How Can AI Make Analytics Invisible?

Key takeaways

Analytics professionals face challenges but have significant opportunities to leverage their skills.
The evolution of analytics has transformed it from a niche concept to a vital business tool.
AI is positioning analytics to be more seamless and integrated within business processes.
Key areas of focus for analytics include customer engagement, resource monitoring, and cost prevention.

What are the things that keep analytics professionals awake at night? Why are we here? Clearly, analytics professionals have the best of opportunities, but how can they make the most of it? Ashish Sharma, Co-Founder and COO at BRIDGEi2i Analytics began his talk on the subject 'AI - Analytics Made Invisible' with these questions at Cypher 2017, India’s most exciting Analytics summit. Just a over a decade ago, analytics was the esoteric buzzword that a few businesses started using to address their critical business problems around growth, cost, risk and more. As analytics became a competitive differentiator for business transformation, and as data started becoming more pervasive and invasive, new innovations came into play. New applications, new buzzwords, new data streams.

AI = Analytics Invisible:

Sharma explained that most analytics professionals may have had their projects revolve around one of the following points, at one time or another:

Interaction or engagement between businesses and customers
Monitoring assets, transactions and resources
Prevention of extra costs, abuse of resources, data leaks, fraud
Increasing growth

“These are some of the actions that we as analytics professionals want to invariably undertake," he said. Sharma added that what's really changing is not the business problems or its transformational needs, but it is the data which is changing the shape and size to take digital dimensions. And we are giving the solutions a new name today, Artificial Intelligence (AI). But even as AI becomes increasingly pervasive in the future, the fact is, AI is nothing but a part of Analytics. Sharma explained that AI was not just the chatbot we interact with for booking our flight tickets or the self-driving car we are dreaming of taking a test-drive on. Analytics is the invisible driver behind the self-drive car and the chatbot trainer training the chatbot’s every answer. And that will not change. AI is Analytics Invisible.

Efficiency, Effectiveness, Engagement:

The three pillars of analytics -- efficiency, effectiveness and engagement -- come from the fact that sometimes analysts need to take a step back and observe their own work. They also need to ask themselves, "What new data can I bring to the table? What new algorithm can I implement here? How can we translate most of it into information?" Sharma also discussed the fact that these questions and the new approach may not just be the result of "analytics invisible" (AI) where information, insights and impact coming together. He also talked about how would the data analysts know the customer needs, and with that objective one can start thinking about how social media can help understand customer need through customer experience data, website navigation pattern can tell you something about what the customer is looking for. "So, I think you should define the need first, define the end objective and then come back. It is not a one day journey; you won’t get the right answer in the first instance. You have to start with some priority in mind and work from there what value you can generate," said Sharma.

Ashish Sharma Of BRIDGEi2i Analytics Talks About 'AI - Analytics Invisible'

Key takeaways

AI = Analytics Invisible:

Efficiency, Effectiveness, Engagement:

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