6 Challenges In Ensuring Data Quality

How does a business decide the pricing of a product?

How do they decide how long a campaign should run in a city?

What makes them choose to promote one product over another?

There’s a common answer to all three questions – data. Data can be a company’s biggest asset that defines their success. However, simply having data isn’t enough. Meeting high quality standards for data is crucial. This is easier said than done. Some of the main challenges to ensure that data meets high quality standards are:

1. Standardization

The way we manage data has changed. Where earlier, it was maintained in separate silos, today, data is integrated into a single database. What has stayed the same is that entries are still made by many different people- data entry technicians, customer service reps and customers themselves. Data may also be acquired during a merger. Because of this, data may be entered with different units of measurement. For example, the distance may be entered in terms of kilometers or miles, temperatures could be mentioned as Celsius or Fahrenheit, etc. Think of the confusion this could cause!

2. Data is Contextual

Just as data is entered by different individuals, it is also used by different people. Thus, they might format data differently according to what they need. This also means that what qualifies as good data for one department may be poor data for another. For the delivery team, the consumer’s name, email, address and phone number may be all that’s needed. But the marketing teams may require additional data like age, gender, family type, etc. This contextual nature of data also may create problems with defining when a record is complete and when it is incomplete.

3. Duplicate Records

A consumer’s name is one of the most basic elements of their records. The trouble is that names can be spelled out in many different ways. The consumer might spell his name as Aakaash but the customer service representative may enter it in the records as Akaash and the salesman may enter it as Aakash. As a result, three records are created for the same individual. Record duplication can be a major problem especially if a change needs to be made to the account later. For example, if the person changes his phone number, it may be edited only in one record while the other records still show the old number.

4. Obsolete Data

Change is constant and when it comes to data quality, to maintain a high standard, it must be constantly updated to reflect the latest changes. Else, it becomes obsolete and useless. For example, a consumer may change his phone number, his email address or even his physical home address. If your records do not show the consumer’s current phone number, you cannot call him. Similarly, if the records have an outdated address, deliveries may be delayed. This has implications on multiple levels. Consumers may lose trust in the company, they may file complaints, etc.

5. Poorly Defined Data

Poor definition of data can cause a lot of confusion. For example, when taking up a new internet connection at an office, the connection may be linked to a person’s name instead of th
e company. Thus, the records and all the data contained within them may appear to be personal instead of corporate. Similarly, when it comes to addresses, entering a single digit wrongly in the pin code may change the demographics completely.

6. Too Much Data

It may sound better to have a lot of data but if a majority of that data does not meet your quality standards, the amount of data you have could be cause for concern. Having too much data means that your data technicians will have to spend a considerable amount of time sifting through the data to find what’s worth keeping and what’s not useful. A small error like entering the wrong title (mr/mrs) could snowball into a much bigger problem. On the other hand, if you had lesser data but the data was good quality data, this effort could be saved and utilized for tasks with a higher ROI.

To Wrap it Up

Poor quality data isn’t just useless for brands, it can also be the cause for major issues. Given how many decisions are based on this data, poor quality data can affect profit margins, future business decisions, etc. It can also cause compliance issues. Failing to meet government and industry mandates can put companies at risk for heavy fines. This is not just a financial blow, it also makes customers lose faith in the company. Thus, working towards cleaning data, enriching it and improving data quality is crucial for the success of every company. You can do this yourself or outsource it to an agency. If you haven’t started already, it’s something you should seriously consider now.