Machine Learning: The Future of Business Intelligence

Author:- Careers of Tomorrow
02/11/2018

The number of self-service tools is increasing thereby enhancing the ability to solve intricate analytical tasks. This is mainly seen in the fields of marketing, business operations, and analytics. Gradually, Business Intelligence will implement features that are well-learned and knowledgeable, or driven by machine learning and artificial intelligence.

Business analysts, consultants, vendors, researches have acknowledged the power of machine learning and calling this as the next trend in BI.

 

What is Business Intelligence?

Before delving into complex concepts in Machine Learning and AI, let’s understand BI. A Gartner definition on Business Intelligence goes:

 

“BI is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance”.

A CIO definition on Business Intelligence:

“BI leverages software and services to transform data into actionable intelligence that informs an organization’s strategic and tactical business decisions. BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business.”

To further understand BI, here is the definition from Forrester Research:

“BI is a set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery. Research coverage includes executive dashboards as well as query and reporting tools.”

The universal consent is that Business Intelligence largely comprises these areas:

1.    offers historic, up-to-date, and analytical interpretations of business operations

2.    takes into account data that has been collected into a data warehouse or data in operation

3.    provides reporting, visualization, enquiries, assessments, dashboards and discovery

4.    uses sources of data in sales, promotion, financial transactions, and many others

5.    provides business intelligence platforms; on-premise or SaaS or a mix of both; hosted by the application service provider (ASP)

 

Where do ML and AI come in?

Business Intelligence + Machine Learning and Artificial Intelligence

BI is grasping the highpoints and capacities that blend machine learning (ML) and artificial intelligence (AI) with conventional BI contributions. Progressed, predictive analytics are tied in with figuring trends and future conceivable outcomes, thereby helping to anticipate potential results and offering recommendations. These capabilities go beyond the conventional reports and inquiries that have characterized (appropriately or wrongly) Business Intelligence in the past.

This moves the conventional role of BI of answering “What happened?” to a futuristic AI driven model which answers "What is next?". The shift is noteworthy since it affects the software, as well as the information, individuals and processes that need to help this development. Poor quality of data is the number one hindrance to the widespread use of machine learning. Garbage in, garbage out (GIGO) is true for any program including ML and BI. This is one great challenge BI teams face and will continue to meet. Hence, data must gain priority to deduce any solution.

 

What are the AI & ML trends in Business Intelligence?

The leaders in the BI industry are attempting slightly different tactics to ML and AI. For example, Tableau Software, a software company announced that it intends to include to its platform a new machine learning recommendations engine that will enable algorithms to surface relevant information contextually. They procured ClearGraph, a platform which they expressed will help upgrade their product using Natural Language Processing for smart data discovery and data analysis. They will likely continue acquiring additional proficiency and products, as the requirements for AI and ML develop.

On the other hand, Looker, a software company, is not anticipating on building machine learning directly. They are concentrating more on preparing and delivering while choosing the duties of machine learning and artificial intelligence to be taken care of by complimentary external systems.

Microsoft is likewise following the same model Looker is to a certain degree. The company is looking to fuse Power BI and the MS Cloud system with Azure SQL and Azure ML. This bodes well given the investments that the company has made in Azure ML. Power BI is a business intelligence cloud service and is the start more complex ML usage.

They have just offered an overarching roadmap at this point. Any AI, or ML capabilities are likely a few years away from being available in their product offerings.

In conclusion, Qlik, a software company has expressed that the contributions of AI and ML should not dismiss individuals from decision-making processes. The company has defined this as "augmented intelligence" which is a mix of the power of machines with human enhancement. They have recently offered a general guide for now. Any AI or ML abilities are likely going to be made available in their products in a couple of years.

BI to continue impacting business performance.

Bearing in mind the trends of BI, from self-administration to Machine Learning to Artificial Intelligence, the main objective of business intelligence continues being: to furnish decision makers with the ability to observe better the link between trends, patterns and practices, that were "concealed" in data, for better decision-making and optimization of resource utilization. In any case, changes in BI platforms will affect accomplishing that end, particularly in the analytical tools we utilize and how teams infer results in view of those insights.

 

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