Two Big Bets Salil Parekh should take as CEO of Infosys

First posted on Medium Dec 3, 2017

The paint on the signboard with the next CEO of Infosys is fresh. Thats fresh cheer for stock markets in Mumbai. Infosys was always a darling! Salil Parekh will quit his executive board position at Capgemini, a French IT services company, to join Infosys. This in spite of prevailing sentiment that this time it has to be an old hand. Especially under the circumstances leading to Vishal’s resignation. The culture argument is that an outsider doesn’t get how Infosys works. Leadership they believe needs to be sitting closer to its headquarters in Bangalore. At least, I did so too, having spent few years there between 1995 and 2006.

Nandan Nilekani, co-founder of Infosys, author of ‘Reimagining India’ is a wise man. As the person tasked with getting Infosys back on track, he is well-aware of challenges ahead at Infosys. Nilekani said “the challenge before companies like Infosys was to get people to be up-to-date on current technology, current development and how they learn the latest.”

Considering his recent experiences in helping build the worlds largest bio metric identity system, Aadhaar, and on his way to build the next high impact ‘societal platform’ at Ekstep; to build scale fast, and to generate critical impact requires a commitment, a no nonsense attitude. Sentiment doesn’t help here!

The choice in Salil Parekh, an outsider to Infosys, yet a veteran at scaling Capgemini India operations in a market that was already witnessing eroding margins coupled with a need to reskill for new technologies reflects this approach. A Reuters report quoted Nandan saying “He (Parekh) has nearly three decades of global experience in the IT services industry. He has a strong track record of executing business turnarounds and managing very successful acquisitions.”

There are several issues to get around once Salil steps into Infosys in January 2018. But two will standout in how he leaves a lasting impact on the organization. OK, three!

#1 BIG BET – Picking up on the foundation laid by Vishal Sikka in Artificial Intelligence will be first big bet. Predictive Analytics Today did a comprehensive analysis of leading AI platforms. In that report, Nia, Infosys’s AI platform ranks fourth alongside Wipro’s Holmes and those from stalwarts such as Google, and Microsoft. Not included in this list is Indian IT leader TCS’s AI platform Ignio. But, the big boy way ahead in the AI game is IBM’s Watson. Reported widely, Watson’s estimated revenue stands at US $100 million over the past three years. IBM has set an ambitious target of $10 Billion by 2023. This is likely a challenge in this otherwise exuberant market, even for Watson.

For Salil at Infosys, the challenge will be similar. Infosys needs to solve its clients problems fast, and show business value from such solutions. He should build on Infosys’s attempt to invigorate the solutions space with its ‘Innovation Hubs’ that hire local talent, which includes user experience and design. Infosys has traditionally had the advantage over its competition, at least the India based ones, in cementing strong client relationships. Salil should quickly press these forces to deliver application ideas for Nia and other advanced technologies it possesses and show results to its customers. And the place for the action for this big bet will be its Innovation Hubs. This is very different from the past model of capturing functional requirements as usecases; a template driven approach.

Here one needs to collaborate, and co-create solutions. If such concerted effort plays out well, it will be the first solid differentiation Infosys can highlight. The transformation will need to be away from its offshore centers and closer to the client location.

Re-skilling its offshore armies of developers and technologists is already underway. Efforts include collaboration with leading online trainer Udacity to deliver ‘Nanodegrees.’

#2 BIG BET — This is more up Salil’s sleeve ie. M&A. For Infosys, India business is not as significant in revenue terms as it is in visibility. The much touted GST, a taxation modernization effort, has been an issue for Infosyswhich built and deployed it for the Government of India. India’s small businesses are up in arms against them for what they claim is poor performance and several glitches, especially its usability. Infosys though defends its record. Quoting from the report “Given the complex nature of the project and rapid change management, there have been several stakeholder concerns that have also been raised. Some of our finest engineers are supporting the GSTN team as they work towards resolving these and serving all stakeholders.”

In the past, rapid growth for service companies such as Infosys has come from implementation and customization of products. Honestly this did not involve that great an amount of thinking and innovation since the problem is already solved by SAP, Oracle, Microsoft, etc. Strategic problem solving skills is a culture and capacity found readily at reputed consulting firms such as McKinsey, BCG, Bain, Deloitte, Booz Allen, so on. Infosys has time and again tried building such a practice but has not delivered on that front as expected. But, the Innovation hubs planned at strategic locations across USA and Europe will help stimulate these problem solving skills and deliver results.

