Shailendra is a thought-leader and visionary in the cognitive and analytics space. He is the Chief Evangelist for Intelligent Enterprise Group in the APJ&GC region. He is responsible for driving innovative ideas and discussions with SAPs clients in the region.
With an experience of over 25 years working with Corporates, Software Vendors and Consulting companies to deliver over One Billion Dollars through advanced analytics, Shailendra joined SAP in July 2017. He has established and lead data science businesses to generate revenue and drive incremental growth by creating multiple cognitive solutions across a variety of sectors, including High Tech, Financial Services, Retail and Public Sector.
With the sole motto of making money out of data, he has helped multiple organisations across the globe to generate incremental revenue or optimise cost using machine learning and advanced analytics techniques.
He has published multiple articles about analytics and cognitive solutions, and recently published an Amazon bestseller “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics.
How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?
Shailendra Kumar: I do a lot of research around what is happening in the marketplace. I do follow a lot of leaders in the field of Artificial Intelligence, Analytics, IoT, Machine Learning.
To answer your point what I have actually done is for people to get the resources, I have created my own Twitter account where I continuously share a lot of information, insights, trends & updates with reference to the Data Analytics space.
I also make sure to put in data and use cases into my account through all the learnings that I have got. I think it is a good resource for people who are looking for information related to Data Analytics.
If someone wants to get more insights and more used cases, they can follow my Twitter handle which is @meisshaily and they would get a lot of content there on what is happening in the Analytics, Machine Learning space on day to day basis.
What I have done is summarized all the informational content at one place so that people don’t have to wander here and there in search of relevant content. So, this is what I have tried to build up.
Apart from that, I have got my LinkedIn profile which also does similar exercise and also informs people about the platforms and location where I share my insights through Public Speaking on what new things are happening in the market place and how they can be leveraged. So, these two resources could be great resources for people.
The third resource is my blog which is www.cognitivetoday.com where I get a lot of blogs and articles around what is happening in the Data Analytics domain. I do a lot of writing on articles as well which gets published not only internally but also on external platforms.
Finally, you can read my book “Making money Out of Data” which is available on Amazon through both kindle and paperback version which contains business stories on how large organisations create incremental value using analytics techniques.
How do you include Data Analytics in your daily business routine?
Shailendra Kumar: Well, that’s a brilliant question. We usually say that if it is not measured, it’s not worth doing. For example, if you work on any project but you do not measure it, then it is not worth doing. Not only my day to day business work but my life when I speak to people or when I do my work at home, that is also monitored and evaluated on what can be done.
So, most of my work in business is measured through KPIs, driven through what I have done and what I have delivered. For example, I do a lot of public speaking as my day job and share a lot of information to spread awareness.
So my KPI is how many people have I influenced. Like last year I touched around about 100,000 people across the world and that was through Public Speaking where I had an opportunity to connect with people, articles that had a large viewership along with social media interactions.
I have also connected with a very small group of people who belong to the higher management like CEOs, CXOs where I have a one on one engagement.
This is how I evaluate and measure most of my work. Further, I also try to automate the things that can be automated to make my life easier.
Is Data Analytics confined to just services or it has emerged in the product market as well?
Shailendra Kumar: Great question again! Up until 4 or 5 years ago, it was primarily a service driven business where people had questions and you had to answer them. That was quite a predictive analysis driven thought. Now what has happened in the last 4-5 years is that there is a lot of automation with the emergence of Machine Learning. This has led to the creation of a lot of products.
Companies like Amazon, Google and SAP have embedded a lot of their analytical or Machine Learning capability into an automated process so that it runs as it is. You can actually buy those processes as a product. So, the market has moved on from being a service-driven analytics business to a product-driven analytics business. But there is a little glitch.
The people who actually work on both the different sides of the business have to have different skill sets. The reason for this is that the people who are doing services driven work are very specialists in that particular industry, whereas product driven people are technical, IT-oriented and would know how to automate most of the stuff that has been analyzed and produced so that there is no way that you can actually get something wrong.
Analytics helps the business to make decisions. If it is a product and the decision taken is incorrect, then that will have a huge impact on the bottom-line of the business. So, it needs to have a lot of confidence in the product. So, it has to be very very accurate to allow the processes to take decisions on its own. This also means that as we move forward to the next era, most of the processes in the world will be automated. This leads to what is called Intelligence Enterprise. Organizations will automate the processes smartly.
For example, in the process of hiring people, the agents generally, have a look at the resume and search for certain keywords and then send it to the hiring manager. But what if that process was automated and also smart enough that identify the CV and match it up with the desired profile not just by looking at the keywords but also summarizing it according to the requirement. This will make the process of “hiring” the right candidate easy.
This automation gets a lot of accuracies to match the right talent for the right job. This is how an organization will work in the new Intelligent Enterprise Era.which will have to be enhanced by innovative technologies such as AI/ML, IoT, and Analytics.
What are the top 3 challenges that you face with Data Analytics & how do you overcome them?
