What is the price of bad data?

Tentive / Blog / What is the price of bad data?

What is the price of bad data?

In one of my previous blog posts, I wrote that organisations are increasingly viewing data as an asset. As a data management and data governance expert, I am of course ecstatic about this development. And it is a fact: high-quality data that organisations can rely on when making strategic decisions is exceptionally valuable, even though it remains difficult to quantity the financial value of data or data quality. The most important reason is that organisations often find it difficult to substantiate the often digital nature of their data. It stands in sharp contrast to the traditional, more tangible assets, such as machines and buildings, which do have a financial value which is expressed on the balance sheet. And that is basically the fundamental problem at the moment. My experience as a consultant is that organisations do acknowledge the importance of data quality, but show limited willingness to invest in it as yet. Why spend money on something intangible?

Because poor-quality data costs money. That’s the bottom line.

Organisations learn from their data and use newly acquired insights to provide added value. Examples include understanding customer behaviour to improve a product or service, or mapping out market trends to develop a better business strategy, etc. Poor-quality data has a negative influence on these decisions. In addition, poor-quality data costs money, and that holds true for each and every organisation (DAMA International, 2017).

What kind of figures are we talking about here? The estimates vary, but generally speaking organisations will miss out on a substantial percentage of their potential turnover as a result of problems with data quality. Ovum Research states that inferior data results in lost revenues for organisations amounting to at least 30% (Geiger, 2014). This means that poor-quality data destroys business value. A Gartner study (2018) indicates that organisations believe that inferior data quality is responsible for losses of at least $15 million annually. IBM (2016) estimates that the costs of inferior data quality in the United States amounted to $3.1 billion in 2016.

This was caused by….

Besides imposed fines, the majority of the costs resulting from inferior data quality are hidden and indirect, and therefore difficult to quantify (Redman, 2016). Costs are caused e.g. by:

Missed opportunities: An organisation can miss out on a unique opportunity to develop a new product or respond to renewed customer needs, while a competitor with more mature data knowledge will capitalise on those opportunities.
Reputation damage: Damage to a company’s good name could vary from minor, routine damages to a veritable PR disaster. For instance, if banks rely on bad data, they could unintentionally end up doing business with organisations or groups that have been blacklisted, simply because the financial institution did not have sufficiently accurate information about the parties they are dealing with. In addition to towering fines imposed by supervisory authorities, such mistakes often also result in negative publicity. A carefully cultivated reputation can be ruined by such blunders.
Lost revenues: Bad data can lead to lost revenues in various ways. For instance, promotional mailings that were not converted into sales due to incorrect customer data.

The benefits of excellent data quality

That is why it is high time for organisations to focus on improving data quality. High-quality data offers advantages in many areas, including:

Decision-making: The better the data quality, the more confident employees will be in the results they produce, which will minimise risks and increase efficiency. The adage ‘garbage in, garbage out’ is true. The opposite is fortunately true as well. When results are reliable, it becomes possible to minimise guesswork and risks in decision-making.
Productivity: High-quality data makes employees more productive. Instead of spending time on validating data and fixing data errors, they can focus on their core activities.
Compliance: In sectors where the legislator determines how customer relationships are established and/or business activities with clients may take place, high-quality data may very well be the difference between confirmed compliance or millions of euros in fines.
Marketing: Better data enables more accurate targeting and effective client communication, especially in omni-channel environments which many organisations try to achieve.
Competitive edge: High-quality data can give organisations a competitive edge, providing better insights into an organisation’s clients, products and processes and making it possible to identify market opportunities more quickly.

Conclusion
The costs and benefits discussed above clearly indicate that data quality management is not a one-time event, but an ongoing process. High-quality data requires planning, commitment and a mindset to permanently safeguard that quality in processes and systems. Improvements in data quality will take some effort, but the organisation will benefit in the long run.


Tentive Solutions offers organisations support in resolving their Data Governance and Data Management issues. Would you like to know more? Feel free to contact our Data Management Team.


Jacco Oudeman

References consulted for this post:

– DAMA International (2017), DAMA-DMBOK, Data Management Body Of Knowledge, Second Edition, Technics Publications, Basking Ridge, New Jersey
IBM (2016)
Gartner (2018)
Redman, Thomas C. (2016)