Taking an Enterprise View Can Optimize Analytics Spending and Increase BI Budgets

In 2023, a majority of respondents (52 percent) report increasing BI budgets. Among these organizations, 16 percent indicate their BI budget increases come from the reallocation of budget from other initiatives.

Data leaders need to not just realize but accept that “competing” sources for analytics exist in their organizations. They need to ensure they understand this full landscape of potentially competing analytic alternatives that exist in their organizations—most commonly, analytic capabilities embedded in enterprise applications, spreadsheets, and AI models. With that awareness established, data leaders then can manage their BI portfolios in the context of this full landscape of analytic capabilities.

By focusing solely on proving BI’s value to justify and obtain overall BI budget increases, data leaders likely are at a minimum possibly passively accepting duplication of analytic capabilities or use of suboptimal non-BI sources for these. They also could miss out on additional sources of potential funding (by using better or more established BI capabilities to replace duplicative or suboptimal non-BI sources, and capturing that spend through reallocation). That is why data leaders need to understand their organizations’ full analytic landscapes and manage their BI budgets in that context.

A data leader not already doing so should complete a full inventory of analytic capabilities currently used in the organization—most commonly application-embedded analytics, spreadsheets, and AI models. After establishing which set of analytic alternatives exist in the organization, a data leader should next identify usage levels for these. Then a data leader should honestly (and without bias) analyze the non-BI analytics being used in the organization and compare these capabilities to those that are or could be delivered through BI.

With this full inventory and analysis matrix, a data leader will now know and understand the full analytic landscape—and can step forward and start to manage it holistically for the organization. With this information, a data leader can work with the executive team to highlight any instances of “shadow” analytics that may deliver suboptimal capabilities to the organization, and educate functional leaders as to where and why BI would provide better value to the organization. Such instances could represent opportunities for additional reallocations that could further increase BI budgets.

But data leaders should not solely view this understanding and analysis as a source of new BI budget funding for many reasons. First, any gains from reallocations likely will be single-instance events; they cannot really recur. Second, not all non-BI analytic alternatives will warrant replacement.

For example, although spreadsheet use exists at high levels in almost all organizations, displacement of this basic tool by BI is both unlikely and unrealistic for many reasons (including licensing considerations, costs, training, and user backlash). Instead, data leaders should look to manage proper use of spreadsheets, eliminating usage that creates “shadow” derivative data that hinders data-governance goals.

Furthermore, this full-landscape inventory and analysis creates for a data leader an opportunity to have a much more strategic discussion with executives about data and analytics in the organization. Not only will awareness increase and improve, but the full context could help open the door to deeper, more meaningful discussions with executives about traditionally more difficult data-related topics, such as the importance of and need for data governance, and where and why additional investments in data infrastructure may be warranted.

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