Is Machine Learning Inevitable for Data Analytics?

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    One of the most watched developments in data analytics and business technology these days is that of machine learning. Becoming something of a business-critical technology, machine learning makes computing processes more efficient, cost-effective, and reliable and may ultimately accelerate every aspect of business decision-making.

    Machine learning has applications in most industries, where it presents a great opportunity to improve upon existing processes. Yet many organizations are slow on the uptake. Recent surveys report that fewer than 25% of businesses have adopted any significant level of machine learning automation; yet it is currently behind some of the most game-changing advancements at Google, PayPal, Netflix, and other industry giants.

    And for good reason. Where data analytics is easily the jet plane of data processing, machine learning could be considered a rocket ship, providing a means to quickly and automatically produce models that analyze larger volumes of highly complex data and deliver results faster and with more accuracy.

    Traditional data analysis has become invaluable to enterprises for its ability to mine their ever-growing data stores, produce reports and models of historical developments, trends, and deliver predictive tools. It helps at every level of the business to help quantify and track goals, cut costs, boost productivity, and improve customer experience. Doing so, it delivers more attuned decision-making that makes a business more profitable and more competitive.

    But there will come a point when every department in a large organization—finance, marketing, IT, operations, development, or anything else—will be able to reap benefits from the accelerated processing that machine learning has to offer.


    Machine learning asks ‘What Do You Want?’

    With traditional data analytics, data models are typically static and can be of limited value when it comes to working with fast-changing and unstructured data. But as more fluid and sophisticated applications become desirable, it becomes necessary to be able to identify relationships between larger numbers of inputs and external factors, all of which produce millions of data points. The exponential growth of relevant data at some point requires heftier measures to mine the treasure of insights that lie within it.

    That’s where traditional data analytics leaves off and ML can begin. Whereas traditional data analysis requires models built on historical data and the inclusion of industry-expert judgment to define the relationships between the variables, machine learning takes a different approach. It “only” requires the goals and objectives as inputs along with the relevant data, and then automatically and autonomously looks for predictor variables and their interactions in order to produce the desired outcomes. By doing so, machine learning accelerates a business’s ability to predict future activity— including trends, behaviors, patterns—based on past behavior and activity. (Sound familiar?) By programming in the outcome you want, it will find out how to get you there.

    This predictive capability can be tremendously valuable to any organization. For example, where markets are concerned, what you can predict, you can respond to. Where behavior is concerned, you can provide more convenience in anticipation of your customer’s desires. And where sales are concerned, you can plan to produce and sell now what you expect your market to value in the future.

    Cutting edge applications

    Think of some of the most advanced computer feats in recent times and you’ve probably identified areas using ML. Facebook would not be Facebook without its learning algorithms that gather behavioral information and predict interests and sell ads on its news feed. From Siri and Alexa’s ability to parse human language and respond to it, to Amazon, Netflix and Spotify’s ability to suggest similar items you might like based on your purchase history, the world is experiencing more of the features of machine learning.

    While those are specific to their products and services, the principles and capabilities of machine learning can bring the ability to process highly complex, time-sensitive, and fluid data for applications in virtually any industry, such as:

    • Tracking customer/client satisfaction in real time
    • Analyzing and responding to market trends
    • Tracking and setting prices according to demand
    • Combating fraud
    • Calculating risk
    • Improving customer service through real-time, predictive analytics
    • Remaining competitive

    Some of the more publicized recent success stories include Google’s use of ML to reduce by 40% the energy it need for cooling its data centers. At PayPal, ML is used to detect fraud and money laundering. And the Icahn School of Medicine at Mount Sinai built a tool that can analyze patients’ medical histories to predict more than 75 diseases up to one year before onset.

    In addition, ML can help to more accurately predict future events. Since machine learning involves a reiterative learning curve, its algorithms constantly improve over time as more data is captured and processed. As a result, the algorithm can make predictions, observe the result, compare it against its predictions, and make adjustments to refine its accuracy.

    Learning from human learning

    How does it work? Essentially, machine learning is a method of data analysis that enables computers to learn similarly to how humans learn—and with minimal human intervention. As a branch of artificial intelligence, it works by automating analytical model building: identifying patterns, defining algorithms, and making decisions. Once the system is sufficiently refined, it is applied to new sets of data and draws its own conclusions—ones that were never programmed for it by humans. Eliminating the need for humans to program precise steps frees organizations from having to make every decision about how the algorithm functions.

    The iterative aspect of machine learning is central to the process; as new data is presented to systems, the systems update and modify their conclusions. And from repeated iterations of these complex mathematical calculations, they produce reliable, repeatable decisions and results. Its ability to automatically apply complex computations to big data, again and again, at accelerated speed, expands the capacity and accuracy of the solutions it finds.

    Getting started with machine learning

    To start using machine learning to improve your data analytics, start here. Note that data analysis and data visualization are critical at almost every part of the machine learning workflow process, from data exploration, data cleaning, and model building, all the way to presenting results.

    • Start with the foundation

    Start by using data analysis to identify the questions that need to be answered, catalog the data you need to answer those questions, and put processes in place to gather the data you need to support machine learning.

    • Learn the basics

    Even if you will be outsourcing the bulk of the work, having an understanding of what machine learning can and can’t do with data will help you recognize how to leverage it for your business.

    • Start simple

    Select a small first project that has potential to provide business value. Choose one that has sufficient data and a clearly-defined outcomes that can be demonstrated. Once you know machine learning is helping you achieve your goals, move on to the next project.

    • Define clear goals

    Set up defined goals for the analytics. What exactly do you want the machine to learn?

    • Use clean data

    Machines are only as good as the information we give them. For effective outcomes, it’s essential to use the best possible data. Clean up your databases and be sure your data sources are feeding you quality data.

    • Hire experts but engage non-experts

    While it will be advisable to get an expert data scientist to work with you and supervise the project, making machine learning successful will involve specialists and non-specialists alike. The organization needs to analyze business cases to determine where machine learning can add the most value while managing the risks of a new methodology.

    • Shift the mindset

    Since using machine learning requires a different approach to solving problems, it often requires a shift in mindset for people used to thinking through functional steps themselves. Patience may be required. But the results will eventually prove themselves.

    Machine learning provides fast and efficient algorithms for real-time processing of data with the primary goal of delivering accurate, highly-scalable, predictive analysis of various kinds. Applications are vast and can be highly valuable to organizations who want to respond to the expanding tech needs of the business environment and the popular culture.

    If you have extensive stores of data for which you want to create complex statistical analysis to detect patterns and predict patterns that are typically too complex for simple trending functions then it is time to get started with machine learning. To prepare for such a step, explore ClicData’s powerful, centralized and fully cloud-integrated data warehousing and streamlined, dynamic dashboards.