Alternative Careers: Series III - Predictive Analytics
The global economy is traveling through a transition phase. A transition wherein business models are evolving into creating newer ecosystems which never existed in the past. We are at the cusp of witnessing some of the most intriguing business models which are likely to dominate the near future. Under such a scenario, it is imperative for parents and students to evolve their thoughts to newer opportunities. A lateral thinking in this direction will help the new generation professionals to adopt new careers which never existed in the past.
This article attempts to bring to the forefront five such lucrative segments which can be explored by new age professionals and perhaps can be a career defining moment for them. Each of these segmented industries has a wider platform for impactful advancements and is still in their growing stages. Information about each of these developing sectors has been broken down into different series by some of the best recruitment consultants in Mumbai for your better understanding. The below information is a peek into one of the new age opportunities that can be adopted as a career.
Predictive Analytics
Figure 1: Predictive Analytics
Quality analysis helps to maintain constant rise or consistency for proactive organizations. Assumptions without collectible data and engaging with the clients are considered to be unsecured and not an intelligent move. Unpredictable events could lead to a downfall pour for companies trying to secure a higher position in the industry. Every business is now looking to top the other and this is where analysis skills assure prominent growth.
Definition
Predictive Analytics is a branch of advanced analytics. They are methods and measures used for predictions about the future events and trends in various industries. Every existing and active business today likes to be prepared for any growth or loss trends. Predictive analytic uses many techniques from data mining, statistics, modeling, machine learning and artificial intelligence to analyze current data to determine future predications.
History
Many people credit the rise of predictive analytics to the technological advances of the last 50 years. However, the history of predictive analytics began in 1689. It’s true that record keeping standard, relational database, faster CPUs, and even newer technologies such as Hadoop and MapReduce have made predictive analytics an accessible tool for decision making. However, the history of predictive analytics shows that it has been used for centuries. One of the first applications of predictive analytics was in underwriting back when shipping and trade was primarily conducted traveling the seas. Lloyd’s of London, one of the first insurance and reinsurance markets ever established, was a catalyst for the dissemination of key information needed for underwriting. Financial bankers that would accept the risk on a given sea voyage in exchange for a premium would write their names under the risk information that was written on a Lloyd’s slip created for this purpose.
Edward Lloyd had established the Lloyd’s coffee house in 1689, which became popular with sailors, merchants, and ship owners because he delivered reliable shipping news that assisted the community in discussing deals, including insurance. Eventually the place became so popular that after his death they carried on the arrangement and eventually formed a committee that became The Society of Lloyd’s.
After Lloyd’s success, it was then in the 1880s that the U.S. Census took eight years to tabulate. In the midst of 1881 due to the influx of census data the Hollerith tabulating machine using punch cards to compile data was invented which not only helped in "taming" the big data but also let them to complete the job in about a year.
Image 1: The Hollerith Tabulating Machine
Figure 2: Data Analytics Process
| Defining Problems & Requirements |
| Data Collection |
| Statistical Modeling |
| Model |
| Deployment |
| Evaluation |
| Implementation |
How Does It Work
A number of data mining, analytical techniques and predictive modeling is used to understand companies market fluctuations and interpretations. This intricate and detailed study gives various companies in IT, Management and the other competitive sectors to identify the opportunities and risk factors involved in the patterns found in the previous and newly gathered data. Using these composite techniques helps in pre-determining if there would be a crisis or a good amount of success in the future
Structured and unstructured data is gathered to put forth the potential abilities of the organization. It provides sensitive and important aspects based on which, only do companies make a move towards their next focused goals to either change the risk factors or to make a complete use of the situation that lies ahead of them. The competitive environment encourages prospective growth through different phases of in-depth research. These are conducted by Data Analysts or Strategic Business Analysts.
Every data mining firm has different methods of evaluation and data management but the basic formula is the same for everyone. Accessing substantial records of the companies to find, if it will face inherent growth or loss is of dire importance to them. The strategic models are applied, based on the type of sector, problem, scenario, data set and the desired outcome.
