Tag: 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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Data Lakes: The Foundation of Big Data Analytics

Posted on April 24, 2023 by Kopius

By Kristina Scott

Data lakes are flexible and scalable architectures that are changing how businesses store and process data.

Businesses are generating and using more data than ever. This data comes from a variety of sources, such as customer interactions, social media, and IoT devices, among others. And it is often stored in different formats, making it challenging to use the data, analyze it, and gain insights. That’s where data lakes come in.

Data lakes are new to the world of big data analytics, and they are rapidly becoming the right choice for organizations. According to a report by MarketsandMarkets, the data lakes market is expected to grow from $7.9 billion in 2019 to $20.1 billion by 2024, at a compound annual growth rate of 20.6%.

Let’s dive deeper into the purpose of data lakes, explore their benefits, and look into their future.

What is a Data Lake?

Data lakes were introduced in the early 2000s by Apache Hadoop as an alternative to the limitations of data warehouses. A data lake is a storage system that allows you to store vast amounts of unstructured, semi-structured, and structured data at a low cost.

Simply put, a data lake is a large repository that stores raw data in its native format. Compared to a data warehouse, which stores data in hierarchical files or folders, a data lake uses a flat architecture and object storage.‍ While traditional data warehouses provide businesses with analytics, they are expensive, rigid, and often not equipped for the use cases companies have today, which is why the demand for data lakes is increasing.

Data lakes consolidate data in a central location where it can be stored as is, without the need to implement any formal structure for how the data is organized. That eliminates the need for preprocessing or transformation of data before storing it, making it an ideal storage solution for a vast amount of data. This raw data can then be processed and analyzed using a range of tools and technologies, such as machine learning algorithms, data visualization, and statistical analysis. Data lakes are built on Hadoop Distributed File System (HDFS) or cloud storage, such as Amazon S3, Microsoft Azure, or Google Cloud Storage.

Why Do Data Lakes Matter?

Often, a business has had big data and just didn’t know it. For instance, data goes unused because current business requirements only use a subset of the data a client or partner exchanges. Data lakes allow a business to consume and ingest vast amounts of raw data, allowing for data discovery in a cheap, efficient, and measurable way. Data-driven businesses tend to focus on future business needs, which require new insights into existing data and using newer technologies such as machine learning for predictive analysis.

Further, data lakes enable organizations to democratize big data access, making data-driven decisions a reality. The most significant advantage of data lakes is that they allow organizations to analyze data more effectively and gain insights faster to empower decision-making.  

Data lakes enable businesses to become more data-driven, as they can access and analyze big data quickly and efficiently, shifting the culture to embrace data-driven thinking across the organization. And it pays off — a Deloitte survey found that companies with the strongest culture around data-driven insights and decision-making were twice as likely to significantly exceed business goals. Data lakes enable that big data-driven culture to thrive and be accessible at all levels of the organization. BCC research also found that companies that use data lake services outperform similar companies by 9% in organic revenue growth.

How are Companies Using Data Lakes?

“The one thing I wish more people knew about data lakes is that it’s a tool that has great potential but can be misused. It’s vital to have a strategy to keep your data organized and avoid turning your lake into a swamp.”

Michael Rounds, Director of Data Engineering and Analysis, Kopius

Companies across a variety of industries are using data lakes to gain insights, improve operations and gain a competitive edge. In a research survey by TDWI, 64% of organizations said that the main purpose and benefit of a unified data lake is being able to get more operations and analytics business value from data. Other top value adds include reducing silos, gaining a better foundation for analytics compared to traditional data types, and storage and cost savings benefits.

Here are some practical use-case examples of organizations implementing data lakes in business operations:

  • Retailers use data lakes to analyze customer behavior and purchase history to offer personalized recommendations and promotions.
  • Healthcare organizations leverage data lakes to store patient data from multiple sources, such as electronic health records and wearables, to better diagnose and treat diseases.
  • Manufacturers implement data lakes to monitor and optimize production processes and analyze product performance, thus reducing operational costs.
  • Financial institutions use data lakes to gain deeper insights into customers’ behaviors, analyze and detect fraudulent activities, improve risk management, and improve customer experience.

