IBM: the Blueprint for a Data-driven Enterprise
“To be a good Chief Data Officer is a true craft; when I walk into an organisation I immerse myself in the business strategy and the detailed end-to-end business processes to know exactly what I’m going to implement, the strategy behind it, measure how well it’s going to unfold and the milestones to be hit,” says Inderpal Bhandari, IBM’s Global Chief Data Officer. With more than 20 years of experience transforming industry-leading organisations, Bhandari has defined the scope, expectations, and deliverables of the modern Chief Data Officer role. Indeed, it wouldn’t be unreasonable to suggest he is the Chief Data Officer.
Bhandari is a recognised expert in transforming data into business value. He improves customer experiences by delivering strategic and innovative capabilities that use data-driven insights to enable growth and streamline productivity. Most recently, he has been doing so at IBM. Bhandari joined the global tech leader in December 2015 to lead IBM’s data strategy and to drive its internal data and artificial intelligence (AI) transformation. This work, which Bhandari joined us to discuss in more detail, has culminated in the company’s Data and AI Enterprise Blueprint - a roadmap for IBM clients to use when embarking on their own data and AI journeys.
His experience and an intimate knowledge of what an effective Chief Data Officer can deliver took shape in the trenches of corporate America where data was becoming the new natural resource. “I became the very first CDO in the healthcare industry in 2006, and was an entrepreneur in data products before that. It was very early on in the profession - at that point there were four of us globally. Today the role has expanded massively with thousands of CDOs, Chief Analytics Officers and Chief Digital Officers, and I’ve been fortunate to have been at the forefront from the very beginning, learning, and understanding the profession.
“Data is a hugely valuable asset and so the importance of the role has transformed within enterprises,” he continues. “My experiences enabled me to contribute to multiple organisations while honing the craft of using data and technology to fuel the powerful and complex transformations that enterprises are undergoing.”
Technology and transformation: driving change
Understandably, during his career Bhandari has been at the forefront of technological innovation. He has also seen data evolve to become a crucial success factor for the modern enterprise.
“The rapid change we’re seeing today really started with the journey to the cloud - cloud computing has been a major game changer at the industry level,” he states, expanding on the technology evolution that has shaped today’s landscape. “And with that came a heightened focus on cybersecurity, data privacy and ethics, which remain core areas for any CDO. Once the resources become available through cloud or, more recently, hybrid cloud, AI and many of the devices at the ‘edge’ can and will come into play, such as robotic technology, the Internet of Things (IoT), and autonomous vehicles.”
The ability to keep ‘building’ on the hybrid cloud, says Bhandari, will accelerate the adoption of AI as part of enterprise transformation. Other important technologies that will be adopted include 5G and blockchain, which he says has the potential to transform data and data ownership. “It’s a rapidly changing industry. But with that, comes an acute awareness and willingness on the part of the c-suite and enterprises to gain a much greater understanding of what these trends and technologies can do in terms of transforming their businesses. Leaders and organisations all over the world have come to realise that it’s not a question of ‘should we do it’, but a question of ‘when do we do it’. If you don’t, you’ll very quickly be irrelevant.”
IBM: The CDO’s Blueprint for Change
When Bhandari joined IBM five years ago, his role was specifically created to facilitate the mission of transforming IBM into a Data and AI enterprise. He built a transformation strategy roadmap based on three steps: develop a clear data strategy; execute enterprise-wide data governance and management systems; and become the central data and AI framework for the IBM enterprise.
The first is crucial for any enterprise embarking on such a journey and starts with one straightforward question: what is your company’s monetisation strategy? This is, says Bhandari, key to a successful data strategy. “Any data strategy has to be tied to the business strategy, and that revolves around how the company is going to make money, maximise business impact, and delight customers,” he explains. “In IBM’s case it was clear that we were going to make money primarily from best-in-class AI and hybrid multi-cloud offerings. But it wasn’t clear what AI meant for an enterprise or how to go about it. Thus we thought why not make IBM itself into an AI enterprise and use it as a showcase for our clients.” Bhandari created a dedicated team that consists of world-class data and AI specialists who collaborate across the entire business.
