it, underscores its pivotal role in the future of data management. However, its success hinges on an organization’s readiness to embrace the cultural and operational shifts it demands. As with all significant transformations, due diligence, meticulous planning, and an understanding of the underlying principles are crucial for its fruitful adoption. Embracing the Data Mesh is more than just a technological shift; it’s a paradigm transformation. Organizations willing to make this leap will find themselves not just keeping up with the rapid pace of data evolution but leading the charge in innovative, data-driven solutions.  

JumpStart Data Success

Innovating technology is crucial, or your business will be left behind. Our expertise in technology and business helps our clients deliver tangible outcomes and accelerate growth. At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.

Kopius has an expert emerging tech team. We bring this expertise to your JumpStart program and help uncover innovative ideas and technologies supporting your business goals. We bring fresh perspectives while focusing on your current operations to ensure the greatest success.Partner with Kopius and JumpStart your future success.

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How the Digital Front Door is Transforming Healthcare

Posted on June 20, 2023 by Kopius

With over 10,000 digital health solutions to choose from and hundreds of applications inside many health services organizations, our healthcare industry is drowning in a chaotic ecosystem of technology management. Furthermore, patients are suffering from poor, disjointed experiences. To combat this phenomenon, companies are adopting what is known as a “Digital Front Door.” This term has become increasingly important in the health services space, representing the virtual omnichannel engagement strategy by which a provider interacts with their patients or members. Unfortunately, the Digital Front Door solutions on the market today are often too narrow in their ability to deliver a seamless experience across the entire customer journey.

Benefits of Implementing a Digital Front Door Strategy

In an age where convenience and personalization are not just preferred, but expected, healthcare must invest in the right technology to understand their people and processes, all while eliminating patient pain points. This requires capabilities that are not just about accessibility. Instead, healthcare organizations should prioritize comprehensive engagement strategies like the Digital Front Door, which focus on transforming the patient experience and improving health outcomes. The resulting benefits can be plentiful:

  • Streamlined Access: Eliminate the need for patients to navigate through complex healthcare systems. With a few clicks, patients should be able to schedule appointments, access health records, engage with healthcare providers, and receive personalized care recommendations.
  • Personalized Care: Deliver personalized care recommendations based on each patient’s unique health profile. This means care that is tailored to the individual, leading to improved health outcomes.
  • Enhanced Patient Engagement: Foster active patient engagement by providing access to health education resources, personalized health reminders, and interactive tools for tracking health progress.
  • Improved Operational Efficiency: Automate routine tasks like appointment scheduling and reminders, thus freeing up valuable time for healthcare providers to focus on what they do best caring for patients.

Designing a Digital Front Door

Coming out of COVID, a major health system in the Southwest United States was dealing with problems across marketing, IT, and patient experience. Our team was asked to design and develop a Digital Front Door solution to deliver seamless, personalized, and intuitive healthcare experiences. We built a patient-oriented digital wrapper that sits around their EMR and other digital investments. This enhances the ease and personalization associated with several patient activities including scheduling appointments, accessing health records, and interacting with healthcare providers.

Partner With Kopius for Digital Front Door Solutions

At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.

Our JumpStart program fast-tracks business results and platform solutions. Connect with us today to enhance your customer satisfaction through a data-driven approach, drive innovation through emerging technologies, and achieve competitive advantage.

Add our brainpower to your operation by contacting our team to JumpStart your business.

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5 Industries Winning at Artificial Intelligence

Posted on February 7, 2023 by Kopius

By Lindsay Cox

Augmented Intelligence (AI) and Machine Learning (ML) were already the technologies on everyone’s radar when the year started, and the release of Foundation Models like ChatGPT only increased the excitement about the ways that data technology can change our lives and our businesses. We are excited about these five industries that are winning at artificial intelligence.

As an organization, data and AI projects are right in our sweet spot. ChatGPT is very much in the news right now (and is a super cool tool – you can check it out here if you haven’t already).

I also enjoyed watching Watson play Jeopardy as a former IBMer 😊

There are a few real-world examples of how five organizations are winning at AI. We have included those use cases along with examples where our clients have been leading the way on AI-related projects.

You can find more case studies about digital transformation, data, and software application development in our Case Studies section of the website.

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Consumer brands: Visualizing made easy

Brands are helping customers to visualize the outcome of their products or services using computer vision and AI. Consumers can virtually try on a new pair of glasses, a new haircut, or a fresh outfit, for example.  AI can also be used to visualize a remodeled bathroom or backyard.