Overall, data lakes help companies make more informed decisions. By storing all their data in one central location, companies can find patterns and trends that were previously hidden. They are empowered to democratize data access, becoming more data-driven, agile, and competitive.

What is the Future of Data Lakes?

The future of data lakes is bright, as businesses continue to invest in big data analytics to stay ahead of the competition. With the increasing dominance of technologies such as artificial intelligence (AI) and machine learning (ML), data lakes can become more intelligent and powerful, able to create predictive models and automate decision-making processes.

McKinsey suggests that businesses take full advantage of data lake technology and its ability to handle computing-intensive functions, like advanced analytics or machine learning. Organizations may want to build data-centric applications on top of the data lake that can seamlessly combine insights gained from both data lake resources and other applications. Data lakes can be used to develop new business models and revenue streams, as businesses seek ways to monetize their data assets.

Ready to harness the power of data lakes in your business? Kopius can help build the future of your data-driven organization by streamlining your data architecture and delivering powerful analytics through data governance, machine learning, data visualization, and more. Learn about our Data Lakes solutions.

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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.

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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

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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

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What Are Foundation Models? How Do They Differ From Regular AI Models?

Posted on January 26, 2023 by Kopius

By Yuri Brigance

This introduces what separates foundation models from regular AI models. We explore the reasons these models are difficult to train and how to understand them in the context of more traditional AI models.

chatGPT Foundation Model

What Are Foundation Models?

What are foundation models, and how are they different from traditional deep learning AI models? The Stanford Institute’s Center of Human-Centered AI defines a foundation model as “any model that is trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks”. This describes a lot of narrow AI models as well, such as MobileNets and ResNets – they too can be fine-tuned and adapted to different tasks.

The key distinctions here are “self-supervision at scale” and “wide range of tasks”.

Foundation models are trained on massive amounts of unlabeled/semi-labeled data, and the model contains orders of magnitude more trainable parameters than a typical deep learning model meant to run on a smartphone. This makes foundation models capable of generalizing to a much wider range of tasks than smaller models trained on domain-specific datasets. It is a common misconception that throwing lots of data at a model will suddenly make it do anything useful without further effort.  Actually, such large models are very good at finding and encoding intricate patterns in the data with little to no supervision – patterns which can be exploited in a variety of interesting ways, but a good amount of work needs to happen in order to use this learned hidden knowledge in a useful way.

The Architecture of AI Foundation Models

Unsupervised, semi-supervised, and transfer learning are not new concepts, and to a degree, foundation models fall into this category as well. These learning techniques trace their roots back to the early days of generative modeling such as Restricted Boltzmann Machines and Autoencoders. These simpler models consist of two parts: an encoder and a decoder. The goal of an autoencoder is to learn a compact representation (known as encoding or latent space) of the input data that captures the important features or characteristics of the data, aka “progressive linear separation” of the features that define the data. This encoding can then be used to reconstruct the original input data or generate entirely new synthetic data by feeding cleverly modified latent variables into the decoder.

An example of a convolutional image autoencoder model architecture is trained to reconstruct its own input, ex: images. Intelligently modifying the latent space allows us to generate entirely new images. One can expand this by adding an extra model that encodes text prompts into latent representations understood by the decoder to enable text-to-image functionality.

Many modern ML models use this architecture, and the encoder portion is sometimes referred to as the backbone with the decoder being referred to as the head. Sometimes the models are symmetrical, but frequently they are not. Many model architectures can serve as the encoder or backbone, and the model’s output can be tailored to a specific problem by modifying the decoder or head. There is no limit to how many heads a model can have, or how many encoders. Backbones, heads, encoders, decoders, and other such higher-level abstractions are modules or blocks built using multiple lower-level linear, convolutional, and other types of basic neural network layers. We can swap and combine them to produce different tailor-fit model architectures, just like we use different third-party frameworks and libraries in traditional software development. This, for example, allows us to encode a phrase into a latent vector which can then be decoded into an image.

