Solving key data challenges with data integrity
To prevail in an increasingly competitive global marketplace – that relies so heavily on data – it’s clear that business leaders need to be able to trust the data they have in order to make confident, strategic decisions. Having and maintaining a foundation of data that has accuracy, consistency, and context is more important now than ever, particularly as businesses continue large-scale digital transformations at a rapid pace. To be able to solve existing data challenges, leaders need robust data management strategies that prioritise data integrity. Below are some of the key challenges we see data leaders facing everyday:
A key challenge for businesses when it comes to their data is one of trust. In fact, a recent Corinium report, which surveyed more than 300 chief data officers, revealed that only a third of respondents actually trust their data when it suggests conclusions that differ from their own assumptions.
Furthermore, 44 per cent of respondents reported that they don’t trust insights from data that don’t confirm their initial gut feeling, and a further 22 per cent stated that they don’t trust the insights from their data overall. This prevents businesses from being able to make the best possible decisions, undermining the ability to achieve better business outcomes and, ultimately, more profitable growth.
The ability to trust data is paramount, but for data to be trustworthy, it needs to have integrity. Businesses must develop a foundation composed of the core pillars of data integrity: data integration, data quality and governance, location intelligence and data enrichment. By doing so, businesses will be better prepared to manage risks, provide better customer experience, reduce costs, and move faster due to confident decision-making.
Findings from the Corinium report also revealed that the average data team spends 40 per cent of its time cleaning, integrating and preparing data before it can be used in analytics, with several respondents even reporting spending up to 80 per cent of their time cleaning data. In spite of this, the use of automation to improve data quality is still limited. Approximately half of industry leaders (51 per cent) reported that they only make limited use of automation in their data practices, and 12 per cent have not engaged with automation at all.
As the availability of data continues to increase in volume and velocity, automation is fast becoming a business imperative. Organisations that lack data quality at scale will end up experiencing a decay in the integrity of their data and risk putting key data management initiatives, including data governance, in jeopardy.
The challenge of achieving data quality at scale will only increase in importance in the years to come as businesses continue to rely on artificial intelligence (AI), machine learning (ML) and other advanced analytics to inform strategic and tactical decisions.
