Large databases, traditionally the domain of the financial departments, are increasingly entering the world of strategic planners. Under the label “business intelligence”, database software and data mining tools are marketed to strategic planners, and their acceptance is quite obviously on the rise. Contributing factors could be a change of generations among planners, more user-friendly tools available, increasing technological experience among those contributing data (who often have a background in marketing rather than technology) and a narrowing cultural gap between strategic management and the technology people necessarily involved in setting up and running such databases.
The main driver behind the spread of strategic management information systems, decision support systems and strategic planning cockpits, however, is the decision makers’ insatiable hunger for definitive answers, clear recommendations and solid data. Where traditional strategic conceps like portfolios or SWAT analyses are highly aggregated and deliberately vague in their conclusions, a strategic database can assign aggregated discounted cashflow numbers to a selection of potential future products, based on data from product and region experts across the company. We have to be aware, however, that the origins of such information about the future remain essentially the same: extrapolation, projection, estimates and, more often than not, educated guesses.
Working with databases in strategic planning offers some obvious advantages:
- Databases help to avoid the chaos of versions and formats that often occurs when strategic information is traded within the company using standard office tools like tables or presentations. The data can be located on a central server or even an external cloud under the control of the corporate IT experts and governed by corporate IT security guidelines. Adequate access rights for the different users can be set individually or by standard rules.
- Database user interfaces and data mining programs provide convenient tools to aggregate and visualize the gathered data, speeding up the process of generating bite-size information for decision makers and potentially reducing the workload in planning departments typically short of resources.
- The standardization of data going into the database and the tools employed to fill it force contributors to address a certain minimum of questions in their planning process, adhere to common conventions and summarize their results in a predefined form.
- Everybody discussing a decision can argue based on one agreed set of data, representing the best available, up-to-date information from experts across the company’s network, which may include external sales partners, market researchers and consultants.
These advantages, however, come at a price:
- The clarity of versions and formats is not so much the result of the database itself, but of the strictly implemented strategic planning process that necessarily comes with it. If the thoughts behind a changed estimate in the database or a quick summary for an executive still end up being communicated in spreadsheets sent by e-mail, the advantage is eroded and the database becomes just one more data format users have to deal with.
- The reduced workload resulting from the use of business intelligence tools has to be compared to the additional resources needed to set up and run the systems. The needed expertise will often not be available within the company, and even for the most user-friendly tools, the actual planning cockpits will in many cases be programmed by external consultants.
- While standardized data structures to be filled define a minimum of questions to be addressed in generating the data, they also discourage any planning going beyond that, which may not fit into the database. Such standardization is particularly detrimental to any qualitative, critical or out-of the-box thinking that could be priceless as an indicator of possible yet unknown threats or as a source of ideas for future growth not included in current planning.
- The uniform view of the future defined by a planning database tends to reduce the awareness that the actual future will always be uncertain. The fact that the one future (or, at best, the generic base/best/worst case structure) defined in the database has been built from the input of many contributors and has been agreed upon between different departments makes it particularly difficult to argue against the results and ask the necessary “what ifs”.
Some of these challenges can be addressed early in the process of setting up the database. Looking for synergies with database solutions already in use in the company, for example in controlling, can reduce the workload and accelerate the learning curve in the introduction phase. However, it also may introduce a bias towards processes and structures that are not ideal for information that contains estimates for an uncertain future rather than numbers from a well-accounted past. Leaving space for unstructured information within the database costs technical efficiency, but it may end up containing the one piece of information that avoids the need for parallel data exachange by e-mail or the decisive warning about an external threat that might otherwise have been unheard. Asking in time if an external support is to work as a consultant or merely as a programmer can save time and effort later and can avoid implementing potentially inefficient structures.
It is important to be aware that databases, data mining tools and even strategic planning cockpits can be an interesting source of information to be taken into account in a decision, but they are not decision tools. Asking the many “what ifs”, evaluating alternative strategies, testing for different external scenarios or analyzing potential competitors’ strategies can be done including information from such a database, but these, the actually decisive steps of strategic planning, are not done by the database. In most cases, the user interfaces employed are optimized for visualizing what’s in the database and are not even very well suited for interactively calculating the effects of assumptions that go beyond the scope of the underlying data structures.
It is, however, possible to develop tools to interactively calculate the impact of many different “what ifs” on the agreed planning basis, draw all the necessary information from the database and even write results for different scenarios back to the database, usually in separate but linked structures. The implementation will depend on the framework used, which will usually be either relational databases or multidimensional cubes. Furthermore, it depends on whether a separate data mining interface is used to access and visualize the data and if it should also provide the interface to the simulation and calculation tool.
In the upcoming weeks, we will look at two case studies on such interactive planning tools linked to pre-existing databases, both allowing the same scenario and strategic alternative evaluations on the same data, but in different database environments. One will be a relational database accessed through a data mining tool, the other a multidimensional cube providing its own user interface. We will look at similarities and differences of the two implementations and suggest ways to work around their respective limitations.
Dr. Holm Gero Hümmler
Uncertainty Managers Consulting GmbH