Who Is Responsible for Data Quality – IT or Business Teams?
This article presents the idea that IT shouldn't be responsible for data quality alone; it's a task that everyone in the organization should be accountable for.
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Imagine that you are a mid-size financial service provider. You implement a CRM solution to enhance your operations only to realize a few months later that it yields poor results. And the reason? Data quality! The details of customers fed into the system are incomplete or inaccurate – invalid addresses, missing contact details, misclassification of customers, inconsistent data formats, duplicate data, etc.
The question we pose is, do you think the IT team should be held accountable for this shockingly poor data quality?
If your answer is yes, it’s high time you revisited your notions about both data and data quality. It’s easy to assume that data quality is an IT problem because data obviously has a lot to do with the information as well as technology. The first step to solving issues related to poor quality data is to take the burden off the shoulders of your IT team.
This takes us back to our original question – if not the IT team, who is responsible for ensuring data quality?
To answer this question, one must dig deeper into three related questions.
- What is data quality?
- What are the points at which data is created in an organization?
- What are the ways in which organizations use data?
What Is Data Quality?
As all complex inquiries start with basic questions, let’s follow suit. So, what does the term data quality mean? What are the factors that contribute to the quality of your data or the lack of it?
While there are many ways to define data quality, data is typically considered to be of high quality if it is captured carefully from sources and transformed into formats such that it serves all its intended purposes. According to the Data Warehousing Institute (TDWI), for data to be trusted, it must be complete, consistent, current, compliant, collaborative, and above all, clean.
As an extension of this definition, if your data suffers from any anomalies that compromise one or more of these properties, it can be said to be of poor quality. A wide range of factors can compromise the quality of your data from typos, missing numbers, characters and details, and outdated information to duplicate records, incorrect classifications, and disparate data sources.
That is to say, if your data contains invalid email addresses or phone numbers without area codes, it can lead to potential data quality issues. Missing surnames or an address that no longer exists can also compromise the quality of your data. Long story short, anything from spelling mistakes to irrelevant information to data silos can put the quality and reliability of your data into question. At this point, one cannot help but ask a related question – who creates and collects data in an organization?
What Are the Points at Which Data Is Created?
In his article published in the Harvard Business Review, Thomas C. Redman, an expert on data quality and analytics, puts forward an interesting augment. According to him, there are two important points in the lifetime of a piece of data – one is the point at which it is created and the second is at which it is used. However, “Most of these moments don’t occur in IT”, says Redman.
Now, isn’t that true? Take, for instance, the case of a manufacturing company or an eCommerce store. Most of their highly valuable data is transactional – that is, it is created through their core business activities, such as production, buying, selling, etc. For an insurance firm, a sizable amount of data is created when managers sign up new customers and at different stages of a customer’s journey – when they create an account, fill an online form, submit their documents for verification, and the like.
You see what we are getting at, don’t you? Business departments contribute more to the creation of data than IT departments. In the words of Redman, “the really interesting and important moments for data occur in the business, not in IT.” If so, isn’t it counterintuitive and illogical on the part of organizations to believe and act as if data quality is an IT problem?
What Are the Ways in Which Data Is Used in an Organization?
Our third line of inquiry is about the use of data in businesses. So, how do businesses use data and the insights they derive from it? And who uses it? The answer seems pretty straightforward – everyone and at every step of their journey. Data analytics is instrumental in strategizing operations, creating new streams of revenue, and smoothening customer relationships, and more.
Businesses all over the world, large corporates as well as SMEs, are diving into big data and making use of its predictive power. People at the management level use it for decision making while for the marketing team, data insights guide marketing strategies. The sales team benefits heavily from the patterns data reveals about buyer behaviors. For customer relationship departments, data is all about improving the customer journey.
So, Is Data Quality an IT Problem?
For the very reasons listed above – that is, business departments have more to do with creating and using data than the IT department – data quality is a problem that has more to do with your business operations than your IT infrastructure. Since data has the power to make or break your business, it is ill-advised as well as counterproductive to let the IT team bear the burden alone.
So, whose responsibility is data quality?
Our answer is, everyone. As business departments stand to gain exponentially from the power of data analytics, they shouldn’t be left out of the responsibility of ensuring data quality. From the top management and middle management to the marketing and sales unit, the onus of ensuring data quality lies with everyone, not just the IT team.
The Collaborative Model for Data Quality
Sharing responsibility for data quality is not as easy as it seems. For organizations that are so used to turning to the IT department for data quality solutions, it might even seem an impossible mission. Yet, it is possible provided that you change your perspective; data quality is not just a process but an organizational approach owned and executed jointly by all data-consuming teams.
Let us state it clear without any ambiguities. Everyone in the organization has something to do to contribute to the quality and reliability of data. Client-facing staff should validate the data, CRM professionals must come up with guidelines for what constitutes a valid data entry, top management must keep articulating the importance of seamless data, and so on.
And the IT team? Let them do what they are originally hired for – managing, storing, delivering, and protecting your data.
While every business understands the power of data, there appears to be a lack of established accountability when it comes to data quality. More often than not, it stems from the misconception that it is the IT team’s job.
However, it is a myth that has to be busted. Data quality is not just about how you manage or store or process your data. It, on the other hand, is a value that should be enforced by every business department in their every single operation. Taking the data quality responsibility off the shoulders of the IT team will enable them to perform their duties well.
And remember, data quality is not a destination where you can magically land in. It’s a journey that everyone in the organization must partake!
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