[Music] hello everybody this is Rudra Pradhan course instructor of business analytics for management decision first of all thank you everybody for choosing this subject and welcome you all to this lectures we are about to start our first lecture and we like to highlight the details before we start the introductory lecture so let me first give you the details course plan so we have altogether 12 weeks serial so in the first week we start with introduction to business analytics second week exploring data and analytics on spreadsheets then in the analytic sites we have altogether four modules first module descriptive analytics that will be covered on week 3 then second module will be inferential analytics that will be covered on week 3 week 4 and week 5 and next to next will be predictive analytics we have week 6 week 7 and week 8 respectively then we have prescriptive analytics these are these are three modules so we have a week nine week 10 and week 11 respectively and then finally we have decision analytics that will be on week 12 so these are all our course plans so today we start with the introduction to business analytics and that through the first week course contents so here we have the details the structure which we like to discuss for this first week we have altogether five different lectures so five lectures will be covered through all these components so first component is what is all about business analytics second one evaluation of business analytics then third module will be classification of business analytics then we will cover trends of business analytics next framework of business analytics then scope of business analytics then data understanding for business analytics and we have decision models and then finally problem-solving and decision-making in fact the course has two different contents so business analytics and then management decision so that means by default the course has two divisions business analytics and management decision so the literary meaning of this particular subject or the basic objective of this course is to take a management decision by using analytics tools of course this is related to some of the business problems so that means we technically have some kind of inner business problems and corresponding the availability of data then the idea is a we like to choose some kind of inner analytics maybe descriptive analytics maybe inferential analytics maybe descriptive analytics maybe prescriptive analytics to solve this business problem and then finally we'll come to take a management decisions since all the business problems are very complex and with respect to dynamics and the changing environment so or the complexity the degree of complexity is very high so as a result we need some kind of you know analytics tool to solve this business problems that means the complex business problems so now before we start the a particular structures so let us know first what is all about the analytics and other these curves and what are the kind of inner trends once you acquainted with all these analytics concept then we'll go to or some kind of you know application area so let us first know what is exactly business analytics and how is this kind of inner trend and then let's see the the application area so let's start with the first what is your about business analytics so the the first lecture of course I have already highlighted so we have five different lectures for this unit and this is the first first lecture and that to basics of business analytics so let me first highlight so what are the items we are supposed to discuss in this particular you know model so first thing we like to know the structure of or the definition of business analytics then we'll cover the importance historical trend of business analytics then classification of tools that is business analytics tools and then some business applications so Business Analytics by default is a multi-dimensional concept we have a plenty of definitions to define the term business analytics so the simple language or the simple understanding is that it is the discovery and communication of meaningful patterns of data and that to some business related problems there are you know various you know definitions are readily available by different authors and one such definition I am citing here is like this business semantic is the scientific process of transferring data into insight for making better decisions this is this is derived from informs so now by these definitions it is altogether movement from data to wisdom so in between so we have information knowledge and then we will come to wisdom so that means and that I will give you basically hint then information to understand the particular concept then knowledge means we'll get some kind of insights then which term means we are going to take some kind of you know better decisions or reliable decisions which may be a as per our requirement or definitely which will be useful for this for any kind of you know problems which you like to highlight or we like to address so Business Analytics is the extensive use of data statistical tools quantitative tools then explanatory and predictive models and text based management to derive decisions and to take some kind of actions so altogether business analytics is the set of couple of attributes so first attribute is the data which is the pillar of this particular you know business analytics and business analytics for management decision then information technology statistical analysis quantitative methods and mathematical or computer-based models the idea is to help managers to get in improved insight about their business operations and make better fixed based decisions so that means technically so we here we have to integrate all these components or attributes to get some kind of you know management insights which which initially a in hidden and then with the help of some kind of you know tools or some kind of in a strategy will be like to highlight or we like to search for the a exact insights so accordingly we can take some kind of you know management decisions so also so the definitions itself clarifies that you know it has lots of you know kind of you know a solid structure through which we can we can get some kind of you know management decision so I will I like to highlight here some of the application through his business analytics can be applied so these areas are pricing decisions financial and marketing activities supply chain management management of customer relationship human resource management enterprise resource planning that CRP that means technically