Future-Proof Your Business with Analytics, Not Artificial Intelligence

In my last few posts, I’ve looked at how the hype surrounding Artificial Intelligence has caused enterprise IT departments to hastily plunge ahead on implementing costly AI solutions without the foundation in place to support them—and how this hype has potentially even limited some business’ ability to build that necessary foundation at all.

Hopefully by now you’re asking yourself whether AI is the right answer for your business right now—there’s no doubt that AI will have a truly transformative effect on business efficiency and operations over the next many years, but by rushing to roll out AI to your enterprise before you’re truly ready, you’re flushing a substantial chunk of your IT budget down the proverbial toilet, never to be seen or heard from again.

With the potential use cases on the horizon for AI in business, as well as the investment dollars and rate of change currently propelling AI, one thing is clear: you’ll need to get your foundation in place sooner, rather than later, to take advantage of the benefits coming to the business world. But how can you do that?

Enter Business Intelligence (or BI). By building the foundation now with this readily available, accessible, and affordable software, businesses can prepare themselves for the future while also reaping the benefits today. Why stumble through a costly AI implementation that won’t yield tangible results for 2-3 years when you can implement BI today, yield results immediately, and layer AI on top of your established BI data to derive new insights and drive greater benefit once the technology matures? You can have the best of both worlds—just let me tell you how.

Regardless of where you’re landing in regards to Artificial Intelligence and Business Intelligence, one thing is true: you’ll need to have data to feed both. Without data to act upon, there’s no ‘intelligence’ in AI or BI. There’s nothing to analyze, or apply a learning algorithm to—when it comes to any intelligence solution, data is the foundation upon which it must be built.

Thankfully, with widespread adoption of cloud computing and the Internet of Things, data has never been more readily available in today’s business world. But the vast reams of data generated daily are presenting a new problem for businesses—what data matters? How should data be tagged, sorted, grouped, and analyzed? Which problems do disparate data points speak to? And how can the data collected across multiple touch points, from retail locations to the supply chain to the factory be easily integrated?

Enter data warehousing. Data warehouses are a means of taking data points from disparate touch points (such as point-of-sale, CRM, inventory, and warehouse management systems), standardizing the data collected, structuring it to extract necessary insights, and running analysis. Enterprise businesses cannot survive without robust data warehousing—data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is quickly going to lose ground to those businesses with integrated data and reporting.

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. Cubes are multi-dimensional data sets that are optimized for analytical processing applications such as AI or BI solutions. Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextual data points to uncover new insights and adjust tactics and decisions on the fly. Chances are good that your average non-technical sales agent or purchasing representative will have difficulty joining multiple tables together with a standard report, but with Business Intelligence cubes, all that is required is to drag and drop the metrics and dimensions that matter to them into their own personalized dashboard.

So how is the data extracted? By using Structured Query Language, or SQL, the language used to manipulate and extract data stored in cubes. SQL was developed as a standard language to communicate with databases, regardless of exactly which type of database was being used, and is ultimately how data in a table is extracted, retrieved, deleted, updated and managed.

When many of today’s business leaders are looking to implement AI, what they really mean is they want more actionable insight into their data. Data warehousing, SQL, and OLAP cubes help address that—and the potential to layer AI algorithms on top of this infrastructure in the future gives enterprise businesses the opportunity to set themselves up for success for years to come - we just aren't there yet. Prepare your business NOW with proper analytics and intelligence, so you can take full advantage of the advances in artificial intelligence that are sure to come. Don’t miss out.

For more about AI vs. BI, download: From AI to BI: Misunderstood Applications of Business Intelligence