Data Quality becomes an increasingly critical part of your business as you rely more and more on it to make important, accurate business decisions. However, humans are the ones inputting this data, and its inevitable that we make mistakes. These mistakes happen, but a habit of small mistakes over a long period of time causes bigger consequences down the road when a large chunk of your data is innacurate.
Like what? According to IBM and TechTarget “Added together, all of those little data quality problems can cause big issues in business processes and result in significant losses of both money and employee productivity.” Yikes. Read on to discover your worst data quality habits and how to get rid of them.
Bad Habit #1: Allowing mistakes in data entry
What’s the big deal about a little error? Mistakes happen, but when you add up little mistakes over a long period of time the result is a large pool of innacurate data. Little mistakes like these are very common in fast-paced environments.
Bust it: Implement internal standards to ensure bad data is rejected. Also, set goals for your staff on upholding these standards as a performance incentive.
Bad Habit #2: Not involving the business when IT makes the rules
In light of these small mistakes, you implement new data standards with IT and communicate them to everyone else. Wrong! In order to change the behaviour, you need to involve the people who will follow these rules when you are making them. “They’re [the users] are likely to challenge the rules if they weren’t part of the process of determining them.” says William McKnight, President of the McKnight Consulting Group. IT and the rest of the business look at this data with different priorities, and getting both sides on the same page is difficult.
Bust it: Relax your ‘political will’ if necessary to get everyone on the same page. You need to engage effective leaders on both sides and make some tough judgements in order to get a consensus on data quality standards.
Bad Habit #3: Allowing bad apples and busy bees
You spend a lot of time and resources deciding on and implementing standards, but you need to uphold them – starting today. David Loshin, President of Consultancy Knowledge Integrity sums it up nicely, “The enforcement of business rules often conflicts with the desire to get the job done.” So humans aren’t perfect, but do not tolerate resistance to change, ignorance, pettiness, laziness and departmental isolation. However, it’s a reality that busy workers have deadlines to meet. There are both ‘bad and understandable’ reasons for poor data quality.
Bust it: You must make sure your data quality standards guarantee at least sufficient (if not totally error free) data. Non-conforming data should be rejected automatically or manually, and you need to evaluate processes to relieve bottlenecks where necessary.
What you can do right now
Maybe you can relate to some of this – but never assume your data is perfect. It’s not. To bust these bad habits, buy-in from organizational leadership is key, but upholding data quality standards day-to-day and making sure users adhere to these rules will be an ongoing challenge. Not sure where to start? Download our data integrity healthcheck or leave a comment below and we will get back to you.
We all have them…what are your worst data quality habits?
This is part 2 of a 3 part blog series:
–4 Key Measures of Data Quality
–Top 3 Worst Data Quality Issues and How to Bust Them