Reduce the financial impact of poor data quality

Companies' focus on data quality varies. Some organizations put a lot of effort into curating their data sets, ensuring there are validation rules and appropriate descriptions next to each attribute. Others focus on rapid development of the data layer, paying little attention to ultimate quality, lineage and data governance.

There's no denying that businesses that refuse to devote the necessary time and resources to managing their data will face financial blowback. This is supported by recent research showing that companies with annual global revenue of more than $5.6 billion lose an average of $406 million annually due to poor data quality.

Bad data primarily affects a company's bottom line because it is the root cause of underperforming business intelligence (BI) reporting and AI models that are built or trained on inaccurate and incomplete data, resulting in inaccurate Reliable responses, which the business then uses as the basis for important decisions.

As a result, organizations need to do a lot of work behind the scenes to truly have confidence in the data they have.

Technology is advancing, but data is stable

It's worth remembering that data tends to outlast all other layers of the application stack. Therefore, if the data architecture is not designed correctly, problems can occur downstream. This often stems from aggressive timelines set by the management team as the project rushes to meet unrealistic goals, resulting in unsatisfactory results.

In many companies, adding new data sets remains a very ad hoc task. Even in large projects involving the ingestion and analysis of several terabytes of data, a lack of data quality often affects the level of subsequent processing. For example, it's surprising how often data sets go through a costly transformation process without even a simple check to see if columns and formats are consistent.

Ultimately, understanding the value of your data and a meticulous approach to validation will yield greater returns when completing a data project than prioritizing speed. If the key foundational elements of an organization’s data are in place—and this doesn’t happen overnight—any effort that relies on this information is more likely to deliver strong results that improve financial performance.

Choose the right solution

The simple fact is that the data world is unrecognizable from where it was 20 years ago. However, before we had a handful of database providers, now development teams may choose from a large number of data solutions available (research suggests there are around 360 tools to choose from).

With a wealth of intuitive and innovative solutions available, data professionals should avoid the natural tendency to stick with tools they are familiar with and have served them well in the past. Being willing to try new technologies and create a more versatile technology stack can increase efficiency in the long run.

Businesses should carefully consider the requirements of the project and the potential future areas it may cover, and use this information to select the right database product for the job. Specialized data teams can also be extremely valuable, and organizations that invest heavily in highly skilled and knowledgeable personnel are more likely to be successful.

Data quality underpins an effective AI strategy

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

These models tend to multiply any problem they encounter, and some AI chatbots can even hallucinate when trained on a perfect set of source information. If the data points are incomplete, mismatched or even contradictory, the GenAI model will not be able to draw satisfactory conclusions from them.

To prevent this from happening, data teams should analyze the business case and the root causes of ongoing data issues. Too often organizations try to solve problems tactically and then let the initial problem grow larger.

At some point, a full analysis of the project is required, depending on the size of the organization and its impact. This should include a lightweight review or a more formal audit, followed by implementation of the recommendations. Fortunately, modern data governance solutions can alleviate much of the pain associated with this process, and in many cases, depending on the size of the technical debt, can make the process smoother.

Empowering a data-driven workforce

Employees who trust and rely on data insights are more productive, feel more supported, and drive improvements in efficiency. Business acceleration driven by data-driven decision-making processes is a true sign of a data-mature organization. Taking this approach ensures that data becomes an asset rather than a vulnerability that costs businesses money.