Businesses need data accuracy to survive and thrive in a changing marketplace. But, what is data accuracy and how do you ensure data completeness and accuracy?
Businesses need data accuracy, a data quality dimension that meets agreed-upon expectations for data correctness and uses in a particular context. Data accuracy fuels business to thrive and survive in a fluctuating marketplace.
Take life insurance. Over the past decade, insurance has “struggled with growth and profitability” from a data accuracy problem.
Traditionally life insurance prices premiums from individual questionnaires and medical exams, assessing risk. That worked twenty years ago. But the life insurance data, collected annually, can lose accuracy, daily and monthly, as people change lifestyle and behaviors through exercise, diet, and doctor visits.
Today’s high-risk mortality person can visibly see the results in regularly implementing healthy habits on wearable biometric devices (like the Apple watch) and share them. As a result, customers expect a finer accuracy threshold on mortality data.
To meet customers’ demands for data accuracy, life insurance companies need to integrate this biometric data to align products with customer engagement and expectations. Data accuracy spans beyond reliable and well-formatted data to its usability.
This guide explores the data accuracy definition from a practical business approach in six sections:
Data accuracy describes agreed upon reliable data representations of business activities within a shared context. Data creators, owners, stakeholders, and users determine what is data accuracy through norms, data governance (formal processes around data), and objective measurements.
Accurate data stays consistent and current with the situation of its intended use. However, consistent and up to date information do not automatically assume accuracy.
For example, you can say the mathematical expression “2+2 = 5” regularly and currently, but the statement is not accurate. Accurate data must be correct to the people that use it.
Say an e-tailer gives a coupon to loyal European and American customers on their birthdays. Should it record and display the customer’s date of birth as MM/DD/YYYY (an American format) or DD/MM/YYY (a European format)?
The online business wants two results: to issue the coupon on the correct day and enhance customer relationships for repeat business. So, accurate birthday data should match the content and format depending on the customer’s location.
If the customer lives in America, that person’s birthday should be in MM/DD/YYYY. If the customer lives in Europe, that person’s birthday should be DD/MM/YYYY. That way, the e-tailer gets the desired results.
Choosing just one format leads to some accurate and inaccurate birthday data, given the circumstances specified above. With only one configuration, the entire data set will be incorrect given the customers’ nationalities.
You need to remember three points to understand what is data accuracy. You do data, and data gets done within a larger defined framework. Accurate data connects both what you do to your context to meet your intention. Radically change how you do data; the environment around that data or your goal and data will become inaccurate.
Data does not sit, unmoving. You may point to a customer relationship management (CRM) system storing a particular contact (Christie Marshall) with the same reliable, correct values.
True. If someone updated Christie’s record with false content in the wrong format, the record would be inaccurate. However, you also need to account for actions, context, and goals to get the accuracy of data meaning.
Say Christie Marshall works for IBM. You record Christie’s email as ‘Christie.Marshall@ibm.com.’ What makes Christie’s email accurate? You can send an electronic transmission to Christie, and she receives the message.
Now, say Christie leaves IBM to work at WinPure. IBM deactivates Christie’s ‘ibm.com’ email account as part of the termination.
‘Christie.Marshall@ibm.com’ turns into inaccurate data. Why? When you send an email to ‘Christie.Marshall@ibm.com,’ it bounces back.
Will changing Christie’s email to ‘Christie.Marshall@winpure.com’ make that data accurate? That depends.
If you work as a customer representative for IBM, the ‘winpure.com’ email becomes inaccurate since she no longer counts as your customer at IBM. In this situation, you would keep Christie Marshall’s email and make it inactive. (The purpose changed from directly communicating with Christie to analyzing historical data). Then you would have accurate data.
In this case study, Centura Health, a hospital network in the United States, wanted accurate data about people who engaged with and valued the organization. But its donor information spread across several datasets.
To identify Centura’s contributors, staff had to manually put together different datasets and figure out the relationships between individuals. This process led to errors in getting a single view of all the people involved with Centura.
In this example of data accuracy, Centura changed how it did data. The health network used WinPure Clean & Match to automate cleansing and matching processes. Centura identified information about individuals more efficiently and correctly, connecting the right person to the right opportunity.
Inaccurate increases business liability. In a study released by KPMG, an internationally regulated accounting and professional services network, 70% of data managers surveyed agree that data and analysis will expose them to reputational risk due to inaccurate predictions. Only 16% of these participants believe they perform well getting accurate models produced are correct.
Most importantly, a lack of data accuracy stunts flexibility and growth. Companies see this problem when trying to adopt newer technologies.
According to a Trifacta study, about 46% of data scientists spend more than ten hours preparing data to analyze and for artificial intelligence (AI) and machine learning (ML) projects. In this survey, data management report that poor data-quality increases AI and ML expenses or reduce expectations from the initiatives.
To improve data accuracy, go back to the data accuracy definition and understanding. Review the three components: the data goal, doing data, and the framework in which data gets done. You need to understand each of these three pieces and know when a change is necessary.
As this blog posts, you will probably be revisiting your data accuracy processes very soon. Fintech applications promise to revolutionize marketplaces. Consequently, data accuracy norms may very well change to match the of Stripe or other fintech business activities.
So, your piece of data’s accuracy, like telephone number or first name, will need to change to match what the fintech company considers accurate. Knowing this possibility will prepare you to improve your data accuracy to provide customers the online payment experiences you want with these fintech tools.
Look at the data accuracy summary below so you can prepare your data accuracy to meet fintech norms before your competitors do.
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