Foundation Models for Natural Language Processing

Modern Natural Language Processing (NLP) models like ChatGPT fall into the category of Transformers. The transformer concept was introduced in the 2017 paper “Attention Is All You Need” by Vaswani et al. and has since become the basis for many state-of-the-art models in NLP. The key innovation of the transformer model is the use of self-attention mechanisms, which allow the model to weigh the importance of different parts of the input when making predictions. These models make use of something called an “embedding”, which is a mathematical representation of a discrete input, such as a word, a character, or an image patch, in a continuous, high-dimensional space. Embeddings are used as input to the self-attention mechanisms and other layers in the transformer model to perform the specific task at hand, such as language translation or text summarization. ChatGPT isn’t the first, nor the only transformer model around. In fact, transformers have been successfully applied in many other domains such as computer vision and sound processing.

So if ChatGPT is built on top of existing concepts, what makes it so different from all the other state-of-the-art model architectures already in use today? A simplified explanation of what distinguishes a foundation model from a “regular” deep learning model is the immense scale of the training dataset as well as the number of trainable parameters that a foundation model has over a traditional generative model. An exceptionally large neural network trained on a truly massive dataset gives the resulting model the ability to generalize to a wider range of use cases than its more narrowly focused brethren, hence serving as a foundation for an untold number of new tasks and applications. Such a large model encodes many useful patterns, features, and relationships in its training data. We can mine this body of knowledge without necessarily re-training the entire encoder portion of the model. We can attach different new heads and use transfer learning and fine-tuning techniques to adapt the same model to different tasks. This is how just one model (like Stable Diffusion) can perform text-to-image, image-to-image, inpainting, super-resolution, and even music generation tasks all at once.

Challenges in Training AI Foundation Models

The GPU computing power and human resources required to train a foundation model like GPT from scratch dwarf those available to individual developers and small teams. The models are simply too large, and the dataset is too unwieldy. Such models cannot (as of now) be cost-effectively trained end-to-end and iterated using commodity hardware.

Although the concepts may be well explained by published research and understood by many data scientists, the engineering skills and eye-watering costs required to wire up hundreds of GPU nodes for months at a time would stretch the budgets of most organizations. And that’s ignoring the costs of dataset access, storage, and data transfer associated with feeding the model massive quantities of training samples.

There are several reasons why foundation models like ChatGPT are currently out of reach for individuals to train:

  1. Data requirements: Training a large language model like ChatGPT requires a massive amount of text data. This data must be high-quality and diverse and is typically obtained from a variety of sources such as books, articles, and websites. This data is also preprocessed to get the best performance, which is an additional task that requires knowledge and expertise. Storage, data transfer, and data loading costs are substantially higher than what is used for more narrowly focused models.
  2. Computational resources: ChatGPT requires significant computational resources to train. This includes networked clusters of powerful GPUs, and a large amount of memory volatile and non-volatile. Running such a computer cluster can easily reach hundreds of thousands per experiment.
  3. Training time: Training a foundation model can take several weeks or even months, depending on the computational resources available. Wiring up and renting this many resources requires a lot of skill and a generous time commitment, not to mention associated cloud computing costs.
  4. Expertise: Getting a training run to complete successfully requires knowledge of machine learning, natural language processing, data engineering, cloud infrastructure, networking, and more. Such a large cross-disciplinary set of skills is not something that can be easily picked up by most individuals.

Accessing Pre-Trained AI Models

That said, there are pre-trained models available, and some can be fine-tuned with a smaller amount of data and resources for a more specific and narrower set of tasks, which is a more accessible option for individuals and smaller organizations.

