First impressions are the most important, and that’s just as true when building out analytic capabilities. That first project is the best chance to jolt an organization into action and is a reliable bellwether of long-term success. The best way to identify that future-defining first project is a solid data and analytics strategy (DAS).
A DAS is vital to quickly deriving real value from data
To stop hemorrhaging money, organizations need to move quickly to build analytics capabilities. That first project is the best bet for building momentum. And a DAS is the best tool to pinpoint the project.
Let’s say your organization is looking to move data from a power or resource-hungry on-premises data center to a cloud-based database. A data and analytics strategy maps out how to undertake this shift, while assuring you have the right people and skills in place to make it count. With your key next steps laid out, it is possible to pinpoint the first project.
An ideal first project fits the RAVE criteria:
Return. This is the ‘R’ in your ROI. If the project is not providing a return, you probably shouldn’t be doing it.
Alignment with company strategy. Alignment means the project affects what matters and moves the company forward. A data and analytics strategy clearly articulates what this strategy is and why it matters to the business.
Visibility. It’s hard to build momentum with a backend solution only four people see.
Ease (relatively speaking). This may be the first foray into unfamiliar territory. You don’t want to start with a whiff. Instead, take aim at a slow-moving meatball.
Sticking with the earlier example, let’s assume a big part of the business’s cash flow rests with customer payment patterns. Better visibility into outstanding payments could help the company lower its accounts receivables line alongside its interest burden. The Return metric here is clear.
If you’re prioritizing a project, start moving that customer payment data into the cloud. Once migrated, you can take advantage of that platform’s seamless integration with state-of-the-art reporting tools.
A visible tool that makes people’s jobs easier will do far more to accelerate a company’s data and analytics strategy than a years-long migration only the team of database administrators see. That backend work is important. It will still have to happen. But it’s important to pair the backend data piping with projects that affect users directly.
Why first impressions matter
A meaningful approach to data analytics is as much about the people than the technology. The best analysis is meaningless if people don’t incorporate it into decision making. And it will fall far short on value if it’s not a part of key processes such as planning and forecasting. Plus, finding the funds for critical data work is a lot easier if leadership can draw a connection between the investment and the outcome.
For data to drive value, people need to be on board. They need to be excited. Most people (data enthusiasts like us excluded) don’t get too excited about well-executed Extract Transform Load code buried in the recesses of a company’s innerworkings. People get excited about tools that make their lives easier. Yes, good data architecture is important. But the theoretical is seldom compelling. People want something they can see and interact with.
Once people see a successful project, they will be far more likely to invest in those less visible but equally important projects that hold the key to quality data and analytics.
Ultimately the road to data and analytics capabilities can be a long one. Having a solid DAS as a starting point allows analytics leaders to think strategically about project timing and plan out projects that make a difference.
RevGen has deep experience in analytics and business strategy. We have helped clients from the Fortune 500 to the mid-market create and execute data and analytics strategies that deliver measurable value. Contact us today to have a conversation about building your own DAS.
Andy Vold is a Senior Architect and RevGen data enthusiast who knows exactly how important a solid strategic foundation is to building more complex data and analytics solutions.
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