Culture inherent to a business is the big elephant in the room and there is no way past that. This applies to India too.

Especially for a high visiblity solution such as GST portal for the Government of India that impacts hundreds of millions of ordinary Indians. Now, who understands the financial thinking of millions of businesses in India better than Bharath Goenka, the founder of Tally ERP, India’s leading a ERP and accounting software product company. The journey of Tally started with a challenge posed to Mr.Goenka by his father, “Are you writing programmes to make the life of the programmer easier or the life of the user easier?”

Early on he understood the culture of accounting preferences of Indian book keepers. There are consultants like Rohit Choudhary who says “It is an accounting software with a soul!” and that “You simply don’t change for the sake of changing. Tally’s interface is very simple, unique and user friendly.” While there is a universal lesson in such philosophy, its important to note that that simplicity has emerged from a focus on what users seek or how they work.

Salil has a radical option of co-opting this deep learning on how millions of Indians prefer managing their business accounts, while they get around to complying with the new tax regulation with over 99% of taxpayers registering in a record time. If we accept the simple assertion that Bharat Goenka understands this behaviour well, then Salil should acquire Tally, convert it into a open platform, and offer it with tighter integration to GST portal, reducing the burden on users to mastering new software, instead, using the Tally like experience to segway into GST portal.

Now, would such an acquisition actually pay off in terms of license fees or subscription fees, assuming it offers a freemium model to millions of users, where they pay to upgrade. It may not. But there is a larger political gain if this is pulled off in a record time before the next general elections, and for Infosys a greater clout and influence on policies that impact its operations. One can only imagine other unknown benefits from a platform such as this when linked to other Government digital programs including Aadhaar. There could be stories of efficiency, inclusion and benefits for millions of Indians.

For Infosys, these two big bets could truly transform it into a next generation solution company, accompanied with impact and influence, built on a robust base of an efficient service culture.

The third bet is ancillary to the previous two. Salil will need to convince the Infosys board and takes them along, including prominent shareholders like Mr.NRN Murthy for these initiatives, and with transparency. But with Nandan as Chairman to guide him, the journey should be easier for Salil as compared to Vishal, relatively speaking. Now, time will tell soon whether he will be successful or not and be that bold transformer Infosys needs. But then again, he is an outsider!

Data Driven Cultures

(first published in 2015 at . Updated 7 Dec 2017)

What drives data driven cultures…besides coffee?

How do businesses deal with intuitive insights and machine generated insights? In a conversation with a brand consultant and travelista about my product Dataswft, sifting through #realtime #bigdata #analytics, he asked “where are the warmer human things that drives AI and ML technologies.” To be ‘data driven’ he pointed out, is a culture, unaware of the Tableau sponsored report from Economist “Fostering a data-driven culture.” To quote the report “IT security is indeed a job for experts, but data are everyone’s business.” I still struggle with the plural nature of data!

“Is Dataswft a technical thingy for data driven, or is it enabling data driven cultures?” the brand expert enquired. Time to act is a key metric for data-driven I explained. And so, to be data driven is an everyday matter as long as it provides value. But, how does that differ from a data driven culture? Then is the constant posing questions, small questions constitute a data driven culture?

Consider this scenario. For an online ad campaign the frequency of tweaks need balancing between regular and not at all. Regular requires minimal amount of data to analyze for metrics such as reach, clicks, CPC so on. Something like a weeks worth of data is good. But that is a heuristic that applies to a human scale of attention and processing. Or, compress it to a day, such that the human manager can take a look end of the day or beginning of the day so on. Also, these tweaks are post fact ie. historical data analysis using heuristic approaches.

Add machine learning, and it opens up two opportunities. First, unlike humans its not limited by fatigue, attention. Of course, we will never discount human creativity and imagination. Especially when dealing with limited information, limited time or limited capacity to process. These constraints are best obviated by humans than machines. Machine learning can give us the speed and capacity to deal with large data sets. The second opportunity for machine learning is the capactity to predict, by utilizing well defined mathematical models or algorithms.

