September 10, 2019
September 10, 2019
Contributor: Katie Costello
Data and analytics are no longer afterthoughts they are fundamental to digital business transformation and have a much more expansive role in generating business value.
Data and analytics continues to evolve as digital business takes off. Ahead of Gartner IT Symposium/Xpo 2019, Smarter With Gartner reached out to experts presenting at the event to ask them how IT leaders should invest in data and analytics.
Data and analytics has a much more expansive role in generating business value. It is a set of enterprise capabilities in support of a wider digital transformation strategy. This requires treating your data and analytics as assets and creating a data-driven strategy that starts with your data. Ask questions such as, "With this data, or this type of insight, how could we fundamentally change the value propositions for our customers or our fundamental ways of working?”
Create a vision of a data-driven enterprise with business peers. Identify and prioritize information-based outcomes, such as internal and external monetization of data assets and improvements to business insights. Also invest in building data-driven competencies across the enterprise — these are critical to your success.
See Mike at his IT Symposium/Xpo sessions:
IT leaders must make data and analytics a strategic priority aligned to business outcomes — delivering on most digital business goals and objectives will depend on it. This means empowering everyone and everything in the organization to exploit data and analytics, introduce new sources of growth, change how work is done and augment the role of people across the organization to drive innovation.
Achieving this will require a balance between investments that drive innovation, change business models and define new ways to interact with and serve customers, partners, suppliers and employees, such as in leading technologies — artificial intelligence (AI), machine learning, blockchain, immersive analytics, and the Internet of Things — and renovating the technology core to build an agile data-centric architecture leveraging the cloud in support of continuous intelligence. Critically, as the use of AI becomes more fundamental to all aspects of business and society, success with data and analytics will depend on building a foundation of trust, accountability, governance and security that respects privacy and promotes digital ethics.
Managing and harnessing change is the biggest challenge facing data and analytics and business leaders alike. It is affecting business models, driving the need for data literacy, treating data as an asset and the need for a data and analytics-centric mindset. Creative thinking must be embraced and encouraged. Specific investments must be made to create new roles (such as the chief data officer) and responsibilities. Leaders must examine and build their organizational culture foundation for adaptability and resilience.
See Rita at her IT Symposium/Xpo sessions:
he fundamentals are to focus on business outcomes and to invest more — a lot more — in people. Technology considerations come last. As data and analytics become pervasive in all aspects of businesses, communities and even our personal lives, the ability to communicate in data terms — that is, being data-literate — is the new organizational readiness factor.
Identifying and quantifying business value is paramount; sell the strategy by finding the “so what.” Organizations need to take into account the major expectations, mindsets and behavioral impacts that arise as a result of the aspirational goals for data-driven business. All of this means there is a need for a deliberate, coordinated and ongoing approach to data and analytics, and investment in the capabilities to execute against the promise of being “data-driven.”
See Alan at his IT Symposium/Xpo sessions:
Data and analytic investments extend beyond purchasing technology. The real key to success is to establish the right organizational model. The optimal organizational model requires a centralized team working in collaboration with a finite number of decentralized teams.
It's a simple enough idea, but data and analytics leaders must choose from numerous permutations to find the ideal balance for their organizations. Each local department should be empowered with a cross-functional team that blends data engineering, data science and domain expertise. And the mandate for decentralized teams to create analytic prototypes, pilots or full-production solutions should be clearly articulated.
This article has been updated from the original published on September 18, 2018, to reflect new events, conditions or research.
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