Posted on February 14, 2025 by Rob Carek, Vice President, Client Solutions
Non-gaming accounts for nearly 17% of total casino revenue, according to the American Gaming Association, but in some cases, it can contribute as much as 70%. That’s a substantial differential, and it demonstrates just how large the opportunity is for casino operators to drive revenue growth through non-gaming channels. In fact, the 2024 LaneTerralever (LT) Non-Gaming Player Insights Report indicates that across all age and income demographics, non-gaming activities and amenities like restaurants, bars, spas, bowling alleys, arcades, and live entertainment, are an important consideration in determining which casino to go to.
For years, casino operators have known that personalized experiences build loyal customers and that player’s clubs and loyalty programs provide valuable data on gaming habits. But they miss a crucial piece of the puzzle—non-gaming spending. As these experiences become increasingly meaningful to guests, how can casinos gain a complete view of customer behavior and drive revenue growth across their entire property?
GenAI is here to help.
By leveraging your existing loyalty program and other data, along with GenAI, you can gain deep insights into non-gaming guest behavior, enabling highly personalized recommendations and incentives that encourage exploration of all the amenities on site.
And Kopius has a Virtual Concierge solution to make it happen.
The Who, What, Where, and Why of Non-Gaming Casino Revenue
While gaming remains important to consumers across all demographics, individual preferences, behaviors, and importantly, the opportunities for casino operators vary widely based on generation and income. The LT report indicates that:
All demographics are increasingly going to casinos in groups, and 70% of them say that non-gaming activities are more important when they are with a group.
79% of affluent consumers consider non-gaming offerings in choosing a casino and are more likely to spend 50% of their time engaged in non-gaming activities, and are particularly interested in live events.
86% of Gen Z consumers visit local casinos in groups. When visiting a destination casino, non-gaming amenities like restaurants and live entertainment are top priorities, but at 14%, they allocate the least amount of total spending to non-gaming relative to other generations.
Non-gaming activities are more important to millennials than to any other generation, with 89% of them saying they have a significant impact on which one they choose and 69% saying they budget specifically for non-gaming.
Only 34% of Gen X consumers say that non-gaming activities impact their loyalty to a casino, but like their boomer and Gen Z counterpart, food matters. Gen X prioritizes non-gaming spending in restaurants.
41% of boomers factor in non-gaming activities when choosing a casino, and they allocate 18% of their spending to them. For boomers, restaurants and bars are the most important non-gaming activity.
Insights like these are incredibly powerful when developing non-gaming offerings for specific demographics. But imagine if you could target offerings even more closely, based on individual casino guest preferences and behaviors. And imagine that based on choices guests made during previous visits to your casino, you could anticipate the types of non-gaming activities they might enjoy during future ones. How would that impact the guest experience? And what would that do for your business in terms of loyalty and increased revenue.
GenAI can close that gap, and it isn’t just a promise of what’s to come—the technology is available today.
The Kopius Virtual Concierge —Personalized Recommendations that Drive Non-Gaming Revenue
The Kopius Virtual Concierge for casinos is a flexible, GenAI-powered app that delivers personalized recommendations, incentives, and service to your guests. It connects to your existing data sources like players clubs, loyalty programs, reservation systems, and builds on that data as guests use non-gaming services. With the Kopius Virtual Concierge, you get a comprehensive view of guest behavior across your entire property, not just on the gaming floor, so you can optimizing your non-gaming offerings for maximum impact.
With the Kopius Virtual Concierge, you will:
Boost dining revenue: Offer targeted deals and recommendations based on guest history and preferences, driving traffic to your restaurants and increasing spend.
Upsell related services: Proactively suggest relevant offerings based on guest bookings and activities—like a golf lesson after a tee time—increasing revenue per guest.
Craft tailored itineraries: Create personalized plans based on past visits, encouraging longer stays and maximizing guest engagement and spending.
Optimize offers with data: Leverage real-time data and guest feedback to refine promotions and personalize experiences, driving non-gaming revenue growth.
The possibilities are endless.
Imagine a casino experience perfectly tailored to each guest. That’s the power of the Kopius Virtual Concierge. The more guests engage with it, the more personalized their experience becomes. Meanwhile, casino operators gain access to invaluable data on guest preferences, creating a continuous feedback loop for optimizing offers and experiences.
Elevate Guest Experiences and Drive Non-Gaming Revenue with the Kopius Virtual Concierge
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.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
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.
