The basic building block of all products, services and customer interactions is, of course, data. And just as water is crucial to life on Earth, fuelling ecosystems across the planet, data is the core component that underpins every business strategy.
It provides the facts, insight and intelligence needed to propel activity and growth across every functional discipline.
But our water supply faces constant threats that affect its quality, purity and ability to sustain life. One only has to look at places such as Flint, Michigan in the US or the Yellow River in China, where the water is so polluted as to be undrinkable, to see the impact this can have.
The same can be said for the purity of an organisation’s data. Among other things, poor data quality can leave businesses facing dissatisfied customers, regulatory disclosure risk, and an inability to accurately forecast earnings.
It’s vital, therefore, to know that this data is of pristine quality. It may look crystal clear on the surface, but it’s what’s underneath that truly matters. Only by using the highest quality data can a business deliver a satisfactory customer experience, reduce the risk of compliance failures, and optimise its operational efficiencies.
In many cases, this will require organisations to purify the data they use.
Filtering out impurities
The purification process should begin with businesses outlining a series of structured steps they can follow to ensure their data is of the highest possible standard. More than anything, formulating a solid data governance and quality strategy has the benefit of offering greater transparency around an organisation’s data: where it comes from, how much of it there is, whether it’s all required, and who should have access to it. Knowing this means that when it comes to implementing the plan, organisations can immediately identify opportunities to cut costs and improve efficiencies.
Prior to embarking on the plan, research should be undertaken on the tools and technologies that will make the process easier for all involved. After all, with a wide range of analytics and machine learning solutions available to carry out the heavy lifting, manual intervention should only be required for exception handling when it comes to improving data quality. It’s now just a case of finding those tools that fit an organisation’s requirements and meet its budgets.
An audit should then be carried out to assess the relevancy of any data currently held by the organisation. The type of data used to help boost sales, for example, will differ greatly from that needed to improve customer service or streamline the supply chain. Likewise, it’s important to take stock of those suppliers and customers that are still relevant to a business, and those which can be left behind. The aim of such an audit is to enable organisations to focus on purifying the data they actually need. As a filtration process removes impurities to leave only clean water, so the removal of irrelevant data will leave only that which matters to a business.
It’s important to avoid cherry-picking data, however. It’s an easy and common mistake for organisations to make – we have a natural tendency to look for the results we want and to ignore less favourable data – but might be too biased or narrow minded. Objectivity is essential. All relevant data – both good and bad – must, therefore, be included in an audit’s findings if businesses are to ensure their data cleansing plan is comprehensive.
Mindsets and mindfulness
As organisations everywhere undergo digital transformation, the volume of data they create is only set to increase. Both operational employees but also management sometimes underestimate the value of data to a business, though, so some education is required on the importance of data quality as a driver of organisational success.
Maintaining a high level of quality – of data purity – will, therefore, require a company-wide change of mindset. One way of achieving this is to assign data advocates to each business unit, keeping their colleagues abreast of the latest developments in – and benefits of – their company’s data purification process, and the ongoing need to ensure that data is kept relevant and up to date.
That said, it’s important not to set the bar too high. It’s possible to over-purify the data supply and thereby removing some of its value. Organisations should be mindful of data protection regulations such as GDPR, too, to ensure they remain compliant without hindering the flow of data between internal systems, and external customers and suppliers. An awareness of established best practices, industry standards, and similar initiatives undertaken by comparable companies will help avoiding this.
Data is essential to the life of every business today; it improves efficiencies and helps organisations maintain their competitive edge. But like our water supply, it must be kept clean of any unnecessary pollutants and allowed to flow freely. By taking a structured, company-wide approach to purifying data, businesses will feel refreshed, reinvigorated and ready for the rewards that relevant and timely data offers.