A few months ago, the Wharton School of the University of Pennsylvania published the article Four Ways Leaders Can Gain Value from AI and Advanced Analytics.
In the article, author Ravi Bapna, a professor of business analytics and information systems at the University of Minnesota’s Carlson School of Management, lays out four issues that make a case for enterprise leaders to consider adopting AI, ML, and advanced analytics.
Those four things are:
- The power and the limitations of tacit knowledge
- Cognitive challenges in processing multi-dimensional spaces
- Weaknesses in counterfactual thinking
- Challenges in combinatorial thinking
We agree that these issues adequately describe reasons enterprise leaders should look to adopt robust data tools for making decisions. But for this post, we’d like to focus on a critical idea that enterprise leaders—especially IT leaders—need to embrace if they’re going to succeed with AI, ML, and advanced analytics.
That idea is this: Go all-in on data to make decisions.
Moving away from gut instinct
While it’s a given that experience and institutional knowledge are critical for enterprises, the fact of the matter is that the massive increase of data—and the tools available to parse it—are fundamentally changing how leadership makes decisions.
Where leaders used to make calls “based on their gut,” more and more enterprises are relying on insights mined from data.
The benefits of this are two-fold:
- As experienced talent leaves, the ability to make smart business decisions doesn’t leave with them
- What Bapna describes as “cognitive challenges in processing multi-dimensional spaces” can be overcome
Seeing in four dimensions
Here’s a question: Is there a correlation between fried chicken consumption and the price of gas?
Now, your first answer to this query is probably: No, why would there be? Your second one might be: Who cares?
Both these answers are understandable, but the reality is there most likely is some correlation between the two—an association that can be pinpointed and surfaced by deep learning technology.
Whether an enterprise can do anything with this unearthed information is beside the point, at least for this post.
What’s important to know is that as more enterprises adopt things like AI, ML, and advanced analytics into their processes, those enterprises—some of them your competitors—will unlock new ways to create products and serve their customers.
Put another way, staying competitive these days means mining data for insights that traditional “gut instinct assumptions” and human brainpower can’t find on their own.
Going all-in on data
So how do you successfully put AI, ML, and advanced analytics to work in your enterprise? Like we said at the top, it means going all-in on using data to make your business decisions.
Merely dabbling in data won’t be enough. It would be best if you had buy-in from every floor of the enterprise, which means technology leaders, in particular, need to make a compelling case for a data-driven approach.
The four points highlighted in Ravi Bapna’s article are a good starting point. For another excellent resource, click here to read An In-Depth Guide to Advanced Analytics.
Keep up with Redapt
- Business Transformation
- Cloud Native
- Artificial Intelligence (AI)
- Advanced Analytics
- Modern Datacenter
- IoT and Edge
- Managed Services
- Emerging Tech
- Systems Integration
- Cloud Engineering
- Dell EMC
- Google Anthos
- Google Cloud
- Hybrid Cloud Implementation
- Google Cloud Platform
- Machine Learning
- Microsoft Azure
- Cloud Cost Assessment
- business continuity
- Application Modernization
- Business Resilience
- Rapid Data Platform Modernization
- Application Development
- Health Bot
- Power BI
- Virtual Desktop