Just as a military commander relies on accurate and timely intelligence to win wars, business leaders need useful insights and information to make an impact on the business front. Today, almost all quality business intelligence is acquired via data processing, and if the data is of poor quality, it stands to reason that the insights will be similarly affected. 

Although it’s easy to relegate data quality to the domain of backroom analysts and spreadsheet mavericks—poor data can have  massive implications on your organisation. And that is why it needs attention at the highest levels of the management. The smallest of inaccuracies in fleet management data can mess up the delivery schedule, a discrepancy in the product information can cause inconsistency between eCommerce channels, a minor glitch in the design prototype can delay the manufacturing turnaround by entire cycles, and so on. Bad data can be undoubtedly catastrophic in any industry, for any use case. 

In fact, according to a survey conducted a few years back, bad data was costing the US businesses over $3 trillion a year – for context, that’s about 4% of annual global economic output.

To prevent poor data from seriously impacting your business, it’s a great idea to start data quality management processes early. This means creating transparent communication structures between the business units and the information technology groups under your domain.

What Does Good Data Even Mean?

But before we get into the ’how’ of data quality management, let’s take a look at the markers of good data. Quality data management churns out information that conforms to these essential pillars:

  • Completeness – No missing bits of information, all relevant data has been collected and collated successfully.
  • Accuracy – The data correctly reflects real-world constructs and positively impacts decision-making. 
  • Consistency – Each entry within the database is collected in a systematic and uniform format.
  • Relevancy – The collected data is actually useful towards the goals and objectives.
  • Currency – The data isn’t outdated and is recent enough to be useful. 

How Does Data Quality Management Affects Your Enterprise?

  • Smarter Business Strategy – Quality data can help you estimate the value of a particular business move, as well as give you a fair idea of the level of success you can expect to achieve. But with poor data, you’ll be blindsided by market trends, important insights, feedbacks, and competitor strategies. While data doesn’t necessarily offer you a confirmed business outcome, it can greatly increase the chances of your strategy’s success. 
  • Managing The Scope Of Data – Quality data management taps into all relevant and critical datasets to process them into curated insights. By not limiting to only the new (or old) data points or only the current applications, it expands the horizons for future market opportunities and holistic decision-making. A rock-solid data management strategy can, in fact, filter out the valuable aspects of yesterday’s information to create a more profitable future. 
  • Creating Business-Driven Workflows – To many modern business leaders, the sheer influx of data from multiple channels, platforms and analysts can be absolutely overwhelming. Businesses today depend on responsive innovation, which is essential to create dynamic, outcome-oriented workflows. Quality data management protocols can help businesses define the changing rules of critical workflows to stay lean and agile and make the most out of market opportunities.
  • Rapid Data Integration & Migration – Data is useless to businesses unless converted into insights. And that is where quality data management can make a huge difference. By integrating fresh, unstructured data into existing system formats, quality data management can aid decision-makers to quickly mine, visualise, and analyse the right data at the right time from their data warehouses. Unmanaged data, however, can be extremely time-intensive to integrate, especially when dealing with legacy systems and out-of-date DBMS architecture. 
  • Data Analytics – In the highly digitised ‘next’ normal, an enterprise that is near real-time, analytically-powered, and directly influences business transactions is truly business resilient with a competitive edge. Quality data management can deliver tangible outcomes and impacts in this perspective via top-notch analytics. It can ensure that clean, standardised data is fed into systems for fast and accurate processing. It also reduces manual effort of logging, end-to-end data curation, and assortments needed in different categories.
  • Advanced Rules Engines – Today, everything from playing chess to marketing automation to logistical scheduling to customer support routing is done via rules engines. With access to good quality data, programmers can build AI/ML-powered advanced rule engines and have access to a wider variety of conditions, instead of predetermined ‘if…then’ rule sets. For example, they can be used to achieve a higher degree of accuracy with marketing campaigns, resulting in a reduced spend for higher returns. As advanced rule engines infuse with different business functions, quality data management will soon become a success factor among enterprises.    

The Way Forward

Putting a data management program in place isn’t particularly easy, but enterprises that have put time and money into these systems are among the most successful in the world. It is a critical competency that has already been leveraged by many leading businesses in different domains – from social media to eCommerce to research to product development and more! And besides business success, good data management is increasingly becoming a prerequisite to regulatory compliance.

To ensure that the quality of records are not compromised at any level of data harvesting, data management systems require a management team that is educated in the importance of data, partners that have the skill, experience and technology to help you build the right systems, and transparent communication protocols between business stakeholders and information technology workers.