Is It Time to Put Your Data Strategy on a Diet?
Organizations today need to streamline their data strategies to be able to lift more value from their data and derive tangible benefits. Mathias Golombek, CTO at Exasol, shares insightful tips for efficient, cost-effective data management and how companies can employ leaner data management systems and processes.
In today’s competitive business environment, data has never been more valuable. It helps inform almost every aspect of business, from product innovation to security to hiring, and can be a critical competitive differentiator for organizations. However, while datavolumes areexpectedto almost double in size from 2022 to 2026,93% of IT decision-makersview storage and data management complexity as impeding innovation and digital transformation. In fact, a recentsurvey[MP1] found that organizations use an average of around 23 different data management tools.
It is clear that we’ve reached a critical crossroads in the data management space. Complexity and costs are growing, hindering companies at a time when an efficient and effective data strategy is needed most. Let’s dive into some of these common data management challenges organizations face and how a streamlined data strategy can help.
There are several common challenges today’s IT teams encounter when it comes to data management. At the top of the list is investing in the incorrect infrastructure for their specific data workload needs. On the one hand, a business can invest in adatabase that requires too big an infrastructure to perform, hindering data-driven operations. On the other hand, a company may begin with a small and affordable solution but will soon see costs exploding if it isn’t scalable and the system grows linearly with usage.
Organizations also face challenges around personnel costs for database administration and implementation, often underestimating them from the onset. Labor costs can quickly add up if the data stack is too complex and extra engineering work is needed. This can occur, for example, if there’s a data system that needs several database administrators (DBAs) tuning the systems at all times or if a business jumps at the opportunity to invest in a data management system that is affordable from a software license perspective, but needs additional engineering work.
Because of these data management complexities and growing expenses,data teams often become the bottlenecks for data processes, leaving their organizations with vast data lakes of expensive, unusable data. Without the ability to lift the real value from the data, businesses are unable to conduct effective data-driven decision-making, leaving them at a large competitive disadvantage. If your organization has reached this point, it’s time to make a change to your data strategy.
