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#ModernDataMasters: Sarit Bose, Cognizant

Kate Tickner, Reltio

    Sarit Bose is the Head of Business Analytics and Insights at Cognizant UK&I. He has more than 18 years’ experience in Business Consulting, Business Development, Implementation and Pre-Sales across multiple domains. Sarit is accustomed to working across large enterprises and driving C-Level initiatives.

    What was your route into IT and data management?

    I always loved Maths and Physics as a child at school. I used to save money from my pocket money and gifts. Before I decided to buy anything, I used to calculate the interest I could earn. The love for numbers and computation led me to do statistics and then computer science at college. For me everything was a formula which is why I quickly realised the vital importance of good input data to work with.

    “We all know the “garbage in, garbage out” saying but unless you are working in data management then it is probably hard to gauge just how important it is to get the data right and the impact it can have on the results of your analytics.”

    I learned this very early on and it is even more important in today’s world of big data.

    How would you define “modern” data management and the connection between that and business analytics?

    I see modern data management as a way to bring a method to the madness in terms of this entire data deluge. Until relatively recently it has been considered “normal” to do data management projects which take 6 months, 1 year or longer.

    “I don’t think that in today’s fast-paced world people have the luxury to do a project that takes a year just to get the data right. By that time some of it will have lost its relevance and a new kind of data will have come in.”

    It is very important that you apply a set of modern techniques to get projects done faster. This means doing things better; applying learnings from the past and automating the application of that learning. That is the way I would differentiate between modern and traditional data management.

    In relation to business analytics there are a huge amount of techniques now that have to be applied to deal with complexities which we were not able to do earlier in a traditional mechanism.

    “There were certain limitations of doing things on an RDBMS and traditional storage – for example being dependent on storage, not being able to get the funds to continuously scale up processing, not being able to incorporate macroeconomic and social factors that impact decisions etc. Some of this definitely meant compromising on the quality of things and giving away the opportunity to look at the bigger picture.”

    This is where modern techniques are really helping. Big data is all about being able to store and process data very fast and then the application of analytics and AI /ML just to manage the data – never mind using it for any other business purpose. Analytics today is everywhere – not just at the business end but also at the data management end.

    “You have to continuously apply Machine Learning to learn how you have been treating the data. Then you can treat any new type of data coming in automatically and you are not dependent on any individual brilliance to figure out what to do with it.”

    How important is experience versus willingness-to-innovate for a modern data master?

    It is a combination. Experience plays a large role – human experience as well as the experience of managing of data – so we can leverage what we have learned and apply other techniques on top of it. That’s where innovation comes in – how you continuously evolve and become more and more efficient – make less mistakes in how you are treating data.

    “If you are going to succeed in this game you need to use the combination of both. Experience has to be combined with continuous innovation – that’s the only way forward – nothing less than that.”

    You really have to see what fits in well and what will give a better ROI. You decide whether to apply the human brain or tried and tested techniques as opposed to something new just for the sake of it.

    “If there is no ROI then it probably does not make business sense. Is it solving a problem? Making things easier? Is it a lower cost? Will it make you scalable for the future? If the answers are yes then innovation will make sense, if not then it is innovation for its own sake. At the end of the day, whatever we do has to make business sense.”

    What are your top 3 tips or resources to share for aspiring modern data masters?

    1. Know the objective before you apply the technique. What is the purpose the data is going to serve? Understand and realise what you are trying to do first and then choose the technique. What business are you in? What are people trying to do with the data? What are their pain points?

    “You apply a solution to a problem so you need to know where your goal post is if you want to score a goal or you will be kicking the ball all around the field.”

    1. Pursue perfection: Always be asking how can it be improved? How can it be done faster, deal better with complexity or deliver better outcomes? Use the combination of technology and human experience to solve these problems.
    2. You have to love data: The numbers should dance in front of you and start making sense for you straightaway. There will never be enough documentation or literature to always tell you what the allowable values should be for a particular field. You have to have an eye for it and develop your judgment and use AI to retrieve it for you if you cannot. You must love data and numbers themselves.

