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 Symposium/ITxpo 2018, Smarter With Gartner reached out to experts presenting at the event to ask them how IT leaders should invest in data and analytics.
The 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 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 Duncan at his Symposium/ITxpo sessions:
- How Can Midsize Enterprises Exploit AI and Data Monetization to Grow Their Business? (Orlando, Florida)
- A Data-Driven Culture Is Vital to Digital Business Success for Midsize Enterprises (Orlando, Florida)
- Making Digital Business More Intelligent — Secrets of Success for a More Data-Driven Culture (Orlando, Florida; Gold Coast, Australia)
IT leaders should be investing in data and analytics, period. Digital business cannot actually exist without some aspects of them. Data exists in analog business, too. But with digital, data becomes more nebulous, more risky and more valuable — and analytics underpins the new intelligent and augmented mode of business.
Even if your organization has not yet decided to adopt a digital business platform, if you are seeking to exploit data and analytics to improve business outcomes, then you still need to put data and analytics at the center of your investment plans. The first thing to realize is that acquiring technology is the easy part; maturing the right workforce skills and being able to organize are just as important. As almost 50% of organizations do not create detailed financial justifications of their IT or data and analytics projects, ensuring work is methodically justified is essential when investing in data and analytics.
IT leaders should invest in data and analytics in three ways:
- Modernize analytic and BI platforms. Use them to support self-service, agility and higher-level insights. Traditional BI platforms, in which IT provisioned a data warehouse and created reports, have served organizations well for decades, but modern analytic and BI platforms that support agile data modeling, visual exploration and augmented analytics are now state of the art and provide better insights, faster.
- Prioritize enablement and data literacy. With modern analytics and BI tools, business users can do more of the heavy lifting, including dashboard authoring and, in some cases, data preparation. This is possible with minimal classroom training — tools are now easier to use and most have online help available. To really shift to self-service, IT must evangelize the value of data, promote best practices and provide forums for sharing them.
- Share the load with the business. Unless the IT organization wants to stay in the infrastructure and report factory business, it has to let go of some of the low-value work and empower business users to share responsibilities. In some organizations, business users demand greater capabilities, so clarifying roles and responsibilities are important.
Data and analytics are no longer afterthoughts — they are fundamental to digital business transformation and they have a much more expansive role in generating business value. To develop a data-driven strategy, start 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 Rollings at his Orlando Symposium/ITxpo session: The CIO and Chief Data Officer Working Relationship (Orlando, Florida)
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 leverage data and analytics to optimize every decision, every process and every action.
Achieving this will require a balance between investments that drive innovation, such as in leading technologies — AI, machine learning, blockchain, virtual/augmented reality, IoT and digital twins — and renovating the technology core to build an agile data-centric architecture in support of continuous intelligence. Critically, in the era of fake news and fake narratives, success with data and analytics will depend on building a foundation of trust, accountability, governance and security that respects privacy and promotes digital ethics.
Specific investments must be made to create new roles (such as the chief data officer) and responsibilities, treat data as an asset, and build data literacy and fluency across the organization. It will be necessary to establish new ways of working and new data-driven approaches that exploit diverse data, thinking and teams to spark creativity and innovation.
See Rita Sallam at her Symposium/ITxpo session: Magic Quadrant: Analytics and BI Platforms (Orlando, Florida)
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.
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