First-party data, collected directly by a business, is a necessity in the modern marketplace. But to put the data to its best and fullest use, it has to be properly gathered, stored, and maintained.
No matter how detailed, information is useless if it can’t be properly processed due to bad data hygiene. When using big data, in fact, keeping it clean is just as important, if not more so, than the actual data. Why? Because data is only as useful as its quality. The problems often start with faulty data management policies, leading to business mistakes and misguided decision-making.
Data problems have consequences. Poor data quality costs the U.S. economy around $3.1 trillion per year, according to IBM. Over in Forbes, Falon Fatemi writes that dirty data continues to cost businesses in real money. “Big data has an Achilles heel: dirty data. Dirty data, which manifests in a multitude of different forms—duplicate data, inaccuracies, and duplicate information, for example–represents the single greatest threat to big data. It wreaks havoc on a company’s bottom line, costing companies a staggering 12% of overall revenue.”
The road to better data management starts with three steps:
- Audit Data Quality: Like most problems, a full-throttle diagnosis is a good first step to assess the depth of the damage. Sometimes the problem is visible and easily correctable. Other times it’s a more complex situation, requiring a more detailed solution. When going through an audit, one of the first tasks is to determine what data is necessary. More data is not always better unless it is properly managed and accessible.
- Address Problems: Data cleaning is the next step. Using automated techniques to correct errors and typos, reformat the information into a usable form, then identify and remove incorrect data.
- Automate Processes: Setting up automated systems that can look for errors, duplications, and other issues that pop up is a key part in keeping a clean machine of data. Why not do it manually? Human error. The fact that people make mistakes is the best reason to set up an automated system to make sure your data is clean.
Remember: Properly using big data can provide transformative insight for a company. But to get the most out of it, data must be clean, useful, and accurate.