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.
Single Source of Truth
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.
Analytics Highlights the Need for Data Cleansing
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.
Data Quality Is Everyone’s Responsibility, Isn’t It?
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.
So now that everyone is involved in managing the data quality, why not make everyone responsible for it. Upon assigning the responsibility to everyone, align it with measurable goals. This will help in assessing the rapid rush in improving overall data quality.
Analytics of Existing Data
Now that you have assigned the responsibility for data quality with measurable goals, it’s time to see what lies beneath in your database. Unless you analyze your existing data, how would you conclude which aspects of data management need immediate or moderate attention?
No organization can know the areas of improvement for employees unless the organization understands the kind of challenges they are facing in terms of data and management for analytics. This includes assessing whether there are major data gaps, missing fields, inaccurate numbers, or information that isn’t in the right format and much more. The answer to these questions will reveal where the data is dirty and that it needs immediate attention and improvement.
The need for clean data is due to the fact that enterprise data with a lot of white noise makes tracking and forecasting unreliable. The easiest way to conclude that your data has problems is when you encounter a lot of duplicates within your database. Every company should make an adequate investment of time and money to train their employees about the correct processes and pushing stronger enforcement of data quality immediately.
Starting small is a good idea. Every organization is struggling to clean their databases, but cleaning it up all at once might make you feel that it is an uphill task. One can start with one of the fields and move forward at a steady pace, which might take a few months to complete. But incrementally it will improve the historical data quality.
Don't Get Complacent
Once the historical data gets cleaned and its quality shows considerable improvement, getting complacent is one of the biggest mistakes an organization can commit. Data quality has the tendency to run downhill if ongoing processes to continually improve it on a real-time basis are not implemented.
Data quality processes should never be static. Keep on asking the question like what data points you intend to access, but are not able to? Do those data points provide required information that you are looking for? Upon getting your hands on that data and information, how fast and impact of what severity would it have on the overall business?
Start Now: Waiting Won't Make the Problem Any Better
Did you know that the company size of any of your clients has an adverse or positive impact on your sales team’s winning ratio? But to conclude this, an organization needs to have reliable data from a single source of truth for accurate reporting. Companies need assistance from data management experts to cleanse data collected from across multiple data fields de-duplicate, replace, correct, delete, organize, validate, modify, classify, and format all data collected.
Attaining a higher level of data quality may seem to be a daunting task, but waiting or overthinking will not make the problem vanish. In the process, you might feel that your data is too far gone and there is no hope, but clean up your data before it cleans you up from the market and the industry.