Posted on January 30, 2025 by Danny Vally, Chief Operating Officer, Kopius
Female freelance developer coding and programming. Coding on two with screens with code language and application.
According to Deloitte’s 2024 Mapping Digital Transformation Value report, there is a sea change in how companies are investing their innovation budgets. Fewer dollars are being invested in transformational change. Instead, “Budgets are going toward more concrete business cases: entering new markets, launching new products, and modernizing the core.”
And a primary driver for this change is value.
The survey indicates increased investments technologies respondents perceived as driving the most value—GenAI, traditional AI, and data architecture are chief among them.
Overcoming Innovation Ambiguity and Identifying Value
Innovation is inherently ambiguous. It is in complete opposition to a concrete business case. You may have a concrete problem but be unclear about whether or how an emerging technology can solve it. Or maybe you know you need to start experimenting with new technology, but you haven’t defined what it can do for your organization. Both those scenarios are a long way from a business case and even further from demonstrating value.
So how do you bridge the gap?
While it may seem counterintuitive, putting some structure around the innovation process can be advantageous, particularly when it comes to narrowing in on a business case and demonstrating value. It can provide you with space to think things through, home in on a problem, and explore solutions from several different angles. It can unearth the unknowns and root out costly gotchas that might prevent you from moving forward. A structured innovation process provides an opportunity to figure out if something.
Innovation often ends with proof of concept that demonstrates that something can be done. That may solve the business-case challenge, but it’s not enough show value. Proof of value is a more modern approach. Proof of value is about more than if something can be done—it’s about whether it should be done. It’s intended to show whether it solves your business problem, is financially and operationally viable, and what it will take to pilot and scale.
JumpStart—A Value-Based, Guided Process for Technology Innovation
Kopius’ flexible future state and product ideation program, JumpStart, is a framework for innovation designed to help you explore possibilities, narrow down business use cases, and demonstrate value. And while JumpStart is a great launchpad for exploring both traditional and GenAI, it’s also a good fit for other types of innovation. Manufacturing, healthcare, and retail organizations, for example, may want to get a better understanding of their physical environment through IoT. And companies of all types have ideas for digital products they’d like to develop, but just don’t have the capacity.
JumpStart is technology agnostic—often, the solution to a particular challenge includes many integrated approach involving AI, IoT, AR/VR, etc.
The possibilities are endless.
Regardless of what you’re interested in exploring, the foundational process is similar, and typically entails—and it typically wraps up with a proof of value.
Research – First, we explore emerging trends in your space, how early adopters in are using new technologies, and what’s happening in other industries that might be applicable.
Dialog & Discovery – Next, we bring all the key stakeholders to the table to explain any relevant new technology, present the research, and discuss challenges and opportunities.
Brainstorming & Whiteboarding – Then, we brainstorm and whiteboard potential solutions, prioritize by need and impact, and provide space for you to consider next steps.
Proof of Value – Last, we bring together a team to prototype a solution that tangibly demonstrates whether something can be done, and what it will take to make it happen.
The goal of the JumpStart process is to for you to have the information you need to determine if the innovation path you are considering makes sense—whether it’s achievable, what the business case is, what the obstacles are, and if it will drive value for your organization.
While JumpStart typically ends with the proof of value, it’s rarely the end of the project lifecycle. If you’re looking to launch a pilot initiative or scale a program or technology across your organization, we can help with that, too.
JumpStart in Action—Three Real-World Examples of How
Innovation looks different.
To get a better understanding of how JumpStart future state and product ideation workshops can help you innovate and solve your most pressing challenges, I’ve pulled together three real-world examples.
An AI solution for triaging automotive warranty claims – A leading auto manufacturer with a large warranty business wanted to explore how AI could help them triage claims. They needed to understand if it was a practical solution and what implementing it would entail. The JumpStart and proof of value process proved their idea was achievable, but it brought to light some underlying issues around data and integrations. They came away with not only a better understanding of what was possible, but also what it would take to get there.
An IoT solution for monitoring vaccines storage temperatures – A global healthcare nonprofit that provides vaccines needed to monitor the temperatures of refrigerators in remote locations with inconsistent power supplies to ensure the vaccines’ efficacy. The JumpStart and proof of value process was the launchpad for a viable solution that included everything from building a circuit board, to operationalizing it with 1G connectivity in the Azure Cloud. Once the initial project was a starting point for a host of related solutions, including refrigerated backpacks and fanny packs.
