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Using Data in Business Decisions

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  • 0:04 Assumptions Can Be Dangerous
  • 1:31 How to Produce Actionable Data
  • 5:46 Lesson Summary
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Lesson Transcript
Instructor: Scott Tuning

Scott has been a faculty member in higher education for over 10 years. He holds an MBA in Management, an MA in counseling, and an M.Div. in Academic Biblical Studies.

Many costly business decisions are the result of acting on incomplete or inaccurate information. This lesson explores how to secure the right data and use it to make high-quality business decisions.

Assumptions Can Be Dangerous

In business, assumptions can be disastrous. One of America's largest department stores learned this the hard way. The retailer was using a pricing strategy known as high-low pricing. In this model, a retailer marks up the price of an item as much as 100% above the expected selling price. After leaving the item on the shelf for a few weeks, the retailer offers deep discounts to entice consumers to buy the product.

The large retailer in this case study decided to upend this pricing model by simply offering their best, lowest price from the beginning. The retailer's conversion to a best-price-first model was a disastrous business decision that was, in part, because of poor use of data to drive its decision. The company surmised that a ''bottom line'' pricing would entice more customers to the store. They also made errors in how they interpreted the data that they acted on.

Some data indicated that customers often walked away from merchandise before the discounts, but the retailer misinterpreted some of the data. The retailer believed that by shifting away from the high-low pricing they would capture business while their competitors were still in the initial six weeks of non-discounted pricing. That didn't turn out to be the case. The company failed to account for the fact that customers actively pursue discounts and sales in order to feel as though they received a good value. The misinterpretation resulted in decisions that drove thousands of customers away, and the company's revenue plummeted.

How to Produce Actionable Data

Having now determined that companies need actionable data to utilize prior to making business decisions, let's take a look at the steps companies should take that will help them to produce this valuable data.

Step 1: Identify the Problem

Before a company can collect and aggregate data for analysis, it must determine the specific information required. This leads to asking the single most important question in business decisions: ''What is the problem we are trying to solve?''

In order to find the correct answer to that question, companies must perform a root cause analysis, the use of a variety of techniques and tools to determine the actual cause of a problem. To perform a root cause analysis in an overly simplistic way, simply ask the question ''why'' at least three to five times. A root cause analysis for underperforming sales might look something like this:

A simplified illustration of the root cause analysis questions.
Fig1

Once a solid root cause has been identified, an organization lists the data they need in order to answer the problem question.

Steps 2 and 3: Collecting and Aggregating Data

The data used by an organization to inform its decisions is often called business intelligence. Business intelligence is high-quality, accurate information that executives use to make sound business decisions. Business intelligence does not come from a single source; it must be collected and aggregated.

Aggregating data means taking information from multiple sources--often both internal and external--and compiling it into a form that can be used for analysis. For a retailer, aggregated data would include external sources like market research and internal sources like data obtained from frequent shopper cards. The purpose of aggregating data is to ensure that executives are looking at the big picture. The case study retailer's mistake wasn't that it relied on inaccurate data; rather, it was that the company did not have all of the relevant data. Aggregating data from multiple sources would have provided better data upon which to make a decision.

Business intelligence is high-quality data that executives can rely on to make sound business decisions.
Fig2

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