We’re pleased to have another guest blog post from Gary Cokins. You can read more about Gary in his bio at the end of this article.

As companies face a thinning margin for decision error, the ability to use business analytics effectively—everything from correlation, segmentation, clustering, regression analysis, as well as forecasting and predicting outcomes—is becoming mission-critical.

There is now a strong need to gain insights, foresight, and inferences from the treasure chest of raw transactional data, both internal and external, that many organizations store in a digital format, typically referred to as data warehousing.

Managers understand the value of using big data—named for the substantially large increases in the volume, variety, and velocity of data—to make better decisions. To assist, software vendors are enhancing business analytics with visualization to more quickly gain insights by “seeing” the information.

Business analytics take statistics to a higher level. For example, sophisticated software may detect anomalies or fraudulent behavior that an auditor is unlikely to detect with the human eye.

There is a misconception, though, that analytical software is expensive, requires exceptional skills, and is primarily used by large organizations. The fact is that there are many analytics software vendors at many price levels. Moreover, the software is designed with features that can be mastered by the casual user. So smaller companies can now leverage analytics to outsmart and out-compete bigger rivals.

When six key components of enterprise performance management (EPM) and corporate performance management (CPM) are embedded with analytics—especially predictive analytics—powerful decision support is provided for insight, foresight and action.

Intelligence vs. analytics

There’s a difference between business intelligence and business analytics.

Business intelligence consumes and packages stored information to answer basic questions. It summarizes historical data, typically in table reports and graphs, as a means for data-mining queries and drill-downs.

Business analytics, however, simplify data to amplify their value by converting huge volumes of data into a much smaller amount of information—in a way that can provide valuable insight. As a result, business analytics produce new information and stimulate more complex questions. Analytics also have the power to answer those questions.

For instance, when analytics are applied to understanding a company’s amount of revenues and profits by product, by channel, and by customer, a company might know what happened—but also why it happened by observing the causal drivers of sales, costs, and profits. It might be observed that a customer with high sales volume may be surprisingly less profitable than expected because the customer constantly requests special services or too frequently orders small quantities. A surcharge price could be charged to the customer.

These kinds of insights, revealed by finance professionals, can lead to higher profits than the sales force might have considered.

Analytics-based EPM and CPM

EPM and CPM are synonymous methods. They provide context for leveraging business intelligence and analytics and determining what to analyze.

When business analytics are embedded into the various methods of EPM and CPM methods—such as strategy maps, scorecards, driver-based rolling financial forecasts, customer profitability analysis, lean management, and Six Sigma productivity initiatives—a good rule to follow is to work backward with the end decision in mind.

Identify the decisions that matter most to your organization and construct analytical models that lead to making better decisions. By understanding the type of decision needed, the type of analysis and its required source data can be defined. For example, if a marketing campaign is targeted to customers in a certain geographical region or demographic, then a filter can exclude all customers that don’t meet the target criteria.

A common observation is that there is no intelligence in business intelligence. But when business analytics are applied to intelligence within an organization, deep insight and foresight are produced. Business analytics are needed to understand the solutions to problems or pursuit of opportunities.

Six main components make up EPM and CPM, and they should be seamlessly integrated in a way that breaks down silos. The components are:

  1. Strategic planning and execution: This is where a strategy map and its associated Balanced Scorecard fit in. Together they serve to navigate the organization to fulfill the organization’s mission and vision and the executive team’s strategy to meet the mission’s calling. The executives’ role is to set the strategic direction to answer the question: “Where do we want to go?” Using correctly defined key performance indicators (KPIs) with targets aligns employees’ priorities, actions, projects, and processes with the executives’ formulated strategy. Correlation analysis can validate the quality of KPIs selected.
  2. Cost visibility and driver behavior: For commercial companies, this is where profitability analysis fits in for products, standard services, channels, and customers. For public-sector government organizations, this is where understanding the costs of their outputs that consume processes and resources fits in. Activity-based costing principles are foundational by modeling cause-and-effect relationships based on business and cost drivers. This involves progressive, not traditional, managerial accounting.
  3. Customer intelligence: This is where powerful marketing and sales methods are applied to retain, grow, win back, and acquire profitable customers. The tools are often referenced as customer relationship management (CRM) software applications. But the CRM data are merely a foundation. Business analytics, supported by software, leverage CRM data to define actions to create more profit lift from customers. They impact the behavior of customers from being satisfied to being loyal.
  4. Forecasting, planning, and predictive analytics: Business intelligence and data mining typically examine historical data “through the rear-view mirror.” This EPM-CPM component shifts attention to look forward through the windshield. The benefit of more accurate forecasts is reduced uncertainty. Forecasts of future volume and mix are core independent variables from which many dependent variables, such as future workforce headcount and spending levels, have relationships and can therefore be calculated and managed. CFOs increasingly look to driver-based budgeting and rolling financial forecasts grounded in activity-based costing principles using this component. By adding business analytics to forecasts, possibilities become probabilities including for what-if scenario analysis.
  5. Enterprise risk management (ERM): ERM serves as a brake to the potentially unbridled gas pedal that EPM and CPM methods are designed to step on hard. ERM requires prudent selection of risk-mitigation projects and insurance for potential risks with higher probabilities of occurrence and a substantial adverse financial or reputational impact if they were to occur. Analytics have become essential for identifying and selecting what are, in effect, appropriate risk-mitigation investments.
  6. Process improvement: This is where lean management and Six Sigma quality initiatives fit in. Their purpose is to remove waste and streamline processes to accelerate and reduce cycle times. They create productivity and efficiency improvements.

CFOs often view financial planning and analysis (FP&A) as synonymous with EPM or CPM. It is better to view FP&A as a subset of EPM and CPM. Although better cost management and process improvements are noble goals, an organization cannot reduce its costs forever to achieve long-term prosperity.

EPM and CPM are not just about the CFO’s department; they are about the integration of often siloed functions such as marketing, operations, sales, and strategy. EPM and CPM are not about monitoring the dials in a strategic Balanced Scorecard or operational dashboards; they are about moving the dials with projects or process improvements identified from better analysis to make better decisions.

About Gary Cokins

Gary Cokins (Cornell University BS IE/OR, 1971; Northwestern University Kellogg MBA 1974) is an internationally recognized expert, speaker, and author in advanced cost management and enterprise performance and risk management systems. He is the founder of Analytics-Based Performance Management LLC, an advisory firm located in Cary, North Carolina at www.garycokins.com .  He began his career in industry with a Fortune 100 company in CFO and operations roles. He then worked 15 years in consulting with Deloitte, KPMG, and EDS (now part of HP). From 1997 until 2013 Gary was a Principal Consultant with SAS, a leading provider of enterprise performance management and business analytics and intelligence software. His two most recent books are Performance Management: Finding the Missing Pieces to Close the Intelligence Gap (ISBN 0-471-57690-5) and Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics (ISBN 978-0-470-44998-1). His most recent book is Predictive Business Analytics (ISBN 978-1-118-17556-9), published by John Wiley & Sons.

Mr. Cokins can be contacted at gcokins@garycokins.com, or you can visit his LinkedIn profile at http://www.linkedin.com/pub/gary-cokins/0/15a/949.