After a few years of reading about the benefits that advanced analytics can offer companies, yours has finally decided to undertake a broad implementation. Not dip a toe in the water. You’re going all in.
Before your company dives in headfirst into the shallow end of the data pool, consider a couple of common missteps that companies make as they rush to implement analytics. By understanding what can go wrong, you can avoid that moment when someone says, “Hey… waitaminnit…”
Silo building. Silos are out, old school, prime for busting. But what many people don’t realize is that when they start implementing new data management strategies, they start building new silos. Companies often initiate projects that address specific business objectives such as improved marketing or reduced customer churn. But the sales data required to feed these is often slightly different.
By thinking and planning ahead, one sales data feed could be developed to satisfy both requirements. But what often happens is that the two initiatives create two separate pools, never cross paths, and keep their projects isolated. Multiply that over a couple of years by dozens of projects, and the data fields look like the plains of Iowa. Data silos as far as the eye can see.
Road to nowhere. Developing a data and analytics program is often driven by one of two considerations: either to integrate data from specific core systems or to consolidate and eliminate existing data marts — both worthy goals. But here’s the rub: There’s often no tie between those efforts and a real business goal. Often, the desire to eliminate data warehouses or integrate data becomes the goal itself, with no direct connection to a business initiative or goal. You’ve checked off a box but you’ve accomplished nothing of value.
Boiling the ocean. To many companies, the idea of developing a data strategy just seems like crazy talk. Especially when you consider that the volume of data is mounting and speed is increasing. Actually, not developing a data strategy is the crazy idea. With the right approach, it is achievable. And considering that successful companies embrace data-driven decision making, it’s essential.
Establish a data strategy
As Aleksey Gurevich and Srijani Dey observe in DXC Technology’s paper “Defining a Data Strategy,” data strategy ensures that all data initiatives follow a common method and structure that is repeatable. Uniform data enables efficient communication throughout the enterprise for rationalizing and defining all solution designs that leverage data in some manner. Data strategy can help companies achieve four important objectives:
- Unifying business and IT perspectives. Having a shared understanding of the company’s direction will enable business and IT units to work together, not at cross purposes.
- Aligning the enterprise vision for leveraging data as an asset. This ensures that different groups in the enterprise view data-related capabilities with consistency, which reduces redundancy and confusion (and silos).
- Defining common success metrics across the company. This reinforces how initiatives are measured, evaluated and tracked across the organization.
- Reducing technology debt. Data strategy can help chart a course for applying innovation that moves the company away from legacy systems with minimal disruption to ongoing business operations.
In a consistent, cohesive environment that’s wrapped with a well-thought-out data strategy, companies can build a utility-like service that provides a “supply chain of insights.” Just as you plug a lamp into an outlet and expect power at the flick of a switch, data strategy can provide the same kind of juice to your applications, delivering insights at the click of a mouse.
Need a plan for advancing your digital transformation journey and treating data as a corporate asset? Read the DXC paper, “Defining a Data Strategy.”