Beyond the container: Elements of a successful data strategy

Data is the lifeblood of every modern enterprise and indeed of our modern world. In fact, the world is expected to generate three times more data in 2025 than it did in 2021. Storing, managing, and analyzing that data is critical for every line of business, from product development to customer service to talent acquisition. Globally, enterprises spent $89 billion on data management in 2022, with that market expected to grow at more than 12% each year through 2030. Those companies that can make the best use of their data stand to gain a valuable edge over competitors. In fact, it’s safe to say that modern data management is a foundational, make-or-break aspect of any digital transformation. Therefore, engineering a data strategy that enables deeper insights into the business while setting the stage for future innovation must be a priority.

When it comes to data, companies are grappling with two main challenges. The first is simply how to capture and store the mountains of data being generated every second. This can include data about sales, inventories, employees, cash flow, and much more. They must determine what makes the most sense for their organization: Should one opt for a well-structured repository, like a data warehouse, a more flexible yet technically demanding data lake, or a decentralized data mesh? Do they go all-in on the cloud, keep their data on-premise, or adopt a hybrid approach? This boils down to choosing the right container for the data. That’s an important decision, to be sure. But the second, more critical challenge, is how to increase the adoption – and therefore the ROI -- of the data ecosystem.

By one estimate, just 32% of the data generated by medium and large enterprises end up getting used. But how can you harness the other 68% of your data to generate insights and further your organization’s business goals? That’s the consumption part, and it’s a much trickier question to answer. Storing all that data on a fancy new platform is pointless if you’re not going to use it effectively. We need to move the needle to increase consumption.

Choosing the right technology is crucial, but it’s only a single component of a more comprehensive strategy. Simply adopting trendy buzzwords won’t miraculously fix an organization’s data challenges. Despite the current hype around technologies like generative AI, it’s essential to pause and consider how they can genuinely benefit your organization. Achieving success with data requires addressing “soft” factors that touch on culture, and organizational change, and fostering the right mindset among employees. Here are a few essential elements of any modern data strategy:

• Promoting a data-centric culture and mindset among employees, such as through data-literacy training.
• Encouraging intuitive and user-friendly tools, interfaces, and data-visualization dashboards that enable self-service analytics.
• Investing in data governance and quality initiatives to establish ownership and collaboration among key stakeholders while maintaining security, privacy, and compliance.
• Identifying use cases that support business goals, such as applying predictive analytics for customer segmentation, or using natural language processing for sentiment analysis.

At Virtusa, our Engineering First mindset asks these basic questions about your organization, starting with, what exactly is the problem you are trying to solve? We think beyond the container to explore how you can use your data to advance your business and organizational goals. And we do that by offering a fit-for-purpose architecture, end-user training to improve adoption, a blueprint for a futuristic global data organization, and more. This approach ensures that your data strategy goes beyond a one-off migration and transforms into the fuel that drives meaningful insights into your business.

Reinforcing the importance of data consumption in driving business success is critical. Technological advancements are important, but they will mean little without organizational readiness. A data strategy that focuses more on increasing consumption through a combination of appropriate technology and soft factors can help ensure that it delivers the returns you expect, for years to come.