Dirty Data Is OK, How You Cleanse It Matters
It has been an unsolved mystery for companies if they should get their data cleansed first to opt for data analytics or if they should opt for data analytics to conclude whether their data is dirty. It comes back to the question, Which came first: the chicken or the egg? There is no absolute answer. Businesses can suffer analysis paralysis without quality data input, and they can never have clean data without the help of analytics to help them identify data errors.
It might sound a bit abrupt, but clean data is a myth. If your data is dirty, so is everyone else’s. Enterprises are more than dependent on data these days, and it is going to stay the same in coming years. They need to collect data in order to analyze it, which necessarily will not be 100% clean, pristine, or perfect in nature.
Nearly all companies face the challenge of dirty data in the form of a lot of duplicates, incorrect fields, and missing values. This happens due to omnichannel data influx, followed by hundreds, if not thousands, of employees wrestling and torturing that data to derive professional outcomes and insights. Don’t forget that even the best of the data has that tendency to decay in few weeks.
It would not be wrong to conclude that just as the chicken and egg conundrum is endless, the debate on data analytics or data cleansing is endless. However, what matters is how that data is handled to gain insights that drive major (and minor) decisions, providing incremental boosts in company performance on a regular basis and even drastic boosts on occasion.
Appropriate handling data for quality enhances organizational ability to access accurate analytics. The organization should have a constantly evolving data cleansing process in place to improve data and drive better operational efficiency and overall performance.
The dirty data that companies are using today is usually due to mistyping, leaving fields blank, and several other small mistakes. These mistakes would double and so their impact would double over a period of time, making it all the more unmanageable. The idea of cleaning enterprise data itself may open up Pandora’s Box, but the organization would have to start from somewhere. It may take a while for them to clean their historical data; but today is the day they should start considering strategies to implement a new process for cleansing data, from the past and going forward.
Marketing, operations, finance, and your customers’ data and its quality are owned by all. But much more of it depends on your organizational structure as well. It is all about how your data moves in bits and pieces, which needs stringent coordination to reach out to that source of truth in terms of customer contact details, previous purchase history, forecast, their billing details, their invoices, etc.
The reason to make a statement that data quality is everyone’s responsibility is that, for example, if a salesperson is made the owner of data quality, you would be surprised to see that role positioned in the finance team, and it would integrate data from disparate systems with the help of the IT department.