Stable Diffusion took $600k to train – the equivalent of 150K GPU hours. That is a cluster of 256 GPUs running 24/7 for nearly a month.  Stable Diffusion is considered a cost reduction compared to GPT. So, while it is indeed possible to train your own foundation model using commercial cloud providers like AWS, GCP, or Azure, the time, effort, required expertise, and overall cost of each iteration impose limitations on their use. There are many workarounds and techniques to re-purpose and partially re-train these models, but for now, if you want to train your own foundation model from scratch your best bet is to apply to one of the few companies which have access to resources necessary to support such an endeavor.

Contact Us to JumpStart Your AI Success

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

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Data Trends: Six Ways Data Will Change Business in 2023 and Beyond

Posted on December 28, 2022 by Kopius

By Kristina Scott

Data is big and getting bigger. We’ve tracked six major data-driven trends for the coming year.

Digital analytics data visualization, financial schedule, monitor screen in perspective

Data is one of the fastest-growing and most innovative opportunities today to shape the way we work and lead. IDC predicts that by 2024, the inability to perform data- and AI-driven strategy will negatively affect 75% of the world’s largest public companies. And by 2025, 50% of those companies will promote data-informed decision-making by embedding analytics in their enterprise software (up from 33% in 2022), boosting demand for more data solutions and data-savvy employees.

Here is how data trends will shift in 2023 and beyond:

1. Data Democratization Drives Data Culture

If you think data is only relevant to analysts with advanced knowledge of data science, we’ve got news for you.  Data democratization is one of the most important trends in data. Gartner research forecasts that 80% of data-driven initiatives that are focused on business outcomes will become essential business functions by 2025.

Organizations are creating a data culture by attracting data-savvy talent and promoting data use and education for employees at all levels. To support data democratization, data must be exact, easily digestible, and accessible.

Research by McKinsey found that high-performing companies have a data leader in the C-suite and make data and self-service tools universally accessible to frontline employees.

2. Hyper-Automation and Real-Time Data Lower Costs

Real-time data and its automation will be the most valuable big data tools for businesses in the coming years. Gartner forecasts that by 2024, rapid hyper-automation will allow organizations to lower operational costs by 30%. And by 2025, the market for hyper-automation software will hit nearly $860 billion.

3. Artificial Intelligence and Machine Learning (AI & ML) Continue to Revolutionize Operations

The ability to implement AI and ML in operations will be a significant differentiator. Verta Insights found that industry leaders that outperform their peers financially, are more than 2x as likely to ship AI projects, products, or features, and have made AI/ML investments at a higher level than their peers.

AI and ML technologies will boost the Natural Language Processing (NLP) market. NLP enables machines to understand and communicate with us in spoken and written human languages. The NLP market size will grow from $15.7 billion in 2022 to $49.4 billion by 2027, according to research from MarketsandMarkets.

We have seen the wave of interest in OpenAI’s ChatGPT, a conversational language-generation software. This highly-scalable technology could revolutionize a range of use cases— from summarizing changes to legal documents to completely changing how we research information through dialogue-like interactions, says CNBC.

This can have implications in many industries. For example, the healthcare sector already employs AI for diagnosis and treatment recommendations, patient engagement, and administrative tasks. 

4. Data Architecture Leads to Modernization

Data architecture accelerates digital transformation because it solves complex data problems through the automation of baseline data processes, increases data quality, and minimizes silos and manual errors. Companies modernize by leaning on data architecture to connect data across platforms and users. Companies will adopt new software, streamline operations, find better ways to use data, and discover new technological needs.

According to MuleSoft, organizations are ready to automate decision-making, dynamically improve data usage, and cut data management efforts by up to 70% by embedding real-time analytics in their data architecture.

5. Multi-Cloud Solutions Optimize Data Storage

Cloud use is accelerating. Companies will increasingly opt for a hybrid cloud, which combines the best aspects of private and public clouds.

Companies can access data collected by third-party cloud services, which reduces the need to build custom data collection and storage systems, which are often complex and expensive.

