Automated Predictive Analytics: The Future

Unemployed Data Scientist!

   They don't own the same depth of data science skills, but automated predictive analytics tools hatch a new breed of 'data scientists' who can produce actionable predictive models.

Businesses don't have access to a souped-up DeLorean that can travel to the future and back. So the next best and most accessible time machine is none other than predictive analytics -- and its newest relative, automated predictive analytics.

I have always been very much of the opinion that data science is best left to data scientists. But there is a new trend and a group of analytic platform developers who are driven to deliver One-Click Data-In Model-Out functionality. In other words, fully Automated Predictive Analytics.

This topic is too broad for one article. One may have thought that the broader issue is whether or not predictive analytics can actually be automated. Yes, It can. Depends on how we look at it. Given the pace and depth of changes in the industry, in the next 3-5 years the process may be fully automated. When you examine some of the companies who deliver these solutions, you'll see that boat has sailed.

So What Exactly Is Automated Predictive Analytics ?

   Automated Predictive Analytics are services that allow a data user to upload data and rapidly build predictive or descriptive models with a minimum of data science knowledge.

There is an element here of trying to make the data user more efficient by automating the tasks that are least creative. Much of that is in data cleansing, normalizing, removing skewness, transforming data for specific algorithm requirements, and even running multiple algorithms in parallel to determine optimized models.

The good news is that automated predictive analytics tools are gaining acceptance and penetration at an ever expanding rate. It's also a compelling motive that while most data scientist have settled in on the one or two automated analytics tools, they prefer to use that this new expanding market does not come with that baggage. To sell here will not require displacing SAS or SPSS or any of the other heavy hitters since those tools don't meet the needs of these new users. However, ever since Gartner seized on the term "Citizen Data Scientist" and projected that this group would grow 5X more quickly than data scientists, analytic platform developers have seen this group possessing a minimum of data science knowledge as a key market for expansion.

These tools are gaining popularity, because the software solutions have been focusing on the "sexy" Machine Learning (aka Modelling) stage of Predictive Analytics. Modelling has always been quick and takes only a small fraction of the project time, hence any improvements in that stage have a marginal impact on the entirety of project effort and timelines.

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Characteristics of any Predictive Analytics Process

Lets have a quick look at the Traditional Predictive Analytics process

While CRISP-DM breaks down 6 stages in the analytical process - in practical delivery mode there are actually 3 major development steps plus deployment.

Step 1: Target Definition

This stage involves defining the behaviour that you would your model to predict e.g. customer churn, product cross-sell, fraudulent activities.

This is very much the core starting point where the future benefits of the project may be won or lost.

Experienced veterans may spend days and sometimes weeks analysing historical usage patterns, brooding over human nature, triggers in our minds, false flags in the data and time-to-action.

It seems that the more experience we have - the more challenging the task can present itself.

Each Target usually converts into a separate Modelling project.

Step 2: Feature Engineering

We define Feature Engineering as a process of transforming the source data - typically residing in a raw non-aggregated form - to the format of a Modelling Dataset, ready to be pushed through Machine Learning.

Typically, we start with a database full of diverse tables, none of which is digestible by Machine Learning.

What we have to do is to transform this data to a format ready for Machine Learning by creating so-called Features (aka Variables, Input Columns, Factors) which roll the raw data-points up to the Target level.

This process consumes ridiculous amounts of effort. This is exactly why there is a consensus in Data Science community that "Data preparation takes 80%-90% of the project time".

It is manual, slow, error-prone and it is also completely open-ended because there is no limit to the number of Features we can generate from the source data.

Each additional Feature can increase the future model's performance - but with the project clock ticking, eventually we have to move to Machine Learning (aka Modelling) task, no matter how satisfied - or not we are with the final Modelling Dataset.

Step 3: Machine Learning

The Machine Learning part, the "magic" in the Data Scientist's arsenal of skills actually takes very little time.

Why? Because it is automated. Always has been.

No one is coding up a Neural Network or a Random Forest manually from scratch. We let the pre-built algorithms do the job.

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The Future - Automated Predictive Analytics

The biggest benefits of automated predictive analytics tools is that more people can now build models and build them faster - a basic necessity for any company working to keep up with and even surpass the competition in all aspects of its business.

Even with automated predictive analytics tools to help turn the unskilled into moderately and passably skilled modelers by easing the complexities of modeling, there needs to be an applied set of checks and balances. If someone builds a model, but doesn't know how to interpret output, for instance, that is a problem if the model is going to be put into production. They need to register models and include metadata about those models, so that they know more about the models in production. They need to monitor the models once they are in production to check for degradation.

Nearly all successful financial and banking institutions rely on automated predictive analytics in their business as it consolidates and simplifies data to help companies maximize profits.

Automating data pipeline

An automated data analysis pipeline gives you control over your business intelligence and real-life info. Just imagine a seamless flow of structured data between all your departments, systems, and applications.

How can data pipeline automation help your business? To name a few:

- Productivity boost. Technologies like AI can save hundreds of man-hours by automating routine operations and streamlining data maintenance. As a result, your employees will focus on other productive tasks, while managers benefit from relevant information.

- Improved revenue. Collecting data is one thing, using it is another. Most corporate financial organizations can't monetize the entire scope of the information they receive. An automated data pipeline helps analyze info for risk assessment, faster decision-making, and successful financial app development.

- Correct data. Automated data analytics tools in the financial services industry can seamlessly detect missing values, typos, and other errors. Consequently, you can be sure you're banking only on valid data for business decisions.

- More growth opportunities. Forrester's 2018 Report indicated that businesses relying on insight and analytical data grow about 30% more than companies with low business intelligence. With automation tools, you can utilize multiple cloud environments for databases and applications.

- Competitive advantage. According to Gartner's 2018 Report, less than 13% of organizations have high business intelligence and analytics maturity. Most companies lack the skills, technology, and resources to exploit machine learning and AI-based analytics to the fullest. However, automation of data analysis will help you surpass your competitors with informed decisions.

Data stands at the core of your systems, therefore, a robust data pipeline will help your business adapt and thrive. And with automated data analysis, you'll be ready to optimize your financial services model and technology to the ever-changing market conditions.

Conclusion

Data is a crucial resource for corporate finance firms. But collecting raw information isn't enough - you need the right tools to gain actionable insights.

Automated predictive analytics is invaluable for gathering intelligence from large magnitudes of internal and external data. In turn, you can use this data to predict how your business, products, and the market will prosper - giving you the heads-up you may need to act accordingly.

Automated Predictive analytics holds massive promise. Business owners and executives can gain significant insights about their customers, trends, and the future. They can make better decisions that will help their business achieve optimal profitability.

At Vyom Data Sciences, we can help you build and accomplish your Data Science strategy or approach that suits your business requirements and your company's objectives. If you want to see how we can assist in your Data Science dreams, schedule an appointment with one of our experts today.

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Looking forward to see you soon, till then Keep Learning !

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