Is Your Data Management Living Up To Its Promises?

It’s clear that data-driven insights will matter more as companies navigate a market landscape ravaged by macroeconomic pressures and the far-reaching effects of the pandemic. It puts data and analytics teams under immense stress to unlock vital insights from their data treasure troves as they battle legacy data architectures and lack of proper governance.

In a recent virtual roundtable, CDOTrends and Denodo invited experts to discuss the challenges and best practices of data management and examine data virtualization's value. Titled “Becoming Data-Driven With A Modern Data Management Strategy,” it looked at how data virtualization allows organizations to break down data silos, increase agility, and improve business outcomes by decoupling data from its physical location.

But as the discussion highlighted, putting theory into practice is never a straight road. Companies also need a more holistic approach to data management, not a piecemeal one.

The missing data audit

Data management can be a complex and daunting task, especially for large companies with different data types spread across multiple silos.

The first challenge is simply understanding what data a company has and where it is located. Once this data is identified, it must be cleansed, transformed, and standardized before being used.

This process can be time-consuming and costly, and it is often difficult to get all stakeholders on board with the changes.

COVID-19 has also created new challenges for data management. With so much uncertainty, organizations need to be able to change their data strategies as the situation evolves rapidly. They must also trust the data they are using to make decisions, which can be difficult when employees are working remotely, and data comes from various sources.

However, it may not be necessary for data insights to extract all the data from the company. One participant, a senior IT director from the healthcare industry, shared his experience and strategies for extracting data from different sources, including hospital billing, lab records, and radiology systems.

He noted that it is essential to extract only the necessary data to support use cases and do so in a way that does not impact the operational system.

Felix Liao, product management director for APAC at Denodo, stressed the increased need for speed and agility and the importance of having a proper data management strategy before a crisis.

He cited the example of RMIT University in Australia, which used data virtualization to quickly develop a one-stop-shop portal for students and staff during the COVID-19 pandemic.

However, having the right platforms and tools in place is not enough. Companies also need the right people and processes to promote holistic data virtualization, make data-driven decisions, and build a culture of data-driven innovation.

Creating the agile data-driven mindset

To be successful, data-driven organizations must have agile data management teams that quickly adapt to changing needs. This requires close collaboration between business and IT and between different departments within an organization.

A managing director at a government-linked investment firm shared how his team is working to build an agile data management team to create a data environment that can rapidly respond to changing business needs. He noted that it is vital to have the right mix of skills on the team, including data scientists, engineers, and business analysts, to converge the suitable environments and get to the right insights quickly.

There is also an ongoing discussion on treating data as a product. Product managers are responsible for the strategy, roadmap, and execution of a product, and many organizations are now looking at data in the same way. The managing director from the investment firm said that this requires a different mindset and set of skills than traditional data management, as product managers must understand both the business and the technical aspects of data.

Furthermore, it is important to encourage technical employees to draw closer to data ownership and work with business stakeholders to understand their needs. By doing so, they will be able to identify opportunities for data-driven innovation better.

Other participants agreed. They shared how their companies identified data stewardship as critical in their data management strategy. Data stewards are responsible for maintaining the quality of the data and ensuring its accuracy. They work with data users to understand their needs and help them find the right data. This close collaboration is essential for ensuring that data is used effectively and efficiently.

According to a vice president of a major Thai bank, there is also a need to look at the entire data landscape, not just individual data sets. This includes understanding how data is generated, how it flows through the organization, and how it is used. By doing so, companies can develop a more holistic view of their data, ensure consistency, and identify gaps and opportunities for improvement.

Re-calibrating the data strategy

To be successful, data-driven organizations must clearly understand their business goals and the outcomes they wish to achieve. Participants noted that this could be done by breaking down the strategy into two parts: operational and growth. The operational strategy focuses on what needs to be fixed in the current environment, while the growth strategy focuses on areas where the organization can scale through data-driven innovation.

It's also important to understand that there is a bespoke element to how each organization's systems work, depending on the domain they operate. For example, data must be processed in a particular order in the financial markets. As such, it's critical to have a clear understanding of how your system should work before implementing a data management strategy.

Then you need to understand how you can structure your teams around these strategies and how your systems work. The investment firm’s managing director advised that companies need to start getting into conversations about harmonizing data structures and ensuring enterprise clusters across different divisions and departments.

Most importantly, all participants agreed that culture is a critical success factor for data-driven organizations. For many, a data-driven culture starts with the senior leadership team and trickles down throughout the organization. It's also essential to have buy-in from all levels of the organization and clear communication on the goals and objectives of the data strategy.

Such a holistic approach may be challenging for many companies grappling with lean budgets and shifting market dynamics. But as the participants noted, it is also necessary if companies truly want to become data-driven and be ready to weather upcoming market shocks.

Image credit: iStockphoto/Pheelings Media