We helped a teledentistry, web-first brand develop a solution using computer vision to show a customer how their smile would look after potential treatment. We paired the computer vision solution with a mobile web application so customers could “see their new selfie.” 

Consumer questions can be resolved faster and more accurately

Customer service can make or break customer loyalty, which is why chatbots and virtual assistants are being deployed at scale to reduce average handle time average speed-of-answer, and increase first-call resolutions.

We worked with a regional healthcare system to design and develop a “digital front door” to improve patient and provider experiences. The solution includes an interactive web search and chatbot functionality. By getting answers to patients and providers more quickly, the healthcare system is able to increase satisfaction and improve patient care and outcomes.

Finance: Preventing fraud

There’s a big opportunity for financial services organizations to use AI and deep learning solutions to recognize doubtful transactions and thwart credit card fraud which help reduce cost. Also known as anomaly detection, banks generate huge volumes of data which can be used to train machine learning models to flag fraudulent transactions.

Agriculture: Supporting ESG goals by operating more sustainably

Data technologies like computer vision can help organizations see things that humans miss. This can help with the climate crisis because it can include water waste, energy waste, and misdirected landfill waste.

The agritech industry is already harnessing data and AI since our food producers and farmers are under extreme pressure to produce more crops with less water. For example, John Deere created a robot called “See and Spray” that uses computer vision technology to monitor and spray weedicide on cotton plants in precise amounts.

We worked with PrecisionHawk to use computer vision combined with drone-based photography to analyze crops and fields to give growers precise information to better manage crops. The data produced through the computer vision project helped farmers to understand their needs and define strategies faster, which is critical in agriculture. (link to case study)

Healthcare: Identify and prevent disease

AI has an important role to play in healthcare, with uses ranging from patient call support to the diagnosis and treatment of patients.

For example, healthcare companies are creating clinical decision support systems that warn a physician in advance when a patient is at risk of having a heart attack or stroke adding critical time to their response window.

AI-supported e-learning is also helping to design learning pathways, personalized tutoring sessions, content analytics, targeted marketing, automatic grading, etc. AI has a role to play in addressing the critical healthcare training need in the wake of a healthcare worker shortage.

Artificial intelligence and machine learning are emerging as the most game-changing technologies at play right now. These are a few examples that highlight the broad use and benefits of data technologies across industries. The actual list of use cases and examples is infinite and expanding.

Kopius supports businesses seeking to govern and utilize AI and ML to build for the future. We’ve designed a program to JumpStart your customer, technology, and data success. 

JumpStart Your Success Today

Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders, equip teams with the skills and mindset needed to:

  • Identify unmet customer, employee, or business needs
  • Align on priorities
  • Plan & define data strategy, quality, and governance for AI and ML
  • Rapidly prototype data & AI solutions
  • And, fast-forward success

Partner with Kopius and JumpStart your future success.

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Addressing AI Bias – Four Critical Questions

Posted on February 6, 2023 by Kopius

By Hayley Pike

As AI becomes even more integrated into business, so does AI bias.

On February 2, 2023, Microsoft released a statement from Vice Chair & President Brad Smith about responsible AI. In the wake of the newfound influence of ChatGPT and Stable Diffusion, considering the history of racial bias in AI technologies is more important than ever.

The discussion around racial bias in AI has been going on for years, and with it, there have been signs of trouble. Google fired two of its researchers, Dr. Timnit Gebru and Dr. Margaret Mitchell after they published research papers outlining how Google’s language and facial recognition AI were biased against women of color. And speech recognition software from Amazon, Microsoft, Apple, Google, and IBM misidentified speech from Black people at a rate of 35%, compared to 19% of speech from White people.

In more recent news, DEI tech startup Textio analyzed ChatGPT showing how it skewed towards writing job postings for younger, male, White candidates- and the bias increased for prompts for more specific jobs.

If you are working on an AI product or project, you should take steps to address AI bias. Here are four important questions to help make your AI more inclusive:

  1. Have we incorporated ethical AI assessments into the production workflow from the beginning of the project? Microsoft’s Responsible AI resources include a project assessment guide.
  2. Are we ready to disclose our data source strengths and limitations? Artificial intelligence is as biased as the data sources it draws from. The project should disclose who the data is prioritizing and who it is excluding.
  3. Is our AI production team diverse? How have you accounted for the perspectives of people who will use your AI product that are not represented in the project team or tech industry?
  4. Have we listened to diverse AI experts? Dr. Joy Buolamwini and Dr. Inioluwa Deborah Raji, currently at the MIT Media Lab, are two black female researchers who are pioneers in the field of racial bias in AI.

