How Machine Learning Has Hit Its Stride With Business Intelligence

Business Intelligence is one of the oldest concepts in data processing. Though it was first coined by Richard Millar Devens in 1865 in the Cyclopædia of Commercial and Business Anecdotes, mining transactional data for business insights has been popular since the 90s. Today, Business Intelligence tools are systems that enable companies to collect, store, access, and analyse enterprise data to allow for efficient decision-making and strategic predictive intelligence for planning. Businesses can also leverage predictive models to help detect patterns in transactional and historical data in order to identify risks and take advantage of the opportune potential of different aspects of machine learning.

Machine Learning Hits Stride With Business Intelligence In India

In the 1990s, the BI business flourished when improved and powerful graphics on PCs entered the market and it became a $17 Bn industry last year. According to the latest Gartner report, the Indian business intelligence (BI) software revenue is forecast to reach $245 Mn (in constant currency) in 2017, a 24.4% growth over the 2016 revenue of $206 Mn.

The key driver is the increased adoption of machine learning tools for better and efficient data management and analytics. The rapid shift to cloud and the adoption of hybrid data models have enabled enterprises to rethink data storage and introduce modern BI platforms. The current times have now called for the evolution of traditional methods for better outcomes and smoother go-to-market strategies through the adoption of innovative technologies.

Shifting Trends In Business Intelligence In India

The enterprise software space is witnessing seeming shifts in both hardware and software. The business intelligence community in India is now realising the potential of their organisation’s immense proliferation of data assets and, thus, the need for the right technology to extract data-driven insights through analytics to make fact-driven business decisions. This is not only a disruption in the traditional enterprise business intelligence solutions, but also quite critically marks the era of data driven smart business intelligence solutions.

Reimagining Social Media

Up until recently, BI was mostly restricted to internal data due to the lack of easy access to data from multiple sources efficiently and seamlessly. At present, next-generation consumer intelligence platforms are enabling companies to reimagine social data and that is going beyond the traditional confines of social listening and measurement.

Social media platforms like Twitter, Instagram, and Facebook essentially are a treasure trove of unstructured data. Tools such as machine learning and predictive AI analytics help to make sense of linguistics, market sentiment and brand research. Semi-structured data-types like URLs, retweets and hashtags are creating frameworks for structured information to drive intelligent business decisions. Made possible by search engine constructs like Hadoop, Big Data’s hype has finally caught up with the possibility of structuring, storing and indexing free-form data.

Push Towards Machine Learning Skills

A recent report by Bengaluru-based analytics school Jigzaw Academy says that IT analytics, big data and machine learning skills are ‘future-proof’ career-wise. On the start-up front, Google chose to mentor six more Indian startups in AI and machine learning this year as a part of their accelerator program. It is noteworthy that India is the third-largest cluster of AI startups in the world. Also, multinational companies like Google, Microsoft, and Intel have launched several machine learning applications to solve and enhance a number of industry specific challenges in India.

Indian IT companies like Infosys, TCS, and Wipro have stepped up their game with automation and AI platforms. These don’t just save time, but also enable faster decision cycles, cut costs as well as boost revenues substantially. Machine learning is providing opportunities to engineering students to imagine careers beyond software services and enable them to adopt a strong research mindset that solve market gaps through critical BI.

Ecommerce Industry And Adoption Of Cloud TPUs

Over 2,000 deals, amounting to $30 Bn, have been made globally in the e-commerce space over the last 5 years. The Indian ecommerce space now seems to be evolving and maturing, creating a niche area of sophisticated analytics. About 50 companies so far have received funding in this space over the last two years.

These companies now have strategies to expand market share by predicting customer behaviour through analytics. Investors are focussed towards companies who are leveraging AI and machine learning across multitude of applications and generating insightful use cases. Before this, AI was never quite as well understood in the mainstream e-commerce space.

This shift can be attributed to Google’s launch of the second generation Cloud tensor processing units (TPUs), which previously served the purpose of carrying out inferences quickly, and have now jolted the industry into the next level of machine learning, bringing in superior computational performance to the training of machine learning (ML) models. Google now uses TPUs in everything from augmenting usage in its data centers, to suggesting auto replies in Gmail. Combining cloud infrastructure with business intelligence (BI), these TPUs do “machine-learning computation and nothing else”.

Cloud TPUs along with the TensorFlow developer toolkit,an open-source ML system designed by Google for conducting research in ML and deep neural networks, now allows businesses to develop machine-learning algorithms and applications for a wide variety of devices and use cases. This has amazing potential for the next phase of the e-commerce industry.

Conclusion: Making The Case For Data-Driven Insights

The ever-expanding digital universe creates a massive cluster of unstructured data sources, which presents organisational challenges. BI is becoming available to a large number of business enterprises that gather data or outsource it to blend industry makeshifts into their operational data scheme. We can now monitor and observe what customers and partners are saying about brands and their competitors, and then scale it against sales patterns. Multi-dimensional BI Analysis and what-if queries are now minutes away from being accurately answered. This opens doors for innovative thinking and for business proliferation through machine learning.

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