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Leveraging Data Analytics For Enterprise Planning

Data analytics integrated with planning applications can deliver significant value

12Oct

'If you can't measure it, you can't improve it.' - Peter Drucker

Enterprise Planning applications have been around for two decades. Financial Planning and Analysis (FP&A) teams have been using these applications for planning, budgeting, forecasting of revenues and expenses, tracking KPI’s on periodic basis.

Enterprise planning was traditionally done in excel sheets where complex models were maintained and circulated across regions and teams with low version control and audit trail. Excel sheets gave way to enterprise software platforms, enabling users to collaboratively plan and view data via dashboard. These applications were known as Enterprise or Corporate Performance Management (EPM / CPM). The last few years have seen a consistent shift of these applications from on-premise to the cloud. The products have rich customer interface, robust modeling capabilities, increased collaborative capabilities, increased dimensionality, slick visual dashboards and seamless integration with ERP and accounting systems.

A key area where most enterprise planning platforms are still lacking is data analytics. Big Data and Analytics in most enterprises are viewed in isolation, with independent groups managing these solutions instead of an FP&A team. There is a strong business case for integrating data analytics in enterprise planning applications. Below are some of the use cases where enterprise application with out-of-box data analytics functionality can create significant value for FP&A:

Revenue enhancement:

a.Predicting Customer Loyalty and Churn

All businesses want to grow, retain their profitable clients, and closely monitor and reduce attrition. Data analytics can help in providing 'early warning signs' by detecting trends in product usage and customer communication. Necessary actions can be taken by the management to cross-sell to existing customers and avoid churn wherever possible. For example, Netflix can predict the customer churn by observing streaming history of its customers over a period of time.

b.Identifying trends

As organizations grow into multiple geographies and business lines, Corporate FP&A teams find it difficult to track actuals vis-à-vis budgeted cost. Chances are that such data may be analyzed post facto. Most EPM applications do have data backup for last few years. A good data analytics solution can help in running the historical data with real time data thus helping management in identifying trends, seasonality, and cyclicality.

Reducing costs and Enhancing Efficiencies:

a.Streamlining working capital and Real time monitoring

Significant working capital of the company is locked in case receivables are delayed. Ability to quickly dissect the delay in receivable at customer level, location level, or even relationship manager level can significantly enhance profitability of the company. A large part of FP&A team’s time is spent on data collation rather than data analysis and business improvements. Analytics solutions can help increase speed of decision making and reducing opex.

b.Recommending better contract terms

Most companies don’t analyze the impact of customer and vendor contracts on the long term financial health of the company, for example, the impact of each contract on long term growth and profitability. Data analytics driven EPM platform can help tie these two together in real time and guide management on possible outcomes.

c.Insights on Efficiency: Data analytics can enable FP&A teams to derive efficiency by linking data from various departments like finance and operations together. For example linking inventory data with downtime information and production schedule can help efficient working capital management.

Redefining KPIs:

Most organizations typically track standard Key Performance Indicators (KPI) like revenue, expense, headcount etc. However, the challenge with this approach is that the FP&A team can get blindfolded with these KPIs to the extent that meaningful metrics can sometimes go unnoticed. One such example is identifying which liquidity metrics to track in short term vs. long term. Data analytics through various mathematical techniques can correlate metrics to business unit performance for past data and pinpoint the right metrics for the future business model.

There are certainly many benefits of having an integrated EPM solution even though technology platforms on which these are built may differ across organization. The value derived by FP&A teams is compelling to natively integrate data analytics into planning applications. With cloud computing, implementing these solutions step-by step is more feasible than anytime earlier.

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