Rediet Adebe is a computer scientist and co-founder of Black in AI. Adebe sums it up like this:

“AI research must also acknowledge that the problems we would like to solve are not purely technical, but rather interact with a complex world full of structural challenges and inequalities. It is therefore crucial that AI researchers collaborate closely with individuals who possess diverse training and domain expertise.”

Ready to JumpStart AI in Your Business?

Kopius supports businesses seeking to govern and utilize AI and ML to build for the future. We’ve designed a program to JumpStart your customer, technology, and data success. 

Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders, equip teams with the skills and mindset needed to:

  • Identify unmet customer, employee, or business needs
  • Align on priorities
  • Plan & define data strategy, quality, and governance for AI and ML
  • Rapidly prototype data & AI solutions
  • And, fast-forward success

Partner with Kopius and JumpStart your future success.

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ChatGPT and Foundation Models: The Future of AI-Assisted Workplace

Posted on February 1, 2023 by Kopius

By Yuri Brigance

The rise of generative models such as ChatGPT and Stable Diffusion has generated a lot of discourse about the future of work and the AI-assisted workplace. There is tremendous excitement about the awesome new capabilities such technology promises, as well as concerns over losing jobs to automation. Let’s look at where we are today, how we can leverage these new AI-generated text technologies to supercharge productivity, and what changes they may signal to a modern workplace.

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Will ChatGPT Take Away Your Job?

That’s the question on everyone’s mind. AI can generate images, music, text, and code. Does this mean that your job as a designer, developer, or copywriter is about to be automated? Well, yes. Your job will be automated in the sense that it is about to become a lot more efficient, but you’ll still be in the driver’s seat.

First, not all automation is bad. Before personal computers became mainstream, taxes were completed with pen and paper. Did modern tax software put accountants out of business? Not at all. It made their job easier by automating repetitive, boring, and boilerplate tasks. Tax accountants are now more efficient than ever and can focus on mastering tax law rather than wasting hours pushing paper. They handle more complicated tax cases, those personalized and tailored to you or your business. Similarly, it’s fair to assume that these new generative AI tools will augment creative jobs and make them more efficient and enjoyable, not supplant them altogether.

Second, generative models are trained on human-created content. This ruffles many feathers, especially those in the creative industry whose art is being used as training data without the artist’s explicit permission, allowing the model to replicate their unique artistic style. Stability.ai plans to address this problem by enabling artists to opt out of having their work be part of the dataset, but realistically there is no way to guarantee compliance and no definitive way to prove whether your art is still being used to train models. But this does open interesting opportunities. What if you licensed your style to an AI company? If you are a successful artist and your work is in demand, there could be a future where you license your work to be used as training data and get paid any time a new image is generated based on your past creations. It is possible that responsible AI creators can calculate the level of gradient updates during training, and the percentage of neuron activation associated to specific samples of data to calculate how much of your licensed art was used by the model to generate an output. Just like Spotify pays a small fee to the musician every time someone plays one of their songs, or how websites like Flaticon.com pay a fee to the designer every time one of their icons is downloaded.  Long story short, it is likely that soon we’ll see more strict controls over how training datasets are constructed regarding licensed work vs public domain.

Let’s look at some positive implications of this AI-assisted workplace and technology as it relates to a few creative roles and how this technology can streamline certain tasks.

As a UI designer, when designing web and mobile interfaces you likely spend significant time searching for stock imagery. The images must be relevant to the business, have the right colors, allow for some space for text to be overlaid, etc. Some images may be obscure and difficult to find. Hours could be spent finding the perfect stock image. With AI, you can simply generate an image based on text prompts. You can ask the model to change the lighting and colors. Need to make room for a title? Use inpainting to clear an area of the image. Need to add a specific item to the image, like an ice cream cone? Show AI where you want it, and it’ll seamlessly blend it in. Need to look up complementary RGB/HEX color codes? Ask ChatGPT to generate some combinations for you.

