data is a core asset for every organization, whether it already serves Business decision-making or will do so in the future. This is why cost-efficient data collection and storage is a priority for companies looking to reap benefits from this asset in the future.
But a lot needs to be done before managers act on data-driven insights. What organizations need to establish first is a solid data management foundation.
To accomplish that, enterprises need to start treating their data as they would treat their product, adopting the right practices to ensure its high quality.
• Good data is timely and up to date: Basing a decision on data that has not been collected for months or years is plain risky.
• It's trustworthy and reliable: There is no point in generating reports if managers don’t trust them to represent what is really going on.
• It comes with privacy and security practices: Data management adheres to both external and internal regulations (e.g., the definition of privacy).
• Storing, processing, visualizing and reporting on data has a positive ROI: If you use data insights to increase sales by 5%, but storing it costs 20% of what you are selling, then it's time to rethink your data strategy.
Enterprises have been dealing with data for decades, and for the past few years, they’ve had efficient Data management tools at their disposal.
What makes managing data still so challenging? It’s all down to the complexity of the data lifecycle at scale, the industry skill set gap, managing multiple data sets and facing integration challenges.
Collecting and processing data may sound simple in theory, but it requires much effort. A typical data lifecycle consists of at least five different stages.
If any step is delayed, it causes a domino effect. An organization with many data sets going through this process will encounter an issue sooner or later. And legacy systems increase the chances of something breaking on the way.
Data is not static by definition. It's a living, breathing thing that is constantly moving through these stages and changing.
The complexity inherent to data management is compounded by the fact that analyzing a data set on its own is not enough. To unveil game-changing insights, teams need to combine several data sets.
Looking at sales data only gives you simple insights (e.g., how much you sold yesterday). This question becomes much more interesting when you combine inventory data to find out which product type sells better than others.
This comes at the price of increased complexity. If a report combines data from sales and inventory, each of these data sets has its own lifecycle where something may go wrong. If the inventory data is delayed or broken, this will impact the entire report.
Humans tend to make mistakes. In the data world, they are usually very expensive.
