A recent Gartner report confirmed that every year, poor data quality costs organisations an average US $12.9 million and highlighted that in addition to the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision making.
Despite this, some organisations prioritise technology solutions or systems upgrades that rely heavily on high-quality data to achieve their implementation goals over long-term data governance and data quality itself. The idea that the next technology program will ‘fix the data issues’ persists, regardless of evidence to the contrary.
COSOL has more than two decades experience running data quality and data migration projects for asset-intensive organisations. When it comes to data quality, we’ve heard it all; and we’re here to debunk some of the common myths and misconceptions about data quality.
Myth 1: Our data is fine; everything's fine
Many business leaders would like to believe there are no data quality concerns in their organisation. Unfortunately, it’s more common that they are simply not fully informed about their data quality issues.
Often, they will remain unaware of data integrity issues until a significant event – such as a data compliance breach, merger or systems upgrade – reveals the actual state of their organisation’s data.
We see this all too often during data migration projects. Dated, duplicated, inconsistent and inaccurate data, legacy data stored in multiple systems with no coherent link or process for management are just some of the issues we encounter.
As a result, most major data migration projects we undertake begin with a Data Quality Assessment before moving into the first phase of data profiling and data remediation.
Myth 2: Data quality is an IT Issue
Data is not gathered, used, or managed in the IT department alone but right across your business. Data quality is a business issue, not an IT issue.
Data is a strategic business asset and as such the processes and rules to create, retrieve, use and manage your data need to be consistent across the business.
Your IT department has a critical function in determining a technical solution to improve and help maintain your data. However, to deliver an optimum business outcome, the importance of data quality needs to be established by leadership and filtered through your organisation as a business priority.
Myth 3: A good data tool will sort that out
A good ‘tool’ will be necessary if you need to undertake a data cleansing process, but a tool is not the start nor the end of the process.
Getting the right tools for the job is important, but people and processes are even more important. Creating a data quality framework that encompasses business rules for cleansing, integration and overall data quality management will provide you with a functional, sustainable data platform for your organisation.
Without this more sophisticated understanding of data in your organisation, you could employ a basic data cleansing tool, but how will you know that you’ve set the appropriate parameters for successful data management?
Myth 4: We can fix any data quality issues ourselves
In complex data environments, data systems, data processes and the data itself are often siloed, with no one person or function having full visibility of the entire operation. This narrowcast view of data inside your organisation means the scale of any data integrity and integration issues may be misunderstood or missed altogether.
So, while it’s true that a data cleansing process can be managed in-house, the question is, what are the risks if you get it wrong and at what cost to your business?
A specialist with niche expertise can help you form an impartial view of your current data and advise on target business states. They can assist in establishing a data governance framework that sets the business rules around data properties and guides capturing, controlling and utilising business data – in addition to providing recommendations on the appropriate technologies to remediate your data in the first instance and successfully manage your data in the future.
Myth 5: One major data cleansing project will fix everything
Speaking of data governance and contrary to common belief, data quality is not a ‘fix and forget’ one time project.
A major data cleansing project will help your organisation improve its data quality. It also comes with the added bonus of helping you build data competency within your organisation. However, a proper structure and data governance framework are essential to head off future data decay.
The real opportunity is to build a sustainable data quality platform. Once you identify and analyse the reasons for data quality issues, the goal is to create an operational environment where your data remains an evergreen strategic asset to your business.
Myth 6: Good data quality is ‘nice to have’ but not critical to my business
If there is one misconception we’d like to lay to rest it’s this: having good data is optional for good business. This infographic on The high cost of low quality data reveals the hard numbers on data quality business risks and opportunities.
Gartner predicts that by 2023, organisations that promote data sharing will outperform their peers on most business value metrics. In our experience running data quality and data migration projects for two decades, organisations that successfully harness and utilise their data are in a better position to drive positive business outcomes.