Business intelligence (BI) is, in essence, new information that is not necessarily common knowledge and is sought to gain a competitive advantage over the competition. Business intelligence comes from a variety of sources, ranging from spies working for the competition to data mining. Contrary to the theme of several current articles, business intelligence is not a combination of tools, best practices, and software programs, but is the result of those tools and software programs. The same is true when using artificial intelligence (AI) to unearth BI. The tool (AI) is not business intelligence, but a source of business intelligence.

A popular form of modern BI focuses on offering insights about the organization itself, with the goal of streamlining the business and thereby making it more profitable. Other forms of BI focus on gathering information about customers or potential customers. The use of AI can provide these types of insights quickly and efficiently. According to Forrester, global decision makers believe improving data, analytics, or platforms that provide insights is the primary reason for implementing Artificial Intelligence. All the major business intelligence vendors – including Microsoft, IBM, and Oracle – are currently researching and developing these AI technologies.

Boris Evelson, a vice president and principal analyst for Forrester Research stated:

“AI has been democratized. Until recently, it required a data scientist to write code. Today, with these business intelligence systems, I can point and click at a few data points, choose the variable I want to predict — like a customer’s propensity to buy — and these predictive models are going to be automatically generated.”

Confusion about AI and ML

The term “artificial intelligence” sounds more exciting than “machine learning.” As a consequence, people are replacing “machine learning” (ML) with “AI” in their advertisements and descriptions. Machine learning started as a training method, and then began being used as a primitive form of AI.

Artificial intelligence was originally seen as an ideal with the goal of creating a computer (or a combination of algorithms) that can communicate and problem solve as effectively as a human. The term “artificial intelligence” is now being used to describe algorithmic machine learning programs that can learn and perform complex tasks – but cannot yet carry on a decent conversation. These Artificial Intelligence platforms – which include a range of ML algorithms – can support business analytics, data visualization, data tools, data mining, and best practices. It is useful to understand that the lines between AI and ML definitions are blurring and becoming interchangeable.

AI seeks BI

MIT Sloan performed a survey asking executives from 168 large organizations about their use of AI/ML. Two of five companies surveyed currently use AI/ML in their marketing and sales efforts.

An AI platform can process large amounts of data much more quickly than a human. Listed below are some of the benefits that come with using an AI platform.

• Natural Language: The recent advances in natural language processing now allow both experienced users and novices to ask questions about analytic outputs and then shift to related information without struggle. For example, a researcher could say, “Find the warmest recorded temperature in Ann Arbor, Michigan,” and follow up with “What about Seattle, Washington?” without restating the original command.
• Actionable Analytics: Actionable analytics places data in the place it will be most useful, working in real time.  It uses plugins and software APIs to provide insights regarding existing software solutions, such as Salesforce. A plugin might be used to review the purchase history of a customer as well their buying preferences during a call. Another example of actionable analytics would be a purchase ordering platform that returns information on a store’s stock levels and the restocking needs each month at a predetermined time.
• Explainable AI: Machine learning algorithms can provide insights but have problems showing how the conclusions were reached. This situation causes decision makers to hesitate in implementing solutions they might have to explain to investors later with no understanding of how the conclusions came about. Explainable artificial intelligence seeks to solve this dilemma by making AI processes more transparent, allowing users to drill down more deeply into the data and understand how the conclusions were reached.
• Streamlining Operational Processes: With intelligent IT automation, productivity can be increased significantly. AI-powered “bots” (programs that can automatically execute actions) are becoming common place in factories, with computer vision and ML software improving safety and quality control.
• Boosting Sales: The majority of sales teams spend much of their time performing repetitive, tedious tasks. Artificial Intelligence takes over the tasks of finding leads, sorting them, monitoring orders, and communicating with both customers and potential customers. Certain AI tools can analyze data, work with numbers, and identify patterns in a very small amount of time. This support makes AI extremely useful in helping salespeople.
• Building Customer Loyalty: Innovative organizations are dropping the more traditional methods of attracting more customers by way of price wars and incremental product improvements and are instead investing in AI platforms that offer a personal touch, attempting to create tailor-made experiences. Algorithms are gradually learning phrases and tones that prompt cozy, friendly feelings from customers.  For example, the information Facebook friends place on their pages is collected and data mined using ML. Based on a person’s gender, location, age, and previous posts on the platform, Facebook tries to create customized sponsored posts and advertisements that might be of interest.

A Sense of Timing & the Agile Philosophy

The popular Agile philosophy promotes the goal of developing and deploying software quickly and efficiently, using the eclectic process of taking the best of different approaches and leaving behind the unnecessary baggage. The Agile philosophy puts great emphasis on speeding up the process and getting the software to its users as quickly as possible. This philosophy has been extended to, and included in, the DevOPs and DataOps processes. However, efforts to apply this speed-driven philosophy to business intelligence models are questionable.

While the process of gathering data can be reduced to efficient mechanical steps, maximizing the profits that can be gained from business intelligence requires a certain amount of  mental flexibility. (A dash of imagination can be quite helpful, too.) While habitual responses, such as getting the product to the market as quickly as possible, can be quite efficient, it is not a replacement for a thoughtful planning that takes in the variables. The mechanical response of following a series of steps is efficient, but lacks the conscious awareness needed for long-term strategies.

Data Mining vs. Knowledge Discovery of Databases (KDD) vs. Knowledge Mining

Data mining (which has been around since the 1700s, see Bayes’ Theorem) is defined as the extraction of useful, previously unknown information from large databases or data sets. But in the 1990s, data mining became a step in a process called Knowledge Discovery of Databases(KDD). The number of steps in KDD vary, depending on the model being used, and data mining, as a step, is not always in the same sequence of steps.

A popular definition of KDD is “The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” (Sound familiar?) While the goals of data mining and KDD are essentially the same, KDD is a more complicated process. The process also includes multidisciplinary activities, with scaling algorithms for massive data sets, data storage and access, and interpretation of the results. The end goal is useful knowledge, rather than information that needs to be interpreted. KDD requires a fair number of researchers for interpretation/knowledge.

Knowledge mining is still a fairly new, evolving process and currently has “flexible limitations.” It is basically an extension of KDD, but uses artificial intelligence and machine learning to replace data scientists and researchers. AI and Machine Learning are used to provide knowledge as well as make decisions.

Knowledge mining provides the ability to categorize and curate massive streams of structured and unstructured content automatically. While knowledge mining can help to uncover insights from all types of data, much of the critical information exists in unstructured formats like images, videos, audio files, PDFs, paper documents, and even handwritten notes. This process is too labor intensive and expensive to performed manually. Knowledge mining can find connections within related information, aid in finding real-time anomalies, and index the data.

AI, BI, and the Cloud

If an organization is planning to purchase artificial intelligence software, it is extremely likely it will be operating it in a public or hybrid cloud. Machine learning and other aspects of artificial intelligence require powerful hardware and lots of memory, and the cloud is the most cost-effective way to access that hardware and scaleable memory.

Very few organizations purchase a single piece of AI software, instead preferring to add a range of machine learning tools such as natural language processing, recommendation engines, and concept mapping. Many organizations find buying all their tools from a single vendor to be both convenient and cost effective.

When using the cloud, it is important to have the necessary security safeguards in place, along with appropriate policies and procedures to help in optimizing cloud expenses — especially when purchasing some of the more expensive services. Plan on starting small and scaling.

Start off with a fairly small AI pilot project, expanding the projects as the staff’s experience grows and they become familiar with the tools. Generally, tools supplied by the cloud are good for scaling, but be aware that an AI project often becomes bigger than originally planned, resulting in greater costs to complete it.

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