IoT, AI, NLP & ML – The Future is Data-Driven Analytics Automation

 In Analytics, Artificial Intelligence, Bigdata Analytics, Blog, Business Innovation, Internet of things, Machine Learning

All these days, we have been aware of data-driven decision making. It implies making use of core information, or data, to arrive at business growth decisions. It involves data processing to make sense of the vast numbers. For example, some popular names of data-driven companies would be Google and Amazon. Then there is analytics-driven decision making. It is more about the approach to be taken for achieving a certain outcome. Or predicting how the market will behave. Companies usually have an analytics team for this purpose. However, with the increasing amount of data which the data scientists have to work around with each passing day, opting for either one of the decision-making processes is proving inadequate. This has given rise to what we are today going to talk about – data-driven analytics.

In today’s complex business scenarios and market volatilities, global businesses realize that data is at the core of their business operations – helping them handle the increasing uncertainties while growing the business and ensuring customer satisfaction.

Importance of data-driven analytics automation

Among several benefits of opting for data-driven analytics, the two most important are –

  • To better the digital decision-making process
  • To extract maximum benefits from the data

When quality data is available at hand, the onus lies on the organization to make maximum use of the same. Company heads also need to make sense of diverse data to arrive at decisions beneficial for their own good. Data-driven analytics allows easy integration of data and that too at a great speed. This not only saves time for decision-making but also allows a company to understand the market/ competitor fully and act ahead of time.

Such analytics based automation works assists in data governance. It tells pertinent information about resource allocation according to the inputs gathered as well as the format in which the data can be utilized so that it reaches the target audience. Now that we have understood how data-driven analytics automation functions, let us look at some of the core areas it involves, which have made this sector so exciting.

The Core Four – IoT, AI, NLP, and ML

Internet of Things (IoT): IoT has been a game changer for data-driven analytics automation. With IoT, networking of information becomes possible. Thus, one is able to access data from some far-off location and use it for working on a particular business problem. For instance, the sensors on a single Boeing aircraft jet engine can generate 20 terabytes of data per hour! Imagine being able to receive such humongous data for varied purposes such as making predictions on geolocation or 3D predictive analysis on manufacturing issues.

Artificial Intelligence (AI): If results can be repeated, performance can be guaranteed. AI allows replication of decisions, procedures, and overall processes. This minimizes human errors to a great degree and provides consistency across all levels. This is actually what data-driven analytics automation is preferred for. Thus, AI bridges the gap between technological inputs and human intelligence to provide complete, clean results.

Natural Language Processing (NLP): This branch is all about providing user-friendliness in business operations. NLP and AI together make a powerful combination in providing a platform which is comfortable to use for all users. Since it understands human language, consumer data available in hundreds of languages and in multiple dialects can now be easily mined and summarized for analytics purpose.

Machine Learning (ML): What ML achieves for data-driven analytics is personalized marketing. ML is an exciting technology which has the power to offer everything made-to-order. Consequently, a company can dream of satisfactory customer service, precise logistic mapping, customized analysis of reports, and so on. According to a survey done by the McKinsey Global Institute in December 2016, 45% of work activities can be automated by the existing technologies, and ML can further be used for automating 80% of those. That is a huge incentive for business heads to be able to focus on other areas for growth.

To conclude, it would not be an exaggeration to say that with the assistance of rapidly-changing technologies, like the ones mentioned above, businesses can enjoy greater efficiency in customer service, operational agility, and data risk management. As a 2016 PwC report on Global Data and Analytics states, almost 61% companies feel that they should rely on data analysis more and less on intuition. Engaging in IoT, NLP, etc. will thus make data-driven analytics automation the key determinant of business growth functions. Companies which have successfully adopted these cutting-edge technologies are experiencing enhanced performance, better productivity, increased throughput, improve decision-making, better outcomes, and more accurate decisions.

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