Data analytics is receiving vast amounts of attention in the trade press, on many websites and at conferences. References are even spilling into the mainstream media.

How does data analytics add business value at your organization? This topic is worth discussing in more depth because much of literature talks about the value of improved decision-making. That’s a very intangible value that many stakeholders are skeptical about.

Data analytics adds business value in these major ways. Emphasize these ways as you plan and communicate the value of data analytics projects within your organization.

Reduce time to business value

Stakeholders always want a higher return on investment (ROI) on every initiative. Examples include capital investment, new product launch, advertising, cost-saving, taxes, business process improvement.

Data analytics increases ROI because it reduces the time to business value for these initiatives. Examples include net income trend analysis, project progress and schedule risk reporting, cost forecasting and competitor performance analysis.

You can respond to this constant clamoring for a higher ROI by:

  1. Structuring your data analytics projects to deliver new visible capability every ninety days to remind stakeholders of the value you’re delivering. Never propose multi-year, multi-million-dollar projects because they won’t reach their goal and often lose the support of management along the way.
  2. Designing straight-forward data analytics that everyone can understand and use to add business value. Avoid moon shots that rely on ambitious analytic techniques, complex software development or vast volumes of data that are difficult to integrate and manage.
  3. Ensuring project sponsorship and dedicated business expertise for the data analytics project. Projects without this support crash and burn while creating collateral damage to the reputation of data analytics.

Focus on tangible business value

Executives are tired of project proposals that include lengthy descriptions of intangible value. Those descriptions may be true but often don’t move the performance needle noticeably. Examples of intangible value include more engaged customers, lower turnover of employees, improved company reputation.

You can ensure data analytics adds tangible business value by focusing on the only two measures that matter:

  1. Income statement improvements that:
    • Reduce cost or increasing margin by using data analytics to find real opportunities. Help the company quit guessing about what opportunities might be real and quit addressing ideas that are counterproductive.
    • Increase revenue or gross sales by using data analytics to find under-served customers, poorly-served markets, or emerging product trends. Quit over-analyzing sales data and financial transaction data.
  2. Balance sheet improvements that:
    • Increase the ratio of revenue to marketing expenditure. Every organization wastes marketing dollars. While the expenditure impact data available from Google and Facebook has helped significantly, data analytics can reduce the waste further.
    • Reduce inventory value. Many organizations still carry a surprising amount of dead stock and too much float for active products. Data analytics can help reduce the wasted capital.

Track business value

Unfortunately, many project teams are so focused on superior execution of their data analytics projects that they do not track the tangible business value they are achieving. That failure leads to the following problems:

  1. Stakeholders have no way of knowing if the business value assertions of the project sponsor or the project team are being exaggerated or not.
  2. The end-user community asserts the business value is due to their excellent work and not due to the new data analytics capabilities delivered by the project team.

These problems undermine the belief that data analytics is contributing value. You can ensure data analytics can confidently claim to add tangible business value when you track that value. For example, if you claim to be adding incremental revenue, then track the amount on a daily, weekly, or monthly basis.

Streamline business processes

Organizations tend to streamline business processes by simply increasing the level of automation in the existing processes. That approach typically delivers only modest improvements. A more effective approach consists of challenging all the existing steps to identify the current problem points, steps that can be eliminated and steps that can be restructured.
You can ensure data analytics adds tangible business value to streamlining business processes by:

  1. Automating the analysis of the data gathered in support of streamlining. The quality of manually-gathered data tends to be uneven.
  2. Quickly analyzing multiple scenarios leading to a dependable recommendation. Without data analytics, there’s usually only capacity to analyze one scenario.
  3. Emphasizing data-driven analysis. This will reduce the risk of making changes that unintentionally produce less effective and more costly business processes.

What considerations have you found effective to ensure that data analytics adds business value and is not simply an exploration of nifty technology?



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