Feature
In all the discussion about artificial intelligence (AI) and its place in the enterprise, the role of business intelligence (BI) and what it can do is often overlooked. Given the level of investment that some enterprises have made over the years in BI this is somewhat surprising until you scratch a little bit deeper and it becomes clear that many BI providers are now looking at ways to pull AI and BI together. Given the faith that many enterprise managers are putting in AI to extract insights from data, is there even a role for BI software? It depends on how you understand BI, AI and their differences. So what is the difference between the two?
AI and BI Uses
The high-level difference is that business data is the collection of data produced by your company: site traffic, purchases, brand engagement, campaign analytics, KPIs, according to Bernard May CEO of Westlake Village, Calif.-based National Positions. Collecting and using this information can give organizations insights as to how business operations and marketing campaigns can be optimized.
Artificial Intelligence, on the other hand, can use your business data along with other industry data to help you in predicting the best moves to make moving forward. A simple example would be a chatbot that uses AI. As the chatbot learns from your users' questions — it can provide more tailored responses to customers using live chat. On a grander scale, AI could help a fashion brand analyze social media data to help predict which colors, fabrics, and accessories are gaining or losing popularity in order to guide production decisions for the next buying season.
BI vs. AI
Preeti Singh is CTO of TechUG, a Dulles, Va.-based mobile app development company. She pointed out that while BI and AI are often thought to be synonymous with each they are quite different. For many, it is not clear that the two technologies are quite distinct from each other and have crucial differences that are contained not just in the definition, but also in the functionalities offered by these two platforms,” she said. So what exactly are they?
1. Artificial Intelligence: Artificial intelligence is all about a computer system that thinks and acts intelligently like humans. The AI system learns from past experiences and mimics human intuitions and judgment.
2. Business Intelligence: Business intelligence is a technology that is used to collect, store, access, and analyze data that helps business users make better and informed decisions. With BI, the data is converted into reports and dashboards. This enables organizations to make better data-driven decisions.
Baseline Differences
There are also three-baseline differences with the two technologies:
AI vs. BI: AI aims to create similar intelligence as that found in humans. BI aids in analyzing business performance through data-driven insights.
Areas of contribution: AI is used in science and technology computer science, math, biology, psychology. In AI, the algorithm follows the patterns of the human brain. While, BI combines analysis tools, including ad-hoc analytics, enterprise, reporting, and OLAP (online analytical processing). The principle purpose of BI is data analysis.
Issues faced: In the field of AI, there are two major issues:
- Privacy breaches
- Data security
With BI there are two major issues:
- Organization and people
- Technology and data
Put all this together and it is clear that BI and AI combined deliver analytical solutions in any business situation. AI-enabled BI helps businesses to automatically review past data and give alerts to new and interesting features. This brings time-saving into analysis and lets the businesses analyze and derive actionable insights more efficiently.
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San Franciso-based Lucidworks CMO, Vivek Sriram, adds that as a result of the way they work, AI cannot swoop in and make decisions or recommendations without the backing of actual human beings. Though machines are good at recognizing patterns, they are terrible at making sense of things the first time around.
Humans have evolved sophisticated fight or flight stimuli over millions of years that let us make sense of things the first time around. Where AI-powered dashboards can help by enabling better BI by doing first order analysis for people. This means we can spend our increasingly fragmented attentions on things which humans are uniquely suited to solve.
AI can replace BI only to the extent that it can point out anomalies or trends. Unless, and until, it can explain why those exist in the first place and what must be done about it, BI will remain the domain of people who excel at solving unique problems.
AI is The New BI?
Despite this, with all the hype surrounding its emergence, AI is fast becoming the new BI — but only if it's the right AI, Arijit Sengupta, CEO and founder of Foster City-based Aible said. “You hear a lot of business users say that their AI is never wrong — it’s just not useful for what they’re trying to accomplish. That’s because most AI today is trained to optimize for academic metrics like accuracy, rather than what businesspeople really want — measurable impact,” he said. “An AI that’s theoretically accurate fails to take into account the unique cost-benefit tradeoffs and operational realities of a business, and that’s useless for a businessperson. Actually, it’s worse than useless, because if you follow what the AI recommends, you won’t be in business very long.”
In order for AI to be truly effective for business, it has to take into account real resource constraints such as marketing budgets and sales capacity. AI that’s trained to achieve the highest accuracy unrealistically assumes that all costs and benefits are equal. What if the benefit of winning a deal is 100 times the cost of pursuing a deal? In that case, you might be willing to pursue and lose 99 deals for a single win. “An AI that finds only one win in 100 tried would be very inaccurate. But it would boost your net revenue. In the end, the goal of AI is simple: better business results,” he said.
BI With AI
BI is certainly getting better in terms of insights and predictions. But the underlying models are generally more focused on traditional statistical approaches, according to Tom Taulli, founder of the online investment company WebIPO and author of the book Artificial intelligence Basics. AI on the other hand, can get more sophisticated.
He cites the example of deep learning. Here there may be patterns that humans or BI software just cannot detect. The reason is that this approach involves sophisticated algorithms that constantly optimize weightings for the parameters — until there is a better outcome.
Now there are issues, such as finding the true reasons for the output of the model. And like you mentioned, there generally needs to be lots of high-quality data that is prepared correctly. But all in all, AI does hold much potential. This is why BI providers are working hard to implement more of this technology in their platforms (a big part of this is with consolidation, as seen with deals for Looker and Tableau). So over time, the difference between AI and BI will fade away.
