Data analytics has become table stakes for competitive differentiation. As a result, businesses require their analytics-driven insights as quickly as possible. The reality is, however, that queries often take weeks or even months to complete.
One of the key logjams to completing queries more quickly is a disconnect that exists between business stakeholders and IT teams. IT has insight into what data the organization is capturing, but typically has little (if any) visibility into business needs. On the flip side, lines of business naturally have insight into the business objectives they are trying to meet with a query, but they do not have visibility into what data is available, or the quality of that data.
A major part of the problem is that data lives across multiple different databases and data warehouses today. At the same time, available data streams and the business needs and questions that are driving analytics queries are constantly changing. The process of aggregating and preparing data has become too complex for IT to be able to keep up with the business.
Simply put, IT teams and data engineers are spending so much time trying to locate and decipher data, when really, they need to become a collaborative and agile partner that delivers value-add actionable insights in a timely manner. Determining what data is available to answer a query, and then figuring out how that data needs to be prepared, takes a very long time when different tools are used for each data source. This approach is no longer feasible especially as analytics queries scale across the enterprise.
Further compounding this problem is the fact that there is typically, very murky visibility into data availability, data context and data relationships. Most IT teams do not even have a holistic perspective into what data their organization is capturing. As a result, analytics queries might not be delivering the most in-depth or the most accurate insights. Additionally, the business might miss out on opportunities because it wasn’t aware that a particular question could even be answered with the data that is available.
To overcome this challenge, IT requires a tool that automatically provide context into data relevance, quality and relationships, across various databases and data warehouses. To learn about this and other challenges that will derail your analytics project – as well as how to overcome those challenges – join Storage Switzerland and Promethium on for our on demand webinar, “The Top Four Challenges of Data Analytics and How to Solve Them.”