see the structure is the management decision so that means this particular tools that's the business analytics tools can be applied to all kinds of you know management it may be marketing it may be a human resource it may be finance it may be operations but the it is like you know called as a potahto it can be connected to any kinds of you know management problems only requirement eg you have to understand the problem then you you have to know all these business analytics tools and you must be in a position to pick up a particular tools as per the particular requirement so once you understand the problem and if you have if you have a knowledge on business on all these tools then only requirement is how to connect this this particular tools to business problems and then by default will get some kind of you know insights so once you do this process then you are in a position to take better management decisions so the suffering importance is concerns there of course I have already highlighted these are the areas which you can actually apply Business Analytics but by the way so it has a lots of you know importance why you need actually business analytics whether it is in marketing or whether it is in operation or whether it is in finance or any kind of you know HR OB problems so we need actually so definitely there is some objectives so the basic objective easy to tech management decisions or you know better decision so now why and for what grounds so this the idea is that you know in any kind of you know business so revenue is the key component or profit is the key component so Business Analytics can be applied to optimize revenue to optimize profit or to minimize cost again some of the important items like you know shareholder returns vendor selections so these are the items you know you can you can forecast in a better way or you can take a decision in a in a in in a attractive way so that means in in looking to these problems or you know these problem areas with simple you know understanding you may not be in a position to would take a good decisions or you know better decision so business analytics is a kind of in a supporting component so it will help you not to take a better decisions some some of the things maybe or some of the insights may be in hidden and with the help of business analytics you can find out the hidden pictures and then you may be in a position to highlight or you know to take better management decisions it can it can increase the understanding of the data sometimes you know the data may be able means readily available with respect to marketing you know problems or finance problem or operation problems but you know once you once you go through all these data through some kind of you know analytics tools maybe descripton Alex maybe inferential analytics maybe predictive predictive analytics then you can you can understand in a much better way so initially by look you cannot understand properly what are the insights in the in the particular you know data set then with the help of analytics analytical tools you can you know you can get better insights and with the help of you know help of these insights you can take a better decisions so similarly business analytics is a vital tool for business to remain productive and competitive so that means it it is a kind of you know why you need actually better management decision because analytics tools will give you some kind of you know foundations and it will give you some kind of you know strategic decisions through this you know your management decision will be more attractive or you know it will be what we can say that you know it will be very excellent as far you know a requirement some sometimes you know we will get some kind of you know quantitative judgment from the business analytics then with the help of you know quantity judgment then we can put some kind of in a qualitative judgment and then finally you are in a better position to address the problems in much attractive way so these are the things we you know we have you know we can you know justify that you know business analytics it has a lots of you know importance particularly no managerial problems or you know business related problems so so accordingly so we you know we are in we are here to know some of the business analytics tools and then we'll connect some of the business related problems so now I only here give you some kind of you know he hint about the historical trend about the business analytics so it is not something new actually so it started long back so historically so the first structure about the business reality energy starting with you know stand study X time study exercise by teller then historical et has a connection with the operation research management science and again with the help of information communication technology so the picture or the particular component is more highlighted or you know the importance ism again more you know more significant then business intelligence and this is some support system and then with the it with the evaluation of you know personal computers of pears so that means technically so today's wals we have a plenty of yunusov tears so with the help of you know sub tears or you know with the readily ability sub tears so now the business analytics and the data and the kind of individual related problems so so it is a kind of in a challenge and by default it is the kind of now like you know demonstrate some kind of you know things we usually get some kind of you know inside and then with the help of all these insights so we are in a position to address certain problems so that means it will give you some kind of you know attractive exposures through which you can solve or solve some some of the business related problem see I mean see you know what you can say that you know it is a kind of inner supply driven kind of an environment since all these things are readily available so it will attract the party to come forward and to solve the business problems right so this is how so we can say that you know there is a high importance of you know business analytics in in the recent environment or we in the digital economic or we can say that you know in the ICT environment so now historically again so this is how the trends which you have already highlighted and historical et has started from 1900 then you know till today the growth eg a growth of you know analytics easy you know in a kind of an increasing rate so so it is the trend is actually a very very significant now and with the help of against what I have mentioned digital economic and then the availability