Organizations across the globe are under constant pressure to reduce the margins of decision errors and focus heavily on improving performance across the enterprise. Data-driven enterprises have identified that the one way to drive enterprise performance management is by leveraging business analytics optimally and gain intelligent insights. It can be achieved by leveraging data from both internal and external sources such as front and back office applications, data warehouses etc. To achieve this, enterprises are adopting Analytics-based Performance Management to improve decision making by relying more on hardcore data than the traditionally applied format of ‘intuition’ or ‘gut feeling’.
What Is Analytics-Based Performance Management?
As the volume, variety, and velocity of data grow incrementally, most organizations are adopting analytics-based performance management to make better decisions. Analytics-based performance management integrates different methodologies such as business analytics, business intelligence, profitability management, segmentation analytics, predictive analytics etc. and takes statistical analytics to the next level to gain deeper insights from their data for better foresight and consequently, better decision making. Analytics-based performance management provides the invested stakeholders with larger and more robust set of tools and techniques to gather and combine data from a wider set of disparate resources for more comprehensive analysis. It helps organizations not only get the answer to ‘what happened’ from the data but also understand ‘what can happen’.
Why Use Analytics-Based Performance Management?
There has been an increase in the adoption of enterprise performance management software that helps organizations manage a vast range of functional areas. Once used primarily for finance management, EPM is now growing to manage a wider range of operations such as budgeting, forecasting, business strategy planning, improving operations, measuring forecast progress, identifying KPI’s to measure performance against strategy etc. As EPM adoption increases, it demands greater integration of these methods with analytics so that organizations are not only in a better position to manage change but also become more capable of driving change.
By embedding analytics with EPM methods such as scorecards, strategy maps, lean management, profitability analysis, productivity initiatives, driver-based budgeting, and financial forecasting etc., it helps in improving organizational performance and ultimately the company bottom line. Analytics-based performance management helps organizations:
- Identify strategic KPI’s and operational performance indicators and optimally align them for success
- Enable better predictive accounting by driver-based budgets/rolling financial forecasts, conduct ‘what if’ analysis and make better outsourcing decisions
- Identify areas that need process improvement
- Identify the correct risk-mitigation investments
- Reduce complexity of management reports
- Improve accountability and control for better performance
- Identify and simplify complex processes to eliminate complexity
- Implement continuous forecasting based on the changing business climate
- Connect sales and operational planning activities with financial forecasting processes
The Key Components of Analytics-Based Performance Management
To improve performance within the enterprise and enable analytics-based performance management, organizations have to embed analytics to the EPM and CPM methods. Gary Cokins, the founder of Analytics-Based Performance Management LLC, lists out the six components of EPM and CPM that should be tightly integrated with analytics so that they do not break down into silos as follows:
A strategy map and a Balance Scorecard explore the methods to achieve an enterprises’ strategic goal and helps invested stakeholders answer the question of, ‘where do we want to go?” By implementing analytics, it becomes easier to identify the main execution points, required target adjustments, activities, tasks and related processes and procedures. Analytics helps in fleshing out the strategic concept to the last detail based on the business model and helps in establishing allocations along the value chain.
Operational Planning, Forecasting, And Budgeting
By integrating the planning systems, driving operation planning from the strategic business planning and forecast, organizations can easily differentiate between ongoing expenses vis-à-vis strategic actions. This also allows the invested stakeholders to leverage ‘driver-based planning’, make more accurate forecasts, and change ‘possibilities to probabilities’ by using predictive analytics.
Embedding analytics in CRM software helps organizations generate more profit from their customers as it provides organizations the insights they need to make their strategies and deals more lucrative to the customer pool.
Cost Visibility and Driver Conduct
Conventional administrative bookkeeping has no place in the enterprise of today. Having a productivity assessment to gauge the productivity levels of products, items, channels, administrative elements etc. and having movement-based costing standards are now critical to design the cause and effect relationship based on the business and cost drivers.
Identifying productivity lags, finding areas of improvement, reducing waste and streamlining processes to accelerate time to market help in bolstering productivity and efficiency and in waste management. Organizations employing analytics to their EPM software gain deep insights into the product and employee productivity cycles and can take real time action to deal with these lags.
By embedding analytics into Enterprise risk management (ERM) organizations can select appropriate risk-mitigation investments and ensure that they make a prudent selection by considering ‘probabilities of occurrence and a substantial adverse financial or reputational impact’.
Analytics-based performance management takes siloed operations and processes them as a single whole while taking into account data that not just belongs to the organization but also relevant external data (such as historical data, consumer information, industry ratio etc.). This enables organizations to gain a holistic account of a situation which helps in gaining deeper insight into the enterprise and helps them gain a better grasp of the future, enables better decision making and improves risk taking capabilities…all of which ultimately help organizations stay ahead of the curve.