Business Analytics for Decision Making - Making It Work

Overview:

This presentation focuses on how the finance and accounting function can leverage analytics, especially predictive ones, embedded in their financial reporting, planning, and decision making.

Finance and accounting professional are typically considered to be very quantitative. They are by nature number-crunchers. But collecting, validating, and reporting data is not the same thing as analyzing the information that can be gleaned from data. Most organizations are drowning in data, but starving for information.

With analytics organizations gain insights for better and more timely decision making. Business intelligence (BI) reporting consumes stored data that first must be cleansed and integrated from disparate source systems and then is transformed into information. Analytics produces new information. Enterprise performance management (EPM) then leverages and deploys the information. EPM requires BI as a foundation. Predictive analytics are important because organizations are shifting from managing by control and reacting to after-the-fact data toward managing with anticipatory planning so they can be proactive and make adjustments before problems arise.

Most companies are far from where they want and need to be when it comes to implementing analytics and are still relying on gut feeling, rather than hard data, when making decisions. What is needed today is the seamless integration of managerial methodologies. Volatility and complexity are the new normal. 

In commerce and government the scientific method is exploding. The Dark Ages are far behind us. Customer relationship management analysts use analytics to determine which types of customers are most attractive to retain, grow, win-back, and acquire - and why. Production and supply chain management analysts determine how to optimally distribute the right products to the right place at the right time. Bank loan analysts apply analytics to credit scoring. Warranty analysts use analytics to detect product-related problems. Weather scientists use analytics predict the paths of storms. Criminal justice professionals apply analytics to detect fraud crime patterns. The list of examples is endless.

There is a common misconception that equates business intelligence (BI) technologies, such as query and reporting techniques, with advanced analytics like data mining and forecasting. But in practice experienced analysts don’t use BI like searching for a diamond in a coal mine and flogging the data until it confesses with the truth. Instead they first speculate that two or more things are related or that some underlying behavior is driving a pattern to be seen in various data. They apply business analytics more as confirmatory than somewhat random exploratory. This requires easy and flexible access and manipulation of data and software to support the process. But IT tends to exhibit gatekeeper behavior proclaiming, “We own the data and if you want a report, we’ll write it for you.”

Analytics with statistics, including regression and correlation analysis, provide organizations with insights to make better decisions and take actions.

Why you should Attend:

After we properly calculate product, channel, and customer profitability, how can we know what drivers cause higher or lower profits?

Are we measuring the most valid KPIs? Can we validate them with correlation analysis? 

Can we apply probabilistic variables to calculate the range of financial outcomes?

How can we validate the selection of cost allocation factors (which are activity drivers used with activity-based costing)?

How can we improve the accuracy of our forecasts of demand and other variables used for planning, budgeting, rolling financial forecasts, and what if scenarios?

Are many of our decisions based on intuition or experience rather than on fact-based data?

How much competency does our organization have with analytics?

How much resistance to change does our organization have that is slowing our adoption rate of progressive managerial methods?

Areas Covered in the Session:

• Learning why business analytics and leveraging Big Data provide a competitive advantage
• Understanding the difference between business intelligence (BI) and business analytics
• How to imbed statistics and analytics into enterprise performance management (EPM) methods
• How to differentiate forecasting from predictive modeling
• Learning alternative approaches to accelerating the adoption rate of business analytics

Who Will Benefit:

• CxO's
• CFO's
• Financial Officers and Controllers
• Managerial and Cost Accountants
• Financial and Business Analysts
• Budget Managers
• Strategic Planners
• Marketing and Sales Managers
• Supply Chain Analysts
• Risk Managers
• CIO and Information Technology Staff
• Board of Directors