Mining, Warehousing, and Sharing Data

Photo of many multicolored candles.

Before we discuss how organizations and operations managers use data to make informed decisions about production processes, we need to specify what data means. The terms data and information are often used interchangeably as though they were the same thing. However, there is an important difference between them. Data refers to a collection of facts, such as numbers, words, measurements, observations, or even just descriptions of things. Data aren’t particularly useful on their own, and don’t become meaningful until they have been analyzed. The result of data analysis is information. Information is that which informs—the answer to some kind of question, for instance. Data represents the values attributed to things, whereas information, like knowledge, represents an assessment or understanding of those values.

Organizations use data all the time to help them understand things like processes, products, customers, finances, and markets. To understand how data are collected and used, we’ll follow a small candle company called Scentfully Yours.

Scentfully Yours collects data from a variety of sources:

  • Processes: As each jar is filled with scented wax, the jars are weighed by an automated scale on the production line. The data are recorded and stored in the scale’s memory, where they can later be retrieved by the quality-assurance team.
  • Retail sales: Data are collected about which candles the sales department sold during the previous month. The retail point-of-sale system (POS) captures data about each transaction, including the candle’s scent and size. These data are transmitted to the national sales manager at the end of each month.
  • Customers: A customer satisfaction survey is included on customers’ sales receipts. If they complete a brief online survey, they are entered into a drawing for a $250 gift certificate, redeemable for Scentfully Yours Candles.
  • Suppliers: Data on delivery time, materials cost, and shortages are captured by the purchasing manager in their material requirement planning (MRP) system.
  • Customer service: The customer service department collects data from every customer who calls the 1-800 number. The type of data they collect are zip code, reason for the call (complaint or question), product purchased, and the date of purchase. These data are entered into the company’s customer resource management (CRM) system, where management can access it.

How does Scentfully Yours turn these different kinds of data into information that are useful for planning and executing the operations?” Most companies enter their production data in an enterprise resource planning (ERP) system, where they transformed into useful planning information. This information is used to answer the following questions:

  • Start with a question: Is the new line of Tub-o-Wax Candles performing as well as the marketing department projected?
  • Identify the data needed to answer the question: Access data from customer surveys, customer service, and retail sales regarding Tub-o-Wax Candles.
  • Analyze the data: The company is looking for trends, so it might compare Tub-o-Wax Candles and other products and analyze actual sales versus the original marketing projections.
  • Act: Based on what the data analysis shows, the company might want to make changes to the Tub-o-Wax Candles. For example, they might decide to address repeated customer complaints that the candles are so large they smoke up the house when burned or drop the “lumber” scent because it just isn’t selling.

The key takeaway is that making changes to the Tub-o-Wax Candle line is informed by the data, but the data (facts and figures) have been transformed into information (sales trends)—and that information provides the knowledge needed to improve the outcomes. For a small company, turning data into information is a simple process, but how does a company with vast amounts of data like General Motors or Apple do it? Much in the same way Scentfully Yours does but on a much bigger scale.

Large firms make use of their data and gain knowledge about their processes through data mining. Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. The key features of data mining are the following:

  • Automatic discovery of patterns
  • Prediction of likely outcomes
  • Creation of actionable information
  • Focus on large data sets and databases

Data mining can answer questions that cannot be addressed through simple query and reporting techniques, as Scentfully Yours did above. Also, because large firms have so much more data, they must consider how to store it. Managers in large companies consider the issue of data warehousing essential to efficient operations. Data warehousing is the electronic storage of a large amount of data by a business. Warehoused data must be stored in a manner that is secure, reliable, easy to retrieve, and easy to manage.

At the same time, to provide the greatest benefit to an organization, data needs to be sharable. It’s no good if the collection, analysis, warehousing, and mining of data takes place within a bubble. Data sharing is the ability to share the same data resource with multiple applications or users. Having this capability is crucial for operations managers, who rely on inputs from many different part of the organization. Data sharing implies that the data are stored in one or more locations in a network and that there is some software mechanism that prevents the same set of data from being changed by two people at the same time. Data sharing is a primary feature of a database management system (DBMS). These systems can range from the very simple (single server) to complex Cloud-based systems. In a small firm, such as Scentfully Yours, sharing data is easy because their size allows them to pass data and information among the departments that need to answer questions. For larger companies, whose data needs and uses are complex, data sharing usually necessitates some sort of database management system.

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