Ways to ensure and sustain data quality
With data processing being more tightly integrated with business operations and companies using data analytics to make informed decisions, there is a greater emphasis on data quality. However, according to 87% of marketers, their company's use of data is underutilised. According to 54% of firms, the biggest problem with data management for marketing is data quality and integrity.
What is data quality?
Data quality is a measurement of the state of the data based on aspects such as accuracy, clarity, coherence, reliability, and relevancy. It is an indicator of the dependability of a given dataset.
Why are data quality checks necessary?
The primary goal of maintaining high-quality datasets is to help firms make decisions that will benefit their bottom line. In today's market, businesses must assess data to comprehend changing consumer preferences and adjust their strategies accordingly. Particularly in marketing analytics, data quality assurance is vital because it enables businesses to make swift and sound judgments about their processes and budgets. If companies base their decisions on data that is erroneous, missing, or otherwise distorted, they face the risk of implementing strategies or policies that are not representative of the preferences of their customers. In light of this, high-quality data is crucial, enabling businesses to forge stronger connections while developing products and marketing initiatives guided by specific consumer needs.
Steps to enhancing data quality
One essential aspect of high-quality data is data accuracy. Low-quality data is often inaccurate with a high degree of error. The data used must be of high quality and accurate to prevent issues and inaccuracies in operational systems and analytics. Businesses use a strict set of regulations to collect and analyse high-quality data to ensure consistency and accuracy. Regular data quality checks and ensuring high-quality data are paramount for any business. Here are some steps you can follow to improve your organisation’s data quality:
Thorough data profiling and control:The most crucial step in data quality control is to perform a thorough review of all incoming data from third parties. Data profiling should be implemented with the help of software and tools that can analyse data patterns and anomalies. Automating data profiling and quality alerts is essential to continuously regulating the quality of incoming data as it is received. In addition, a central catalogue and a KPI dashboard should be set up to precisely record and monitor data quality. Finally, each piece of data should be managed by adhering to the same standards and practices.
Prevention of data duplication:Duplicate data is a significant problem for businesses as it is almost always out of sync, generates different outcomes, and has consequences on numerous systems and databases. Businesses must establish and carefully construct a data pipeline in areas such as data assets, data modelling, business operations, and architecture to prevent data duplication. Furthermore, strong communication is necessary to encourage and enforce data sharing within the company, increasing the overall productivity and minimising any possible issues with data quality due to duplicate data.
Enforcement of data integration processes:Referential integrity has become more challenging in today’s big data environment. Not all datasets can exist in a single database system as the volume of data expands, along with the number of data sources and deliverables. Without maintaining the integrity from the start, the referenced data may become obsolete, inadequate, or delayed, which ultimately causes significant data quality problems. Therefore, processes that adhere to the best practices of data governance must be incorporated to ensure the integrity of the data.
Deployment of data quality assurance teams:Having a quality assurance team significantly helps your company improve data quality. When modifications are carried out, the quality of the programmes and software is examined by the quality assurance team. This team’s meticulous change management is crucial to maintaining data quality even through constant transformations with data-intensive apps.
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How can Infosys BPM help?
Our data analytics services incorporate enterprise-grade AI to provide strategic insights into your company’s data management. Our data analytics technology and expertise can help you overcome your business challenges and maintain profitability. Our data quality check framework includes the following solutions:
- Automated data ingestion for seamless integration without any changes to internal systems
- Automation of supplier and product harmonisation
- AI-enabled data cleansing, quality checks, transformation, and de-duplication