Partha Mitra
Members
-
Joined
-
Last visited
Everything posted by Partha Mitra
-
Thanks Rahul for your appreciation ...
-
Hi Prabhat, Thanks for showing interest in the article. As far your query is concerned my response will be in affirmative. People may try to avoid the responsibility of any wrong decision and hence the importance of appropriate training before introducing D3M (as you call it) is critical. Managers must learn that Decision support systems are provided to them to make them more effective and efficient at work and reduce mundane tasks of data analysis through software, but at the end of the day the decision authority and responsibility lies with him because of the non linear nature of situations where decision is to be made. Hence there is a need for continuous reminding of the manager regarding the effectiveness and limitations of decision support systems which is like a weapon in his baggage to deal with situations but the warrior has to be him.
-
Hi Gaurav, Thank you for your much asked and very relevant question. Most managers do feel to be downgraded because of D3M. Well this is selective perception I must say. Like a collection or compilation of recipes cannot replace the importance or taste that a Mother's hand can impart, similarly a D3M cannot substitute the human element that is crucial in business decision making. The D3M is only a predictive tool which lets you know various outcomes of a decision situation through rigorous data analysis and industry best practices which comes in handy so that the decision maker does not miss them during decision making. They can then use their expertise, domain knowledge to customize the decisions as per the situation which would involve some amount of non linear thinking at times and not algorithm driven thinking as most situations would differ from each other. Data only assists in decision making.How to use data effectively & efficiently to arrive at a decision is where the role of manager gets demonstrated. Hope this answers your query...
-
Hi Govind, Thank you so much for showing interest in my article. Well as far as your query is concerned let me answer it like this, we are talking about Data Driven Decision Making. Though the name implies many things it does hide a few, to be comprehended by the reader - A way to keep him/her interested and curious, as they say "Less is More". To come to the point let me clarify that whenever we talk about Data Driven Decision making please be advised that we mean Decision Support Systems(DSS). That is the system which is going to support or facilitate the decision making process to be done by a manager or any professional as such by providing him/her a predictive anaysis of the various situation outcomes. Hence the ultimate authority lies with the human itself and not computer. However there are situations where software does have some decision making authority as in the case of artificial intelligence say in some space systems, which will be referred to as Decision automation systems (DAS). DSS is in the current scope on discussion and not DAS which can be adressed separately in a separate article or thread.
-
Thanks Sebanti for appreciating my work. To answer your question, Yes decision making systems does required access to huge personal data which might lead to concerns about privacy, ownership & regulation. You would agree that people have this tendency to be less apprehensive about data fetching if it is aimed at improving services to them. In this scenario they would be glad if the banking or any other sector does access their data to improve services delivered to them. Such data collection of course would involve their consent and hence minimize conflicts. The problem however arises when we access the same data for marketing new products which they feel as inappropriate usage, for company’s own interests. Exhaustive research needs to be done to strike a balance between the 2 scenarios discussed above so that people see the data usage as predominantly beneficial with some instances of promotion as well. Besides, the customers should also be convinced that there can be situations where the promotions address their own concerns. In this way slowly and gradually we can develop a mutual trust situation where data access conflicts can be minimized. It must be noted that D3M is the future and we eventually have to accept it as a way of life. The only question is When?
-
Anghsuman Sarkar: Hii ... Thank you for your interest in my article and really appreciate your comments. You have asked 2 questions which I will take separately. 1. Issue of small Sample size : Please note that the primary reasons for smaller sample size are : 1. Data Aquisition bottleneck caused due to huge amount of data and limited capability of data analysis API(s) which forces analysts to use a small amount of data & 2. Data Complexity and the corresponding cost of analysis which is also needed to be kept in mind. Hence due to these 2 limitations we select a smaller sample size. But as further technological advancements happen, the capability to handle larget datasets would also increase, allowing us to use larger samples and make more acccurate predictions. As of now we try to make as accurate predictions as possible with our limited sample size. Hence the responsibility lies with the analysts who needs to judiciously choose samples from a diverse set of users representative of the topology of a certain social graph (groups of related users) independent of other factors. The process is known as Graph sampling which has limitations of not considering the similarity of shared content and other contextual information like frequency of status changes, location etc... which are useful in the study of social diffusion (process of transfer of ideas & influences from more to less advanced people when they come in contact, which can be tapped for innovations). Hence to address this we need to do attribute based sampling with Activity (Status Updates), location & randomness as attributes and carry out analysis individually and ultimately merging them to come to a concrete conclusion. Thus utilizing topology and user contextual attributes makes the analysis more accurate but this is again helping us do predictive analysis which cannot be 100% accurate. However with technological advancements and more data analysis our prediction only gets better and better. 2. Unawareness of Customers of what they really need and corresponding erroneous data collection: Predictive analysis of Customer data is done to guess what he/she might need in the future but he/she is not currently aware of. This is precisely the role of a marketer to anticipate the future needs and convert them to Wants whose accuracy is improved by data analysis. However before actually launching the product, customer needs to be educated and promotions done, after which customer reactions needs to be studied and then only the product launch is done. This improves the probability of success. However even then instances of failure are there which cannot be avoided and we can only improve our success rate through data analysis and related marketing efforts.
