A Forrester study found that roughly 30% of analysts spend 40% of their time confirming and analysing their data before using it for algorithmic implementations and strategic decision-making. These stats demonstrate the severity of the data quality issue, and it's not a simple issue either. Take the example of the healthcare sector, where poor data quality can make errors in problem diagnosis or treatment recommendations for urgent conditions.

Data accuracy can be determined as the true potential of any organisation's value. The data's ability to fulfil a certain goal is a crucial component of a data-driven organisation. Higher quality data helps ensure and prioritise the optimum use of resources by providing timely and accurate information to manage accountability and services. 

Data quality matters

Maintaining high-quality data produces accurate analytics and pertinent insights for decision-makers. Organisations acquire enormous volumes of data due to the expansion of big data and AI, and ensuring its quality is becoming increasingly important daily.

However, the main issue begins when companies expand, their mission-critical data becomes fragmented. There is no overall picture as the data gets dispersed across applications, including on-premise applications. Business-critical data becomes inconsistent due to all of this change, and it is unclear which application holds the most recent data. As per a study by Gartner, poor data quality costs businesses $12.9 million yearly on average. Further to having an immediate negative effect on revenue, bad data over time makes data ecosystems more complex and results in poor decision-making.

The situation creates problems with data engineers, as they lack the context to recognise problems when something breaks or goes wrong. As a result, data teams are under even greater pressure to monitor everything adequately. End-to-end visibility into the condition of a firm's data and data pipelines is provided by data observability, which also provides context for deciphering why things work or don't work as expected.

Poor data quality can harm a company's reputation in addition to costing money. Organisations continue to encounter inefficiencies, extra expenditures, compliance concerns, and customer satisfaction problems due to their (often incorrect) assumptions about the quality of their data. Yet, in practice, they don't manage data quality in their company.

In the worst-case scenario, even customers can use social media to communicate their unpleasant experiences, which damages a company's reputation. In addition, when data inconsistencies go uncorrected, employees may also begin to doubt the accuracy of the underlying data.

Although there are no universally accepted standards, data is said to be of good quality if it serves the purpose for which it is being used. The qualities of correctness, completeness, relevance, timeliness, and consistency are used to characterise high-quality data. All of a company's initiatives are built on the foundation provided by data quality management. In order to provide accurate and trustworthy results, it combines data, technology, and organisational culture.

Road to good quality data

The machine becomes as excellent as the data it is trained on and learns the statistical correlations from the historical data. As a result, high-quality data becomes essential and a fundamental component of an ML pipeline. Only the training set can make an ML model as good as it can be. Some of the steps need to be kept in mind. 

To begin, start by comprehending difficulties rather than attempting to solve them. Data quality problems frequently have a long history within departments. Therefore, it's crucial to establish issues, gather data, and comprehend every data issue's breadth and fundamental causes via an "issue-driven path." Once done with finding the challenges lying in the data, next is to have a data steward in the team to plug the gaps and control and manage the operational data.

Third, it is good to have a team precisely looking and working as a single point of contact. The team's main responsibility should be to look after how each department across a firm manages data. Further, the team can set a framework to bring uniformity to the process of collecting and disseminating data, thereby helping the data steward about what's happening with the data. Finally, don't initially provide the organisation's staff with specialised tools for identifying, evaluating, cleaning up, and correcting data quality issues. Instead, begin with the manual but well-defined techniques to reclaim knowledge of data's past.

Bad data can kill the organisation's ambition for growth. So it's better to start now rather than sitting idle and trying to invest heavily in the technology side.