Next, the focus rested on consolidating critical enterprise data and making it available as a service. Rapid integration of critical data into a single, consolidated data platform can bring unprecedented connection and is the moment, Bhandari says, where a data strategy ‘really comes alive.’ He adds that a unified data platform is the fundamental enabler of advanced AI solutions in an organisation, and brings new business capabilities, drives efficiency and top-line improvement.
“Our aim was to transform IBM into a Data and AI enterprise,” he notes. According to Bhandari, AI systems - all of which are underpinned by data - have four main attributes: they learn from data, they support forms of expression more natural for human interaction, their primary value is their expertise, and their learning evolves continuously as they encounter new information or scenarios.
IBM’s Data and AI Blueprint encompasses technology, organisational considerations, data, and business process transformation. For example, cognitive services give the ability to see and contextualise across all data as well as infuse insights into the workflow of business processes. Hybrid cloud solutions encompassing public cloud, private cloud and on-premise environments, provide enterprise cloud container support while implementing and enforcing security standards and privacy policies. Further, the convergence and curation of data that would have previously been siloed and fragmented across an organisation, to create a reliable and trusted source that AI systems can build upon.
Cognition also brings distinct advantages to points of business that require significant human judgement. In Bhandari’s Global Chief Data Office, for example, several projects utilise IBM’s Data and AI Platform. “As an example,” he adds, “take a role within our business, which essentially revolves around an employee having to classify whether a client that we’re working with is a government-owned entity. We hold ourselves to very high standards, and so that kind of classification and judgement is crucial. We have to get it right. Previously, that classification would have involved research, communication with other parties and eventually arriving at the classification. This is a perfect example of where AI comes into its own. Using our IBM Watson capabilities, we can now access historical data as to how we have previously labelled companies, and we can sift and monitor real-time information about those same businesses.
“That capability makes the decision more quickly, accurately and improves efficiency of the work,” he continues. “As an example, it may be at a lower level in terms of the vast amounts of activity we undertake at IBM, but you multiply it one million-fold across every significant business decision that we make, and you have an insight into the potential of AI. That’s what I envisioned when I began this journey - we have to work AI into every business process, whether it’s supply chain, finance, marketing, accounts payable. There’s no area it can’t bring significant value to.”
The challenge, says Bhandari, is for enterprises to undertake that scaling of data and AI solutions. Core to that, he says, is a strong central function which is the role that he and his immediate team provide. However, he also adds that in his experience, many organisations are still in the early stages of their AI adoption journey. IBM’s learnings from its data and AI transformation form the basis of how IBM interacts with clients.
Take the company’s AI Enterprise Accelerator as an example. This collaborative cross-enterprise initiative builds on IBM’s AI transformation and is designed to help leaders quickly ramp up their AI solutions and processes and drive business value covering areas including data strategy and architecture, automating business metadata, data privacy and trust, and AI applications.
“When I started the Global Chief Data Office at IBM, we were effectively at ‘ground zero’ in terms of this,” Bhandari notes. “But our success allows us to share and take it to a completely new level for both internal transformation and with our clients. Our Enterprise Data and AI platform was established at the end of 2017 and by year end 2019 we had over 100,000 active users on it internally, who were infusing AI into their business processes, and several clients replicating these use cases externally. That’s a really high rate of adoption for both internal use and by our customers.”
A Blueprint for long-term success
Any transformation journey is an ongoing process, even with a CDO as capable as Bhandari at the helm. He freely admits to adopting a ‘fail fast’ approach to his role and mission at IBM and is not afraid to admit - even with his experience - that he and his team are always learning. “If you are the CDO then you must be aware it’s a transformation role and you are primarily a change agent,” he explains. “You have to change the enterprise to transform it, so the data and AI blueprint that we developed has all of the aspects that any change agent should focus on.
“You use the data and the technology to affect the change, of course, but there’s also the people and culture factor that is equally important. You can’t neglect any aspect of that blueprint if you want to succeed. Technology will continue to evolve, and so we must evolve too. The move to the hybrid cloud is only accelerating, for example, and things like cybersecurity, privacy and data ownership will become more and more relevant. Similarly, edge computing will be a critical driver as data comes from millions of devices at the edge. That and technologies such as quantum computing have the potential to revolutionise everything we do. It’s an exciting prospect and a very exciting time for Chief Data and Technology Officers at the forefront of these transformations.”
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