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Ways Machine Learning Can Make Your BI Better

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AI has been simmering in research labs since a group of computer scientists talking about the term at the Dartmouth Conferences in 1956 that lead to the birth of the AI field. Since its birth, people herald AI as the key to the future. According to the MIT Sloan Management Review, “About 76% of enterprises say they are using machine learning to improve their sales growth.”

Over the past years and especially since 2015, AI has exploded. A more extensive availability of GPUs has made parallel processing cheaper, faster, and more powerful. This also has to do with the simultaneous one-two punch of infinite storage, and a bulk of data now available, thanks to the whole Big Data movement.

As data sources are proliferating along with the computing power to access them, a machine learning course is increasingly used by professionals to gain efficiency in the field of data analysis. You will be able to harness predictive power as it brings together statistics and computer science.

This post helps you gain an in-depth understanding of machine learning and how it makes your business intelligence better.

Better Investment Opportunities

Investors can use data interpretation and data analytics tools to find the right investment opportunities along with associated risks. Moreover, investors can effectively manage risks throughout multi-asset portfolios that lead to better and smarter investment decisions. With on-time insights into the right portfolio data, investors can leverage “high ticket items” and remodel the portfolio for higher ROI and less exposure. It supports them with factor analysis, manager assessment, and manager strategy overlap.

Understanding of Consumer Behavior

The analytics technologies help the wealth management space to leverage behavioral science. As financial planning is highly emotional, wealth management firms are using data analytics to understand the psychological behavior of their customers globally.

By monitoring the customers’ decisions, social media feeds, and spending patterns; they can better understand the customers’ attitudes and personalities.

Empowered by this knowledge, the investment managers can better shape their investment strategies. The losses that occur due to panic selling are utterly avoidable with Big data analytics.

Now, wealth management firms can create a conducive work relationship using these technologies to match advisors to clients by their mutual personalities.

Application Development

The best cloud service providers such as Amazon, Google, Microsoft, IBM offer MLaaS, which are either independent or integrated with other platforms. Recently Twitter has underscored the importance of Machine Learning by acquiring the Machine Learning startup, Whetlab.

Many providers provide MLaaS as an offering used together with their cloud systems, which shows us that companies are looking to become data-driven, both offsite and on-site.

Moreover, the capability of developers to use machine learning algorithms in their applications minimizes their dependency on data scientists and the intricacy associated with developing these algorithms.

Applications based on machine learning involve an assortment of uses from criminal detection and recommender engines to clustering, pattern mining, and various other e-commerce aspects that depend on Big Data analytics. With MLaaS, developers can take on more responsibility for Data Modeling and Data Science.

Intelligent advice

Either it is IoT or big data analytics, businesses have a bulk of data to base their decisions on, and data-driven decision making is natural. The next step that follows data-driven choices is decision support systems and even automation as well. Is your business ready for intelligent assistants with business advice?

According to a recent study of 50,000 American manufacturing organizations, “The data-driven decisions usage had tripled between 2005 and 2010.

Further, Avanade’s new study of smart technologies tells business leaders expect to be using automated intelligence and digital assistants for problem-solving, data analysis, collaborating and decision making with an estimation to increase revenue by more than a third. With those kinds of attitudes; 54 percent said they would prefer to work with those systems.

Accenture has talked to those who are using machine learning to enhance the way they control and manage customer service, risk and compliance, and financial resources in sales and marketing and in developing new fields of business. These businesses found exponential and significant business profits in revenue, costs and customer compliance, by using a mix of “perceptual intelligence” using voice biometrics and natural language, analytics and business decision support.

Those gains cut down costs up to 70 percent thus increasing revenue up to 20 times by monitoring buyer behavior speedily and making customers happier by managing call routing accurately.

Surpassing Hadoop

Apache Hadoop is the open data storage solution that has been the talk of the BI industry, but more alternatives have come like Apache Spark.

The in-memory data processing engine has been promoted for the past few years now, but as per Baer notes in his report, the facility to deploy Spark in the cloud is gaining traction significantly. According to him,”The presence of cloud-based Spark and related machine learning along with IoT services will provide options for businesses considering Hadoop.”

Data visualization experts added that late adopters of Hadoop could use self-service data preparation tools to solve their data-related issues in 2017. The Self-service data prep tools allow Hadoop data for prepping at a source and present the data available in the form of snapshots for more comfortable and quicker exploration.

Danish Wadhwa
Varun Datta is a serial Entrepreneur and a vivid writer who loves to share what he has learnt in his Entrepreneurial Journey. He has founded multiple companies, out of which 4New.io is the most innovative one. It is a waste to energy enterprise which is wholly focused on the production of electricity in order to power the mining of popular cryptocurrencies.

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