-
Hari Shankar Shasvat : Hii... First of of all I would like to thank you for showing interest in my article. To answer your question, Yes my article is centred around banking sector or it could have been any sector for that matter. The purpose of the article was to influence a Non-Data driven decision maker with substantial years of work experience in the sector (10 years to be precise). Now I thought that to influence a person with that amount of domain knowledge something generic could not be written. Inorder to influence him to adopt the data driven approach one needs to give him instances from his relevant area of knowledge & expertise, then only he might follow your direction else there is high probability of his rejecting your idea. Hence I assumed that the manager would be from banking industry and written the article accordingly. This was a mere assumption and it could well have been Marketing firm or IT Consulting in which case the article would have been centered on those sectors. Hope this clarifies your query...
-
Anirban Sharma : Thank You so much for the appreciation ... Do feel free to ask any queries that you may have ...
-
Gaurav Agarwal : Really Appreciate ur comments ...
-
Data Driven Decision Making - injecting rationality in your gut feeling Sector: Banking Sector in India The data centricity of banking industry is the universal truth. Traditionally banking has been the one sector which handled the maximum data about any person that are particularly critical and dear to those persons as they deposit their trust with the banks in the form of their finances. In the recent times this has been further reinforced with the RBI implementing the “Know your Customer†mandate that aims to compulsorily maintain the customer data which should be relevant, concurrent & authentic. Despite the proliferation of such data, effective analytics and data mining techniques has been at its elusive best. The information industry has grown leaps and bounds and the remarkable advances in analytics software and its processing power aided by the cloud computing systems is just the tip of huge iceberg of potential that such data is capable of achieving. As the industry tries to grow out of the recent financial crisis towards the shady future of uncertainty, banking and retail banking in particular must inculcate the power of analytics in them to be able to improve decision making, indulge in constant innovations which ought to become the bread and butter for survival & be more compliant with the stringent financial regulatory environment that the RBI is supposed to impose for greater control. The siloed approach to banking should give way to enterprise wide resource planning (data being the most critical resource) for fostering greater transparency, efficiency & effectiveness through integration and unified image of the entire sector. This will also help in garnering greater customer trust & rejuvenate customer relationship which is the single most critical factor for survival in times of uncertainty, mistrust & risk. The recessionary trends have forced the clientele of banks to a more frugal approach to managing their funds. Careless consumption has been replaced by need based one and “ROI†has suddenly become the buzzword which never had such a great reputation except amongst the business houses. However it is interesting to note that despite reduced spending the world has not stopped itself from the adoption of latest technologies. Be it smart phones or social media presence the huge numbers are truly defiant of the existing economic conditions and its implications. Such behaviour re-confirms the value of innovations in today’s society besides such channels could provide source of huge data tapping which can help retail bankers to provide a more rewarding experience to their customers enhancing their brand loyalty. The usage of “Big Data†as the new window to the world of increased productivity, innovation & competition is important to be considered here. The rapid adoption of analytical tools would help banks process the information they have into market knowledge which would enable them to differentiate themselves through service excellence. It may sound contradictory that previous paragraphs talked of unified image and integration and now differentiation is promoted. Well, competition has been and will always be the root for future growth without which the need for existence of mankind comes under the radar. Rather we should look at a new dimension of competition – “Competition through Cooperation†where competitors would be on the same page with respect to technology and new inventions yet they would have to constantly evolve themselves to be relevant. Advanced analytics provides the banks with a new path of continuing business by overcoming the obstacles of risk and uncertainty, the prime growth drivers being stricter regulation, better risk management, effective strategising and stronger CRM. The various ways to achieve data salvation is revealed as below: The analytics software would speed up the financial and risk reporting services as required by the new norms as and when implemented by the government ensuring service delivery with no or minimal cost. The usage of Enterprise wide data architecture would provide a single version of banking creating transparency and restoring customer confidence. Data crunching would enforce better risk management by identifying malicious transactions and preventing its recurrence. Usage of technology to combine past and current data can help in predicting future scenarios with greater accuracy and provides an opportunity to face the shady future in a planned manner with confidence. Besides the data analytics tools may be used to boost revenues as well like, Customer data analytics – enhance service and bring more clients Investment analytics – improve lending process Process analytics – find process inefficiencies and take corrective measures thereby reducing costs, to name a few. [*]Data collection from various sources like KYC, social media websites etc... and it analysis using Big Data and relevant technologies can help in providing customized banking solutions, new financial products to suit customer needs & gather feedback on marketing campaigns launched. This would lead to greater customer satisfaction and tighter relationship. Mobile banking is the new brainchild of the banking sector that allows customers carry out transactions on the move. This means greater volume of transactions to be handled and the usage of analytic software to integrate data across channels become essential. Also multichannel banking is constantly evolving with the endeavour of providing cross channel banking across websites. So far the discussions lead us to the conclusion that usage of Data Driven Decision Making through Data Analytics & ERP is imperative to the future competitiveness in banking industry. But there are major speed breakers in the path to this rediscovery, which are as follows: Modifying existing IT infrastructures and the corresponding data migration might incur substantial initial costs Using analytics at the strategic level would require identification of relevant data and standardization of processes and data structures Resolving frequent data issues & inconsistencies that exists in the customer data in the baking domain Required expertise in analysing data points, process expertise & technical expertise is important Support & Initiative of key stakeholders Finally the embracing of analytics as a service depends on the internal culture and dynamics of the organization. Hence to successfully implement the same nurturing of the employees to convince them of the power of data driven decision making is very crucial. However the conviction in employees can be developed only if the leaders & top management of the enterprise believe in the vision of “Analytics as the future of bankingâ€. Hence the purpose of this article would be to inspire the top management, so that they can realize the importance of using data in their organizational decision making and inject rationality in their decision making. Note - Visitors shall not be able to comment on this article until they are logged in.
