SKILL SETS NEED FOR ANALYTICS- DESCRIPTIVE, PREDICTIVE AND PRESCRIPTIVE ABSTRACT

Transcription

1 ISSN: (Online), (print) Volume I, Issue 3 August2015 International Journal of Multidisciplinary Research Centre Research Article / Survey Paper / Case Study SKILL SETS NEED FOR ANALYTICS- DESCRIPTIVE, PREDICTIVE AND PRESCRIPTIVE Authors Details Name:Miss Trupti Nanjappa Affiliation: Danfoss A/S COUNTRY:Denmark E mail Id : truptinanjappa@gmail.com ABSTRACT This paper introduce Use of Descriptive statistics when you need to understand at an aggregate level what is going on in your company, and when you want to summarize and describe different aspects of your business Use of Predictive analysis any time you need to know something about the future, or fill in the information that you do not have. Use prescriptive statistics anytime you need to provide users with advice on what action to take. Key Words: Predictive analysis, Descriptive statistics,manipulate, mapping INTRODUCTION All companies should address data challenges to support decision-making or risk falling behind. Businesses are collecting, storing and analyzing more data than ever before, and the trend is continuing to gain momentum. However, it s not who has the most data that wins. There is a big data revolution, says Gary King, director of Harvard University s Institute for Quantitative Social Science. But it is not the quantity of data that is revolutionary. The big data revolution is that now we can do something with the data.1 Since much has been written about descriptive and diagnostic analytics, we will focus on predictive and prescriptive analytics technologies that will transform the finance function by providing forwardlooking insights; aligning the enterprise to the optimal course of action; quantifying trade-offs fast and with a low cost of ownership; and increasing the ability to communicate and collaborate across functions. These transformative characteristics will lead to significant performance improvements. "Big data" is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. IJMRC All Rights Reserved ISSN: (Online), (Print) Page 1

2 What Skill Sets Need for Analytics? Understand Data Integrate Manipulate QA Prep. Know Analytics Appropriate techniques Interpret data and diagnose models Meet business requirements Focus on the Business Goals Constraints Decisions Communication of results Descriptive: Analytics: insight into the past Descriptive analysis or statistics does exactly what the name implies they Describe, or summarize raw data and make it something that is interpretable by humans. They are analytics that describe the past. The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes. IJMRC All Rights Reserved ISSN: (Online), (Print) Page 2

3 The vast majority of the statistics we use fall into this category. (Think basic arithmetic like sums, averages, percent changes). Usually, the underlying data is a count, or aggregate of a filtered column of data to which basic math is applied. For all practical purposes, there are an infinite number of these statistics. Descriptive statistics are useful to show things like, total stock in inventory, average dollars spent per customer and Year over year change in sales. Common examples of descriptive analytics are reports that provide historical insights regarding the company s production, financials, operations, sales, finance, inventory and customers. Descriptive research focuses on investigating and mapping (describing) problems, processes, relationships (especially causal relationships), or other existing phenomena. For example, a thesis that focused on understanding how information is disseminated from a fusion center to local police departments and where the barriers exist to the flow of information would fall under this type of research. A case study unpacking all aspects of a single event in order to identify the characteristics, dynamics, and chronology and to discern cause and effect could also be descriptive in purpose. Research questions for theses involving descriptive research usually ask what happened, what the relationship is between one thing and another, or what we know about something. Think of this paradigm as a camera, taking snapshots or video of something that hasn t been studied before. Prescriptive Analytics: advice on possible outcomes The relatively new field of prescriptive analytics allows users to prescribe a number of different possible actions to and guide them towards a solution. In a nut-shell, these analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions. These analytics go beyond descriptive and predictive analytics by recommending one or more possible courses of action. Essentially they predict multiple futures and allow companies to assess a number of possible outcomes based upon their actions. Prescriptive analytics use a combination of techniques and tools such as business rules, algorithms, machine learning and computational modeling procedures. These techniques are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data. Prescriptive analytics are relatively complex to administer, and most companies are not yet using them in their daily course of business. When implemented correctly, they can have a large impact on how businesses make decisions, and on the company s bottom line. Larger companies are successfully using prescriptive analytics to optimize production; scheduling and inventory in the supply chain to make sure that are delivering the right products at the right time and optimizing the customer experience. IJMRC All Rights Reserved ISSN: (Online), (Print) Page 3

