In my previous piece on the power of data, Data – the new jet fuel for digital transformation, I talked about how data is revolutionizing almost every aspect of business. Here, I’m going to explore artificial intelligence and look at how we got to where we are today.
Human beings are not top of the food chain for being the fastest, strongest or biggest. We are on top because of our intellect and our ability to build and use tools. Think of the hammers and cutting tools of the Stone Age, or the swords and helmets of the Bronze Age.
The past decade was all about another tool, the computer, and it has ushered in the age of AI. Thinking about it, AI makes sense in the natural order of things.
We can clearly see a pattern in history. Humans have been around for roughly 200,000 years but the real intellectual or intelligent evolution happened in the past 50,000 years. We tend to think of this cognitive evolution as an accident. But our ability to produce and process data has increased exponentially over millennia. And it’s now catapulting us into the AI Age.
In recent years, most enterprises have become data-driven. But with so much data in play, traditional tools of analysis are no longer enough. For example, the first human genome sequencing project took 13 years to complete, but it now takes less than 24 hours thanks to technological developments.
We can safely conclude that companies need to embrace AI to properly and quickly analyze data and use it to make decisions.
Making the most of data
Data is being generated at a rate that even a decade ago was unthinkable.
We all have smart phones which are faster, better and more powerful. We have the computer processing power as well. In order to make the most of these two things, we need a clear strategic framework to guide us.
I believe there are three critical steps in this process: data collection, data curation and data analysis and insights.
Data collection is self-explanatory – it’s all about identifying the sources and collecting them. Sometimes organizations can collect narrow data focused on their business lines, or they can cast a broad net and collect as much data as possible. Each approach has its downsides and benefits.
When the data collection is narrow, you have more control and the quality is good. Traceability, auditability and granularity is transparent, and analysis is very good for diagnostic and descriptive analytics.
While these parameters are important, at the same time they severely limit the ability to look beyond the curve. Organizations are realizing the potential of combining data from unusual sources to unlock business values.
At Tata Consultancy Services (TCS), we have been helping organizations identify and leverage such data sources. This puts a lot of stress on various factors like data quality, data availability and so on, but the potential value it unlocks outweighs the risks.
Once sources have been identified and data collected, the next important step is curation.
Fundamentally, data curation is all about organizing the collected data and integrating it. But it also extends to authentication, archiving, preservation, retrieval and representation. It’s a topic in its own right, but to summarize, data curation is all about contextual metadata. At TCS, we have in-house frameworks and methodologies to quickly implement and scale up data curation, which can be a time- and resource-consuming process.
Once these steps are in place, we get to the part business is currently focusing on: analytics and insights.
Analytics is a mathematical way of synthesizing data and metrics over a period of time to highlight business trends and sentiments. It provides the knowledge base for business leaders to form insights and change business actions and responses.
At TCS, we have dared to venture a step further and we believe enterprises must go through a radical cultural transformation and absorb the philosophy of “Data Driven Everything”.
Many technological platforms can be used to achieve such levels of data maturity. But talking to numerous executives, we have realized technology is not always the challenge. Rather, a plethora of other factors, like organizational culture and regulatory authorities, are at play.
When I discuss this with peers and clients in the industry, I always come up with a Data Aggressiveness Appetite framework. I bluntly ask them how aggressive they want to be and how much they can have on their plate. And based on that I create a strategic plan to help them realize their goals and objectives.
The crystal ball
The moment organizations start transforming towards a data-driven culture they see immediate value in it.
It makes my life at TCS interesting because every organization has its unique challenges and needs complex analytical business thinking to solve them. At TCS, we’re proud to help our clients achieve their business goals. The next step is always to look beyond and take a glimpse at the future – and there is nothing better than an AI initiative.
The amount of reliable information about the AI market is less than ideal, and far less quantified than more mature and established markets. Regardless, there is still insight to be gained, including that healthcare, marketing, and finance consistently appear as areas of AI focus.
Intelligence and analytics firm CB Insights claims healthcare has been the domain of the greatest deal flow in AI. Google’s DeepMind is focused on healthcare, IBM set its sights on that sector years ago (and continues to burrow into the market), and many of the biggest “broad AI” players like Ayasdi are jumping into the area too.
Healthcare also offers benefits other areas do not. AI companies who begin by working on Wall Street may be perceived as simply profit-driven, or possibly helping the wrong party. But a company devoting itself to curing disease or improving treatment, even if it’s doing so for profit, may be viewed in a different light.
I believe companies interested in moving towards strong AI will have to progress with “friendly” steps into fields like medicine to dispel some of the fear around machines that may, one day, become more intelligent than humans.
Marketing and finance also represent huge areas of AI focus. Sentient Technologies’ Aware software promises to deliver better conversion rates for e-commerce vendors, and Cortica’s myriad applications for e-commerce and marketing will be fleshed out in the coming months and years.
All three of these commonly targeted AI segments – health, marketing and finance – involve a tremendous amount of high-volume information, and all three are nearly infinite in size.
I’m of the belief that the convoluted sales cycles and market forces in healthcare will lead to finance, e-commerce, and marketing leaping ahead in terms of relative AI adoption and innovation, though only the future will tell.
What seems certain is that these three fields will be a big focus for AI firms, and these application areas are themselves likely to spawn many scientific innovations in AI itself.