“Transformational” was what Cindy Bates, Vice President of Microsoft, had to say about the impact of Business Intelligence to the economy especially small-to-medium businesses.
In 2015, more businesses turned to the prospect of cloud computing when Microsoft Azure and Amazon WebService went toe-to-toe to launch products that further innovated Business Intelligence. This 2016, we’re about to see a new era of data as we shift our priorities from regular reporting analytics to smarter and faster features. Here are five trends that will dictate the BI and BA scene in the coming years.
What you'll find in this article
A heavy shift to Business Intelligence as a service
More companies are embracing Business Intelligence not as an isolated tool to be used by only business analysts and data scientists, but as a critical service in operations. The cloud has made it easier to upload data for later retrieval, but Business Intelligence as a service has created a richer set of features that makes it usable to every stakeholder.
We have shifted from manually mining information to having applications provide smarter tools. These would include on-demand, customizable dashboards for managers, which gives them the ability to change what they want to see depending on their business objectives. Moreover, as a service, Business Intelligence leaves much-needed legroom for analysts as they are freed from manually mining reports for internal clients. They have now shifted to maintaining their BI service, making sure that all users have consistent access to what they need.
Accessibility and usability will also greatly improve because service providers frequently update their applications. Better user interface means that even non-IT driven personnel will soon have dashboards in their hands. According to Dan Sommer of Gartner, an IT research firm, by 2020 Business Intelligence will be adopted by 100% of stakeholders in companies.
Business Intelligence becomes a core concern in business models
Aside from being integral to business operations, some companies have relied on business intelligence and analytics as a key driver in determining their product offerings and services.
Case 1 – Netflix and House of Cards
A very successful firm that has fully embraced Business Analytics in their business model is Netflix. Using several data points they have gathered from historical data of users, they found out that most viewers wanted a specific director, actor and a theme which ultimately led them to produce the hit political drama series House of Cards.
Case 2 – Better sales conversions by AirBnB
AirBnb, a disruptive and rapidly growing rental platform, relies heavily on data science because they have limited personal contact with users, due to the nature of their services. However, using data science, AirBnB is able to create a detailed analysis of customer expectations through their behavior while using their platform.
These methods gave them more insight into what kind of houses people want (based on the total clicks per ad); the real sentiments of user reviews through natural language processing (because reviews tend to be inflated because the user and the landowner had a personal experience); and the accuracy of their recommendations (based on user search behavior).
From these two cases, it’s easy to see that Business Intelligence has a growing stake in determining the success of companies.
IOT and BA will bring tremendous value to business
Usually, the Internet of Things (IOT) is associated with automatic lights and smart refrigerators that are accessible to users from miles away. However, IOT has a lot more potential in improving business processes. The possibilities are endless, but right now pioneers are already utilizing it primarily to save costs.
Case 1 – Interva utilizing real-time data for better machine efficiency
Interva, a manufacturing firm, has been analyzing their machines and equipment for a long time. They manually track data like temperature and other environmental factors but, much to their distress, much of the data get stacked up and remain useless. But when their CIO, Dennis Hodge, implemented Sight Machine, a business intelligence tool for machines and equipment, they finally saw the treasure trove of data they have left unused.
Sight Machine enabled them to see in real time the status of their equipment and correlate those with other factors. For example, through Business Intelligence, they were able to find out that their second factory would consume less electricity if they started production at night. The real-time data also meant that they are able to foresee possible breakdowns based on machine behavior.
In the first few months of implementation, they were able to reduce total scrap and drastically improve quality.
Case 2 – Haemonetics saving costs through proactive replacements
Haemonetics, a medical firm that specializes in blood and plasma supplies, produced HaemoCloud, which enabled them to gain insight into a variety of medical concerns. One important feature of the HaemoCloud suite is its ability to quickly see equipment that must be immediately replaced.
Walt Hauck, VP for Product Development is impressed with the cost-savings HaemoCloud has brought them. Usually, a machine would exhibit subtle signs of breaking down several times in a month, and it would only be remedied when a support staff would notice it. Usually, it would already be too late, but with HaemoCloud, the support staff are able to replace expensive equipment from stocks rather than buying a new model.
Embedded Analytics: “the Future of BA”
According to Dan Sommes of Gartner, “by 2017, analytics applications offered by software vendors will be indistinguishable from service providers.” There is a growing trend of software companies leaning towards integrating Business Intelligence in all their applications. This is a big jump from today’s current market, wherein companies buy software suites and purchase a business intelligence tool separately. This integration is called Embedded Analytics.
The Eckerson Group even asserts that Embedded Analytics is the last mile of Business intelligence. According to them, the growth of Business Intelligence always came in alternating waves of reporting and analyzing. With embedded analytics, where analytics becomes part and parcel of enterprise applications, innovations would lead to the last mile of integrating analysis and reporting.
Currently, the most prevalent sign of Embedded Analytics can be seen in Application Program Interfaces (APIs). APIs enable a high degree of flexibility because companies can directly input Business Intelligence APIs into their enterprise applications with minimal set-up time.
The Rise of Prescriptive Analytics
The prevalent kind of analytics being used is predictive analytics. It uses historical data from several sources and tries to predict the likelihood of events happening again. Although this has been helpful in predicting important data like sales demand, prescriptive analytics is beginning to see higher rates of adoption.
Guy Yehiav, the CEO of Profitect, believes that Prescriptive Analytics is the next level of Predictive Analytics. Instead of relying on large quantities of historical data, Prescriptive Analytics utilizes current data and checks for variances. If it notices a significant variance, it notifies the staff concerned and prescribes a recommendation. Instead of just delivering real-time data, Prescriptive Analytics scrutinizes every data and attempts to correlate it with other data points.
Cindy Bates was right to claim that Business Intelligence has become transformational. But with these trends, we can expect further transformations in the way we conduct business. Considering the pace at which analytics is growing, it would greatly help to attend a Business Intelligence and Analytics course to keep you one step ahead of the game.
