Data quality management refers to the acquisition, use, and implementation of data. Startups must think about how they handle and maintain data. It’s estimated that poor data quality costs the US economy $3.1 trillion every single year.
That’s because as well as the obvious consequences of not having a data management strategy, companies are missing out on potential revenue streams.
Your startup must have a data quality management strategy if it’s going to take advantage of the full power of its data.
Do You Have the Right People?
An efficient data quality management strategy begins with having the right people in charge of it. In other words, you need to make sure you have someone specifically responsible for managing your data.
From the point of view of a startup, you likely can’t afford to have an entire department taking control of this.
But whomever you do choose must be able to do the job, have an industrious mind, and be a good communicator. Upper management must provide regular direction.
Who is Checking Your Data for Quality?
Many startups have a policy on the collection and storage of data, but what they often lack is the power of attribution.
It’s estimated that in 30% of cases there are missing blanks in the information company marketing departments receive, thus indicating poor data quality.
Someone must be responsible for regular data auditing to continually monitor the quality of the data stored by your company.
Data quality is influenced by everything from the way it’s collected to the way it’s managed. It’s also impacted by whether the data was collected in a manual or automatic way.
What Do You Consider to Be ‘Quality Data’?
There’s no universally accepted definition of what quality data is. It depends on your requirements and your objectives. Data quality is an extremely abstract concept, so defining what quality data is should be the foundation of your strategy.
Any data you clean and analyze must be measured against this defined concept.
How is Data Reported?
Ultimately, data means nothing unless it’s defined in the correct way. You must define metrics and group data based on what your startup needs.
Companies generate more data than ever before. Approximately 83% of senior executives said they feel data blind due to the sheer volume of data they need to work with.
Is this because they don’t know how to handle the data presented to them? Not so. The problem is companies have a lot of useless data. Unless you’re actively doing something with the data generated, that data is useless.
Your startup needs to cut through the useless data and ensure only the data relevant to your aims and objectives is presented to key decision makers.
So a key part of your data quality management strategy is the way in which data is reported to upper management.
Regular Data Cleaning
Entrepreneurs often underestimate just how much data is generated on a daily basis. It’s fair to say that every business in existence in 2019 is a business fueled by data.
Data cleaning is a huge part of any data quality management strategy. The more data you have the more difficult it becomes to differentiate between the data you need and the data that’s just taking up storage space.
The data cleaning process is a minefield for businesses because it’s not just a matter of deleting that which doesn’t align with your business objectives.
There are legal aspects to take into account. Certain types of businesses must keep specific types of data for certain periods of time. Companies also need to delete data in a secure manner or it could create legal problems in future.
For example, the data you receive from the IRS when you file your taxes won’t align with your business objectives, but you legally have to keep that data on record for a specific period of time.
This is something that’s defined by the field your startup operates in, so make sure you’re aware of your obligations before you implement a data cleaning process.
Define a Process for Your Startup
These are the areas you need to focus on. However, the actual implementation requires you to assign specific roles and regularly monitor whether the tasks in question are being fulfilled.
In terms of a startup, the chances are a single person is responsible for your data quality management strategy.
Make sure you setup regular meetings to ensure that your data quality management efforts are continuing to yield results.
Last Word – Review Your Data Quality Management Strategy Regularly
It’s amazing to see how the world of data has changed in such a short period of time. You must review your data quality management strategy on a regular basis to ensure that you’re keeping up with the demands of the time.
At least once per year you should review how you handle your data so it continues to meet the needs of your startup.
Do you have a data quality management strategy yet?