5 ways the Business Intelligence landscape changed in 2016

A year passed and the business intelligence world has changed. Technology is pushing us further and we’ve been experiencing more developments in the last few years than in the past decade. Take a look at the five biggest changes in BI this year.

1 — Modern BI

In the beginning of 2016, Gartner published the BI and Analytics Magic Quadrant in which traditional vendors, like Oracle, were removed from the report and the rest was shifted down.

Gartner believes that BI has achieved a tipping point. It shifted from a “IT-led reporting to business-led self-service analytics” and the Magic Quadrant must reflect such change.

Gartner calls it Modern BI. This means IT teams will assume more of a facilitator role, leaving analytics authorship and data preparation for business users. Gartner states that a Modern BI should have these three features: Natural Language Query and Search; Self-Service Data Ingestion; Big Data Source Connectivity.

2 — Data Citizen Scientist

Also this year, Gartner coined the expression Citizen Data Scientist, to refer to “a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.”

In a simpler way, Citizen Data Scientists are users who went tired of looking to the same dashboards and reports and want to explore data themselves.

This new type of Data Scientists appeared for two main reasons: first, the volume of data and the need for extracting insights from it are quickly increasing and secondly, it is hard and expensive to hire data scientists.

This wave of self-service BI tools that automates the most complex parts of data analysis is increasing the number and variety of tasks business users can perform by themselves. Becoming a Citizen Data Scientist can be as easy as the BI tool you have in hands.

3 — IoT

With IoT’s we can’t avoid the feeling that we are on the edge of achieving a breakthrough but something is holding back. The number and variety of IoT devices keeps growing: wearables, connected homes and factories, drones, autonomous cars and smart cities. According to Gartner there will be six billion connected things requesting data support by 2018.

Why was IoT important for BI this year?

Dresner Advisory Services’ 2016 The Internet of Things and Business Intelligence Market Study, published in October, states that for organizations, IoT is a core justification for investing in and implementing big data analytics and architectures. Even sales are ranking IoT highly, indicating that companies are attempting to launch business models and derive revenue from IoT. This is an important change on how companies perceive IoT, from something distant to something that can boost its businesses.

For BI tools, this means they must be able to deal with even greater amounts of data, preferably in real time and by presenting results, even of advanced analytics with powerful visualisations.

4 — Bots

Chatbots for Business Intelligence is the newest attempt to make these tools more user friendly. Chatbots mimic conversation with people using AI. The reason why chatbots are so popular now is because consumers now prefer to communicate through messaging — people spend more time in messaging apps than in social media.

When it comes to apply chatbots to Business Intelligence, I would say the idea is still in an embrionary phase, but it’s worth to mention.

However, a major challenge immediately cross my mind: how do chatbots deal with ambiguity? For instance, when you ask for “Results by city”, how will the bot know if you are referring to the city of the account, the sales rep or the billing address? It will be interesting to see the next developments in this field.

5 — Natural Language/Search-based BI

While traditional and even agile BI tools have limited usage to power users and IT teams, Natural Language BI is business user-led. Users just need to type everyday language queries in a search box to analyse their data and get automatic visualisations.

In 2016, we saw the rise of the first four players on this field: Wizdee, Power BI, Thoughtspot and Google. With Gartner defending that Natural Language Search is a must-have for a Modern BI we’ll see more players adding this type of capabilities. We can say that finally self-service analytics will evolve from being just self-service for power users to be for the masses.

Most of these concepts and trends will continue to grow in 2017, till they become the new norm.

Do you agree with our picks?

What else would you add to this list?


Originally published at wizdee.com on December 6, 2016.

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