Master Data Management

Data cleansing for effective master data management

Oceans of data, generated from every business angle, have driven digital transformations over the past decade.* Increasingly, for business success, data quality is as critical, if not more, than the data quantity. Business intelligence and analytics applications, the building blocks of decision-making systems in today’s businesses, need accurate and relevant data for better business outcomes and performance.

However, with quintillions of data being generated daily, data clean-up in master data management (MDM), or data cleansing as it is called, has become one of the major challenges for contemporary businesses. And without quality, reliable, and accurate data, a business is sure to face many pitfalls and challenges.

Data clean-up is one of the first steps in ensuring the quality of available data. Effective data cleansing strategies ensure that the data available is complete, accurate, and reliable.

The need for data clean-up

Incomplete, fragmented, and inconsistent data can create a host of issues for a business. Some of the key data quality issues include:

Lack of consistency or standardisation of data:

Inconsistent data formatting and presentation can lead to inaccurate insights from the available information, with higher chances of value being lost or drowned in data mounds. The presence of multiple copies of the same data across different business functionalities translates to data inconsistency. The same data being represented differently in different source systems within the same organisation is another reason for inconsistent data.

Incomplete or incorrect data:

Human error in data entry can lead to inaccuracies or incomplete entries within the master data. Such missing data can lead to lost critical insights — be it for customers, markets, or business processes. This is a major hindrance for the business decision-support or decision-making systems.

Duplication of data:

Availability of multiple copies of data across business functionalities can often lead to duplication of information within the MDM system. Data duplication, a common result of non-integrated systems for data collection, can lead to inaccurate insights and unreliable reporting.

Outdated data:

The presence of old or outdated data within the MDM system can deter relevant and timely business decision-making. Identification and cleansing of such data points within the master data can prove to be challenging, and if not done right, it can cause businesses to lose crucial real-time trends insights.

These issues in data quality can lead to inaccurate insights and have grave implications for the business. Attempting to manually correct these issues can be time-consuming, costly, and unreliable. However, these can be overcome with the help of effective data cleansing strategies.

Benefits of data cleansing in master data management

A data clean-up strategy can improve the efficiency of an MDM system and aid decisions for enhanced customer experience, smoother operational processes, and better performance. Implementing data cleansing strategies in MDM can:

Promote agility and adaptability:

Access to reliable and refined data within the MDM system offers better visibility and control for real-time insights into the challenges and opportunities within the market and different customer segments. As a result, the business can be agile, adapting to the shifts in market trends effectively.

Enhance data visibility and accessibility:

Data cleansing strategies ensure consistency within the master data across the organisation. This can promote data visibility and accessibility across different business domains for faster decision-making and transparency in operational processes. Moreover, such consistency also ensures better data compliance and reduces potential security risks.

Lower error rates and improve performance efficiency:

Data cleansing strategies ensure the accuracy and authenticity of master data. Such reliable data offers accurate insights into market trends, customer engagement, consumer sentiment and satisfaction, and business processes. This results in lower error rates in decision-making and improved performance of the business as a whole.

How can Infosys BPM help?

Infosys BPM master data management (MDM) solutions offer end-to-end solutions for streamlining business processes and delivering maximum benefits to the clients through enhanced operational efficiency and effectiveness. The Infosys BPM Data Quality Diagnostic Tool is a low-investment solution with a quick turnaround for understanding data quality within a specific master data domain. This tool offers insights into data quality parameters, such as completeness, accuracy, consistency, and duplicates. This tool sheds light on the impact of these parameters on business processes, benefiting businesses by highlighting the opportunities for data quality improvement.

*For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive yet modular suite of services is doing exactly that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.