Master Data Management

The reasons and practices to improve data quality

Here is a classic example of the importance of data quality:

In 1998, NASA launched the Mars Climate Orbiter, a robotic probe, to study the Martian atmosphere. The scientific community expected the mission to spearhead breakthroughs in space exploration. But the probe deviated from its expected orbit and disintegrated into the Martian atmosphere, costing NASA $193 million and a bruised reputation.

The navigational error occurred due to a data mismatch. The probe’s software could not translate English units to the metric system, leading to a lack of uniformity between the spacecraft’s information system and a piece of ground software.

A quarter century later, bad data continues to impact bottom lines, outcomes, and reputations. Poor quality data costs organisations close to $13 million annually. It accounts for 20 to 30% of operating expenses. 21% of businesses admit to reputation damage due to bad data. The statistics elucidate why data quality is so important.

Importance of data quality for your business

Bad data is inconsequential at best and detrimental at worst. It can lead to costly mistakes, ill-informed decisions, and missed business opportunities.  Improving your data quality has tangible benefits for your business. Here are the key reasons why you should invest in improving your data:

More informed decisions

Good quality data provides better insights and improves decision-making. This mitigates risk and improves outcomes.

Better marketing strategies

Good data enables you to better understand your audience and their needs. With improved data, you can build more effective marketing campaigns, generate leads, and target the right audiences.

Improved productivity

Having high-quality data at their fingertips improves your employees’ overall efficiency. A feature of good data is consistency across all departments, so your teams can collaborate more effectively.

Competitive edge

In a dynamic and evolving market, high-quality data is your most valuable asset. It enables you to discover opportunities faster than your competitors, anticipate audience needs and upcoming trends, reduce time-to-market, and improve customer relations.

Profitability

Increased profitability is one of the prime reasons why data quality is important for your business. Good data helps you devise strategies for cost-effectiveness, reduce operational costs, and identify opportunities for revenue generation.

Improved Data quality standards and ways to accomplish them

To ensure improved data quality, you must ascertain that your data conforms to high standards of completeness, validity, accuracy, uniformity, consistency, and relevance.

Completeness

A high standard of completeness implies more comprehensive information and minimal gaps or missing information in a dataset. You can enhance data completeness by:

• Regular data profiling
• Using a improved data quality assessment tool with anomaly detection software to identify outliers and data gaps
• Tackling structural errors and missing information at the source

Validity

Data is valid only when it conforms to defined business formats or rules. Some ways to ensure data validity are:

• Validating the source database by establishing relevant values and constraints
• Validating balances, counts, and totals while transferring source data to other systems

Accuracy

Accuracy is a measure of the correctness of data. Data accuracy measures include:

• Adopting robust data-entry standards such as matching, geo-coding, and data-linking
• Identifying inaccurate data sources through Improved data quality assessment tools
• Automating error reports

Uniformity

Uniform data conforms to the same metrics or units of measure throughout the company’s datasets. It is easy to establish uniformity by applying the same metrics to diverse datasets across the organisation.

Consistency

Data is consistent when its values are matched across various records in the organization. Ensuring data consistency requires:

• Establishing referential integrity
• Using locks while updating databases

Relevance

Relevance is a subjective measure of data quality. Data is relevant to a specific user only when it meets the requirements for its intended use and is accessible at the right time. You can ensure that the relevant data is available to authorised end-users by:

• Enlisting cross-departmental support
• Creating KPIs to meet specific business needs
• Defining access policies

How to ensure data quality?

To ensure data quality, you need to deploy data governance and data quality assurance teams within the enterprise and invest in data quality assessment tools. You also require a strategy that incorporates the following practices:

• Define acceptable data quality standards
• Standardise your data
• Implement data stewardship
• Automate reporting
• Maximise buy-in

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 living organisms will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely 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.

How can Infosys BPM help with improving the data quality?

Our data quality assessment tool provides deep insights into key parameters such as completeness, duplications, and consistency. It also includes the following features:

• Understanding current data maturity levels
• Identifying potential data cleansing opportunities
• Analysing key master data fields

Know more about data quality parameters.