For many organizations, the use of data analysis tools, analytics programs, or BI software is…
Daily life is an endless string of decisions. What to eat? How to spend our time best? With whom? And they are all based on thousands of comparisons. Do I look better wearing this or that? Will I work better with or without another cup of coffee? You get the idea.
This process informs all of our business decisions as well. We either consciously or unconsciously base business decisions on comparisons of the options before us. Without comparisons, there really can be no decisions. For example, we need to expand manufacturing, and have two best options either in the US or Europe… which provides the best combination of quality, performance and return on investment? Or we need to capitalize on new delivery channels… which will provide the most profitable growth opportunities?
Making the right decision requires following a specific process: 1) Understand the situation and goal, 2) identify alternatives, 3) Compare alternatives, and 4) choose an alternative and act on it.
Today’s business managers increasingly look to BI and analytics software to inform the best decisions. And the same rules apply – We compare relative risks vs. benefits, costs vs. income, and so on. And the promise of analytics software is that we can make many more decisions with far higher levels of accuracy than by relying only on gut feelings or consensus of advisors.
But analytics software alone does not truly support decision-making. If it did, then why are the majority of today’s business managers still using 20th century spreadsheets (Excel) as the basis of their comparative models. According to analysts’ estimates, more than 78% of global businesses are still basing most of their decisions on Excel, which is much like using your riding lawnmower to commute to work.
These Business decision makers are still using Excel to create forward-looking data models to represent the complex environment they face in the real world so they can then predict alternate outcomes. For example, your industry trade association has specific data on the costs and quality levels of contract manufactures in country X, which you can incorporate into your existing model of relative costs and quality levels of your US based manufacturing. Run the comparisons, and you expect you’ll have the bases for a solid decision.
Excel’s greatest strengths are the simplicity and flexibility it provides for modeling. However, those are also its greatest weaknesses. If the data you are trying to model is in Excel, you face the problem that the logic is defined within hundreds or thousands of individual spreadsheet cells rather than a centralized database. For complex models, the logic only makes sense to the person who built it. Maintaining and extending decision-making applications based on Excel quickly becomes confusing, labor intensive, and runs the risk of data quality errors.
Managers need models that will scale and provide a clear representation of the model as well as ensure security and data quality, all of which are lacking with Excel-based models. Excel-based models also limit users to a two-dimensional perspective, which does not address the complex multidimensional decisions facing an organization.
But, you may ask, what about all of those Business Intelligence vendors out there with offerings that will provide insights into my business with hierarchies, security and data quality? Here’s where we need to look at the big picture, and differentiate between generating insights and enabling decision-making.
BI delivers reports that promise nothing more than insights, which is an overused word that implies far more than it delivers. In fact, those insights may define the nature of a problem, but usually just generate another set of questions about the potential solution to that problem. As valuable as that may appear, handing off “insights” to humans as bases of their finding solutions, it still doesn’t get us where we need to be for real decision-making.
BI reports are limited to historical observations, but decision-making involves comparing options that will help determine future outcomes. And that requires robust, forward-looking multidimensional models that can scale to leverage all the operational detail available in the BI system, not just the highly summarized slices of information available in spreadsheets.
For that, businesses need systems and platforms that will take comparative modeling to the next level with the integration of truly robust and scalable analytics.