Gartner Grapevine 2018 Wrap-Up: Day Two

In yesterday’s Gartner Grapevine wrap-up post, Boaz discussed what Gartner believes are the challenges that need to be addressed as…

Ani Manian avatar image
In yesterday’s Gartner Grapevine wrap-up post, Boaz discussed what Gartner believes are the challenges that need to be addressed as the BI market moves more and more toward AI. If there’s one hot topic on the Gartner floor it’s AI, so it only makes sense that the highlight session I attended yesterday, “From BI to AI: Focus on Business Outcomes to Architect Your Data and Analytics Platform” given by Gartner’s Joao Tapadinhas, also touches on it. With so many options of analytics capabilities for BI professionals, this session focused on how exactly to make the right choice for your organization – and it starts with the end, the business outcomes. Let’s break down a little bit of what Joao talked about.

Why Focus On Business Outcomes?

Data and analytics are complicated, and as they evolve, they only become more so. Data sources alone can range from IoT sensors to data lakes and ad hoc files. The options to transform this data into digestible insights is even greater. Does your organization need behavior analytics? Audio and video analytics? What about prescriptive and predictive analysis? The list goes on and on and on. With so many choices it can be overwhelming and difficult to pick out what will best make sense of your data and serve your end users. According to Joao, if you focus on technology first, your deployments will fail to deliver the expected levels of business outcomes. However, if you start your plan with business outcomes, followed by the business context – like business processes, ecosystems and data available – and then decide what analytics capabilities will serve those outcomes, you’re more likely to succeed.

How Can You Implement This Structure?

To implement an analytics plan that is focused on the business outcomes, it’s important to create a group of business users, data and analytics professionals, and IT representatives to work together on the project. This is also something we recommend to our customers to increase adoption. Once you’ve done that, Gartner breaks down seven steps:
  1. List and prioritize business outcomes – work with business users to identify target business outcomes based on your organization’s strategy and goals.
  2. Define the business context – work with business users and technical stakeholders to describe the business processes, IT systems, customers, and other things that will be impacted.
  3. Select analytics blocks – work with the data and analytics team to select analytics capabilities and review how they will integrate with your existing technical architecture, people, and processes.
  4. Assess organizational readiness – work with the data and analytics team to identify requirements and gaps.
  5. Identify people and data that will make analytics hubs relevant – work with business users and the data and analytics team to identify data sets, inputs and outputs required.
  6. Review business outcome priorities – work with business users to review business priorities and take into account business readiness, limitations, and risks.
  7. Design and set timelines – work with business users and the data and analytics team to put together the implementation roadmap.

Moving BI Forward

Being goal oriented is nothing new when it comes to business. However, Joao’s focus on making your analytics implementation business-goal oriented offers a new way for BI professionals to think about their work. It also creates a more self-service and flexible solution for end users in the long run. Did you attend Joao’s session? Are you still at Gartner Grapevine? Head over to the Sisense booth (#801), and I’d love to hear what you think about his framework. And stay tuned to the Sisense blog for another wrap-up with key takeaways tomorrow! gartner big data magic quadrant
Tags: |