By now, any company born before the digital age is likely to be contemplating or undergoing substantial transformation. More than 82 per cent of CEOs have a digital transformation (DX) or management initiative in place, up from 62 per cent in 2018. Taking the digital leap is focused on making businesses fit for purpose: they need to remain relevant to customers, create new revenue streams, compete (and win) in the digital economy, and be able to recalibrate faster in an increasingly unpredictable and rapidly evolving world.
To take advantage of opportunities in the interconnected economy, businesses must be data-driven and insight-led. It’s not enough to simply amass or generate data. Competitive advantage comes from being able to spot transformational possibilities before they become obvious to everyone else, and act on intelligence proactively and predictively. These capabilities are fundamental to transforming customer experience, discovering unmet needs, and delivering new business models and monetisation opportunities, exemplified by the US ‘big four’ of Google, Amazon, Facebook and Apple, and industry disruptors such as Uber and Netflix. And of course, it’s not just about customer-facing initiatives – organisations need to be smarter about how they collaborate with suppliers and business partners by finding new, more effective and efficient ways to work together.
Shiny tech, dirty data?
The intrinsic value of any DX programme hinges on producing actionable insights. Businesses are eyeing advanced tools and technologies like predictive analytics, robotic process automation (RPA), artificial intelligence (AI), machine learning, the Internet of Things and Blockchain, to help improve or invent new products, services and business models. For example, almost a quarter (23 per cent) of businesses are actively using predictive analytics, while over half (51 per cent) are considering or evaluating tools to enable predictive capability. But no matter what the headline innovation, it’s data that’s the unsung hero, whether fuelling processes behind the scenes or in front-end applications.
In the hot pursuit of actionable intelligence, what’s often overlooked is that any indicator or predictor is only as reliable as the quality and availability of the underlying data. As we become accustomed to taking analytics at face value, incomplete or inaccurate data will inevitably translate into misleading insights and poor decisions – ones that could negatively impact operations, planning, projections and the bottom line. And any high-profile DX project will almost certainly fail to deliver on its promise if the underlying data isn’t up to scratch. To illustrate, almost eight out of ten (78 per cent) AI and machine learning projects reportedly stall due to poor data quality, while a staggering 96 per cent have run into problems with data quality, the data labelling required to train AI, and building model confidence.
Over time, these issues will be compounded. Not only do services and processes based on tech such as AI and RPA feed on data – they generate mountains of the stuff, too. This data needs to be collected, aggregated and analysed to be of any use or value. As the volume, variety and scope of data created and acquired grows exponentially, pity the business users who are expected to find, understand and trust information to do their jobs.
Left unaddressed, companies’ legacy of siloed data and systems will only entrench siloed practices, processes and cultures. This will keep them firmly stuck in a state of being process-defined, rather than data-driven, and prevent them from detecting blind spots or delivering the frictionless experiences today’s digitally-savvy customers have been conditioned to expect.
Data governance: the prerequisite to transformation
While data science is a hot commodity, the foundation of DX is, in fact, data governance: the blueprint of policies, processes and stewards for the end-to-end lifecycle of data that, with enterprise data management, turns data into a shared, company-wide resource through continuous availability, usability and integrity. Before running with ambitious DX initiatives, businesses must learn to learn to walk, by getting better at maintaining trusted, accurate and complete data until this becomes a core competence – and a competitive differentiator in itself.
Data cannot be democratised without giving the consumers of that data an understanding of its trustworthiness and relevance to the business. That means having a firm grasp of the context, quality and business value of all available information sources – both inside and outside the organisation.
Data governance is fundamental to enabling businesses to give their executives a holistic view of the metrics that matter and empower them to make agile, evidence-based decisions. It allows data scientists to focus on answering business questions and training AI models with confidence in the outcomes. It enables more and more workflows to be informed or transformed by putting contextual insight or predictive capability in the hands of non-technical users. And when provided within a framework of privacy, data can actively help to preserve customer trust as well as driving automation and delivering intelligent, engaging customer experiences.
Amid the great DX gold rush, data needs to be perceived and treated in the same way as any other strategic asset, like people and facilities: managed with the right tools and governed by the appropriate policies and practices. At a time when data has growing potential to determine or derail business outcomes, how organisations rise to the challenge of looking after their mission-critical information will dictate the success of their DX initiatives.
Matt Dunnett, Managing Director UKI, Informatica