You don’t need an authority to tell you that poor data quality data implies a multitude of negative consequences in a company. It has a ripple effect. It creates competitive disadvantage, bad strategy, lost productivity, customer relationship and financial loss.
Common complaints include incomplete data, outdated records, inaccurate records, duplicate data, and typographical errors.
Let’s put things in perspective. In marketing, bad data make it more difficult to know the potential client. In procurement and logistics, bad data send deliveries off in the wrong direction. In manufacturing, bad data means components don’t fit together properly.
Want to anger a customer? Work for a mobile phone company, and send him or her an incorrect bill. It happened to me several times, and believe me, it’s a mood changer.
Poor data can result in missed opportunities – causing your business to fall behind competitor launches because your data didn’t show the trend. In short, accurate data makes things simple, while bad data makes everything complicated.
Now take a look at these stats provided by the Harvard Business Review:
- 88 % of all data integration projects either fail completely or significantly over-run their budget tweet this
- 75 % of organizations have identified costs stemming from dirty data tweet this
- 33 % of organizations have delayed or cancelled new IT systems because of poor data tweet this
- $611 billion per year is lost in the US in poorly targeted mailings and staff overheads alone tweet this
- According to Gartner, bad data is the number one cause of CRM system failure tweet this
- Business intelligence (BI) projects often fail due to dirty data, so it is imperative that BI-based business decisions are based on clean data tweet this
- Only 15 % of companies are very confident in the quality of external data supplied to them tweet this
To try and prevent or eliminate these kinds of errors, some companies are using specialized software. 38% of companies surveyed use software to check data for errors, while 34% use software that cleans incorrect data after it has been collected.
As an organization, you need to have a proactive approach when it comes to data cleansing in order to lessen the occurrence of bad data and mitigate its effects.
- Admit you have a data quality problem. Just like any program, admitting the problem half (or in this case, a quarter of) the solution. Then identify the problem with the use of a data profiling or audit tool to identify types and locations of data defects. From there, follow the next three steps for data improvement.
- Resolve the problem by using a cleansing tool to clean data, remove errors and fix basic problems. Focus on the data you expose to customers and take a careful look at your system of controls. Make sure that the right controls are in place, but that you’re actually using them. Check out these Data Profiling Tools.
- Define and implement an advanced data quality program, making sure data is correct and properly vetted before it is used or shared. You need a quality program that does so. Prevent inaccuracy by using real-time safeguards to prevent new errors from entering the system.
- Maintain whatever you have started by appointing a data guardian to be responsible for long-term monitoring, measurement and management of data quality. The guardian, and your organization as a whole should take a hard look at the way you treat data more generally. Almost everyone readily acknowledges that “data are among our most important assets” but they don’t manage them that way. You need to get an aggressive data program and guardian with real talent and teeth to guard it.
Having bad data is a necessary evil. No industry, organization, or department is immune to it. It is inevitable, but it we can all minimize its effects. If not acknowledged and fixed early on, bad data can cause serious problems. Everybody acknowledges this fact, but still, it can often be pushed aside in the rush to manage all of your other responsibilities. But there’s too much at stake to ignore your data. Poor data quality can lead to disastrous business decisions, lost deals, and may even take a company down. Take care of your data, and it will take care of you.