Today, we’re thrilled to sit down with Chloe Maraina, a trailblazer in Business Intelligence with a deep passion for crafting compelling visual stories through big data analysis. With her expertise in data science and a forward-thinking vision for data management and integration, Chloe has become a guiding light for organizations aiming to harness the power of data for business success. In this conversation, we’ll explore the essence of data management, its critical components, the day-to-day activities that drive it, the importance of security and privacy, and how it fuels digital transformation, alongside the tangible benefits it brings to businesses.
How would you define data management in a way that anyone could understand?
Data management, at its core, is about taking care of data so it can be useful for a business. Think of it as organizing, protecting, and making sense of all the information a company collects—whether it’s customer details, sales numbers, or operational stats. It’s a mix of strategies, rules, and hands-on tasks that ensure data is accurate, accessible, and secure, helping companies make better decisions and run smoother.
What do you see as the primary goals of data management for a business?
The main goals are to turn data into a valuable asset. That means ensuring data is reliable and consistent so businesses can trust it for decision-making. It’s also about improving efficiency—streamlining how data flows across departments—and reducing risks like breaches or compliance issues. Ultimately, data management aims to support growth by uncovering insights that drive new opportunities and better customer experiences.
Could you share some of the everyday tasks you’ve handled in data management?
Absolutely. On a typical day, I might be working on cleaning up datasets to remove errors or duplicates, ensuring data quality. I’ve also spent time setting up dashboards for teams to visualize key metrics, or collaborating with IT to integrate data from different systems. Another big part is monitoring access controls to make sure sensitive information stays secure. It’s a mix of technical work and coordination to keep data usable and relevant.
Can you walk us through the essential pieces of a data management framework?
Sure. A solid data management framework has a few key pillars. First, there’s Data Strategy, which sets the direction—how data should support business goals. Then, Data Governance, which is about the rules and roles that keep data consistent and compliant. Data Architecture is another piece; it’s the blueprint for how data systems are built and connected across an organization. Together, these create a structure to manage data from creation to use, ensuring it’s handled responsibly at every step.
How does data governance tie into the broader scope of data management?
Data governance is like the backbone of data management. It’s the set of policies, standards, and roles that define who can do what with data and how it should be handled. While data management covers everything from storage to analysis, governance specifically ensures accountability and quality. For example, it dictates how data is classified or who gets access, which prevents misuse and keeps everything aligned with business and legal needs.
What’s the importance of data architecture in managing data effectively?
Data architecture is critical because it’s essentially the foundation of how data is organized and stored within a company. It’s the design of the systems, databases, and connections that make data accessible and usable. Without a well-thought-out architecture, you’d have silos—data stuck in separate places with no way to connect the dots. A good architecture ensures data flows seamlessly, supports scalability, and makes integration easier as the business grows.
How do you distinguish between data management and data governance in practical terms?
I see data management as the big picture—it’s all the activities, tools, and processes involved in handling data throughout its lifecycle, from collecting it to analyzing it. Data governance, on the other hand, is a subset focused on the rules and oversight. For instance, data management includes the tech side, like running backups or building reports, while governance is more about deciding who’s responsible for data quality or setting privacy policies. They overlap, but governance is more about control, and management is about execution.
Can you describe how data security plays a role in data management based on your experience?
Data security is a non-negotiable part of data management. It’s all about protecting data from threats—whether that’s unauthorized access, leaks, or corruption. In my work, I’ve implemented encryption for sensitive data and set up strict access controls so only the right people can see certain information. Security isn’t just a one-time setup; it’s woven into every stage of managing data, ensuring trust and compliance while preventing costly breaches that could damage a company’s reputation.
How has data management supported digital transformation in projects you’ve worked on?
Data management is often the fuel for digital transformation. In one project, I helped a company modernize by consolidating their scattered customer data into a single, accessible system. This made it possible to adopt new AI tools for personalized marketing, which was a game-changer. Without clean, organized data—a result of solid data management—they couldn’t have leveraged that technology. It’s about preparing the data foundation so new tools and processes can actually deliver value.
What do you believe are the most significant benefits of effective data management for a business?
The benefits are huge. First, it boosts efficiency—when data is well-managed, teams spend less time hunting for information and more time using it. It also improves decision-making with accurate, real-time insights, which can lead to better strategies or faster product launches. Then there’s risk reduction; good data management cuts down on errors, breaches, or compliance fines. Plus, it enhances customer experiences by enabling personalized services. Overall, it’s a competitive edge that saves money and drives growth.
What’s your forecast for the future of data management in the coming years?
I think data management will become even more central as data volumes keep exploding and technologies like AI advance. We’ll see a bigger push for automation in managing data—think smarter tools for quality checks or integration. Privacy and security will also take center stage with stricter regulations worldwide. I expect more companies to adopt data fabrics or unified platforms to handle complexity. Ultimately, data management will evolve to not just support business, but to predict and shape its future through proactive insights.