In the Flexera State of Cloud Report, 89% of respondents have a multi-cloud strategy, and 80% are taking a hybrid approach.

6. Enhanced Data Governance and Regulation Protect Users

Effective data governance will become the foundation for impactful and valuable data. 

As more countries introduce laws to regulate the use of various types of data, data governance comes to the forefront of data practices. European GDPR, Canadian PIPEDA, and Chinese PIPL won’t be the last laws that are introduced to protect citizen data.

Gartner has predicted that by 2023, 65% of the world’s population will be covered by regulations like GDPR. In turn, users will be more likely to trust companies with their data if they know it is more regulated.

Valence works with clients to implement a governance framework, find sources of data and data risk, and activate the organization around this innovative approach to data and process governance, including education, training, and process development. Learn more.

What these data trends add up to

As we step into 2023, organizations that understand current data trends can harness data to become more innovative, strategic, and adaptable. Our team helps clients with data assessments, by designing and structuring data assets, and by building modern data management solutions. We strategically integrate data into client businesses, use machine learning and artificial intelligence to create proactive insights, and create data visualizations and dashboards to make data meaningful.  

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

JumpStart Your Data Transformation

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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Women in Technology – Meet Aravinda Gollapudi

Posted on December 27, 2022 by Kopius

“Technology is an enabler that will be a game changer in shaping society. Women have a role in how that technology is used and how society will be changed.” Aravinda Gollapudi, Head of Platform and Technology at Sage

We sat with Aravinda Gollapudi, Head of Platform and Technology at Sage, a $2B company that provides small and medium-sized businesses with finance, HR, and payroll software. At Sage, she leads a globally disbursed organization of roughly 270 employees across product, technology, release management, program management, and more. Aravinda also rounds out her technical work by serving as a board advisor for Artifcts and Loopr.

We spoke with Aravinda about technology and how women fit into this industry. Here are the highlights of that exchange.

What is your role in technology? What are you doing today?

I create an operating model for platforms, processes, and organizations to combine speed and scale. This drives market leadership and innovative solutions while accelerating velocity through organizational structure. I do this by leading the technology organization for cloud-native financial services for midmarket at Sage while also driving the product roadmap and strategy for platform as a business unit leader.

I also advise, mentor, and partner with CEOs of startup companies as a board advisor around technologies like AI/ML, SAAS, Cloud, Organizational strategy, and business models. I also help bring my network together to drive go-to-market activities.

I have a unique opportunity with my role to drive the convergence of business outcomes and technology/investment enablers by Identifying, blueprinting, and leading solutions to market – for today, tomorrow, and the future.

How did you get started in technology?

I think my inclination toward technology may be attributed to my interest in mathematics. I am old enough to appreciate how personal computers fueled the exponential adoption of technology!

The current generation of technologists have immense computing power at their fingertips, but I started my foray into technology when I had to use UNIX servers to do my work.

Early in my career, I wanted to go into academia and research in physics. I was working on research work in quantum optics after I graduated with my Master’s in Physics. I spent a lot of time with programming models in Fortran language, which is used in scientific computing. Fortran introduced me to computer programming.

My interest in technology got stronger while I was pursuing my second Masters in Computer Engineering.  Although I was taking coursework on hardware and software, I gravitated toward software programming.

What can you tell us about the people who paved the way for you? How did mentors factor into your success?

A big part of my career was shaped very early by my parents, who are both teachers. They instilled the importance of learning and hard work. My dad was a teacher and a principal and was a role model in raising the bar on work ethic, discipline, respect, and courage. My mom, through her multiple master’s degrees and pursuit of continual learning, showed us that it was important to keep learning.

Later in my career, I was fortunate to have the support of my managers, mentors, and colleagues. I leveraged them to learn the craft around software but also around organizational design, product strategy, and overall leadership. I am fortunate to have mentors who challenged me to be better and watch for my blindsides. I still lean on them to this day. A few of my mentors include Christine Heckart, Jeff Collins, Himanshu Baxi, Keith Olsen, and Kathleen Wilson. They have been my managers or mentors who gave me candid feedback, motivated me, and helped me grow my leadership skills.