4 Prescriptive research, like Evaluative research, is applied rather than theoretical. It differs from Evaluative research in that it goes a step further, beyond identifying success or performance or outcomes, and actually recommends solutions or new ideas. Prescriptive research (also known as normative research), comes up with an assertion, a solution, a proposal for how to address a known problem space. The implication of most research questions in Prescriptive research is what we should do now: how a policy should be changed or improved; how an organization can achieve specific outcomes or meet requirements; a set of recommendations or solutions or ideas that involve change and action. The research question in Prescriptive research often uses words like can and should How can we accomplish/improve/change x, or what should we do now about x? For this reason, you can think of Prescriptive as symbolized by a doctor s prescription pad, writing out the fix for an ailment. Now that we have predictive insight, what course of action should be taken? For some companies, this is a billion-dollar question. Most businesses are a complex set of nonlinear relationships with constraints across demand, supply and financials. Senior management s job is to gain clarity and determine the actions to be taken at all levels. They must determine where to allocate capital; decide which products to fund and cut; establish policies across the business; and create operational schedules. These actions all have the same purpose to maximize the company s primary objective. Prescriptive analytics is not statistical modeling; it is deterministic. The purpose is to quantify trade-offs and understand the impact of various positions before action is taken. With the ability to apply optimization to these scenarios, finance executives can discover significant value. Predictive Analytics: understanding the future Predictive analytics has its roots in the ability to Predict what might happen. These analytics are about understanding the future. Predictive analytics provides companies with actionable insights based on data. Predictive analytics provide estimates about the likelihood of a future outcome. It is important to remember that no statistical algorithm can predict the future with 100% certainty. Companies use these statistics to forecast what might happen in the future. This is because the foundation of predictive analytics is based on probabilities. These statistics try to take the data that you have, and fill in the missing data with best guesses. They combine historical data found in ERP, CRM, HR and POS systems to identify patterns in the data and apply statistical models and algorithms to capture relationships between various data sets. Companies use Predictive statistics and analytics anytime they want to look into the future. Predictive analytics can be used throughout the organization, from forecasting customer behavior and purchasing patterns to identifying trends in sales activities. They also help forecast demand for inputs from the supply chain, operations and inventory. One common application most people are familiar with is the use of predictive analytics to produce a credit score. These scores are used by financial services to determine the probability of customers making future credit payments on time. Typical business uses include, understanding how sales might close at the end of the year, predicting what items customers will purchase together, or forecasting inventory levels based upon a myriad of variables. IJMRC All Rights Reserved ISSN: (Online), (Print) Page 4

5 Predictive research is chiefly concerned with forecasting (predicting) outcomes, consequences, costs, or effects. This type of research tries to extrapolate from the analysis of existing phenomena, policies, or other entities in order to predict something that has not been tried, tested, or proposed before. A Predictive research project often asks how - or how well something might work, or what the impact of something might be. Whereas Prescriptive (Normative) research makes applicable and tangible recommendations, Predictive research is often more hypothetical, theoretical, or experimental it concerns ideas that haven t been tried, might not be testable, or didn t previously exist. The Predictive paradigm is a lot like a crystal ball trying to tell the future of something The value of all this big data is drawing a deeper understanding of the behavior of something important to your business and what that behavior may look like in the future. The most common example is the demand for products and services. Others include understanding how the price of oil impacts mode of distribution, or how weather patterns might impact premiums for an insurance company heavily invested in agriculture in the Midwest or home insurance in Florida. You gain these insights through statistical modeling that draws on the power to analyze trends, relationships and drivers. The result is some level of confidence in understanding how an important variable to your business will trend in the future. CONCLUSIION This paper gives a brief explanation of descriptive, predictive and prescriptive analysis. It analyzes current global and domestic position of researchers in some specific research area and does provides prescriptive advice aspects to enhance their R&D competitiveness. Descriptive analysis is therefore an important source to determine what to do next and with predictive analytics such data can be turned into information regarding the likely future outcome of an event. Predictive analytics has really taken off in the big data era and there are many tools available for organizations to predict future outcomes. With predictive analytics it is important to have as much data as possible. More data means better predictions. Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. REFERENCES Michael J. Beller, Alan Barnett, Next Generation Business Analytics Technolgy Trends, Business-Analytics-Presentation Prescriptive Analytics, IJMRC All Rights Reserved ISSN: (Online), (Print) Page 5

6 Gartner Inc., Hype Cycle for Emerging Technologies, 2013, J. Kim, M. Hwang, D. Jeong, S. Song, and H. Jung, InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention, In Proceedings of the Semantic Web Challenge co-located with ISWC Lustig, I., Dietrich, B., Johnson, C., Dziekan, C.: The Analytics Journey. Journal of Analytics (November/December 2010) M. Lee, S. Lee, H. Jung, P. Kim and D. Seo, Decision- Making Support Service based on Technology Opportunity Discovery Model, CCIS 264, pp , J. Kim, M. Hwang, S. Song, D. Jeong, S. Lee, and H. Jung, Intelligent Research Performance Appraisal Model based on Internal/Environmental Evaluation Feature, ATSBD IJMRC All Rights Reserved ISSN: (Online), (Print) Page 6