We often hear that data is the fuel of modern business, but we think food provides an even better analogy. When we go to fill up our car at the pump, very few of us prefer a particular brand, we just want a full tank. But when it comes to what we eat, it's not enough to have a full stomach; We need good food that is nutritious and tasty. It is the same with data. Stocking up on information doesn't necessarily improve a business; in fact, the wrong kind of data can have a very detrimental effect on the health of the entire organization. Indeed, in the age of the connected business, the effects of bad data are not limited to the system in which it resides. Instead, it spreads to a range of other applications and business processes that rely on this information.
About the author Nick Goode, Product EVP, Sage. Companies may not know it, but misinformation is a serious and costly problem. In 2016, IBM estimated that bad data costs more than €3 trillion in the United States alone. (For comparison, the size of the entire big data industry in the same year, according to IDC, was €136 billion.) However, this can only be an estimate, as it is difficult to put a price on lost opportunities, reputational damage, and lost revenue as a result of misinformation, not to mention the time and effort required. to find and fix them. . Knowledge workers spend too much time investigating and fixing data errors. Other researchers provide further evidence of the devastating impact of misinformation. Gartner found that the average cost to organizations is €15 million per year, while a Royal Mail report suggests it results in a loss of six per cent of annual turnover. . Why can't companies solve a problem that has such a direct impact on their bottom line, especially given today's fixation on data-driven information?
The domino effect of bad data.
You hope the past issues give you some food for thought, especially since every industry, from merchandising to finance, customer service to supply chain, now relies full of accurate data on which to base its insights. However, in our pursuit of the quantity of data, we seem to have forgotten one of the oldest tenets of the information age: "Garbage in, garbage out." Too often, companies do not have a consistent data integration strategy, which means that inaccurate or incomplete data causes a ripple effect throughout the organization. Nothing highlights the interconnected nature of modern businesses better than the problem of bad data. If one department does a poor job of keeping data clean, up-to-date, and accurate, it affects all the other departments that depend on that data. This means that the effects are not limited to those responsible for managing records and updating systems; instead, they have spread throughout the organization. This leads to all kinds of problems: from mistargeted marketing campaigns to poor customer service results, including errors in HR and payroll, resource allocation, and product development. Another serious consequence of inaccurate data is that it can make people mistrust the information they are getting and even feel sorry for the data creators who allowed the wrong information to infiltrate their systems.
A recipe for success
Despite all the hype around data-driven insights, companies face a data credibility issue, as business intelligence and performance measures are heavily skewed by inaccurate data. So while no one overlooks the importance of having large data sets from which to derive insights, the most pressing challenge organizations face is improving the quality and Accuracy of the data they hold. Just as the food we eat has a direct effect on our well-being, the quality of your information affects the health of a business. That's why they should treat data as a delicacy, rather than just fuel. By focusing on data quality, they can ensure a positive ripple effect throughout the organization, with departments and workers able to trust the analytics insights they derive from it. To do this, every organization should conduct a regular data quality audit that not only verifies the accuracy of the information retained, but also examines internal processes and workflows associated with collecting and storing information. For example, the organization must have full confidence that employees capture all relevant information in systems such as ERP systems, and that all data is accurately captured and updated. This should include cross-referencing with information held in other systems such as CRM, ensuring that the business can trust the data on which it bases its most important decisions. The recipe for success is simple: be as picky about your data as you are about the food you put in your mouth: prioritize data quality to ensure accurate insights.