You may identify that you know one or one-fourth of the policies are getting redundant or it has changed as you know as your employee base has changed from you know more young generation has come and you need to revamp the entire strategy so we need to do the maximum transformation basis the number that we gather across the organization for the employee behavior and no doubt it is going to improve your profitability as well now I want you to look at your organizational landscape so what is the landscape of analytics is we have started the practice of looking at the standard reports we generally have the practice like either we create into the excel sheets either we have the automated system.

We do the love ad hoc reporting we do the query drill down and the alerts that we have for the organizer so imagine that the value that you are gaining by this in hoc reports and the kind of business, as usual, is not going to be like what you're going to read from the advanced analytics start once you start Atlas doing the hypothesis testing on the data or you start certain inferential in analytics you start building some of the forecasting models which is going to help you that you know what is the scenario going to come if I have the certain you know the business scenario taking place.

Let's say like competitor launching a new product so you need to have some of the forecasting engines to help you in that scenario you want to have the product of predictive models which are going to tell you where to focus and who are the likely customer going to get converted and optimization where either you are designing your supply chain procurement you have to have the optimized business strategy with the help of the numbers so as you move from dashboarding and reporting to forecasting predictive modeling and prescription the value to the organization increases.

Also if I talk now that what do you need to become a part of the analytics journey then you should start looking that you should have the ability in terms of the programming languages where you can perform statistical analysis either you can forecast some of the business scenario you can predict the events events can be like defaults it can be purchase it can be you know churn from the customer so you should have the ability to forecast then the optimization is like creating the millions of equations where you can think of the optimum allocation of your resources to optimize the benefit of the business so that's how the value enhances multiple times when you move from business as usual to the predictive and prescriptive engine however the investment and skills are going to be multiplied in a way if you want to achieve the efficiency on the analytics and AI-based applications so I want you to think that if you if you're looking to take a journey so you should like some of you have said that you are already working in a date in a capacity of data management.

So definitely you can start adding the value by providing the descriptive analytics and diagnostics analytics we call it as a more and a hint sides and once you have the descriptive and diagnostics you are going to have the predictive engines which are going to help you in forecasting and the predicting the events and then recommending the basis of the action the predictions that you have so now the question comes that what are the engines that are going to help me to bring the capability that is going to transform you from you know diagnostics and descriptive to predictive and prescriptive.

So we will get a quick look I have just summarized some of the tools that are getting used which really in the various functions let me just quickly touch upon so as I talk that we are going to have an understanding of the advanced analytics labs so the term is quite an unclear anyone who is pulling even the reports they call that, okay I am doing analytics and no doubt this is analytics however when you move to the advanced analytics the most of the application where you need to understand that what kind of the predictive engines are going to help in different business scenarios so we call it as a data science lab so the data science labs are the group of people either you can come from engineering background having a quantitative orientation there are people from the mathematical and statistical educational background there are people from the management side of it who can link the business with the data and the data with the processes.

Who can innovate around the business so data Sciences lab is basically created to bring the high computational ability to bring the role of the statistical programming is a little bit similar or even so different than the system programming which is more intended to solve the any of the statistical models in nature which is quite difficult in terms of calculation using any of the Excel or other you know our day-to-day platform so what I call it your data science lab will have intensive activity in building some of the decision trees maybe the multiple forms of the linear and the nonlinear regressions there can be a lot of unsupervised learning that is like a cross ring and there can be a various form of the class thing it can be k-means it can be the hierarchical it can be the multiple other forms as well.

So now if I try to help you that you should have in your data sciences lab ability to you know transform the unstructured data to a structured data and where you can have the competencies where you can do the exploration around the big data you can think about the process refinement basis the data what story it is building so data science labs are generally a cross-functional view you can have some of the folks coming from the business expertise as well which can bring lot of the business insights and the business specific trends and behavior which are data scientist trying can try to fit into the equation to predict and simulate the real time scenarios so what is the current time challenge.

So a lot of the old organization they work in silos findings example there may be a lot of information can be available with the marketing but it is not getting monetized with the sales and Rauf information can with the service which is not getting monetized by the sales function so you can think of the data science labs works as integration of you know multifunctional information into the data building engine which is going to help you to build that predictive and prescriptive foundation engine and then you are going to provide the business actionable insights to the various functions that you have so that's how the kind of the lab works now let me quickly touch upon.

I will just jumble up because the question is what makes you today's analytics leader so so I am just going to discuss in brief however before starting this I would like you to address the poll that is should yield for you so you can check your panel where you can have the pool section and you can just express your thoughts on the poll and I will be just able to see your results and then we'll come on what makes you today's analytics leader yeah so let me talk about the skills that you need.

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