Six Tips on Scaling Analytics and AI
by Axis Group, on Jan 25, 2022 9:27:40 AM
Axis Group partners with the Darla Moore School of Business to bring together industry experts and academics along with data and analytics practitioners as part of the semi-annual Analytics Think Tank Roundtable. The group recently discussed how data-driven organizations are pushing innovation by scaling analytics to drive growth and create new business opportunities. Read on for the key highlights from the event.
Scalability Must Be Part of Data Strategy – You Don’t Know What You Don’t Know
When you think about scalability, think of it like any other governance process for data quality or compliance. Scalability must be part of the data strategy in order to effectively deliver business solutions and business outcomes. Business needs are always evolving, and algorithms need to be constantly modified as well. Ultimately, the hardest part about scalability is managing the unknowns and planning for changes that have not yet been defined.
The Best Analytics Result When Data Sources Are Brought Together
A big part of the importance of scaling analytics is the ability to combine data from different sources in a meaningful way to answer new questions and drive new behaviors. These new combinations of analytics allow you create to create compelling stories that are actionable and can improve your business or organization.
Scaling Makes Data and Analytics Accessible to Everyone
While there are top priorities for any organization, more resources tend to be allocated for the priorities deemed most important. However, when discussing scaling, it is important to focus on the concept not just for the top priorities, but for the longer tail of analytics. You need to understand and address who your analytics serve and how to not only give them secure access, but also prepare them for the insights they will receive.
It Is Important to Know What to Ask
Even if data is accessible to everyone, a lot of analytics tools require someone who knows what questions to ask to obtain insight from the data. Data by itself is great, but it does not create action. This means that part of scaling is putting in place the right strategy, technologies, processes, and preparation to achieve insights that can drive action much faster and easier.
Scalability Requires Culture Change for Larger Organizations
There is a cultural transformation that must happen for scalability to occur within an organization. Developers may understand data coming from their “shop,” but they might not understand data from another “shop.” They also must be in regular communication with decision makers and business leaders. Analytics must be continually addressed, not just launched and forgotten about. Once-and-done is not an effective strategy for scaling analytics and AI.
Scaling Is More Complex Than Finding a Needle in a Haystack
Instead, you have to think about this as if you were searching a pile of needles to find a specific needle. Prioritizing which problems to solve and when to solve them and who each problem concerns and the business outcomes they must drive are all critical inputs in the scaling process. Scaling analytics and AI is complex and challenging, and requires not just technology, but effective people management and process management, too!
Watch the Replay
To hear directly from our amazing panelists, check out the replay of the Analytics Think Tank Roundtable: Scaling Analytics & AI event.
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