    A combination of these three can make somebody very successful.

    You have a lot of experience and success in digital transformation – can you tell us about a project where things did not go so well; what you did to fix this and what you learned from that?

    I have failed many times in my life but I’ve picked myself up and moved on. Each of these failures has taught me something and helped me to improve myself as a human being in general. The same thing applies in work – let’s start with these:

    1. Not knowing what you are trying to solve: Just applying techniques and finally realising that what the business needs and what you have delivered is a wide gap. This is the root cause of many project failures.
    2. Timing is of the essence: When do people need that data? If you have a project plan that takes too long then by the time it is finished the need for it may have vanished and people will have found other ways around it.

    “One reason why many MDM projects have been disbanded in the past is because people cannot wait infinitely for the project to complete. They will figure out a short cut and then what you did is not relevant anymore.”

    1. Be nimble: If you assume your scope of work is watertight and you don’t want to complicate things by change requests, then what you deliver may be on time and in budget, but the deliverable might not be usable. Business needs change fast you need to figure out how to address that within the project. Figure out how to take the customer into your confidence and make them understand that they may need to invest in a change – that’s the way to move ahead.

    “Saying no or hiding behind the scope of work does not pay off – you might be able to deliver a project but a project that does not enable people to change the way things happen is not a successful project. People are not going to remember you for that.”

    What trends or changes do you predict to the data management arena in the next few years?

    IOT is going to become one of the key data sources going forward so data management solutions need to prepare to deal with this right now. Having APIs to deal with it will be very important.

    Blockchain is going to play a big role to giving more trustworthiness to the data.

    “If you are going to create a single version of the truth it needs to be a single version of SECURED truth. It is not evolved to that extent yet, but like AI and ML have evolved, Blockchain is going to become a must.”

    The data space will become AI and ML driven – the way you acquire data, map it, manage it, find unknown relationships, do data lineage, generate insight on the fly and provide it in real time will all be impacted. That is where agility comes into play.

    Is there a question you would like to have been asked?

    Q: As practitioners should we ever be happy with the solutions we are providing?

    A: No. Always ask yourself is this all that I could have done? I think that sometimes we become complacent when we think we are the best without realising that someone else is doing the same thing but better. Once you get a pedestal you are always at risk of losing it because for the others in that space, everything is up for grabs.

    “For each interaction I have I would like to leave an impression and be remembered for any value I delivered. But it cannot be the same kind of value every time – so I have to continuously improve that value – that’s the way to be a great practitioner or a great organisation.”

    What do you like to do outside of work?

    One thing that I love, is a walk in the morning when I do not speak to anyone else. It gives me some time to spend with myself and gives me a lot of energy.

    I also love to people-watch – sometimes I will sit in an airport and just watch people around me – we can become so absorbed in ourselves, our lives, our families that we forget to stop and look around. We lose the broader plot – we stop being human and become more like robots.

    “It’s really important to get out of your routine, spend time with yourself, ask yourself whatever you did yesterday as a family man, as a human being, as an employee? Are you happy?”

    If you are not happy you are not going to last very long and you are just going to drag yourself into things. If you don’t love yourself you can’t expect anyone else to love you!

    Which is your favourite fiction book, programme or film and why?

    I am a book worm. I read anything and everything that comes in my way. I will watch movies if I have time to watch the whole thing because I hate to leave things unfinished.

    One thing I often read is a book from India called Mahabharat. It is a very ancient text -similar to the Iliad and a lot of the aspects that are covered in it:

    “It is fascinating to me that someone wrote a book on family, world politics, science, life lessons, philosophy, so long ago which is so pertinent today. This book helps me look at things with a very different perspective – for example what inspires people to do good or what obsession can lead to. The essence of the story is still very, very relevant today. It’s one book that teaches me something new – every time I read it.”