Possessing predominant qualities dont help in preparing for what is to come in further accounts. Placing importance towards getting fortifying and concrete data is a must. Strategizing for business associates is required to drive up sales or to make a smart and effective move towards building a profitable future. The basic process contains the following:
Problem Definition: Driving predictive analytics based on input from business executives ensures the alignment of the outcome with business objectives. Success of the outcome depends completely upon the ability to define the business requirement and the problem before addressing it. You should determine what business needs will be addressed and what business value will be delivered.
Data Collection & Analysis: Predictive analytics begins with the definition of the data set to be used. The identified data set is profiled, cleansed and enriched to ensure data quality and to provide the complete picture. This can be summed up as defining the relevant data set, identifying relationships in data elements and ensuring data quality.
Statistical Modeling: Once the data is ready, a statistical modeling framework is designed. All assumptions are authenticated using standard statistical models involving statistical tests and procedures.
Predictive Model: Prediction of accurate models about the future and choosing the best solution with multi-model evaluation. The last phase of deployment is infusing analytical results in the day to day decision making process.
Deployment: The last phase of deployment is infusing analytical results in the day to day decision making process.
Evaluation: The outcome of this process is used to refine the business strategy and to act on strategic and tactical initiatives.
The framework manages the link between goals being served and the end-to-end process required to provide the actionable predictions to achieve those goals. It incorporates people, processes and technology to work with the steps of data exploration, data selection, data preparation, technology selection, statistical modeling, expert insights and analysis of the results.
IT recruitment agencies feel that the key to achieving success in predictive analytics relies on the data set selection, the quality of the data being fed to the model and the statistical models being used to analyze the data. Reliability of the outcome also depends on whether the data set of interest is complete. Inability to correctly identify the control set, the treatment set and incorrect use of the statistical procedures derails the analysis. This could result in probabilistic observations and guesswork, which can easily lead to confusion
Opportunities
Anticipatory outcomes are looked forward by powerful players as well as newly inhibited ones. Forecasting predictability is as nearly as impossible without researching existential data. Both traditional and transactional data are required to lay down the groundwork for current and non-current competitors in the industry. Analytical firms are growing realizing that they have a chance to expand their methods and to gain innumerable clients belonging to various domains. Exploring new and innovative software’s to compile productive data.
The research field has wide variants for individuals to gain in into an impactful and new domain. There is no doubt that the field of predictive analysis still needs to be explored on a large basis. According to placement agencies in Mumbai a professional looking to tap into this field must possess technological skills, model building and strategic thinking skills. Job opportunities like
| Data Analyst | Web Researcher | Strategic Planner | Business Analyst | Business Process Specialist | Project Manager | Statistician | Data Scientists, etc. |
Designation varies as per experience and individuals with positive characteristics. From entry level to higher posts, candidates are required to possess good mathematical skills along with coding, debugging, using statistical tools like SAS, SPSS, Microsoft Excel, PowerPoint, etc. Interests in qualitative measurement methods catering to analytical field is pushed forth. Technical Engineers are in high demands for these positions. An educational qualification in mathematics and computer science is highly valued. There are opportunities even for those with no such educational background but in depth technical computing knowledge must be acquired before proceeding to apply in Analytical firms. You can earn up to 1,00,000 - 15,00,000 p.a. depending on the basis of educational, mathematical and technical computing skills.
The Potential It Has
In today’s world mining of text, Web and unstructured data plus structured data mining, the term information mining is a more of an appropriate label. Static and stagnant predictive skills of the past don’t work well in the world we live in today.
Advanced knowledge in Statistics and ability to bring data together for determining solutions to problems, better communication and business related skills are well appreciated. Predictive analytics is becoming more main stream as a result of advanced machine learning capabilities and technology advancements which are highly in demand as per top search firms in India. It has huge potential and the recent trends (as represented in the graph below) supports the fact that it is a very promising and defining field.
Figure 3: Usage of Predictive Analytics
For e.g. Insurance companies use credit score as one of their key factor in deciding policy premiums for its customers, then the insurance firm has to demonstrate how various other factors such as education, employment and marital status are being used along with credit score to decide the policy premium.
With the current state of data mining technology, the accuracy of the outcomes needs to be precise and complete. Predictive analytics is just an enabler and not a substitute for people who deep understanding of the issues at concern.