of software's ability of you know data so now the particular you know tool was scared so the particular field eg a very high value or you know high importance so that is how we are here to know all these techniques and we'll connect some of the business problem so that we can know how it is happening and you know what are the ways we can solve some of the business problems so now in the next component we like to highlight here the divisions or classification of you know business analytics so what I have already mentioned that business analytics there is a history by a history behind it and then here's we Li classify some of the tools as the is for the particular requirement so by default here's we have a three different classifications so far as a business analytics is concerned so the first first division is the descriptive analytics second division is a predictive analytics and third division is a prescriptive analytics in the descriptive analytics so the idea is e just to use data and then to understand the past and present pattern so what you li this is just like you know inspections by inspection just you look the look into the data then you understand the first and you know present scenario because usually data is recorded over the time then by default they by default we call as a historical data so now when data is recorded over the time then obviously there is a past and there is a present so we like to know what is the past trend and we like what is the you know present trend so so the information basket will give you some kind of you know inference so in the so accordingly we prefer analyze as for the particular you know business requirement then second one is the predictive analytics so here we like to analyze the first performance of a particular business problem then we like to predict the futures so now when you have actually passed informations then you can understand the past pattern and then on the basis of you know past trend then you can actually predict the future one but over the times when we when we are looking for a particular business problem so within the particular problems we have a several variables which may have some kind of inner relationship until unless you establish this relationship you are not in a position to predict the future so now we are we are that is how we are in the process of knowing relative analytics it is not just to know the first trend and to present trend then we are here to know the a possibility of relationship among B yes problems and that to that to business-related you know issues so now so the last one is the prescriptive analytics here we like to use optimization techniques to address the business problems so that means so historically or suffer the structure is constant business analytic structures is concerned so we have a three different you know all together so first one is the descriptive one then the second one is the predictive one then the third one is the prescriptive one in the descriptive one just you have to look into the particular data then connect with the particular business problem so so it is just you know kind of you know informal kind of you know message to to indicate something so but predictive analytics will establish the relationship with the help of you know data and with the help of you know business problem and then it will help to predict the futures so now once you find out the past trend present trend and the future trend then on the basis of that then we have a prescriptive analytics to take a decision what are the possible values or decision variables or this is some kind of enough things so that you know management management can be reached at the highest level so that is how the broad objective of this particular you know subject that the business analytics so that means these three analytics has a high correlations or you know so you know the ultimate you know structure is the prescriptive analytics but prescriptive analytics has a connection with the predictive analytics and predictive analytics has a connection with the descriptive analogies so if if you miss any one then you you may not in a position to to to highlight the problems in a more attractive way so that is why so what is the what is the solution on that you you are supposed to look into the data then connect with the descriptive analytics connect with the predictive analytics then connect with you know prescriptive analytics this is how you have to reach a destiny and then that that restin will give you some kind of in a better decisions and on the basis of being on decision you can actually predict clear you know business performance so I am here to highlight some of the important applications so like whatever we have discussed the kind of inner structure the classification history and the kind of inner connectivity so now I will give you some of the practical areas very actually Business Analytics are frequently used so some of the important applications and this is basically industry specific so we have a plenty of examples and some of the important one I we are highlighting here so first one is the McDonald's Walmart then Proctor Gamble's coca-cola Southwest Airlines Amazon ian's being Aurora Health Care's and then I'll go in details so to know how actually these are all you know these companies are you know applying business analytics to predict certain things for instance McDonald it's a company name and the they use the business analytics tools for their you know industry food and we bridge so just to you know understand the business reality or you know business environment since it is actually food and beverage industry so now obviously the first and requirement easy to go for you know customer satisfactions so that is the a customer side and again surprise a industry she comes from they have to look into their you know profit side so with the help of business analytics so they are in a position to detect how to go for you know customer satisfaction so until unless you go for you know customer satisfaction then company may not actually reach in a position to enhance their inner cells and enhance their you know profit so here I am citing some of the examples connected to you know McDonald's and that to the huge through business analogies to credit certain situations so here with assessing data on consumer behaviors company money can that means McDonald can learn what promotes a customer to stick around you know longer as well as learn more about their you know customer features and person habits in order to improve marketing efforts and boost profits that's what I have