The highly anticipated Board 11 decision-making platform has now been released, bringing with it a whole host of new…
For many organizations the use of data analysis tools, analytics programs, or BI software is becoming standard practice. The implementation of these solutions is usually born out of a need to replace inefficient spreadsheet-based data collation and analysis, with the purpose of gaining insights for better business decision-making.
Typically these data solutions work on either a departmental or business level and visualize datasets to help identify trends and examine outliers. But the reality is that gaining information from reports or analytics dashboards is not necessarily enough to deliver decision-making support, as highlighted in a keynote by Dan Vesset, Group VP for Analytics and Information Management at market intelligence firm IDC, which was delivered at our Boardville User Conference.
The focus of Dan’s presentation was that data visualization tools only go so far in supporting business decision-making, because the decision-making process steps are much more complex than simply reviewing reported information and making a decision. He noted that in at least 50% of decision-making scenarios, further spreadsheet-based analysis is required because visualization tools fail to provide the relevant information in the first place. Not a good advert for a standalone BI solution, but a real home truth for businesses looking to significantly improve the efficiency and effectiveness of their decisions.
There are generally several stages to all strategic, operational, or tactical decisions:
Though the complexity of each of the above stages may vary depending on the type of decision, there are always several factors to consider before making the final choice.
Let’s consider an example. A manufacturing company wants to increase sales of a certain product and is considering employing additional salespeople to achieve this. How do they make the decision whether to go ahead with the recruitment? The management team could perhaps use a BI tool to analyze the sales figures for different products, and the output capacity of the production lines, and combine this information with their own experience to decide whether to recruit. But is this really the most efficient way to do it? Or the most reliable? What if increased product demand generated by new salespeople actually created too much pressure for the production line, or what if additional heads were not the most cost-effective way to increase sales in the first place? These questions are not answered by the BI tool alone, creating the need for additional insight.
Decision-Making Platforms, which combine analytics, simulation, and reporting functionality in a unified solution, allow users to not only perform Business Intelligence style reporting and analysis, but also to use that same information for Enterprise Performance Management activities such as scenario modeling and planning.
If we relate Decision-Making Platforms back to the Decision-Making Process discussed above, and also our Manufacturing company example, we can see why they provide the missing link and bridge the gap between data and decisions:
Identification: The business need is identified, in this case the desire to increase the sales of a particular product.
Research: The Decision-Making Platform incorporates detailed Business Intelligence capabilities, which can be used to analyze data from across the business. This could be sales figures by product, headcount, and production capability, displayed in interactive, visual dashboards or reports. This provides background information which could affect the final decision to recruit, but this information is not enough. For it to really be useful, it requires the application of further analysis or insight.
Evaluation: This is where the power of the Decision-Making Platform is realized, and where the missing link between data and decisions is overcome. Thanks to their unified nature, Decision-Making Platforms can take the same information from the Business Intelligence analysis and apply logic to it, giving the user the power to quickly evaluate different options by seeing their potential effect on the business. Different business scenarios can even be preset – e.g. best case or worst case – which can then be further analyzed to see how a change in one factor might alter the outcome.
For our manufacturing company, this means the ability to test different scenarios in minutes and predict the impact on the bottom line. For example, how much are sales likely to increase on the product line if more salespeople are recruited, based on a comparison of historical sales and headcount? Is there a need to recruit more operatives on the production line before pushing on sales in order to meet increased demand? Is an increase in marketing spend for the product line likely to have just the same increase in sales but cost considerably less?
The choice: These different scenarios, and more, can then be used to provide a supported, logical decision on how best to proceed. Because the underlying data is unified from sources across the business and can be treated as ‘one version of the truth’, it can be trusted as a solid foundation for the decision
Review: Finally, the outcome of the informed decision can be monitored and reported on through the analysis of real-time data, ready to be used to inform future decisions, and the decision-making process can begin again.
Strategic decisions can have a profound impact on the success of a business, yet many managers and executives still base their decisions on a combination of data analysis and gut feel. But the tools are out there to drive better business decisions, and organizations who are using them are already reaping the benefits.
IDC Perspective - A Call to Rethink Decision Support Software Capabilities: Business Intelligence and Analytics in the Era of AI