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.
Can data analytics add business value to your organization? This topic is worth discussing in more depth because much of the literature talks about the value of improved decision-making. That’s a very intangible value that many stakeholders are skeptical about.
The following are the significant ways data analytics adds business value. Emphasize these ways as you plan and communicate the value of data analytics projects within your organization.
Reduce time to business value
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Stakeholders always want a higher return on investment (ROI) on every initiative. Examples include capital investment, new product launches, advertising, cost-saving, taxes, and 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 clamouring for a higher ROI by:
- Structuring your data analytics projects to deliver new visible capabilities every 90 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.
- Designing straightforward 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.
- 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 accurate but often don’t move the performance needle noticeably. Examples of intangible value include more engaged customers, lower turnover of employees, and improved company reputation.
You can ensure data analytics adds tangible business value by focusing on the only two measures that matter:
- Income statement improvements that:
- Reduce cost or increase margin by using data analytics to find real opportunities. Help the company stop guessing what opportunities might be real and stop addressing counterproductive ideas.
- Increase revenue or gross sales using data analytics to find under-served customers, poorly-served markets, or emerging product trends. Quit over-analyzing sales data and financial transaction data.
- Balance sheet improvements that:
- Increase the ratio of revenue to marketing expenditure. Every organization wastes marketing dollars. While the expenditure impact data from Google and social media sites have helped significantly, data analytics can further reduce waste.
- 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 wasted capital.
Track business value
Unfortunately, many project teams are so focused on the smooth 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:
- Stakeholders have no way of knowing if the project sponsor’s or the project team’s business value assertions are being exaggerated.
- 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 add incremental revenue, then track the amount daily, weekly, or monthly.
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:
- Automating the analysis of the data gathered in support of streamlining. The quality of manually-gathered data tends to be uneven.
- Quickly analyzing multiple scenarios leading to a dependable recommendation. Without data analytics, there’s usually only the capacity to analyze one scenario.
- Emphasizing data-driven analysis will reduce the risk of making changes that unintentionally produce less effective and more costly business processes.
When you follow these best practices, data analytics adds significant business value to your organization.
Yogi Schulz has over 40 years of information technology experience in various industries. Yogi works extensively in the petroleum industry. He manages projects that arise from changes in business requirements, the need to leverage technology opportunities, and mergers. His specialties include IT strategy, web strategy and project management.
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