already explained so generally so these are the industry that means the corporate environment slightly different than in other environment because the beauty of this current environment that's B or business environment eg they have you know plenty of you know data and some data are already recorded and some data are with the help of digital economy now on on the process actually so that means over the time so we have actually plenty of you know ability of you know data for instance the industry like you know McDonald so they they have the data you know every hours or you know every hour every day month basis day basis week Macy's annually so it is you know beautiful classifications of rigid order structure is concerned so these are that is how analogies can be applied here in a big way to solve their you know problems of course they are you know they are doing the they are doing their you know best in the you know competitive environment but still business analytics can help them more competitive and that too they can attract more customers and they end that at the same times they can also enhance their you know profit circles ii ii modules is you know almot and this is the company and they they apply business analytics in there you know retail sectors so again same same way so they are you know assessing the data from the customers from multiple sources such as you know social media data transaction history companies in say you know with the help of you know social data and transaction history the company can better you know create a better segment and target the arena customers obviously a in business market segmentation customer attractions are you know you know very keywords and you know sometimes you know without the help of you know business analytics tools so we are not in a position position to have a good market segmentations or you know good customer attraction strategy so you we must have some kind of you know plants and that too with the help of you know business analogies tool to take some kind of in a better decisions the another example eg a Procter and Gamble and that they apply a business analyst tools for their you know household little sectors against cm similar you know strategy so they they try to you know assess the data and they can go for you know optimizing their product selections then pricings weather therefore they will go for you know differential pricing policy or uniform pricing policy or some kind of in a customer attraction strategy so that means these are the areas you know you know so every companies are trying to you know explore so but you know by simple structures you are not in a position to find out to a better management you know decisions so business analytics again help help them and to come to their you know particular objectives so far as you know maximizing their profit is concerned and attracting customer is concerned the another example is again coca-cola and they you know it's a very interesting a where they have applied the business analytics and they have actually a created a kind of you know or toolbox you know or you know algorithm they can connect with you know said satellite so they which can you know predict the corporal's consumer preference and details about the arena flavor and that makes the kind of you know makes the kind of you know product as far as the customer requirement so that is you know what I can say that you know business analytics has a several kind of you know you know importance in a real-life scenario so it is you know it the requirement is you know how to apply and how to integrate your business in a more attractive is the you know Kiki kind of in a requirement so we have to think how we can actually connect sometimes you know some of the industry they have the data but they have no idea how to connect and how to predict the a you know situation as per the business requirement of course these are the big companies they have they are in a particular strategy but but the fact is that you know the this particular field business analytics abilities you know you know it is emerging like anything and with the help of you know software's and then you know digital economics so the scope and the kind of you know the use is much higher and that win your different way in you know twenty to thirty years big we we we may not have actually solid software to apply these techniques to pretty certain kind of you know business related problems but nowadays a nowadays with the help of you know sub tears and the ICT technology so we are in a better position to to connect the data connect the techniques and to predict the business you know problems or you know business objectives so that means in totals so what I can do we can summarize that you know business analytics can give you know better decisions once you properly connect with data connect with techniques and connect with the a particularly know business problems it's not the connection to a particular business problem so you have to first you know understand the business problem and then we have to understand the business analytics and then you have to understand the business data so now all these three you can should go together or they can go parallel II so that you know you are in a position to analyze predict and then then you can you know optimize as for the business requirement so likewise so we have you know couple of other examples so which I have already highlighted so the same way so they have actually applied the business analytics and then they you know they try to increase the area of it levels by increasing sales reducing cost and you know attracting customers so these are actually these are you know common objectives or broad objectives of you know every industry so now so there they are on the way how to actually optimize all these things but you know but still business and analytics can be applied in more in depth and then they can get better insights and then they can predict accordingly as per their you know a requirement so likewise is some of the other important areas also like you know security threats prediction etcetera by Amazon then fraud detections by this engines banks so likewise you know we have a couple of this is a healthcare industry and sometimes they use business analytics to you know to understand the health environment and then accordingly they can you know predict their you know business structures so this this is another examples so and with this we we can you know we can summarize summarize here or close closing these particular you know lectures thank you everybody have a nice day