Will this put photographers out of business? Most likely not. New devices continue to come out, and they need to be incorporated into the training data periodically. If we are clever about licensing such assets for training purposes, you might end up making more revenue than before, since AI can use a part of your image and pay you a partial fee for each request many times a day, rather than having one user buy one license at a time. Yes, work needs to be done to enable this functionality, so it is important to bring this up now and work toward a solution that benefits everyone. But generative models trained today will be woefully outdated in ten years, so the models will continue to require fresh human-generated real-world data to keep them relevant. AI companies will have a competitive edge if they can license high-quality datasets, and you never know which of your images the AI will use – you might even figure out which photos to take more of to maximize that revenue stream.

Software engineers, especially those in professional services frequently need to switch between multiple programming languages. Even on the same project, they might use Python, JavaScript / TypeScript, and Bash at the same time. It is difficult to context switch and remember all the peculiarities of a particular language’s syntax. How to efficiently do a for-loop in Python vs Bash? How to deploy a Cognito User Pool with a Lambda authorizer using AWS CDK? We end up Googling these snippets because working with this many languages forces us to remember high-level concepts rather than specific syntactic sugar. GitHub Gist exists for the sole purpose of offloading snippets of useful code from local memory (your brain) to external storage. With so much to learn, and things constantly evolving, it’s easier to be aware that a particular technique or algorithm exists (and where to look it up) rather than remember it in excruciating detail as if reciting a poem. Tools like ChatGPT integrated directly into the IDE would reduce the amount of time developers spend remembering how to create a new class in a language they haven’t used in a while, how to set up branching logic or build a script that moves a bunch of files to AWS S3. They could simply ask the IDE to fill in this boilerplate to move on to solving the more interesting algorithmic challenges.

An example of asking ChatGPT how to use Python decorators. The text and example code snippet is very informative.

For copywriters, it can be difficult to overcome the writer’s block of not knowing where to start or how to conclude an article. Sometimes it’s challenging to concisely describe a complicated concept. ChatGPT can be helpful in this regard, especially as a tool to quickly look up clarifying information about a topic. Though caution is justified as demonstrated recently by Stephen Wolfram, CEO of Wolfram Alpha who makes a compelling argument that ChatGPT’s answers should not always be taken at face value.. So doing your own research is key. That being the case, OpenAI’s model usually provides a good starting point at explaining a concept, and at the very least it can provide pointers for further research. But for now, writers should always verify their answers. Let’s also be reminded that ChatGPT has not been trained on any new information created after the year 2021, so it is not aware of new developments on the war in Ukraine, current inflation figures, or the recent fluctuations of the stock market, for example.

In Conclusion

Foundation models like ChatGPT and Stable Diffusion can augment and streamline workflows, and they are still far from being able to directly threaten a job. They are useful tools that are far more capable than narrowly focused deep learning models, and they require a degree of supervision and caution. Will these models become even better 5-10 years from now? Undoubtedly so. And by that time, we might just get used to them and have several years of experience working with these AI agents, including their quirks and bugs.

There is one important thing to take away about Foundation Models and the future of the AI-assisted workplace: today they are still very expensive to train. They are not connected to the internet and can’t consume information in real-time, in online incremental training mode. There is no database to load new data into, which means that to incorporate new knowledge, the dataset must grow to encapsulate recent information, and the model must be fine-tuned or re-trained from scratch on this larger dataset. It’s difficult to verify that the model outputs factually correct information since the training dataset is unlabeled and the training procedure is not fully supervised. There are interesting open source alternatives on the horizon (such as the U-Net-based StableDiffusion), and techniques to fine-tune portions of the larger model to a specific task at hand, but those are more narrowly focused, require a lot of tinkering with hyperparameters, and generally out of scope for this particular article.

It is difficult to predict exactly where foundation models will be in five years and how they will impact the AI-assisted workplace since the field of machine learning is rapidly evolving. However, it is likely that foundation models will continue to improve in terms of their accuracy and ability to handle more complex tasks. For now, though, it feels like we still have a bit of time before seriously worrying about losing our jobs to AI. We should take advantage of this opportunity to hold important conversations now to ensure that the future development of such systems maintains an ethical trajectory.

JumpStart Your Success Today

Kopius supports businesses seeking to govern and utilize AI and ML to build for the future. We’ve designed a program to JumpStart your customer, technology, and data success. 

Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders, equip teams with the skills and mindset needed to:

  • Identify unmet customer, employee, or business needs
  • Align on priorities
  • Plan & define data strategy, quality, and governance for AI and ML
  • Rapidly prototype data & AI solutions
  • And, fast-forward success

Partner with Kopius and JumpStart your future success.

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