2017 Trends in Business Intelligence

Artificial intelligence and machine learning have become the newest trend in business intelligence.  Companies are rushing to secure resources to incorporate these new technologies into their business model.  For the first time in decades, businesses have the opportunity to open up a new channel for business intelligence delivery to support informed business decision making with fast turnaround.  In response to this trend in business intelligence, the labor market has started to demand more Data Scientists and Data Architects while demand for other careers decrease.  What are these new technologies and how can they revolutionize the way companies do business?

What is Machine Learning (ML)?

How many times do you put three words in a search engine and it auto completes the sentence for you? That is machine learning which is the precursor and building block for artificial intelligence. The missing link in creating artificial intelligence, AI, is that the AI has awareness, which technically we are not there yet. The primary components of machine learning are Natural Language Processing (NLP) and Natural Language Understanding (NLU). This is a specialized field of expertise, which is seeing a heightened demand for experienced data scientist, and miners, which is reshaping the labor market when it comes to the importance of business intelligence experts.

What is fueling the 2018 trend for artificial intelligence in Business Intelligence?

Technology has accelerate the change cycle within the world markets. Companies now need to adapt within months in order to fend off the competition and secure their market share. In an environment where decision need to be made at lightning speed a wrong decision will ruin a company. On top of that, the quantity of data that is being accumulated is expanding where the base measure of storage is no longer megabytes but terabytes.

Decision makers no longer have the luxury of waiting for a business intelligence team to weed through the data to find trending and patterns and business intelligence teams no longer have the time to produce reports while still making sense of the abundance of information. This is leading to the rise of machine learning, where data structures can be integrated with decision trees so that executives can access the data they need, when they want it, in the channel that they prefer instead of relying on a secondary resources and static reporting.

Assistance Any Time/Any Place with Smart AI

Machine learning is a powerful tool and has one major limitation, which can be turned into a powerful asset.  Machine learning has no graphic user interface for users to ask it questions, which seems restricting at first glance since the thought of creating a user interface can be overwhelming and detracting from the ultimate goal, to give the information to the decision makers when they need it.

Luckily, many companies have stepped forward and offer developer friendly APIs and SDKs that allow for easy integration into their systems. Imagine being able to ask a Bot through Skype which invoices are still outstanding for the month; or asking Cortana when Christmas inventory should be purchased based on the previous year’s sales.

An executive can be on any device and in any channel since the machine learning is universal to all devices and is interface agnostic. True power at the hands of the employees through the medium that they prefer with no need to additional applications or training.


Machine learning also provides a mechanism for simplifying businesses process. Imagine the complex expense management process, which is designed to remove the risk of unauthorized transactions to be paid for by the company. Utilizing customization, a director could ask the system to provide an alert if any expense other than gas is submitted. These personalized preferences will now be stored in the machine learning library and will only alert the business lead if the conditions are met, removing their need to continually monitor the system or to even run reports for review.

Powerful Analytical Insights

Machine learning can also provide forward-looking insights to users in the organization. Through the processing of organizations’ big data and its algorithms, which were developed by the business intelligence team preemptively, it will be able to provide deeper and more accurate analytical insights to its users.

Think of a director asking what the estimated power costs for 2020 will be for a manufacturing plant located in city X. By asking the question using a natural language processing, the machine learning system would be able to return the data to the director immediately. The director could then ask the system to change certain variables in its calculations such as power cost thresholds.

The ability to change models on the fly with little to no effort that is driving business into the machine learning world but it is a big change, which requires significant change to both organizational resourcing and data warehousing.

What About Report Designers?

If businesses can use “what were my fiscal sales for last year excluding Christmas”, and a smart AI can take that data, substantiate it, and return it into a requester in a consumable format, what happens to the existing presentation layer? Do report writers become a career of the past?

Request to data teams to process data will no longer need to be done because if the “intent” is designed in the AI correctly, all the requester needs to do is request the information in an English format.  An AI can interface with robust data structures with to provide immediate results.

Why are we going to need dashboards when the AI (personal assistant) is attached to the enterprise business intelligence framework, and can provide an alert when sales dip below X, or a project is projected to go over budget, due to forecasting algorithms that the artificial intelligence would apply to current data. Currently, consumer behavior is that they will open dashboards only to monitor a specific item. AI will provide another channel for business intelligence to provide this data to the consumer at real-time, or near real-time.

The Path to AI

As mentioned earlier, AI is only as smart as the datasets it is based on. If the AI is built incorrectly, with any spreadmart or bad data store, the executives will get bad data – which will ultimately affect the bottom line.

To meet the requirements for machine learning which leads to artificial intelligence, your organization needs to have an underlining data architecture following proper data governance guidelines. This should include a clear naming convention to support natural language processing, data integrity standards and data architecture that can support fast extractions of data.  On this path, we will see an increase in demand for Data Architects, Business Intelligence Analysts, and Data Scientists.

Rise of the Data Scientist

To achieve a machine learning library, a new role has moved to the forefront: the data scientist. Since this field is so new, there is a huge gap in the availability of natural language processing and natural language understanding skillsets in labor force. Many larger companies have already consumed the available data scientists to support development of their own machine learning initiatives on a path to business intelligence solution involving artificial intelligence.

Demand for Data Scientists will continue to trend higher in coming years and exceed the market supply of data scientists as companies continue to push to attempt to incorporate artificial intelligence in their business intelligence solutions.

Hitachi Solutions offers guided workshops and hands-on learning for the Azure Machine learning suite, as well as other business intelligence solutions that support decision making in this evolving technological environment. Large companies are starting to apply machine learning to gain competitive advantages in the marketplace.

Many companies have been in the news for their application of innovative solutions based on machine learning, and for the advanced analytics that support their decisions. Feel free to contact us to discuss how machine learning can benefit your business decision-making processes.

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