In a recent IDC Perspectives paper – A Call to Rethink Decision Support Software Capabilities – analysts Dan Vesset and Chandana Gopal identified a key flaw in the way organizations are using business intelligence software. Many companies have implemented BI solutions with the view that decision-making will be enhanced by delivering information in dashboards, however decision-making is a process formed of multiple tasks and activities which involves stakeholders in departments across the business. The presentation of a graph or chart might provide insight at one stage of this process, but intuition and experience are still required to make the final decision.
This poses a key question for organizations. Are your standalone business intelligence technologies really supporting decision-making, or are they just presenting part of the story? For most, it’s likely the latter is true.
To make the move from the analysis and reporting of data to the support of future decisions, a more comprehensive set of tools is required, which must provide actionable and meaningful insight across the entire planning, analytics, and business modeling process. To put this into context, let’s explore the elements which contribute to a more-informed business decision.
To know where you want to be you need clarity over where you’re currently at. Analyses and reports, generated by Business Intelligence solutions, help you to achieve this, but still leave certain aspects open to interpretation. For example, your goal may be to double your revenue in the next year, and your decision revolves around a strategy for achieving this. You may have invested in sales and marketing this year, resulting in X revenue, but does that mean doubling sales and marketing investment next year will generate twice X? More insight is required to evaluate this question, and standalone business intelligence software only provides part of the picture.
The introduction of Corporate Performance Management capabilities adds a new dimension. By seamlessly linking the data used for analysis and reporting, users can undertake planning and forecasting activities based on that data. Plus, by modeling different scenarios, users can help to create clarity over the best course of action.
Thinking back to the above revenue example, it may now be possible to see that doubling sales and marketing efforts will require additional investment in production to meet the increased output, and even with all that investment, revenue is not forecasted to double. Having this insight provides vital information to help shape the direction of the decision.
Predictive Analytics capabilities add another string to the decision-maker’s bow. By analyzing data from multiple sources and applying relevant models, forward-thinking analytics software can predict the direction of future activities or results. This calculation would be formed based on past performance (as with forecasting), but also other factors which may influence the outcome – in a fully integrated system this could include everything from time of year to staff sickness.
Coming back to our revenue scenario, it may be predicted that revenue will grow by 50% if everything stays as is. But it may be the case that a reduction in staff sickness days, combined with a focus on production quality and a smaller investment in sales, could bring the company closer to the 100% growth goal than the original idea of investing in both sales and marketing.
Without each of these elements, a decision is only ever going to be as good as the experience, or gut feel, of the individual(s) who are making it. Support throughout the decision-making process could provide that key insight which augments the decision and prevents the wrong path being chosen. It is time to invest in more comprehensive decision-making technology which goes beyond the presentation of data. If you’re not already considering it, know that your competitors are.
This Executive Snapshot by IDC helps enterprises to evaluate a new generation of Business Intelligence and Analytics (BIA) software solutions, focusing on the role of artificial intelligence (AI) and machine learning (ML).