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DQ Blog Master Data Management Data Quality shouldn’t be a Goal – It should be a Habit

Data Quality shouldn’t be a Goal – It should be a Habit

Martin Doyle April 4th, 2018 Master Data Management
Data Goals

We are what we repeatedly do. Excellence, then, is not an act, but a habit.”—

Will Durant

We all know quality stems from habit, as much as we’d like to believe otherwise. So, why is it we don’t follow such sage advice? Instead, we allow less desirable actions to creep into our routines.

If we don’t apply focus to areas most important to our daily success, there is every risk that we will fall foul of our misgivings, only mastering skills that add little by way of tangible value.

Such a position has never been more valid than in today’s data-centric world.

Never Follow Your Gut

While the route to many people’s heart is through their stomach, where data is concerned, this should never be the case.

Gut feelings have little place in a data-rich world where we have plenty of information for making an optimal decision. There is no longer room for excuses that disparate data sets across multiple databases – or incompatible formatting – is disrupting the decision-making process.

It is time to kick these bad habits into touch and align with a better data management strategy.

Too often cognitive dissonance leads management to believe that ‘if it ain’t broke, don’t fix it’ but the truth of the matter is – it probably is broke, you just haven’t taken the time to check. In fact, data experts Experian have shown that 83% of companies are concerned their revenue suffers from poor data management – such as inaccurate or incomplete customer data.

The Bad Habit Black Hole

Big Data is at the forefront of business in today’s hyper-connected world. Decisions are supported – or countered – by reams of high-quality information that allow management to optimise everything from order fulfilment to customer management to the bottom line.

Conversely, inadequate data comes at a significant cost, draining resources and negatively impacting the quality of decisions. Too many businesses toe the line when it comes to data management, stuck in the belief that what they have is good enough; though this is rarely the case. Just because others believe that hundreds of unlinked databases with different storage formats is sufficient to power big business, this is not the example to follow.

Quite the opposite, as such a system predicates further issues of un-scalability – or systems that will, one day, topple over. Experian notes that 88% of businesses suffer adverse effects due to poor data management, with revenue declines of as much as 12% as a direct result of bad habits.

If your company relies heavily on outbound marketing, how would you feel knowing that one-in-twelve communications arrived at the wrong address due to out-of-date data? That is not only money, but time lost through small inaccuracies, and the issue extends way beyond marketing.

Up to 20% of employee productivity – that’s one full day – disappears through searching for the data they need to carry out daily tasks. The simple act of retrieving a record – perhaps accounts reconciling sales – is a drain that you could avoid through optimising your process.

Steps to Change

Breaking the status quo takes guts – we admit it, this is the one time to follow. Once you implement the right habits to improve your data, your efforts will pay handsome returns.


The first task is to help others understand the power of clean data. Communicate why the new approach will simplify your employee’s lives as much as pushing the bottom line, as you are more likely to achieve buy-in.

Demonstrate how customer service can deliver a more efficient response, if only they collect this one additional piece of information.

Establish Requirements

Work with key stakeholders to define what data will propel your business forward. Understanding the data required will help you outline best methods for collecting, storing and reporting information, making life easier as you progress.

Outlining which fields are essential – and who needs access to what – are foundational to the success of your long-term data management strategy.

Maintain Standards

You’ve built a robust system, so be sure your policies preserve the data’s integrity. A considered process will ensure each member of your team understands how to store and update records, guaranteeing you never end up back where you started.

Schedule Updates

The fact you are working to build a new system demonstrates your appreciation that data quality can decline at a rapid rate. Scheduled data cleansing avoids a gradual build-up of sub-optimal information and mitigates human error (we all make mistakes once in a while), so be prudent with your data management strategy.

Invest in Quality Software

Every system requires the right tools to function at its best: This does not mean a massive outlay, just an integrated system that presents company-wide data under one roof.

Couple with an efficient data-cleansing software and you will be well on your way to a data powerhouse free of duplicate entries or incorrect formatting.

Changing your Data Quality habits will ensure you have data you can trust to make informed business decisions.

If you’re concerned about the quality of your Customer Data, book a DQ Discovery Call with us today


Written by Martin Doyle

Martin is CEO and founder of DQ Global, a Data Quality Software company based in the UK. With an engineering background, Martin previously ran a CRM Software business. He has gained a wealth of knowledge and experience over the years and has established himself as a Data Quality Improvement Evangelist and an industry expert.