Jacob Hill, global data quality and insights manager at British American Tobacco, explores the role data quality plays in creating seamless processes, trusted reporting and driving informed decision-making.

In today's data-driven landscape, the adage "rubbish in, rubbish out" remains true, or maybe - with the current supercharged digital transformations - “rubbish in, rubbish everywhere” seems more fitting. As organisations across all industries reach to new technologies to gain the next competitive advantage, the quality of the data that runs through them becomes paramount to achieving meaningful outcomes and insights. This is where a strong data management discipline, including data quality reporting, becomes critical.

Bad master data causes loss throughout global businesses

Incorrect master data creates loss from an operational perspective. At best, incorrect data gets fixed before issues arise incurring a small amount of rework and loss through productivity friction but, at its worst, it completely halts processes from running and/or creates significant financial loss. Any process across plan/buy/make/move/sell can collapse due to missing or incorrect data.

These principles hold true for finance as well, where incorrect data can lead to misallocated funds, erroneous invoicing and a plethora of financial irregularities.

While incorrect reporting can lead to fines and reputational damage through inaccurate financial reports, incorrect data for analytics can slow down or lead to incorrect decision-making with far-reaching consequences.

The value of accurate data

Although putting a pound figure on the value of data is notoriously subjective, for modern businesses today, agility is key. Organisations need to pivot swiftly to seize opportunities or mitigate risks. However, agile decision-making hinges on accurate data. When data quality is high, organisations can allocate resources optimally, eliminate loss, automate effectively and seize competitive advantages.

Accurate data also unlocks opportunities. Automation and machine learning offer seismic transformation and value to those companies who can harness it. However, the risk is also magnified when data is incorrect. An automated workforce can become a non-existent workforce, customer service becomes customer frustration and artificial intelligence becomes artificial stupidity.

Data quality rules and reports: a remedy and foundation of trust

Once a single source of truth and definition has been established, data quality measures the accuracy of the data in the systems against the agreed definition. These reports serve as an assessment of the condition of a key company asset, its data.

By setting up rules such as completeness, validity, consistency, integrity and timeliness in collaboration with SMEs (subject matter experts), we identify the accuracy of our business-critical data set and begin to understand the potential risks associated.

Reporting on the reliability of the data through metrics and visualisations is a fundamental element of achieving trusted data. Data quality reports create a transparent system where stakeholders can understand the state of the data they're using and correct the data if they are responsible for maintaining it. Running data quality across a large function or organisation requires getting the right data errors and results to the right people, and as data is entered once and used by many, how the data quality is reported plays a key role in connecting the impacted party with the responsible data maintainer.

Success through reporting and communication

There are few outside of data management teams that find data quality an exciting topic therefore, the story and messaging around data quality results is a fine balancing act. Establishing and maintaining the engagement of your sponsor is essential to achieving any lasting success. The mistake many make is focusing communication solely on the data without relating back to the business benefit it addresses.

Ultimately, the quality of data underpins any business strategy to increase revenue, reduce spending, save time or mitigate risk and to achieve this at scale, clear and consistent communication is needed by sponsors, SMEs, data maintainers, data users and a data management team.