Data-driven digital transformation is now critical. But how do you morph your business model to exploit the opportunities of big data? Read on
Pre-Covid-19, organisations, at varying speed, were on a journey towards a digital business model. But the scale of the pandemic has forced a dramatic acceleration, with even greater focus and investment on digital transformation.
In the mid-spring of 2020, deep into the pandemic’s first phase, Satya Nadella, Microsoft’s CEO, said that we’ve seen “two years of digital transformation in two months.” The move to remote work and dramatic shifting consumer behaviour has challenged companies.
Data-driven digital transformation is more critical to success now, when we return to our (new) normality and further down the line when we return to business as (new) usual. Now, chief data officers (CDOs) are increasingly being asked to take the lead on digital transformation initiatives as the digital business itself transforms to business as usual.
According to Gartner, 72 per cent of data and analytics leaders with digital initiatives are either leading or heavily involved in their organisation’s digital transformation initiatives.
“The results indicate that more organisations now understand the synergy between building a data-driven business and leading digital transformation,” said Debra Logan, distinguished research vice president at Gartner. “D&A strategy is a business strategy infused with D&A thinking; it has a primary role in digital business strategy, affecting everything the organisation does.”
However, embracing a data-driven digital transformation journey is not simple, and there are no shortcuts to real success. Instead, it’s about taking a data-driven approach, putting the information and skills you have at the heart of the project, and building outwards with the right technology to deliver insights at speed.
The key to moving business fully online needs a business model that’s distinct from analytics and algorithms, it needs a map that unifies the data of the business. A systematic “digital twin” serves as that map, replicating the people, places, and things relevant to the business in a digital model. It’s the programmatic, algorithmic manipulation of those stand-ins or proxies that is the way of digital transformation.
To succeed with a digital twin strategy, businesses need a machine-understandable enterprise knowledge graph (EKG) that fuses data management and knowledge management techniques. These systems allow enterprises to discover hidden facts and relationships through inference mechanisms that would otherwise be unable to catch on at large scale without the accelerating power of machines because moving from data to knowledge and achieving higher degrees of automation requires insight into, and leverage over, the relationships between things in the world.
Managing data that rely on rigid storage-based techniques is giving way to modern data integration approaches in 2021 and beyond.
Semantic data models
Also, to manage data algorithmically, a business must first represent what is actually meaningful about the data in ways that are accessible to algorithmic design. Here, semantic graph data models, which is one of the fastest-growing new technologies, is the best way to represent data that is natively stored in other structures.
Semantic data models not only store data but also have the tools for interpreting it in a way that suits different information needs and helps gain different perspectives.
Functionally, semantic data modelling is about understanding what the data is about and making the knowledge locked in it more explicit. It’s about translating disparate data into information that can be consumed (queries, visualisation) for different decision-making purposes.
Organisations can look to semantic graphs to connect data from structured, semistructured, and unstructured sources to gain a full picture of connected enterprise data and to understand the relationships and nuances that exist.
Meanwhile, data fabrics, which have the ability to weave together existing data management systems, will continue to be the next step forward in the maturation of the data management space because data lakes and warehouse store the data itself but don’t give the picture of data in motion as data fabrics do. Gartner identifies data fabrics as a means to frictionless access across a distributed network. Traditional data management options have gone obsolete as data itself changes, and our ability to process that data changes. Data fabric enlightens the team and the decision-makers to entire data operation.
Companies can make data usable and reusable at enterprise scale by applying powerful query-answering services in data fabric systems that connect all the disparate parts of the fragmented data landscape. Data fabrics will help to derive insights, and make data actionable.
“It’s not enough to manage data and create insights,” said Logan. “These activities must deliver measurable business outcomes. Data sharing is the way to optimise higher quality data and more robust data and analytics to solve business challenges and goals. Data sharing is an important KPI and a business necessity. It accelerates digital business transformation.”
Gartner predicts that by 2023, organisations that promote data sharing will outperform their peers on most business value metrics. Recently, Microsoft commissioned The Economist Intelligence Unit to conduct an independent study across eight industries about the lasting changes brought about by pandemic-driven waves of digital transformation.The research reveals digital preparedness and resilience are key to transformation.
Overwhelmingly, business leaders cited digital preparedness as key to their ability to adapt. The report, The Transformation Imperative, unlocked insights from the past year and focused on the way forward. While with graphs and metrics people have become more used to interrogating and understanding data, and making informed decisions, it’s more critical now than ever to up-skill the workforce. Globally, 74 per cent of employees report feeling overwhelmed or unhappy when working with data, yet data proficiency is critical to digital transformation and data-driven success.
A data-driven company where only the C-Suite and a few select employees lead with data will always lose out to one that has data literacy spread through every tier of the organisation. Now is the time for data democratisation, where everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway to the data.
Also, one of the most important analytic approaches is machine learning (ML), which can help find connections and trends in the data that human data analysts may not even know to look for. It’s time to consider opportunities to enable ML to solutions and focus on forward-looking insight, ensuring these are able to automatically convert current data into real, actionable insight, which can be easily understood across the business functions.