Data as a new currency of business innovation
Almost two decades ago Clive Humby – a British Mathematician, had coined the phrase “Data is the new oil.” The phrase signified the importance of processing raw data – i.e., distilling, reshaping, transforming, blending amongst others, to create new business values. We have witnessed the rapid emergence of Artificial Intelligence (AI) and Machine Learning (ML) technologies and their wide proliferation across industries in recent years. With the advent of AI, data has further ascended to the position of a new currency of business innovation and gaining a competitive edge.
In the data-driven business paradigm, business firms have been actively adopting AI/ML technologies in diverse functional domains to realize the potential of business innovation and enhanced productivity. 2023 Global Trends in AI Report from S&P Global highlighted that AI technology is already a catalyst for transformation — 69% of surveyed organizations report having at least one AI project in production, while 28% have reached enterprise scale. Another report from Grand View Research estimated the global AI market size valued at USD 196.63 billion in 2023. The report projected the AI market size to expand at a compounded annual growth rate (CAGR) of 36.6% from 2024 to 2030 and reach USD 1.81 trillion by 2030.
Data Science and AI refocusing on the entire gamut of Data Management
In recent years data science– primarily focused on data discovery, insights exploration and advanced analytics – are the recent additions to the data stream in an enterprise. Harnessing varied forms of data – structured, semi-structured and unstructured from formal and informal (alternative) sources, data science equips firms with unconventional insights for timely business decisions, operational agility and improved customer engagement. While these disciplines have gained increased attention along with prioritized resource allocation, it is the foundational plumbing aspects of data management across the data lifecycle that hold the key to the realization of data-driven business values.
Keeping a realistic view of mastering the technological complexities and establishing a better-governed data and AI environment, the AI adoption strategy presents a proverbial chicken-and-egg situation for most organizations. The fragmented data landscape across business and application silos and technology tools constraints creates acute challenges in the realignment of the data engineering foundations and smooth integration of AI technologies in enterprises. Thus, the success of firms’ drives to acquire AI-powered capabilities for exploiting business opportunities depends on the existence of a well-functioning data foundation enabled by a streamlined data operating model and governance mechanism. These become vital for effectively holding a resilient data ecosystem on which the hefty superstructure of data science can be securely anchored.
The evolving role of the Chief Data Officer (CDO) in the enterprise data landscape
As organizations embark on a data-led innovation journey, the role of the Chief Data Officer has come into a sharp focus. In the evolving business landscape, the CDO occupies a pivotal position in charting the data and analytics roadmap and translating the foundational contours of the strategy into an agile data-driven organizational functioning. To ably fulfill the business needs of data-backed decisions across a wide functional spectrum– e.g. CRM, product and services innovation, operational efficiency, risk management, legal and compliance, reporting, finance, HR, IT and others, establishing a self-reliant and synchronized foundation bed of data-driven business ecosystem becomes an utmost priority for a CDO.
On the one hand, a CDO’s role demands resolutely attaining the critical measures across hard nuances like right data sourcing and integration, quality and governance – including integrity, timeliness, consistency, traceability, security and privacy aspects and ensuring the availability of right data for the business users. On the other side, it requires deeper influence across business layers in the organization to inculcate and nurture softer nuances of data-centricity – i.e., data culture, data literacy and data democratization to create a self-sustained data continuum anchoring self-discovery paradigm for empowering business users. Thus, a CDO’s success in driving a data-led innovation agenda relies on his/her abilities of tactful calibration and flawless orchestration of the data-focused gameplan involving the business and technology stakeholders.
Importantly, the execution of such a gameplan requires frictionless binding of enterprise data management competencies to enable the realization of the vital KPIs of the business groups. Depending on an organization’s data maturity level, some of these competencies – i.e., data strategy, data architecture, data modelling, data integration, data quality management, data governance, data lifecycle management, data security and privacy, business intelligence, analytics, data science, ML operations (MLOps) amongst others - can be pooled from within the enterprise whereas some others are to be strategically sourced from external partners to progressively build up such competencies.
The pivotal role of the CDO in accelerating data-led innovation journey
Anchoring a sound data-backed decision-making paradigm in an organization essentially involves efficient assimilation of requisite data management competencies to ably exploit the flux of varied data from diverse sources to fulfill the business needs. Towards this, a CDO plays a multifaceted role - as an able captain, custodian, curator and steward in the enterprise data landscape – to propel the data-led innovation journey. While driving enhanced readiness of data processes, platforms, models and analytics tools, governance methods, a CDO can significantly uplift data discovery and experimentation practices to accelerate business contextual data insights, data product formulation and data monetization strategy.
Importantly, the success and effectiveness of a CDO’s role is not limited to narrow confines of data and AI outcomes, rather these are the critical determinants of an organization’s ability to realize their data and AI strategy to stand ahead in the competitive race. It is no surprise that CDOs - donning a data strategist and innovator hat- find an enviable position at the high table of data-driven business organizations.
This is the first part of the article to outline the significance of the role of the Chief Data Officer (CDO) as the head of data organization in charting the data-driven business roadmap and translating the foundational contours of data and analytics strategy of business firms. The second part of the article will provide a broad overview of key functional streams and associated roles in the data ecosystem of an enterprise.
About the Author
Indra is a Senior Industry Advisor in the Banking, Financial Services and Insurance (BFSI) unit at Tata Consultancy Services (TCS). With 28 years of global experience in business and IT consulting, he spearheads CXO advisory, strategic planning, design and implementation of digital and data led transformation initiatives and innovation-focused thought leadership engagements. Apart from his extensive expertise in financial services domains, he also maintains a keen interest in sustainability, corporate governance and organizational culture issues.
ipchour@hotmail.com
LinkedIn: www.linkedin.com/in/indra-chourasia-bfsi-business-architect
Twitter: @ChourasiaIndra