A competitive gap analysis for a robotics company’s app – A manufacturer of robotic household appliances wanted to benchmark their app experience and physical against their competitors’ solutions. They needed objective real-world insights into their strengths and opportunities for improvement, so they knew where to focus future development efforts. The JumpStart and proof of value process led to tangible enhancements to both their app and product that improved customer experience.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
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.
Posted on January 30, 2025 by Danny Vally, Chief Operating Officer, Kopius
Male IT Specialist Holds Laptop and Discusses Work with Female Server Technician. They’re Standing in Data Center, Rack Server Cabinet with Cloud Server Icon and Visualization.
Enterprise technology adoption typically follows an S curve, with organizations first undertaking frontier innovation, then experimenting, piloting, and scaling, as McKinsey explains in their latest Technology Trends Outlook. But innovation has never been easy, and staying ahead of the emerging technology frontier is an infinite race. It’s hard to take care of today’s problems when you’re focused on tomorrow. Consider AI or GenAI—you may not have in-house expertise, or if you do, their efforts are focused on existing programs. Maybe you just don’t have the time and space to figure out what types of problems emerging technologies can solve and what the best use cases are in your business.
To take advantage of AI and GenAI, innovation and experimentation are necessary first steps. It’s the only way to figure out what’s going to work for your company and to get buy-in before investing.
That’s why more companies are looking to external partners for help.
Technology Experimentation and the Art of the Possible
When IoT was first introduced, it was complicated, and companies didn’t know what it meant for their businesses. So, at Kopius, we started holding future state workshops to help our clients understand what it was all about, the types of problems it could solve, and to identify some specific use cases within their businesses. We quickly came to think of it as the art of the possible.
We’re seeing much the same thing with AI, and to meet the need, we’ve designed a full-fledged innovation and experimentation service offering we call JumpStart.
Our JumpStart process provides a roadmap for frontier innovation and experimentation. It entails:
Research – Over the years we’ve learned how valuable it is to come to the table with a big picture understanding of your business, what the emerging trends are in your space, how early adopters in your industry are using it, and even if other industries are using it in ways that might be applicable.
Dialog & Discovery – Once we’ve wrapped our heads around all that, we bring all the key stakeholders to the table to explain the technology and present the research, which then generates good discussions about the business problems you’re having and opportunities you could potentially take advantage of.
Brainstorming & Whiteboarding – This is where the art of the possible comes to life. Using a design thinking approach, we brainstorm and whiteboard potential solutions to your most challenging problems and opportunities. Then, we prioritize them based on greatest need and impact. We also give you some space to think about what the best opportunity to move forward with is.
Proof of Value – The last step of the process is to demonstrate proof of value. We bring together a multi-functional team to do some light, rapid prototyping that tangibly demonstrates the use case. Proof of value isn’t just about whether something can be done—it’s about whether it should be done. Does it solve your business problem? Is it financially and operationally viable? What will it take to pilot and scale?
While the proof of value typically marks the end of the JumpStart process, it doesn’t necessarily mark the end of our partnership. For many of our clients, it’s just the beginning. Kopius can also pull together the right resources to pilot the project and scale it across your organization.
JumpStart in Action – For a Leading Auto Manufacturer, JumpStart Brought Critical Information to Light
A leading auto manufacturer with a large warranty business wanted to use AI to make more informed decisions about what claims to automatically approve vs. look into more deeply. Before fully investing in the project, they needed to better understand if it was a practical solution and what implementing it would entail.
After some initial research, we brought key stakeholders to the table for in-depth discussions about the possibilities, the opportunities, and their business challenges. Then, we brainstormed and white boarded potential solutions. A big part of this was mapping out a detailed service blueprint that detailed every step of the warranty claim process.
Next, we tackled the proof of value, which brought critical information to light. While the AI solution was possible, they needed to address some upstream challenges before it could be implemented. Since claims were being submitted by so many different people through so many different systems, they would first need to build integrations and standardize the way data was coming in.
This gave the auto manufacturer the necessary insight to weigh the effort and expense vs. the long-term benefits of the undertaking and make an informed decision about moving forward.
The Value of a Fresh Perspective
When you are laser focused on solving today’s problems, it’s hard to break away and orient yourself to the larger world of advancing technology. That’s why an external technology partner like Kopius can be a real asset. We bring fresh perspectives, objectivity, and use cases from within your industry and outside of it to help you drive innovation, experiment, pilot, and scale.
