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  • Writer's pictureKarol Tajduś

Shifting Paradigms: Why a Value-Driven Approach Outweighs Technology-Driven in Data Lake

The discussion on how to approach data lake implementation is tale old as time but in today's digital world, data is like the new oil, and data lakes are the refineries turning raw information into actionable insights. The way we build these "refineries" or data lakes, though, can make a world of difference in how well they work. Traditionally, businesses have taken a tech-first attitude when setting up their data lakes. However, a shift is underway, with recent trends and research pointing towards a more business-focused or value-driven approach as the new secret sauce for success.

Understanding the Technology-Driven Approach:

The technology-driven approach to data lake implementation is somewhat akin to a child mesmerized by shiny new toys - it's human nature to be drawn to the new and exciting. This outlook translates into the world of data, with businesses sometimes getting captivated by the allure of cutting-edge technology, and eager to procure and implement the latest data tools.

This approach primarily revolves around collecting and storing vast amounts of data, predicated on the belief that we'll be better equipped for the future if we have data readily available. However, this often occurs without a concrete plan for converting this raw data into actionable insights - similar to a child amassing toys without a clear idea of how to use them effectively.

While it might seem technologically advanced and prepared for any data scenario, its limitations have become increasingly evident. Just as a child can end up with a room full of unused toys, businesses can end up with sprawling data repositories that are heavy on technology but light on strategic value.

Moreover, despite the initial fascination with the latest data technology, the absence of an overarching strategy often leads to underutilized resources, inefficiencies, and escalating costs over time. Just as a shiny new toy loses its appeal when it's not played with, technology for its own sake becomes an extravagant exercise if it's not serving the broader goals of the business. This underlines the inherent limitations of a purely technology-driven approach to data lake implementation.

Unveiling the Value-Driven Approach:

On the flip side of the technology-driven approach is the value-driven approach. Here, the focus shifts from the tools and technologies employed to the value and insights that the data can offer. This perspective puts the emphasis on the business outcomes, rather than the means of achieving them. Imagine a master chef who is less concerned about the novelty of their kitchen gadgets and more focused on the quality and taste of the dish they are preparing. That's the essence of a value-driven approach.

In the value-driven approach, data isn't just collected and stored for the sake of it; rather, each piece of data is viewed as a potential nugget of wisdom that can be leveraged to generate insights, solve business problems, and drive business objectives. It's about creating a clear vision for data utility from the get-go, one that aligns with the broader business strategy. This means that right from the data collection stage, there's already a plan in place on how this data will be harnessed to drive value.

Notably, a value-driven approach to data lakes doesn't just present theoretical benefits. Concrete evidence supports its superiority over the technology-driven model. According to leading research and advisory company Gartner, organizations employing a value-driven data lake approach are expected to outperform their peers by 10% in key performance indicators by 2023. This statistical prediction reinforces the shift in data management philosophy that's occurring across industries, advocating for a greater focus on the strategic value of data rather than the mere accumulation of it.

In the grand scheme of things, a value-driven approach resonates with the ultimate goal of businesses: to derive value from their operations. By aligning data strategy with business objectives, this approach ensures that the implementation of data lakes contributes positively and effectively to the achievement of business goals.

Pitfalls of a Technology-Driven Approach:

The technology-driven approach, once hailed as the gold standard, has come under scrutiny for its inefficiencies and limitations. A startling study by IDC revealed that up to 73% of data in an enterprise goes unused for analytics, with technology-driven data lakes contributing substantially to this data wastage.

One of the most glaring pitfalls of this approach is the creation of 'data swamps' — expansive reservoirs of data with no clear structure or categorization. These data swamps are essentially digital quagmires, where valuable insights can easily get lost or become inaccessible due to the lack of proper organization and management. This problem often stems from an overemphasis on collecting and storing data, while neglecting the critical aspect of data organization and usability from a business perspective.

Harvard Business Review, in highlighting this issue, notes that data swamps can lead to poor returns on investment, as businesses often find it challenging to extract valuable insights from these disorganized and chaotic data repositories. The technology and resources invested in storing vast amounts of data yield minimal value if the data remains underutilized and inaccessible.