(i) First things first. Data Analytics is a business area but what has happened is that most of the time people put that into an IT function because it has got some coding involved. But the problem is that when you do that, you actually get the whole thing wrong because you put the analytics guys in the IT space and they will be doing coding without understanding what business is trying to achieve. And that a big NO NO! This is one of the biggest challenges that I see in the market that they do not know where to place the Analytics Team, in business or IT. This is one of the challenges that I see.
(ii) The second challenge that I see is the skill set. The problem is that around the world, people have changed their titles from doing some BI or data work to becoming a Data Scientist or Analytics person. Actually, there are 2 different skill sets. You need to learn a lot of statistics and to be honest, I’m telling you this from personal experience, we had a chapter of statistics in class 10th.
And most of the people including me actually left that chapter. We took that as an option because everyone thought that statistics was a bit difficult. Now what has happened is that when those people came into this industry suddenly wanted to become Data Scientist who actually did not do that particular chapter. Now they had to use the same skill set that they actually left earlier in the school.
The foundation is missing which is essentially important. So, there are people who come up to me and ask me that they are looking forward to doing analytics. I ask them the same question regarding the statistics chapter of class 10. I ask them whether they left their chapter or not and most of them actually left that chapter. Therefore, I ask them to go back, get their fundamentals correct. Also, most of the people have changed their job titles just because it is going to pay them more.
People think that analytics is the place where people are going to get a lot of money. If they were getting X thousand dollars/rupees, now they want to get Y thousand dollars/rupees. But actually, that’s a problem. Data Analytics is not an IT function. You need to understand business & statistics which are the key components of analytics. So, it is very important.
(iii) Thirdly, the “C” level does not understand analytics altogether, they ask their analytics team to build an analytics practice without understanding the capability. So, when they don’t understand it, all they expect is a report/analysis which is never executed & because it was done for the sake of doing it. You need to get the “C” level engagement and also the business to be part of the analytics environment.
The business execution is very important because I can get a very good model and analysis but what if it is not executed, it’s worthless and has no value. You have to make sure that the “C” level sponsorship is there and the business is engaged while they are getting the analytics delivery model done. This way, when you build a model, the business who has been on the journey right from the beginning is able to execute it.
According to you, what are the top skills, both technical and soft skills that are needed for Data Analysts and Data Scientists?
Shailendra Kumar: Let’s take the technical skills. To enhance technical skills, you need to know analytics, statistics, languages like Python, R, SAS. These are the core analytics skills that you would want. But the foundation of statistics is a must. You should know how to play with data, you need to understand data. You need to literally eat, drink and sleep data. It is very important for people to think and understand how data is flowing, working and creating insights within the business.
On the other side of things, you would need data management skills to manage data. From the perspective of soft skills, it is very important to be a good communicator. Now what I’m trying to describe is a true data scientist but in reality, they do not exist because you can’t find all the skills in one person.
Thus, you need to right people to get the right job. Soft skills including good communication skills, presentation writing, interaction and convincing power are a must. More of a sales savvy person who is able to sell the concept. Also, the person should be able to translate the statistical model into pain English & take it to the business because the business people do not always understand statistics.
Generally, we ask for technical, sales, consultancy and data skills in one person, which is not possible. Usually what successful companies have done is that they actually create three different types of people. One, the people who play with data, who actually get data, manipulate data, the second set of people who can build model & people who can actually translate that data in plain English & share it with businesses to convince them to execute it.
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
Shailendra Kumar: Get your fundamentals right. Go back & read your statistics books from your school & college. Have good communication skills, have good presentation skills & get your data skills right. Also, have patience because, in analytics, things won’t work for you overnight & give you success. Analytical models will work over a period of time & they will take to get better as they go forward. So the confidence/accuracy level may be 50-70% to start with & as you run more iterations, it will go up to 80%. The problem is that the expectations of the business are that you need to get around 90% of accuracy which is quite impossible. To ensure to get more accuracy, you have to make sure that you understand the requirements of the business to get your statistical model right.
What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?
Shailendra Kumar: I would request people to keep up to date with the new statistical techniques that are coming in. Most of the time, people have thrown in some new jargons. You need to do a lot of research to learn about the new techniques and models of Data Analytics to further apply them. Nothing succeeds like success. You need to make sure that you create successful models and get them delivered. The other thing is that you can create the templates of what you have done & use them as you go forward. With Machine Learning, automation will play a key role, how you build the model in the first place is very important as that model will surely evolve over a period of time.
You recently led a webinar for Digital Vidya on the “Making Money Out of Data”. How was your experience?
Shailendra Kumar: I really enjoyed delivering the webinar and would love to do a few more webinars in the future. I hope the audience enjoyed it as well.
Would you like to share a few words about the work we are doing at Digital Vidya in developing Data Analytics Talent for the industry?
Shailendra Kumar: Digital Vidya is one of the leading Data Science and Digital Marketing training companies setting a platform for aspiring data scientists to groom their analytical skills. Data Science is in vogue all around the world and this has lead to an increased demand for data science professionals. Digital Vidya, with their comprehensive Data Science and Analytics course, is laser-focused in bridging the gap between the demand and supply of data science professionals.
Are you inspired by the opportunity of Data Science? Start your journey by attending our upcoming orientation session on Data Science for Career & Business Growth. It’s online and Free :).