AI Driven Business Intelligence - Where to Start?

The buzz around artificial intelligence (AI) has reached a fever pitch in 2024, largely due to fascination with tools like ChatGPT. While it's tempting to get swept up in the hype, the true value of AI lies in its ability to enhance and transform existing business functions. Business Intelligence (BI) is one such area ripe for AI-powered disruption.

Contrary to the current belief, AI-driven BI isn't a novel concept. It's been simmering beneath the surface for over a decade, primarily within the tech industry, under monikers like "Self-Serve Analytics" or "Smart Analytics". But with AI now at the forefront of business innovation, it's time to explore how it can revolutionize BI for companies of all sizes.

A Word of Caution: AI Isn't a Magic Bullet

Before we dive in, it's crucial to address a common misconception. AI isn't about replacing human intelligence; it's about amplifying it. Someone, somehwere on the internet posted something a while back which said, "ChatGPT is making stupid people more stupid and smart ones smarter", and we couldn't agree more. Blindly implementing AI tools without a clear strategy is a recipe for a smelly junkyard filled with half baked and over engineered ideas without any real use case. The companies that will truly benefit from AI-powered BI are those that leverage it to sharpen their competitive edge, not those chasing after every shiny new AI-powered gadget.

Let's explore three basic yet extremely effective key areas where AI can deliver tangible results in BI, starting today:

1. Adhoc Automation - Where massive ETL isn't the answer to all BI needs If you are an analyst, engineer or a BI leader, at mutiple points in your journey you would have come across situations where mundane repititive tasks suck the soul out of your body. Think automated data cleaning or even basic analysis. As a decision maker and power user you still own the decision making based on the outcome, but imagine not having to fix data type errors in all the excel sheets you are consuming for an adhoc report generation at the end of each month. And areas where ETLing the crap out of it does not have any cost or resource benefits.
2. Adhoc Analytics - Finding that Needle in the Haystack All or most analytics folks have faced a situation where they are sifting through days of data looking at hundreds or few thousands of records trying to figure out the spike OR a dip OR which intermediate table broke in a long line of tables & data pipeline OR the root cause of why the marketing campaign suddenly stopped being effective. We already know what we are looking for, and most reliable AI tools can read and process information at an incredible rate, then why not ask for clues?
3. Data Quality Checks - The Knight in Shining Armor Poor data quality is the Achilles' heel of a BI team, in most cases because of the quality of data set way upstream or over which the team has no control over. Remember Business Intelligence (BI) as a function is not only working with existing data but it is to bring about a state of Nirvana in the organization's data ecosystem. Again, the idea here is to NOT Over Engineer the heck out of a solution but to create small & consistent processes or solutions that saves time and resources.

The Path Forward: Strategic AI Driven BI Implementation

The convergence of BI and AI is not a distant vision; it's happening right now. Companies that embrace this evolution will gain a decisive competitive edge. The key to success with AI-powered BI lies in strategic implementation. At White Box Data, we don't just throw AI tools at the problem; we partner with you to develop a tailored data strategy that aligns with your specific business goals.

1. Develop a Customized Data Strategy: Align your BI initiatives with your broader business goals.
2. Implement AI-Powered BI Tools: Choose the right technologies and platforms to unlock your data's potential.
3. Build a Data-Driven Culture: Empower your team to make informed decisions based on actionable insights.

Note - This article is a copy of the original article published on White Box Data's website

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