Wherever you are on your AI innovation journey, we’d love to help you explore the art of the possible.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
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.
Posted on January 23, 2025 by Hieu (Sam) To, Manager, UX/UI Design
Design thinking is about empathy—putting yourself in another person’s shoes to solve a problem they are facing. User-centered design is about, well, useability. It narrows in on the part of that solution that is tied to a digital or online experience, whether that’s a product, an application, or a website. The two concepts go hand-in-hand, and as Kopius went about building Tucson Medical Center’s (TMC Health) digital front door, both were at the forefront of our thinking.
The reason? Trust.
It’s important for people to be able to trust their health care system and its providers. And we wanted to send that message loud and clear in every digital interaction. Empathy and useability were the keys.
Key Strategies for Overcoming Complexity
Healthcare can be complicated, overwhelming—even scary. A digital front door, which is an online portal or platform where a health care system, its staff, patients, and even their families or other caregivers, can easily interact and access the information, should be designed to make it less so. Design thinking and user-centered design drove every aspect of our approach to building the new site, which we did using the Payload content management system.
Among the many strategies we used, three stand out. First, every decision we made was centered on the user journey, which was a bit tricky, since there was more than one user. Second, we made access to critical information as straightforward as possible. And third, we used visual branding to simplify and guide users.
While these are particularly critical in a health care setting, they are truly universal and applicable when developing any digital product or solution.
1. Prioritize the User Journey—Even on the Backend
When developing any digital product, the user journey should always be your top priority. But in TMC Health’s case, they needed to welcome both new and established patients, and their journeys are very different. For example, new patients are often looking for educational and marketing materials about what the health system offers while returning patients need to quickly find specific services and providers, schedule appointments, etc. We had to create pathways for both.
Digging deeper, we realized those aren’t the only two user personas that matter. TMC Health’s team uses the site to upload and manage content. They had their own user journey that had to be addressed. Not only did we need to structure the backend so they could work efficiently, but we also needed to build guardrails so that they uploaded new content, they didn’t make changes that would impact the user experience.
Websites often must address the needs of more than one user persona, both on the front end and the back. You may not be able to tackle everything at once. That was the case with the TMC Health project, so we took a phased approach. First, we addressed the established patient journey, then the needs of new patients.
2. Make Sure Important Information is Just Two Clicks Away
TMC Health’s previous website grew to include more than 1,300 pages of content. It was a maze to navigate. Our challenge was to simplify it so people could find what they needed with minimal effort. We started by conducting a content audit and inventory, then we built a restructured site map with improved hierarchy that prioritized important information. We also condensed content and sunset out of date information. In the end, we were able to get those 1,300 down to about 400, so that no critical information was more than two clicks away.
Next, we turned our attention to TMC’s internal users. To make sure they could add necessary content without overwhelming the site or patients using it, we developed content writing guidelines tailored for healthcare that focus on clarity, accessibility, and relevance. Then, we streamlined the back end to make it simpler for content writers to manage and update information across the network and reduce the need for training. We also added formatting and character count limits in the CMS to ensure new content was concise and is easy to skim.
While finding the information you need fast is critical when your health is on the line, it’s true on any website. Many companies, especially in the business world, overcomplicate their sites—they want potential customers to spend time on it. But I would caution to pick your moments. Customers come to your site for many reasons—sometimes they need information fast, and other times they’re there to learn. Prioritize accordingly.
3. Use Visual Branding to Create Cohesiveness—and Differentiation
TMC Health is comprised of 10 clinics and facilities. On their previous site, these were all visually branded the same. Typically, consistent branding is a best practice, but in this case, it created confusion for users. Our challenge was to find a way to create alignment with the primary TMC Health brand structure while making it easy for people to quickly differentiate between locations. We solved this by developing an overarching color scheme and using different but visually related colors for each location. Importantly, though, we kept the page layout consistent so users could quickly find or navigate to the information they needed.
This situation isn’t exclusive to healthcare—large corporations with multiple lines of business often face similar challenges. The big takeaway here is that color can provide cohesiveness, but in a situation where everything else is consistent, it can be a differentiator that helps the user—in this case a patient—quickly understand that they are in the right place.
