“Aoccdrnig to rscheearch at Cmabrigde Uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe.”
Judy Mandell, The Observer
I think you will agree that the above paragraph makes little sense at first glance. However, we as humans have a miraculous ability to read it, this is a phenomenon called Typoglycemia.
Now, let's take this paragraph:
Data ……… great importance………several re3sons. ………. and banana……… making well-informed. When …….. high quality, organisations……… conclusions drawn from it,..... trends, pattErns,..... and resource allocation.
We can get the gist of what's being said (with a little effort and head rotation), even with the gaps and the typos and the rogue incidence of a banana.
Technology exists to unjumble the words and fill in the gaps to improve the quality of the data, but its reliability is questionable, and might only add to the poor quality of the available data. It is currently only the human brain that can do this with a great degree of accuracy. When looking at the second paragraph, context and experience are essential to fill in the gaps correctly. Which, even with the prevalence and capability of Machine Learning cannot replicate the human brain in this area. Where does that leave us?
We live in a world of data, which organisations rely heavily on to drive decision-making, gain insights, and maintain a competitive edge within their industry. However, the value of data lies not only in its quantity but also in its quality. Data quality refers to the accuracy, consistency, completeness, and reliability of data. Poor data quality can have significant negative consequences for organisations, while high-quality data can enhance efficiency, effectiveness, and decision-making.
Importance of Data Quality
Data quality is so important for organisations for several reasons. Firstly, accurate and reliable data forms the foundation for making well-informed decisions. When data is of high quality, organisations can have confidence in the insights and conclusions drawn from it, leading to more effective and successful decision-making processes. It enables organisations to identify trends, patterns, and opportunities, resulting in better strategic planning and resource allocation.
Secondly, data quality directly affects operational process performance. Reliable data ensures that organisations can execute their operations efficiently and effectively. Whether it is managing inventory, processing orders, or analysing customer behaviour, high-quality data yields accurate and dependable results, reducing errors, redundancies, and inefficiencies.
Within the customer service industries or where touch points with customers are frequent, data quality plays a crucial role in customer satisfaction and loyalty. Organisations that possess accurate and up-to-date customer data can deliver personalised experiences, targeted marketing campaigns, and timely support. This fosters enhanced customer satisfaction, increased loyalty, and positive brand perception - ultimately improving the likelihood of customer retention.
Legally, data quality is essential for regulatory compliance. Organisations must adhere to various data protection and privacy regulations, such as the General Data Protection Regulation (GDPR), CCPA, Schrems II, and industry-specific compliance standards. Ensuring data quality helps organisations meet these requirements, avoid penalties, and maintain their reputation and trustworthiness.
The Impact of Poor Data Quality
The consequences of poor data quality can be far-reaching and detrimental to organisations. Firstly, it can result in flawed analyses and erroneous insights. When decisions are based on inaccurate or incomplete data, organisations may end up making misguided strategic choices, leading to financial losses, missed opportunities, and a damaged reputation.
Poor data quality impacts the operational efficiency of organisations. Inaccurate data can lead to supply chain disruptions, production delays, and errors in financial reporting. Inefficiencies caused by poor data quality increase costs, reduce productivity and compromise customer experiences.
Poor data quality will also negatively affect customer relationships. Inaccurate customer data can result in communication errors, failed deliveries, and subpar customer service. These experiences erode customer trust, contribute to customer churn, and damage the organisation's reputation.
Lastly, non-compliance with data protection regulations due to poor data quality can result in severe consequences. Organisations may face legal penalties, reputational damage, and loss of customer trust. Compliance with data privacy regulations requires accurate, up-to-date, and secure data handling practices, all of which are closely tied to data quality.
Improving Data Quality
Improving data quality necessitates a systematic approach and the implementation of best practices. Organisations can follow these key steps to enhance data quality:
- Data Governance: Establishing a robust data governance framework helps ensure that data is managed effectively, with clearly defined roles, responsibilities, and processes. Data governance ensures data standards, policies, and procedures are in place to maintain data quality.
- Data Profiling: Conducting data profiling involves analysing and understanding the characteristics, patterns, and quality of data. This helps identify data anomalies, inconsistencies, and areas for improvement.
- Data Validation and Cleansing: Implementing validation checks and data cleansing processes helps identify and correct errors, inconsistencies, and duplicates. This ensures data accuracy and integrity.
- Data Integration: Integrating data from various sources ensures consistency and eliminates data silos. Proper data integration processes and technologies facilitate the seamless flow of data across systems, reducing redundancy and enhancing data quality.
- Data Documentation: Documenting data definitions, metadata, and lineage provides clarity and transparency regarding data sources, transformations, and business rules. This documentation aids in maintaining data quality and facilitating data understanding.
- Data Quality Monitoring: Implementing data quality monitoring mechanisms allows organisations to continuously track data quality, identify issues, and take corrective actions in real-time. Regular data quality assessments and audits help measure progress and maintain data quality standards.
To quantify some of this I referred to the Forrester Wave Report produced last year - https://www.reltio.com/forrester. Within it the analysis of Reltio customers indicates that:
“every incremental ~1% improvement in Data Quality, yields ~3% incremental conversion rates for top-of-line revenue.”
Forrester TEI Study 2022 Benefit Atr, pp.8-9.
This commonly means $340k to $1.54M in additional annual revenue. To get more of a complete understanding of how the quality of your data can be managed effectively, please review our latest Webinar - on Real-Time Data Quality