Business intelligence (BI) and machine learning (ML) — at first glance, the two disciplines could not be more dissimilar. True, both center around number-crunching, but AI looks to the future while ML is mainly retrospective.
Moreover, ML is ultra-sophisticated and BI’s statistical analysis involves basic counts. So, it’s easy to see why industries treat business intelligence as yesterday’s news while portraying ML as a new, useful tool.
The truth is, we’ve been going about it all wrong; instead of treating machine learning and business intelligence as wholly separate, its better if we combine the two and bring attention to the advantages of ML-powered BI. Let’s see what they can together.
1. Reimagine social media
Due to the absence of seamless and efficient data access from different sources, business intelligence (which may or may not exist at Facebook right now, but that is another topic) has mostly been limited to internal data. Until now. The next-gen consumer intelligence platforms have made it possible for enterprises to reimagine social data beyond the normal confines of social measurement and listening.
Sites like Instagram, Facebook, and Twitter hold vast amounts of unstructured data, but using tools like predictive AI analytics and machine learning allows us to understand market sentiment, brand research, and linguistics.
Apart from that, semi-structured data elements such as hashtags, URLs, and retweets have helped develop frameworks so that structured data can drive intelligent decisions. Thanks to Hadoop and other search engine constructs, Big Data now allows for the storing, structuring, and indexing of freeform data.
2. Promotes machine learning skills
ML, Big Data, and IT analytics are “future-proof” skills that boost careers. For example, six Indian startups were chosen by Google’s accelerator program in 2017 for mentorship in machine learning and AI.
Why India, you ask? Because it’s the third biggest cluster of artificial intelligence startups on the globe. Plus, tech giants like Intel, Microsoft, and Google have launched various machine learning apps to improve Indian industry-specific challenges.
And, if that’s not all, IT companies have made huge strides in AI and automation platforms. So, now they don’t merely save time, but reduce expenses, boost revenues, and offer quicker decision cycles.
What this all means is that machine learning provides career opportunities to engineering students beyond basic software services. Moreover, it inculcates a strong research mindset in them that fills market gaps via critical business intelligence.
3. Locates anomalies in real time
Does your company have systems that stream data constantly? While there is merit in collecting that data and processing it, you will benefit more from identifying outliers while they happen. The process of finding outliers in real time helps you to take action immediately.
So, you no longer need to worry about online buying behaviors, fraud analysis, and other activities since immediate information drives valuable decisions. Normally, the models developed by data scientists were meant to do just that by streaming analytics software. However, the evolution of machine learning has increased the effectiveness of the process. DataTorrent, IBM streams, and other software promote this, and those anomalies lead to bigger actions.
You should feed the details back into your business intelligence system for better policy changes and product development. While it’s good to catch problems while they occur, it’s better to correct the system so that the mistakes don’t take place at all. And when you combine advanced analytics and machine learning with a modern business intelligence platform, you’re able to do just that.
4. Uses a search-based business intelligence platform
Business intelligence has normally been about experienced analysts providing answers for others. Though over the last two decades, the industry has worked tirelessly to make those answers more sophisticated, not much else has changed. Now, the amazing success of LinkedIn, Google, Yelp, and other search-based consumer information systems is changing all that.
Companies, like Microsoft’s Power BI and ThoughtSpot, are implementing this exact search experience into the lives of working individuals. However, we should always remember that the business and consumer success garnered by these platforms depends on machine learning.
So, the platform and data need to be trained properly for answers rather than manually building every answer in advance. Amazon and other products have adopted such an approach. They understand how search suggestions change along with people’s questions. The product’s appearance also changes depending on behavior.
These changes do not take place manually. They are created using machine learning. Use search-based business intelligence the right way and you succeed in closing the last mile for end users, thereby revolutionizing BI.
5. Provides deeper insights
When ML algorithms are deployed in business intelligence software, they inform businesses about their historical data, especially the standout features. At present, analysts must define metrics for business intelligence tools to track.
The process is wholly manual and makes visible the information considered important by the organization. However, ML-enabled BI can delve deeper into the unknown aspects of a business, discovering data that was earlier left unexamined. Moreover, AI-powered BI can utilize natural language generation abilities to explain what these insights mean for the business and how they should act on them.
6. Grants a look at the future
Gone are the days when forecasts meant crystal balls and tea leaves. Businesses now prefer more intelligence, relying more on demand modeling than gut instinct. However, things are beginning to change once more as machine learning enters the fray.
Whether it’s optimizing your supply chain or estimating future sales, forecasting now predicts the future using past data. And everyone knows that machines are the best way to churn through huge volumes of data. So, ML technology enables forecasting, allowing you to make precise estimates of future behavior as accessible as past data.
7. Hires a bot
Business intelligence easily gets the proper data to the point of precision. And talking is the easiest way there is. This is where the bots come into the picture. While it’s true the initial set of bots offloaded simple questions from support rather than help people, today’s bots serve the function of introducing data to existing workflows in low-impact ways.
Nowadays, you don’t have to arrive at the answer; the bots bring the answer to you. And this is all becoming a reality thanks to ML, along with AI.
There is a lot of hype around machine learning, but once it begins to wear off, the differences between ML and BI will not seem as great. True, the applications haven’t been perfected yet, but machine learning suits the BI landscape accurately, making the tools gradually smarter.
As the industry explores the full impact of machine learning on business intelligence, you should prepare yourself to take full advantage once it does.
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