Design Thinking: Empathy Builds Trust
Patient care begins at the digital front door. It’s a healthcare system’s first opportunity to build trust and demonstrate the level of care people can expect throughout their healthcare journey, from routine family care to urgent help in an emergency. A digital front door built on a solid foundation of design thinking and that prioritizes the user journey, can make a real difference in moments that matter, perhaps even saving lives.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
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.
Posted on November 5, 2024 by Diego Anfossi, Vice President of Delivery, and Matias Mazzucchelli, Managing Director
Everywhere you go, emerging technology like generative AI (GenAI) is top of mind. Organizations are wisely racing to incorporate it into their workflows to gain insights and efficiencies that will drive customer value and give them a competitive edge. But many of the fundamental challenges that faced IT and development teams prior to the advent of GenAI remain, and chief among them are simply bandwidth and budget. In fact, in the Skillsoft 2023-2024 IT Skills and Salary Report, more than 5,700 respondents identified resource and budget constraints as the number one challenge their organizations face.
At Kopius, we hear this from our clients every day. Your senior team members are so bogged down with day-to-day responsibilities, they don’t have the time to address emergent business needs, much less innovate. But adding headcount is both time-consuming and costly. It takes time and effort to find the right people with the right skills. You don’t always have the budget for full-time staff, or you may only need extra help for a short period of time.
At Kopius, we are excited to introduce Kopius Labs, a new resourcing solution designed to meet you where you are, so you can quickly and cost effectively stand up a team for a pressing, usually short-term project.
Your Team, Your Way—Flexible and Cost-Effective Blended Talent Teams
If you’ve worked with Kopius before, you know our team of inspired realists is our superpower. What you might not realize is how much work we put in behind the scenes to identify the best talent. And we don’t stop there—we also provide continuing education to make sure they’re always at the top of their game. Our near shore, LatAm-based teams are a blend of experts in a broad range of technologies and principles, people with solid, mid-level experience, emerging talent fresh out of Kopius Academy, our certification program, and everything in between.
If you have a small project or short-term need, we can quickly and cost effectively stand-up a Kopius Lab—a team of people with blended levels of expertise, some who are between longer term projects, to close the gap.
Kopius Labs is a win-win for both you and our team members. You benefit from rapidly advancing design thinking, accelerated feature development, and groundbreaking R&D work, and our teams gain rewarding opportunities and valuable experience working on cutting edge projects. All of this is delivered through a cost-effective, blended team structure, ensuring high-impact results without the expense of high-priced resources.
Just tell us what problem you’re trying to solve, and we’ll spin up a custom Kopius Lab to resource it.
Kopius Labs—A Right-Sized Resource Solution
At Kopius, we’re still focused on digital leadership: developing digital products and custom applications powered by technology, data, and IoT. And we still deliver services through all our usual resourcing approaches: future-state workshops, end-to-end project delivery, managed services, and with embedded team members. Now, with the addition of Kopius Labs, we can help our customers quickly fill technical gaps between those larger scale and longer-term projects.
Here are just a few ways Kopius Labs can help:
Managing Daily Operations Every company has operational upkeep—tasks you must do to keep things running smoothly. But it shouldn’t keep your senior team members from contributing where you need them most. Kopius can spin up a Lab to handle the everyday so you can use your team more effectively.
Addressing Emergent Needs No matter how well you plan, something unexpected always comes up. Need to quickly ramp up your technical resources to handle an ad hoc project or augment your team during busy season? Kopius can spin up a Lab so you can scale your team quickly.
Experimenting and Innovating Sometimes, you just need to understand if something is the right approach for your company. Looking to explore a new idea, test something quickly, or whip up a quick proof of concept? Kopius, can spin up a Lab to make sure you’re headed in the right direction.
Kopius Labs is all about scale, flexibility, and speed—at a competitive price, of course.
Innovate and Scale—Quickly and Cost Effectively—with Kopius Labs!
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 Kopius Labs so you can innovate and scale quickly and cost effectively
Posted on November 5, 2024 by Rob Carek, Vice President, Client Solutions
There is so much intellectual consideration given to the application development lifecycle.
Methodologies like Agile, DevOps, and DevSecOps, which are designed to drive more value by getting new features and enhancements to market faster, with fewer issues, are evidence of this. But very few people think about managing the code lifecycle, at least not beyond a single product. We often just accept the limitations and inefficiencies around code development.
AI-fueled CodeOps is here to change that.
