After studying this section you should be able to do the following:
Despite being awash in data, many organizations are data rich but information poor. A survey by consulting firm Accenture found 57 percent of companies reporting that they didn’t have a beneficial, consistently updated, companywide analytical capability. Among major decisions, only 60 percent were backed by analytics—40 percent were made by intuition and gut instinct.R. King, “Business Intelligence Software’s Time Is Now,” BusinessWeek, March 2, 2009. The big culprit limiting BI initiatives is getting data into a form where it can be used, analyzed, and turned into information. Here’s a look at some factors holding back information advantages.
Just because data is collected doesn’t mean it can be used. This limit is a big problem for large firms that have legacy systemsOlder information systems that are often incompatible with other systems, technologies, and ways of conducting business. Incompatible legacy systems can be a major roadblock to turning data into information, and they can inhibit firm agility, holding back operational and strategic initiatives., outdated information systems that were not designed to share data, aren’t compatible with newer technologies, and aren’t aligned with the firm’s current business needs. The problem can be made worse by mergers and acquisitions, especially if a firm depends on operational systems that are incompatible with its partner. And the elimination of incompatible systems isn’t just a technical issue. Firms might be under extended agreement with different vendors or outsourcers, and breaking a contract or invoking an escape clause may be costly. Folks working in M&A (the area of investment banking focused on valuing and facilitating mergers and acquisitions) beware—it’s critical to uncover these hidden costs of technology integration before deciding if a deal makes financial sense.
The experience of one Fortune 100 firm that your author has worked with illustrates how incompatible information systems can actually hold back strategy. This firm was the largest in its category, and sold identical commodity products sourced from its many plants worldwide. Being the biggest should have given the firm scale advantages. But many of the firm’s manufacturing facilities and international locations developed or purchased separate, incompatible systems. Still more plants were acquired through acquisition, each coming with its own legacy systems.
The plants with different information systems used different part numbers and naming conventions even though they sold identical products. As a result, the firm had no timely information on how much of a particular item was sold to which worldwide customers. The company was essentially operating as a collection of smaller, regional businesses, rather than as the worldwide behemoth that it was.
After the firm developed an information system that standardized data across these plants, it was, for the first time, able to get a single view of worldwide sales. The firm then used this data to approach their biggest customers, negotiating lower prices in exchange for increased commitments in worldwide purchasing. This trade let the firm take share from regional rivals. It also gave the firm the ability to shift manufacturing capacity globally, as currency prices, labor conditions, disaster, and other factors impacted sourcing. The new information system in effect liberated the latent strategic asset of scale, increasing sales by well over a billion and a half dollars in the four years following implementation.
Another problem when turning data into information is that most transactional databases aren’t set up to be simultaneously accessed for reporting and analysis. When a customer buys something from a cash register, that action may post a sales record and deduct an item from the firm’s inventory. In most TPS systems, requests made to the database can usually be performed pretty quickly—the system adds or modifies the few records involved and it’s done—in and out in a flash.
But if a manager asks a database to analyze historic sales trends showing the most and least profitable products over time, they may be asking a computer to look at thousands of transaction records, comparing results, and neatly ordering findings. That’s not a quick in-and-out task, and it may very well require significant processing to come up with the request. Do this against the very databases you’re using to record your transactions, and you might grind your computers to a halt.
Getting data into systems that can support analytics is where data warehouses and data marts come in, the topic of our next section.
