Aligning data priorities with business goals


Leaders in data and business intelligence (BI) organizations are tasked with delivering measurable business outcomes from their data-driven initiatives. This can be challenging against a backdrop of evolving business objectives, multiple ongoing projects, and the need to manage risks, achieve hiring goals, and manage a budget. When initiatives are not tied to clearly defined metrics, it can be difficult to link them to tangible results.

Quantifying the value a foundational project brings to your organization may be challenging, yet it is important to demonstrate impact not only on the bottom line, but also to functional business areas and overarching corporate strategic initiatives. As a result, 82% of organizations are prioritizing analytics and business intelligence within budgets for technologies and cloud-based services in 2019, and 50% say a stronger focus on metrics and key performance indicators (KPIs) are a major driver of investment in analytics and BI, according to a survey quoted by Forbes.

Companies need “analyses that solve key commercial and operational problems,” states a Harvard Business Review article ; “what’s missing, more often than not, is a clear strategy and operational model for using these capabilities in ways that are specific to the company’s business requirements.” This effort depends on three elements:

  1. People who can effectively combine commercial expertise and advanced analytics methods and applications.
  2. An evidence-based approach to translate analytical capabilities and an understanding of business problems into actionable insights.
  3. A small team of analytics professionals (not necessarily data scientists) to devise tools and techniques and provide internal advice and training so that these can be deployed within the enterprise.

Establishing a cross-functional data team

As an example, let’s say your data organization is planning a Master Data Management (MDM) program: Before beginning this foundational project, your team should define a process to track success metrics. A cross-functional data team should be brought together for a brainstorming session since such projects impact multiple IT functions and it is crucial to document a comprehensive list of data metrics at this point. These might include percentage data cleansed, percentage data deduplicated, and increase in data processing time: A protocol should then be established for tracking each metric. Though this can be challenging when there is no single ‘source of truth,’ it is important for ensuring accuracy as the project advances. If the metric calculation or source information changes, an established change management protocol should be followed.

Partnering with the business

The next step should be to establish a team of functional business leaders to examine overarching business goals aimed at reducing costs or increasing revenue. For example, implementing an MDM program can reduce data latency and increase processing time. The resulting efficiencies in corporate finance reporting could reduce FTE hours and create new and more accurate financial reports: During this stage, the cross-functional business team should meet frequently to incorporate feedback and gain shared ownership of the agreed business metrics. Too often, data and BI teams work independently with disconnected metrics and competing budgets, even though they are often working towards similar corporate goals. When data organizations and functional business groups work together to find those synergies, everyone wins.

Liberty Advisor Group can bridge the gap

Whether you’re just beginning to identify future data projects or seeking to establish metrics to demonstrate the success of an existing project, Liberty Advisor Group can help. We bring a mix of business experience and deep technical knowledge with proven frameworks for helping IT and functional business areas realize success together. Please reach out to for more information on how we can best help your organization succeed.


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