Building a business case for data quality must deal with both the consequences of poor data quality and the opportunities created by trusted data. In this post, we explore the reality that for many organisations data is a liability

1. Badly managed data is a liability
2. Some reasons why data can be considered a liability:
   1. Customer data degrades at 30% per annum
3. A Data Quality Rule of Thumb
4. Quality data requires maintenance

While the idea of data as an asset has been around for several years, the consequence of this reality is that data can be a liability. This was highlighted by a colleague at Standard Bank South Africa, Kaiser Manasoe, who suggested the following:

Whilst we may view data as an organizational asset, the reality is that some data is actually a liability especially where the quality is poor or the data collected isn’t fit for purpose and obsolete. The Infonomics of the Data throughout the value chain needs to be understood in order to best leverage it for Digital Transformation efforts. #WestAfricaDataConference #Ghana #HealthyData

I could not agree more!

Badly managed data is a liability

While well governed, good quality data delivers trusted results – bad data is a liability.

According to IBM bad data costs the US economy $3.1trillion  per annum (that’s $3,100,000,000,000,000,000)

This is almost identical to the current US government spending of $3.98 trillion per year.

In other words, if we could clean up our data quality issues the US could run for a year without taxes. That’s got to sting.

Some reasons why data can be considered a liability:

1. Privacy and security risks: Data breaches and unauthorized access to sensitive data can result in serious privacy and security concerns. If personal or confidential information falls into the wrong hands, it can lead to identity theft, fraud, or other malicious activities. Organizations that fail to adequately protect data may face legal consequences and reputational damage.
2. Compliance and regulatory issues: Many industries have strict regulations regarding the collection, storage, and use of data, such as the General Data Protection Regulation (GDPR) in the European Union. Non-compliance with these regulations can lead to penalties, fines, and legal actions. Ensuring data compliance requires resources and ongoing efforts, which can be seen as a liability.
3. Data quality and accuracy: If data is incomplete, outdated, or inaccurate, it can have negative consequences. Decision-making based on faulty data can lead to poor business outcomes or ineffective strategies. Maintaining data quality requires continuous monitoring, verification, and cleansing efforts, which can be resource-intensive.
4. Data governance and ethical considerations: As data collection and usage grow, ethical considerations become more significant. Organizations need to ensure they have appropriate data governance practices in place to address issues such as data bias, discrimination, and fairness. Failing to address these concerns can lead to reputational harm and legal repercussions.
5. Data storage and maintenance costs: Storing and maintaining large volumes of data can be expensive, especially considering the need for secure infrastructure, backup systems, and disaster recovery plans. Additionally, as data accumulates over time, organizations may face challenges in managing and retrieving relevant information efficiently.

It is important for organizations and individuals to recognize these potential liabilities and implement appropriate measures to mitigate risks associated with data. This includes investing in data security measures, adhering to privacy regulations, ensuring data accuracy, implementing ethical data practices, and considering the costs and benefits of data collection and storage

Customer data degrades at 30% per annum

We like to believe that our data is accurate.

But, even if we capture data correctly (which is statistically highly unlikely), customer data degrades by between 30% and up to 70% per annum.

Life happens.

People move, change jobs, update their banking accounts, or get a new improved mobile phone from a different service provider.

Very often, they don’t tell us.

Or they call our call centre but the updated details do not trickle through to other business areas or systems.

The lack of a single customer view hinders the ability to maintain customer data quality.

A Data Quality Rule of Thumb

A commonly used rule of thumb to calculate the cost of poor data quality is the 1:10:100 rule.

This rule states that every R1 saves R10 in recovery costs.

More worryingly, ignoring poor data quality costs R100 based on lost profits due to rework or process failures.

Quality data requires maintenance

Many of us in South Africa are experiencing periodic outages in the delivery of basic services, such as electricity and water,

In many cases, these outages are due to a lack of maintenance on ageing infrastructure, as budgets are allocated to other priorities.

Like our infrastructure, data requires maintenance in order to remain valuable.

When pondering strategic decisions, one question looms large: Does data integrity matter for decision making? The answer lies in the very fabric of your data.

Without a data quality strategy, and appropriate data quality tools, data can quickly become a liability

This explains why improving data quality remains one of the key business drivers for data governance

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