Data Analysis Is A Journey, Not A Destination

Business analysis tools continue to evolve in sophistication. Yet, according to Forbes we only use .5% of the business data available to us. That equates to numbers between 1 and 12 million gigabytes of accessible data that grows exponentially each year. Uniting your siloed data and other important measures involves strategy and the right tools. It’s a journey to analytics that’s important for every organization.

With so much data at our fingertips, why aren’t we making more use of it?

• Siloed data: It can be exceedingly difficult to aggregate data sources. Sales data, competency certification, revenue targets, partner maturity – each of these important measures are typically stored in disparate databases internally and externally to the organization.

• Fails to meet integrity standards: Certain standards are required to generate reliable measures including data aging, source integrity, and change authority. A good channel data management and governance plan is a must-have.

• Concerns around privacy: Some privacy rules such as GDPR limit the ability to match leads to marketing initiatives.

• Difficult sales cycles and influences: Attribution can be challenging, especially with complex, high-value sales involving sales and marketing initiatives along with decision influencers.

Don’t short-change this part of the exercise

We are finding that 60% of an analytical effort is typically dedicated to data management itself. If you or your audience don’t trust the data going into the analysis, the result of the analysis won’t be trusted either.

There are 3 key pillars that build strong analytics:

1. Operational Metrics: These are measures of workflow efficiency taken at points in time and designed to measure the speed at which transactions move through their workflow.Impacts cost base and satisfaction.
2. Program Performance: This informs about program activity and trends. The metrics are used to refine program rules, identify training needs and resolve trouble-spots.Impacts satisfaction and revenue.
3. Investment Analysis: Complex algorithms and predictive models that utilize market data, product life cycle, segmentation, predictive analytics, and machine learning are leveraged. This is the point at which data scientists should be engaged, as the analysis leads to insight that simply can’t be gained through spreadsheets.Impacts revenue, margin, and growth.

It’s also important to think about the audience who needs the analysis. Use visuals for at-a-glance interpretation and tune the visuals to the analysis. (For more on this topic, consider investing in a course or book by Edward Tufte.)

As you keep in mind that analytics is a journey, not a destination, be prepared to leave the “so what?” metrics behind. Alternatively, refine useful metrics and explore new theories, data sets and correlations.