By Emily Washington, senior vice president of product management at Precisely

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:

Lack of trust in data

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.

Achieving data quality at scale

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.

The pressure of real-time analytics

The rise of streaming and real-time insights has created new challenges for businesses seeking a unified view of their data, something that was supported by findings in a survey conducted by 451 Research. Businesses are facing the challenge of ensuring they have the necessary IT and logistics support in place to deliver real-time analytics, but also need to make sure they are maintaining the integrity of their data while processing it at speed.

The problem is that data is dynamic in nature. So, it needs to be kept fully updated and analysed at the speed of the business. As more and more data is streamed, enterprises need to ensure that they have the right systems in place for monitoring changes in data patterns in real time. The right processes need to be put in place to flag anomalies, and to provide reliable assessments and recommendations so actions can be taken quickly. Ensuring the accuracy, validity, and completeness of the data – at a pace that meets the demand of real-time insights – is something that businesses must prioritise.

Challenges with data integration

The biggest challenge many enterprises face with respect to data integration is a shortage of employees with the right knowledge and expertise. But the increased size and complexity of corporate IT landscapes is also playing a factor. 77 per cent of Corinium survey respondents said that processing high volumes of data is at least ‘quite challenging’, with 73 per cent of teams also indicating that they find it at least ‘quite challenging’ to manage multiple data sources and complex data formats.

Challenges in staffing, coupled with the problem of complexity and change, means that many organisations are turning to low-code and no-code integration tools, which offer more agility and flexibility than traditional coding frameworks. With the right enterprise-grade integration platform, streaming data pipelines can be developed easily, deployed anywhere across the corporate IT landscape, and modified quickly without introducing undue risk. When data is critical to business operations, robust and resilient integration tools enable business continuity, even when a connection is disrupted.

Integration of complex and diverse data sources is also critically important. Mainframe data can be particularly challenging, given the intricacies of hierarchical databases, COBOL copybooks, and other complexities associated with mainframe computing systems. Unlocking the data and making it available to cloud analytics platforms, as well as to other applications, is critical if enterprises want a complete picture of what’s happening in their businesses.

Many enterprises have established a basic foundation for data-driven decision-making and automation, but they are also still reporting significant struggles in the quest to develop and maintain data integrity at scale. As the pressures of digital transformation continue to increase, there is a way to go before many business leaders can say that they truly trust their data. To respond to these growing challenges, data executives should be prepared to build a sound data integrity strategy to lay the foundations for success.