With artifical intelligence the same campaign can now run with greater efficiency, more frequent tweaks, instead of weekly or daily windows. It can be real time, though more relevant to IT security and fraud management. Data driven coupled with such system intelligence gives us the opportunity to ask several ‘small questions’ that you can liken to ‘infinetesimal element’ in decomposing physical forces.

Representative image to demonstrate the concept of an infinitesimal element as a tangible, simple model-able, mathematical quantity (source: )

This finer abstraction, I conjecture, will allow for more accurate sampling of data and analysis by the machine. For the campaign manager, these frequent ‘small question’ analyses can present a visualization that is richer, provide better trending on the data and lead to better decisions.

If data driven represents our ability not limited to capture and store of data, but to process it continuously, and asking of it ‘small questions’ that are well-modelled, then our ability to connect with the output of a data driven process, coupled with human intuition stands for a data driven culture.

Providing answers instantly is what technology does well. With the technology, its the culture that realizes potential and pushes the envelope.

Consider an investment bank that needs to run value at risk calculations covering a host of financial products invested in by clients, touples of market price data for months, hundreds of sophisticated risk models designed to predict risk against different scenarios. To this person, its important to know how much money is to be set aside against the dynamic risk and how much capital can be unlocked to earn. Calculations here can run into over 15 billion and to execute in under 30 seconds can make a huge difference to these money managers. This scenario is possible only in a data driven culture and keep a handle on risk in a volatile market that involves many asset classes.

Data driven cultures are those we see at the top of the ‘culture pyramid’, crunching all their data by the second, minute and hour and not some end of the day or end of the week event. But as one moves higher up the pyramid, the response times that the culture will accept reduces from minutes and hours to mere seconds. That is not to say every industry out there needs to optimize at nano second level, real time analytics, but each industry should choose based on where the opportunity lies and where demand lies. Security industry can only survive in a real time processing of events. Social media marketing may find untapped opportunity in an hourly cycle. Education and learning industry may find it suitable by moving to end of day, from end of term, so on. In all these, the data driven culture consists of small questions we ask of the data.

Put another way, data driven cultures are those complementing their solely heuristic decision making process (read gut feel) with a data driven approach and thus; what do the data say! That is not just about the quantity of it, but also a quality that heuristic rules and human cognition are likely to be overwhelmed given the volume, variety, velocity.

In a digital world, even when its the same question asked few minutes back, or yesterday and last quarter, so on, paradoxically, the answer is never the same, instead gets better or is likely different.

“Time…and data, are like a river. You never touch the same water twice!”

The business impact of Bigdata

First published on 21 dec, 2012

As a company engaged in Big data before the term became as common as it is today, we are constantly having conversations around solutions that have a big data problem. Naturally, a lot of talk ranges around Hadoop, NoSQL, and other such technologies. 

But what we notice is a  pattern in how this is impacting business. There is a company that caters to researchers who till recently were dealing with petabytes of data. This is a client company and we helped implement our HSearch real time big data search engine for Hadoop. Before this intervention, the norm was to wait for upto 3 days at times to receive a report for a query spanning the petabytes of distributed information that was characterized by huge volumes and lot of variety. Today, the norm has changed with big data solution and it is about sub second response times.

Similarly, in a conversation with a Telecom industry veteran, we were told that the health of telecom has always been networks monitored across large volume of transmission towers and together generate over 1 Terabyte of data each day as machine logs, sensor data, etc. The norm here was to receive health reports compiled at a weekly frequency. Now, some players are not satisfied with that and want to receive these reports on a daily basis, and possibly hourly or even in real time.

Not stopping at reporting as it happens, or in near real-time, the next question business is asking, if you can tell so fast, can you predict it will happen, especially in  a world of monitoring IT systems and machine generated data. We will leave predicting around human generated data analytics (read – social feed analysis) out of the story for the moment. Predictive analysis could mean predicting that a certain purple shade large size polo neck is soon going to run out of stock for a certain market region given other events. Or it could mean, more feasible, that a machine serving rising number of visitors to a site is likely to go down soon since its current sensor data indicates a historical pattern, therefore, alert the adminstrator or better still bring up a new node on demand and keep it warm and ready. 

So it seems the value of big data is in its degree of freshness and actionability, and at most basic level, simply get the analysis or insight out faster by a manifold factor!