Business Intelligence: Machine Learning and AI The Future Of BI?
If you work in business operations, marketing or analytics, you probably noticed that the number of self-service tools is growing, as well as their ability to solve complex analytics tasks. Increasingly, BI will adopt features that are informed, or powered by, machine learning (ML) and artificial intelligence (AI). Vendors, researches and consultants are talking about this as the next “wave” with BI. An article on this topic by Doug Henschen, at Constellation Research, is worth a read: “How Machine Learning & Artificial Intelligence Will Change BI & Analytics”.
What is Business Intelligence?
Before jumping into advanced concepts like AI and ML, let’s baseline a traditional definition for BI. Gartner defines Business intelligence (BI) as following:
“BI is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance”.
CIO defines BI as:
“BI leverages software and services to transform data into actionable intelligence that informs an organization’s strategic and tactical business decisions. BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business.”
Last, but not least, a definition of BI from Forrester Research:
“BI is a set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery. Research coverage includes executive dashboards as well as query and reporting tools.”
The general consensus is that BI generally covers these five areas:
- provides historical, current, and predictive views of business operations
- leverages data that has been gathered into a data warehouse, data mart or operational data
- provides reporting, dashboards, visualization, queries, analysis, and discovery
- uses sales, marketing, operations, finance, and many other sources of data
- provides on-premise software installed or SaaS-based which is hosted by the application service provider (ASP) or a mix of both on-premise and SaaS.
So, where do AI and ML fit into this mix?
Business Intelligence, Machine Learning (ML) and Artificial Intelligence (AI)
Increasingly, BI is embracing features and capabilities that fuse machine learning (ML) and artificial intelligence (AI) with traditional BI offerings. Advanced, predictive analytics is about calculating trends and future possibilities, predicting potential outcomes and making recommendations. That goes well beyond the traditional queries and reports that have defined (rightly or wrongly) BI in the past. This shifts the traditional role of BI from ”What happened?” to an AI-driven model which layers in answers to ”What will happen next?”. This is a significant shift, because it not only impacts the software, but the data, people, and processes that need to support this evolution. For example, HBR recently published an article “If Your Data Is Bad, Your Machine Learning Tools Are Useless” and stated that “Poor data quality is enemy number one to the widespread, profitable use of machine learning. While the caustic observation, “garbage-in, garbage-out” has plagued analytics and decision-making for generations, it carries a special warning for machine learning.”
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The “garbage-in, garbage-out” for data is true not just for ML, but for any use of data, including BI. This highlights one of the major challenges BI teams have faced and will face in the future; data must be a first-class citizen in the architecture and design of a solution, not an afterthought.
What Are the AI & ML Trends In the BI Industry?
The BI platform leaders are taking slightly different approaches to ML and AI.
Tableau says it plans to add a new machine learning recommendations engine to its platform that will help algorithms surface contextually relevant data. Tableau acquired ClearGraph which they have stated will help enhance their product with smart data discovery and data analysis through Natural Language Processing (NLP). Tableau will likely continue to acquire additional expertise and products, as the needs for AI and ML mature.
Interestingly, Looker is not planning on building machine learning or artificial intelligence directly into their product. They are focusing on data preparation and delivery while looking at machine learning and artificial intelligence duties being handled by complimentary external systems.
Microsoft is also following the Looker model to a certain degree, as they are looking at fusing Power BI and the MS Cloud ecosystem with Azure SQL and Azure ML. This makes sense given the investments Microsoft has made in Azure ML. Power BI becomes a “front-end” of sorts to more complex ML implementations in the Microsoft analytics stack.
Lastly, Qlik has stated that AI and ML offerings should not remove people from decision-making workflows. Qlik has described this as “augmented intelligence” which is a blend of machines with human refinement. They have just offered an overarching roadmap at this point. Any AI or ML capabilities are likely a few years away from being available in their product offerings.
BI is Still About Having a Positive Impact on Business Performance
Considering BI trends, from self-service to ML to AI, the principal goal of business intelligence remains the same: to provide decision makers with the capacity to better see the relationship between trends, patterns and behaviors, previously “hidden” in data, for better decision-making and optimization of resource (budgets, people…) deployment. However, any functional changes in BI platforms will have an impact in achieving that end, especially in the tools we use and how teams deliver outcomes based on those insights.
DWant to discuss further? Need a platform and team of experts to kickstart your data and analytic efforts? We can help! Getting traction adopting new technologies, especially if it means your team is working in different and unfamiliar ways, can be a roadblock for success. This is especially true in a self-service only world. If you want to discuss a proof-of-concept, pilot, project or any other effort, the Openbridge platform and team of data experts are ready to help.
Reach out to us at hello@openbridge.com. Prefer to talk to someone? Set up a call with our team of data experts.
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