I would be remiss if I didn’t mention the support and encouragement from my husband! He has always pushed me to take on challenges and supported me while we both balanced family and work.

How can we improve tech for women?

If we want to improve tech for women, we must invest in girls in technology. Hiring managers need to overcome unconscious bias and create early career opportunities for girls.

Mentorship is crucial: We need to acknowledge that the learning and career path is often different for women. Having a strong mentor irrespective of gender helps women learn how to deal with situational issues and career development. Women leaders who can take on this mantle to share their experience and mentor rising stars will help those who do not have a straight journey line in their careers.

Given the smaller percentage of women represented in technology, I am happy to see the trend in the recent past elevating this topic at all levels.  By becoming mentors, diversity champions can make a real impact on improving the trend.

We need to invest in allyship and mentorship and elevate the importance of gender diversity. For example, with board searches, organizations like 50/50 Women on Boards elevate the value of having gender diversity and work on legislative support. We need more of that or else we leave behind half the population.

What is one thing you wish more people knew to support women in technology?

I wish more people understood the impact of unconscious bias. Most people do not intend to be biased, but human nature makes us lean toward certain decisions or actions. The tech industry would greatly improve if more people took simple steps to avoid their unconscious bias, like making decisions in several settings (avoiding the time of day impact), ensuring that names and accents don’t impact hiring decisions, and investing in diversity.

What’s around the corner in technology? What trends excite you?

There are three significant trends that excite me right now: Artificial Intelligence/Machine Learning, data, and sustainable tech.

We are living in the world of AI Everywhere and there is more to come. Five out of six Americans use AI services daily. I expect AI to keep shaping automation and intelligent interactions, and drive efficiency.

This is also an exciting time to work with data. We are moving towards a hyper-connected digital world, and the old way of doing things required us to harness vast amounts of digital data from siloed sources. The trend is moving toward driving networks and connections that will fuel more complex machine-to-machine interactions. This data connectivity will impact our lives at home, schools, offices, etc., and will dramatically change how we conduct business.

And I am particularly excited about trends in sustainable technology. We will see more investment in technologies that reduce the impact of compute-hungry technology. I’m anticipating an evolution to more environmentally sustainable investments, which will help us reduce the usage of wasteful resources such as data centers, storage, and computing.

What does the tech world need now more than ever?

The tech world needs better data security and more diversity.

Data is highly accessible in our lives (in part thanks to social media), so we need more investment in privacy and security. We already have seen the impact of this need across personal lives, the political landscape, and business.

For too long, we have not invested enough in diversity, so we have a lot of catching up to do. In the world of technology, we are woefully behind in diversity in leadership positions, particularly in the US tech sector where about 20% of technology leadership positions are held by women.

Data is ubiquitous, tools and frameworks are at our fingertips, and technology is covered earlier in schools, so we are seeing a younger starting age for people getting involved in building technology products/applications. We’ve reduced the barrier of entry (languages, frameworks, low code/no-code tools sets) to make it easier to adopt technology without the overhead of complex coursework.  With all these improvements, why are we still so behind on diversity? In the US tech sector, 62% of jobs are held by white Americans. Asian Americans hold 20% of jobs. Latinx Americans hold 8% of jobs. Black Americans hold 7% of jobs. Only 26.7% of tech jobs are held by women.

We have the tools and training. Now we need to change the profile of the workforce to include a more diverse community.

What’s one piece of advice that you would share with anyone reading this?

For women who are reading this, I strongly encourage you to avoid self-doubt and gain confidence by arming yourselves with knowledge and mentors. By bringing our best selves forward, we can focus on opportunities and not obstacles. Technology is an enabler that will be a game changer in shaping society. Women have a role in how that technology is used and how society will be changed.

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