CodeOps is designed to relieve developers of repetitive coding so they can focus on higher level work and accelerate the velocity at which they can get new features and enhancements to market.
Repetitive Coding—and Testing—Is Inefficient
Companies typically align development teams to a single product, and those teams rarely branch outside their own area of focus. The approach has both advantages and disadvantages. On the upside, developers have greater context for their work. They know their products and can build on code they created. The downside, especially for companies with multiple products, is that developers waste large amounts of time writing code that does things that already exist within other products. They’re writing code to do the same thing again and again.
It’s wildly inefficient.
And it’s just not developing code—it’s testing it, too. You develop the code, you develop the test code, you identify and address issues, you release, you fix bugs. The inefficiencies grow exponentially, especially across multiple products. You can see how this might open the company to greater exposure from a security standpoint, as well.
But what if you could find similarities between requirements, develop code to address them, and use it everywhere those requirements exist? What impact would it have on your company, customers, and development teams?
CodeOps Accelerates Velocity—and Value—at Scale
Enter CodeOps.
CodeOps is a methodology that prioritizes reuse of existing code wherever possible. Organizations can use it to reduce development time and get new products, features, and enhancements faster and more securely by reusing, repurposing, or building on code they already have. It entails adopting new ways of thinking, putting new practices and processes in place, and using technology like GenAI to match requirements with reusable, modular pieces of code stored in a code library, so new code is written only when it’s not in the library and/or is truly unique to a single product.
The obvious gains are consistency, efficiency, and security. Products are more structurally similar, and developers aren’t spending hours recoding the same thing dozens of times—or testing it. You already know it works. If your organization uses DevSecOps practices, you know security was a primary consideration in its development. And if there is an issue, once a patch is deployed, it is fixed everywhere it is in use.
But CodeOps is more than just an efficiency play. By using code from the library, even as a starting point, developers can put more time and effort into coding things that are going to have a big impact on your products—things that drive value to your customers and create value for your company. And from a developer’s perspective, that is more interesting, rewarding, and desirable work.
As with agile, DevOps, and DevSecOps, CodeOps requires cultural and process changes. Developers must adopt new ways of working, but they also must be willing to trust the code.
Ignite CodeOps Adoption with an External Catalyst
All the major code platforms—Jira, GitHub, Azure DevOps, Slack—are actively exploring how to integrate CodeOps into their solutions, and third-party tools are emerging, as well. They are all nascent, with some working better than others, which makes it difficult to determine which one will best serve you in the long run. In addition, adopting CodeOps is more than just bolting on a technology solution. Like Agile and DevOps before it, CodeOps requires a cultural shift. Developers must adopt a new mindset and new ways of working. And they must learn to trust the existing code modules enough to incorporate and build on them.
These technical, organizational, and cultural barriers make it challenging to figure out how to get started, especially when your teams have so much to do. Sometimes, it takes an external catalyst to make CodeOps real. At Kopius, we’ve developed a solution to help organizations adopt CodeOps without having to tackle the organization and cultural transformation or make a long-term commitment to a platform that is still figuring out its approach.
First, we use GenAI to intelligently review your backlog and identify commonalities in new requests. Next, we aggregate those requests and develop requirements to address them. Then, we develop code to cover the bulk of those commonalities and validate it with your developers to get their feedback and buy in. The code is stored in the code library and pushed to the right code repositories. Then, when you’re ready to tackle one of those new requests in a sprint, your developers simply pull the relevant code from the repository and use it as-is or as a starting point. As additional new requests come in, the process is repeated.
Companies gain the advantages that come with looking at code across their entire portfolio and maintaining it by feature and functionality, without disrupting existing development processes.
It’s a smart point of entry for any organization wanting to get started with CodeOps today.
CodeOps: A GenAI Approach to Working Smarter, Not Harder
Ultimately, CodeOps solves a fundamental problem that many organizations have—writing the same requirements and code for multiple products. It’s a hard challenge to overcome because the organizational constructs inherent in development teams lend themselves to a product-by-product approach.
But with a little help from GenAI and an external catalyst like Kopius developers can work smarter, not harder and accelerate the velocity at which they can deliver value.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
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.
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
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
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
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.
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:
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.
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.
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.
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.
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.
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
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.
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:
Subject-oriented: Data warehouses allow users to choose a single subject, such as sales, to exclude unwanted information from analysis and decision-making.
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.
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.
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.
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.
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:
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
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.
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.
