Data is crucial to decision-making, even more so when facing an unexpected challenge. Companies need to make sure they are serving the right data, to the right people, at the right time
Data is the foundation of any strong business. If you don’t have insight into how your business is operating then you won’t be able to manage - let alone see - the risks you’re taking. Recent history has taught us the value of planning for the unexpected. Yet beyond global pandemics, there are new technical, legal and business challenges springing up all the time. As this happens, the companies that succeed are those which can best exploit the data they have to gain insights and make decisions about where to go next.
The issue often isn’t a lack of data but that companies can end up with too much data spread across different systems that require different skills to access and analyse. Nearly four in five organisations make use of data from more than 100 different sources, with 30% making use of over 1,000, while nearly 80% store more than half of that data across multiple cloud services. The data usually exists, somewhere, but all too often cannot be accessed or analysed to give useful insights.
Organisations need more than just data, they need ‘data maturity’, which means serving the right data, to the right people, at the right time. The data needs to be high quality, highly relevant and compliant with regulations. The correct people need access to it - whether that’s the CEO who needs high-level strategic insights or a marketing manager who wants to understand the performance of a specific campaign. And the time needs to be right: it’s not good enough to understand what’s already happened, you need to be able to see what’s happening now and have a view of the future through tools such as predictive analytics.
Yet with the business landscape constantly changing, even the data a company is managing can present risks as the important information they need to collect evolves. For instance, the amount of data relating to environmental, social and governance (ESG) issues that a business needs to understand is increasing. In the next few months, new regulations for firms operating in the UK will require reporting on the risks and opportunities presented by climate change, while those operating in the EU will need to abide by new rules requiring disclosure of the impact the firm has on climate change mitigation and adaptation.
Beyond regulation, firms are dealing with consumers who have a growing environmental, political, and social conscience about what they buy, how they buy and who they buy from. It’s not enough to label a product as sustainable, businesses need to truly understand their entire supply chain to ensure every part of it actually lives up to the environmental and societal impacts they want to claim on the final product. Conversely, suppliers need to ensure they can deliver high-quality data about what they’re supplying; companies themselves will increasingly make purchasing decisions based on the accountability of the supply chain they’re hooking into.
One answer is to use a standardised data management platform that can deliver this level of maturity by ensuring the right level of data quality, compliance and access is available to help staff drive business decisions. This process doesn’t necessarily require thousands of hours of manual work; increasingly machine learning and AI can be leveraged to ensure that the data is of high quality and that its presentation complies with GDPR and other governance rules, for example masking personal data where necessary.
With this in place, firms can better understand the data they’ve gathered and turn it into actionable insight. As Greg Hanson of Informatica puts it: “Our intelligent data management cloud has helped organisations drive acquisition and retention with a more accurate view of a customer and their interactions with the business.”
He points to the example of Verizon, who gained better insight into their customers journeys through having a cohesive data management platform. As a result they were able to deliver self-service digital resources that ultimately reduced call service volumes by 26 million a year.
The pandemic saw organisations of all kinds pivoting to a digital-first approach and dealing with fast-changing levels of demand. Those that were able to implement, or were already implementing, intelligent data management experienced huge benefits. NYC Health + Hospitals, the operator of New York’s public health system, was able to make use of intelligent data management to streamline its response. This covered everything from ensuring healthcare workers had better diagnostic tools to the rapid creation of dashboards to document and forecast the impacts on the service. The technology now in place can be reapplied to any future, large-scale health events.
In a different field, meal delivery firm HelloFresh was able to rapidly scale as it experienced increased demand as people opted to eat at home. This is because its robust analytics and forecasting systems meant that change in demand was immediately obvious to those who needed to see it, even with most employees working remotely. By definition, it is not possible to plan for unpredictable events, but when they happen, ensuring that high-quality data is immediately accessible to the right decision-makers means they can respond quickly and appropriately.
The pandemic and ESG regulations are just two examples of how the risks faced by businesses will continue to shift - both through sudden shocks and as legislation, technology and the consumer environment change. Businesses need to evolve to match changing risks. Those that succeed will be the companies that use data on past performance alongside real-time updates and predictive forecasting to make high-quality choices about how they operate.
Too often, data management as a discipline hasn’t received the priority or focus it deserves, but if it’s done intelligently it can actually push a business forward. Understanding the risks means understanding the opportunities. As Hanson puts it: “Digital maturity is a continuous process, not an endpoint. Truly mature organisations will rethink data management implementations and make the strategic decisions that will allow them to identify risks, pivot quickly and drive value.”
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