Overcoming the Financial Implications of Poor Data Quality

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Approaches to data quality vary from company to company. Some organisations put a lot of effort into curating their data sets, ensuring there are validation rules and proper descriptions next to each attribute. Others concentrate on rapid development of the data layer with very little focus on eventual quality, lineage, and data governance.

What’s undeniable is that businesses refusing to funnel the necessary time and resources into managing their data will face a financial backlash. This is supported by recent research, revealing that companies generating over $5.6 billion in annual global revenue lose a yearly average of $406 million as a direct result of low-quality data.

Bad data primarily impacts company bottom lines by acting as the bedrock of underperforming business intelligence reports and AI models – set up or trained on inaccurate and incomplete data – that produce unreliable responses, which businesses then use as the basis for important decisions.

As a result, significant work behind the scenes is required for organisations to be truly confident in the data at their disposal.

Tech Evolves, But Data Lives On

It’s worth remembering that data tends to outlive all other layers of the application stack. Therefore, if data architecture isn’t designed correctly, there could be issues downstream. This often stems from aggressive timelines set by management teams, as projects are rushed in order to meet unrealistic objectives, leading to a less-than-desirable outcome.

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Adding new datasets still tends to be a very ad-hoc task in many companies. Even in bigger projects that involve ingesting and analysing terabytes of data, a lack of data quality frequently impacts subsequent levels of processing. For example, it’s surprisingly common for datasets to be run through costly transformation processes without even brief checks to see if columns and formatting are consistent.

Ultimately, understanding the value of a patient and meticulous approach to validation will yield far greater rewards than prioritising speed when it comes to completing data projects. If the crucial foundational elements of an organisation’s data are in place–which doesn’t happen overnight–any work that relies on this information is more likely to lead to strong results that improve financial performance.

Selecting the Right Solutions

It’s a simple fact that the data world is no longer recognisable from where we were 20 years ago. Whereas before we had a handful of database providers, now development teams may pick one of a whole host of data solutions that are available (research shows there are roughly 360 tools to choose from).

With an abundance of intuitive and innovative solutions on offer, data specialists should avoid the natural inclination to stick to tools that they are familiar with and have served them well in the past. A willingness to experiment with new technology and create a more versatile tech stack can increase efficiency in the long run.

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Businesses should carefully consider the requirements of the project and potential future areas that it might cover, and use this information to select a database product suitable for the job. Specialist data teams can also be extremely valuable, with organisations that invest heavily in highly skilled and knowledgeable personnel more likely to succeed.

Data Quality Underpins an Effective AI Strategy

An integral aspect of why high-quality data is important in today’s business landscape is because companies across industries are rushing to train and deploy classical machine learning as well as GenAI models.

These models tend to multiply whatever issues they encounter, with some AI chatbots even hallucinating when trained on a perfect set of source information. If data points are incomplete, mismatched, or even contradictory, the GenAI model won’t be able to draw satisfactory conclusions from them.

To prevent this from happening, data teams should analyse the business case and the roots of ongoing data issues. Too often organisations aim to tactically fix problems and then allow the original issue to grow bigger and bigger.

At some point, a holistic analysis of the architectural landscape needs to be done, depending on the scale of the organisation and its impact. This should consist of a lightweight review or a more formalised audit where recommendations are then implemented. Fortunately, modern data governance solutions can mitigate a lot of the pain connected with such a process and in many cases make it smoother, depending on the size of the technical debt.

Empowering a Data-Driven Workforce

Employees who trust and rely on data insights work far more effectively, feel more supported and drive improvements in efficiency. Business acceleration powered by a data-driven decision-making process is a true signal of a data-mature organisation. Adopting this approach ensures that data becomes an asset, instead of a vulnerability costing the business money.

About the author: Tomasz Jędrośka is the Head of Data Engineering at STX Next, a leading European provider of Python and AI/ML consulting services. Jędrośka has nearly 20 years of experience delivering software products and solutions for various domains, including retail and investment banking, telco, and blockchain.

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