Over the last few years there have been so many headlines about big data, business intelligence (BI), planning analytics, performance management, data lakes, and AI, that attention has been diverted from the ultimate reason for these solutions — decision support. Implementing all of these tools is meant to help humans make better decisions, to augment the ability of a person to decide with the assistance of information. So, it’s important that we return our attention to the decision-making process that forms the basis of smart business operations.
These decisions, ranging from high-level strategic to operational to customer-facing, are made by executives, managers, and operational employees. In each case, the decision-making is really a process. Decision support is not simply information delivery or reporting or creating a dashboard or a spreadsheet. It is a series of steps enabling a person to make the best possible decision based on the information available at that moment.
Different types of decisions have different characteristics such as:
- Risk – what is the risk if the wrong decision is made? Is the decision easily changed, or is it a major decision?
- Time – how much time is available to make a decision or resolve an issue?
- Variability – to what extent is the issue routine or ad hoc? Are there precedents to this decision, or is this an exceptional decision?
These and related characteristics determine the types of technical and process capabilities in which an organisation may want to invest. Decision support capabilities can be segmented into five related categories, each of which is deployed to answer different types of questions:
- Planning analytics: What is our plan?
- Descriptive analytics: What happened?
- Diagnostic analytics: Why did it happen?
- Predictive analytics: What will happen next?
- Prescriptive analytics: What should be done about it?
As specialist Planning Analytics Consultants, we will focus on Planning Analytics.
It all starts with a plan. Whether it’s the overall corporate plan or one of many lower-level plans, this is usually associated with activities including financial planning, budgeting, and forecasting. Planning is in a unique position compared with the other analytics categories, because it relies on outputs of all the other steps. It requires an understanding of past performance, identification of deviations from the norm (plan versus actual), evaluation of possible scenarios, prediction of likely outcomes, and assessment of risks and constraints.
Many businesses rely heavily on spreadsheets for planning activities, despite the complexity and significance that planning has in guiding business strategy and its execution. Spreadsheets per se are not the problem; they remain the original “killer app” of business software. The real problem is that the disconnected, siloed, and ungoverned use of spreadsheets does not foster efficient planning processes, and spreadsheets cannot scale effectively in large organizations.
In a recent survey conducted by IDC of 300 businesses, 88% of respondents said that they use spreadsheets for performing what-if analysis. The result being that decision makes fill in spreadsheet-based plans and email them back and forth with finance managers to arrive at the final plan. Managing multiple versions of spreadsheets in such a manual process that it makes it virtually impossible to maintain governance and reach an agreed upon “single version of the truth.” In addition, because the planning process is so time-consuming, plans are usually only revisited at the end of the period to determine performance variance. That does not provide an opportunity to course-correct during the period.
In the same study, 49% of respondents admitted to using manual copy-and-paste methods to enter their data into spreadsheets. This process is rife with human errors and inefficiency.
Some organisations string together spreadsheets in impressive feats of data manipulation. For example, in a recent interview with a company, they showed that 72-tabbed linked spreadsheets were used to create budgets, and that the budget-to-actual update process took four months to complete. With a new planning application, the company reduced that period to just two weeks. In another example, a manufacturing company used the predictive capabilities in their planning application to estimate actuals several days prior to period close, a process that previously took a team of 40 analysts a full week.
Spreadsheets have a place, but as a company’s processes become more complex, they dedicated, connected tools that help collect, prepare, and analyse data; adjust planning models and deliver them to downstream processes or applications; and provide insight to upstream decision makers.
Businesses are most successful when planning is continuous, active, and collaborative — and when business users work in tandem with financial analysts and planners throughout the entire analytics cycle to improve enterprise operations and decision making. These needs cannot be met by manual processes reliant on spreadsheets, which is why planning analytics solutions are a key part of ensuring that timely and accurate decisions are made with the best and most up-to-date data.
To find out more about Planning Analytics contact us.