Artificial Intelligence is coming for the enterprise. Long the domain of science fiction and dystopian movies, computers that are capable of simulating human intelligence are poised to have a transformative impact on the business world over the next decade, and as investment dollars flow in and use cases are proven out, that impact only stands to increase.
But potential use cases for AI in business are still nascent, and widely misunderstood. In a recent McKinsey survey of 3,000 business executives, 41% responded that they were uncertain of the benefits of AI for their business. Many of the features frequently attributed to AI in business, such as automation, analytics, and data modeling aren’t actually features of AI at all. And while AI algorithms are certainly poised to make an impact in each of these areas, enterprise businesses need to first invest in building the infrastructure to support them.
The road to AI supremacy in enterprise business starts with investment in an area most businesses might not think to look at first. But business intelligence software, built to give businesses the opportunity to collect, unify, sort, tag, analyze, and report on the vast amounts of data at their disposal, must be a focus for businesses hoping to gain an AI advantage down the road.
It All Starts with Data
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 touchpoints, 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 touchpoints (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 rapidly 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 datasets 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 dragging and dropping 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—but how else can modern business intelligence solutions provide the necessary insight into business data, with or without the involvement of AI?
Consider the amount of data the human brain parses on a daily basis—according to some estimates, up to 400 billion bits per second. Now imagine throwing an advanced AI algorithm into the mind of a newborn child. In their rush to implement AI without first building a data foundation, this is essentially what businesses are doing. Only by investing in building a BI foundation today will forward-thinking enterprises be able to set themselves up for AI success tomorrow.
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