At UDig, we geek out over data projects and enjoy helping organizations realize the full potential of their data as they try to better understand their internal performance metrics, as well as the profile and habits of their customers, partners and competitors. There have been enough blogs on the value of data and, in a digital age, knowledge about your data is power – the power to make better decisions, take better actions and get better results.
In many organizations we see more partnership between the business and IT on data strategy – especially when there’s a “data-as-an-asset” culture instead of it being a managed expense…or worse…a liability. However, as companies invest stakeholder hours and budgets into projects which help them glean better insights from their data, the problem we continue to see in the market is the proverbial cart-before-the-horse: Fixing the front-end data visualization before back-end systems and data streams are fully integrated.
We get it. It’s easy to doctor up the graphics on those excel spreadmarts or sink money into a BI tool to present a quick, appealing ROI. In fact, most intelligence software provide out of the box connectors to commonly used data sources (google analytics, social media, etc.) for easy adoption and quick wins on reports and dashboards. But good data practices would tell you those tools are not true data warehouses and don’t solve for data quality, governance, and integration problems.
The problem? An intelligence house built on sand…
- Accuracy – Improper governance leads to poor data quality: mismatched fields and formats which present inaccurate or incomplete results. Manual remediation creates a soft-cost impact to team productivity.
- Timeliness – Poorly integrated data leads to more complicated extracts which leads to longer report times and congested system performance.
- Cost – Cumbersome and complex data architecture grows expenses in storage and maintenance. Costs can compound if problems are addressed in the wrong order (e.g. investing in a BI tool before you know it’ll actually fit your business needs and technical environment).
- Narrow scope– Lack of visibility into historical data for comparative modeling or an inability to blend data from different sources for advanced insights.
- Wasted effort – Rebuilding visualization models after foundational issues are addressed.
The solution? A house built on rock…solid data architecture.
- Start at the beginning, but with the end in sight. Meaning, before investing in sexy analytical tools, assess the landscape for desired outcomes, seek to understand the business needs to visualize data and prioritize those values.
- Uncover the present-state architecture and design the roadmap to a proper data management strategy that promotes sound analytics of the key values.
- Invest, Implement, Iterate.
Are you confident in the trustworthiness of your reports? Are you only seeing part of the picture when it comes to business and performance metrics? The right partner, like UDig, can help set up your organization for success from strategy to implementation.