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 data volumes are expected to almost double in size from 2022 to 2026, 93% of IT decision-makers view storage and data management complexity as impeding innovation and digital transformation. In fact, a recent survey [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. What’s Driving Data Management Costs? 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 a database 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. See More: Data Orchestration Basics: How to Organize Data to Gain Real-Time Insights Tips for a Successful Top-down, Streamlined Data Strategy Pivoting to a streamlined data strategy can help businesses salvage their data management and analytics capabilities.

A top-level, consolidated data approach is instrumental to determining the right priorities and where to invest in helping minimize complexity and costs and ultimately enable an organization to leverage its data better. Here are some critical considerations for businesses to keep in mind when implementing this approach: 1. Structure your organization to enable a data-led culture Work to determine the right organizational structure to foster a data-led culture.

This comes down to assigning data asset ownership, filling any data skills gaps, and hiring accordingly. For example, hiring a chief data officer (CDO[MP2] ) can simplify this process and help orchestrate and oversee this change. In fact, CDOs have been in very high demand in recent years for this reason. Seen as a specialist in translating and delivering real value from data, this person doesn’t necessarily need to have deep technical expertise but rather an understanding of how to transform a business culture at a higher level successfully. 2. Prioritize operational business intelligence (BI) Solutions should be measured against output so the ROI can be monitored on an operational basis. Instead of just collecting the departmental requirements for specific data reporting capabilities, compile a thorough data strategy that contains goals and priorities.

Change your mindset from primarily leveraging data to create transparency through streamlined KPIs and metrics to using data as a driving source for data-driven decision-making. Ingraining data analytics into the business chain enables organizations to optimize their business; this is the ultimate transformation from traditional BI to operational BI. 3. Streamline and simplify your data stack Invest in cloud infrastructure, software as service (SaaS) offerings and other modern data solutions that use infrastructure more efficiently to help simplify technical stacks, enabling companies to be more agile in their data strategies. SaaS, for example, helps reduce complexity within an IT team’s own organization on multiple levels by eliminating the need to maintain solutions themselves, offering lower costs and enhanced scalability.

While data strategy consolidation should always be a goal for organizations, we must also keep in mind that it is not a silver bullet to all data management problems. Leveraging best-of-breed solutions should not be fully discounted, as businesses will always need to mix and match tools to a certain extent depending on their unique needs. Data teams must continue to learn, evolve and adjust to meet their changing data demands.


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