To connect their business data to business innovations, growth, and cost savings, most companies look toward a mix of multiple clouds and on-premises platforms to employ AI similar to what they use for their everyday IT workloads. High performers aiming to extract the most value from their AI initiatives typically use cloud infrastructure much more than their peers do: 64 percent of their AI workloads run on public or hybrid cloud, compared with 44 percent at other companies. (1)
The immediate goal is to paint a picture of what’s possible. A picture painted by the data that can be read and analyzed by both humans and machines will help the organization visualize the problem, challenge, and solution. This visualization can go a long way in figuring out the best ways to utilize the data you have on hand.
This picture can be used to communicate the objectives, the expected outcomes, and the potential risks with the executive leadership team, business unit leaders, and the operational and technical teams that will see the program through.
With your data sets in hand, you can begin to analyze and observe the information that comes to life as data elements are connected and correlated. You can start to look for interesting patterns that could tell a story of value to the busines
With multiple patterns, scenarios, and stories surfacing, it likely makes sense to automate some of the activities further to help speed up the observation and analysis process. Any business objectives, values-based guidelines, and operating policies that were defined and documented earlier in the process could potentially be converted to code, and machine learning algorithms can be used to begin finding the most effective and efficient methods through which the data can be curated, analyzed, and put into value-based stories.
It’s these stories that we’re after, the ones that represent where the most oomph can come from the data you have available.