Adding to these concerns are issues around data security and compliance. The technology-driven approach, with its emphasis on amassing data, may overlook or underestimate the importance of robust data governance. This can lead to increased risks around data breaches, non-compliance with data protection regulations, and resultant reputational damage and financial penalties.

Additionally, a technology-driven approach can create data silos within organizations. Different departments might use different systems and technologies for their data needs, leading to a lack of interoperability and data sharing. This siloed approach hinders the creation of a unified, organization-wide view of data, limiting the potential for comprehensive analytics and insight generation.

In sum, while a technology-driven approach may seem appealing due to its emphasis on cutting-edge tools and systems, it has several significant drawbacks. These include the creation of data swamps, underutilized data, data security risks, compliance issues, and the proliferation of data silos — all of which underscore the necessity of a strategic shift towards a value-driven approach.

The Upside of a Value-Driven Approach:

Conversely, a value-driven approach to data lakes offers a myriad of strategic advantages that translate into tangible business outcomes. By prioritizing the extraction of value from data, this approach facilitates the generation of strategic insights, the enhancement of decision-making, and the improvement of operational efficiency.

In contrast to the technology-centric model, the value-driven approach does not perceive data as merely a byproduct of business operations. Instead, data is considered a vital asset that, when appropriately analyzed and interpreted, can unlock significant business value. This perspective transforms the data lake from a passive repository into a dynamic, insight-generating engine.

Supporting this perspective, a report from the McKinsey Global Institute presents compelling evidence of the benefits of a value-driven approach. The study found that companies embracing this approach saw a substantial 5-6% increase in output and productivity. This uptick can be attributed to the enhanced ability to make informed, data-backed decisions that drive efficiency and optimization in business processes.

Moreover, the value-driven approach is centered around achieving specific business objectives. Instead of indiscriminately collecting all available data, the focus shifts to gathering and analyzing data that directly contributes to the accomplishment of these objectives. This means that the data collected is, by design, usable, accessible, and prepared for insight extraction.

For example, if one business objective is to improve customer retention, a value-driven data lake might prioritize collecting data about customer behaviors, feedback, and churn. This information can then be analyzed to gain insights about why customers are leaving and how to convince them to stay.

Furthermore, a value-driven approach ensures better alignment between the data strategy and the overarching business goals. This harmonization leads to a more effective utilization of resources, eliminates redundancies, and improves coordination between different business units. The result is a higher return on investment as the data lake's outputs - in the form of insights and enhanced decision-making - contribute directly to achieving business objectives.

Thus, the value-driven approach elevates the role of data lakes from being mere data repositories to strategic tools for business growth and success. By prioritizing value extraction from data over mere data accumulation, it optimizes the return on investment and drives better business outcomes.

Making the Shift to a Value-Driven Approach:

To shift from a technology-driven approach to a value-driven one, it's vital to realign your data strategy with your business objectives. The longest and arguably the most challenging part of the process is gaining buy-in from business leadership. Here's a step-by-step strategy to do this:

Step 1: Identify and Communicate Business Value

Start by identifying the business value that a data lake can offer. This could be strategic insights, improved decision-making, cost savings, or any other benefit relevant to your business. The next step is communicating this value to business leaders. Use concrete examples and case studies to make your case more compelling.

Step 2: Foster Collaboration

A successful shift requires collaboration between business and technical stakeholders. Bridge the gap by fostering a collaborative environment where both parties understand and work towards common goals.

Step 3: Develop a Value-Driven Data Strategy

Design a data strategy centered around business value. Prioritize data that can generate the most value and insights for your business.

Step 4: Implement Strong Data Governance

Ensure your data is accurate, reliable, and secure

. Implement strong data governance practices to maintain the quality and integrity of your data.

Step 5: Measure and Report on Success

Once your value-driven data lake is in operation, track its success using relevant KPIs. Regularly report these findings to business leaders to demonstrate the ongoing value and success of the initiative.


Adopting a value-driven approach to data lake implementation has the potential to revolutionize your business decision-making process. While the shift may require effort and cultural change, the benefits — strategic insights, improved decision-making, and higher ROI — make it a worthwhile endeavor.

Consider your current data lake strategy. Could a shift towards a value-driven approach unlock your data's true potential? Take the first step today and start your journey towards a more efficient, productive, and insightful future. For more information and resources on making this shift, click here.

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