We have come a long way since then, and practical analytics solutions have furthered growth in so many different industries. The internet has had a profound effect on this; it has enabled every click to be stored and analyzed. More data is being collected and stored, some with very little effort, than ever before. That in itself has enabled more industries to enter predictive analytics.

One industry that has embraced PA for quite a long time is marketing. Marketing has always been concerned with customer acquisition and retention, and has developed predictive models involving various promotional offers and customer touch points, all geared to keeping customers and acquiring new ones. This is very pronounced in certain segments of marking, such as wireless and online shopping cards, in which customers are always searching for the best deal.

Specifically, advanced analytics can help answer questions such as, If I offer a customer 10% off with free shipping, will that yield more revenue than 15% off with no free shipping? The 360-degree view of the customer has expanded the number of ways one can engage with the customer, therefore enabling marketing mix and attribution modeling to become increasingly important. Location-based devices have enabled marketing predictive applications to incorporate real-time data to issue recommendation to the customer while in the store.

Predictive analytics in healthcare has its roots in clinical trials, which use carefully selected samples to test the efficacy of drugs and treatments. However, healthcare has been going beyond this. With the advent of sensors, data can be incorporated into predictive analytics to monitor patients with critical illness, and to send alerts to the patient when he is at risk. Healthcare companies can now predict which individual patients will comply with courses of treatment advocated by health providers. This will send early warning signs to all parties, which will prevent future complications, as well as lower the total cost of treatment.

Other examples can be found in just about every other industry. Here are just a few:

• Finance:
  • Fraud detection is a huge area. Financial institutions are able to monitor client's internal and external transactions for fraud, through pattern recognition and other machine learning algorithms, and then alert a customer concerning suspicious activity. Analytics are often performed in real time. This is a big advantage, as criminals can be very sophisticated and be one step ahead of the previous analysis.
  • Wall street program trading. Trading algorithms will predict intraday highs and lows, and will decide when to buy and sell securities.
• Sports management:
  • Sports management analytics is able to predict which sports events will yield the greatest attendance and institute variable ticket pricing based upon audience interest.
  • In baseball, a pitcher's entire game can be recorded and then digitally analyzed. Sensors can also be attached to their arm, to alert when future injury might occur.
• Higher education:
  • Colleges can predict how many, and which kind of, students are likely to attend the next semester, and be able to plan resources accordingly. This is a challenge which is beginning to surface now, many schools may be looking at how scoring changes made to the SAT in 2016 are affecting admissions.
  • Time-based assessments of online modules can enable professors to identify students' potential problems areas, and tailor individual instruction.
• Government:
  
  • Federal and State Governments have embraced the open data concept and have made more data available to the public, which has empowered Citizen Data Scientists to help solve critical social and governmental problems.
  • The potential use of data for the purpose of emergency services, traffic safety, and healthcare use is overwhelmingly positive.

Although these industries can be quite different, the goals of predictive analytics are typically implemented to increase revenue, decrease costs, or alter outcomes for the better.

So what skills do you need to be successful in predictive analytics? I believe that there are three basic skills that are needed:

• Algorithmic/statistical/programming skills: These are the actual technical skills needed to implement a technical solution to a problem. I bundle these all together since these skills are typically used in tandem. Will it be a purely statistical solution, or will there need to be a bit of programming thrown in to customize an algorithm, and clean the data? There are always multiple ways of doing the same task and it will be up to you, the predictive modeler, to determine how it is to be done.
• Business skills: These are the skills needed for communicating thoughts and ideas among groups of the interested parties. Business and data analysts who have worked in certain industries for long periods of time, and know their business very well, are increasingly being called upon to participate in predictive analytics projects. Data science is becoming a team sport and most projects include working with others in the organization, summarizing findings, and having good presentation and documentation skills are important. You will often hear the term domain knowledge associated with this. Domain knowledge is important since it allows you to apply your particular analytics skills to the particular analytic problems of whatever business you are (or wish to) work within. Everyone business has its own nuances when it comes to solving analytic problems. If you do not have the time or inclination to learn all about the inner workings of the problem at hand yourself, partner with someone who does. That will be the start of a great team!
• Data storage/Extract Transform and Load (ETL) skills: This can refer to specialized knowledge regarding extracting data, and storing it in a relational or non-relational NoSQL data store. Historically, these tasks were handled exclusively within a data warehouse. But now that the age of big data is upon us, specialists have emerged who understand the intricacies of data storage, and the best way to organize it.

Along with the term predictive analytics, here are some terms that are very much related:

• Predictive modeling: This specifically means using a mathematical/statistical model to predict the likelihood of a dependent or target variable. You may still be able to predict; however, if there is no underlying model, it is not a predictive model.
• Artificial intelligence (AI): A broader term for how machines are able to rationalize and solve problems. AI's early days were rooted in neural networks.
• Machine learning: A subset of AI. Specifically deals with how a machine learns automatically from data, usually to try to replicate human decision-making or to best it. At this point, everyone knows about Watson, who beat two human opponents in Jeopardy.
• Data science: Data science encompasses predictive analytics but also adds algorithmic development via coding, and good presentation skills via visualization.
• Data engineering: Data engineering concentrates on data extraction and data preparation processes, which allow raw data to be transformed into a form suitable for analytics. A knowledge of system architecture is important. The data engineer will typically produce the data to be used by the predictive analysts (or data scientists).
• Data analyst/business analyst/domain expert: This is an umbrella term for someone who is well versed in the way the business at hand works, and is an invaluable person to learn from in terms of what may have meaning, and what may not.
• Statistics: The classical form of inference, typically done via hypothesis testing. Statistics also forms the basis for the probability distributions used in machine learning, and is closely tied with predictive analytics and data science.

Originally, predictive analytics was performed by hand, by statisticians on mainframe computers using a progression of various language such as FORTRAN. Some of these languages are still very much in use today. FORTRAN, for example, is still one of the fastest-performing languages around, and operates with very little memory. So, although it may no longer be as widespread in predictive model development as other languages, it certain can be used to implement models in a production environment.

Nowadays, there are many choices about which software to use, and many loyalists remain true to their chosen software. The reality is that for solving a specific type of predictive analytics problem, there exists a certain amount of overlap, and certainly the goal is the same. Once you get the hang of the methodologies used for predictive analytics in one software package, it should be fairly easy to translate your skills to another package.

Open source emphasizes agile development and community sharing. Of course, open source software is free, but free must also be balanced in the context of Total Cost Of Ownership (TCO). TCO costs include everything that is factored into a softwares cost over a period of time: that not only includes the cost of the software itself, but includes training, infrastructure setup, maintenance, people costs, as well as other expenses associated with the quick upgrade and development cycles which exist in some products.

Closed source (or proprietary) software such as SAS and SPSS was at the forefront of predictive analytics, and has continued to this day to extend its reach beyond the traditional realm of statistics and machine learning. Closed source software emphasizes stability, better support, and security, with better memory management, which are important factors for some companies.

There is much debate nowadays regarding which one is better. My prediction is that they both will coexist peacefully, with one not replacing the other. Data sharing and common APIs will become more common. Each has its place within the data architecture and ecosystem that are deemed correct for a company. Each company will emphasize certain factors, and both open and closed software systems are constantly improving themselves. So, in terms of learning one or the other, it is not an either/or decision. Predictive analytics, per second does not care what software you use. Please be open to the advantages offered by both open and closed software. If you do, that will certainly open up possibilities for working for different kinds of companies and technologies