Introduction

It’s been another exciting year for analytics and BI technology—and it’s only going to get more interesting in 2019. Once again, AI and machine-learning underpin many of the leading developments, from the proliferation of embedded analytics and computers that imitate the human brain to dashboards that suggest which actions to take next.

Armed with this powerful technology, we continue moving towards a data landscape set up for non-scientists and non-technical users, empowering organizations to make faster, smarter, data-driven decisions without inflated budgets. Other intriguing trends on the horizon include a growing interest in “data for good”—the ethics and corporate culture of BI vendors.

Without further ado, here are our top 9 trends for 2019.

Immersive Analytics Will Be Everywhere

Embedded analytics have been gradually creeping into more and more of our tools for the past few years, but in 2019, we’ll see analytics incorporated into pretty much all the software we use. We’ll be able to analyze the swathes of data produced by our digital worlds and the software we use and packaging it into something we can make sense of—and act on—immediately.

2019 is the year when we’ll
see "insights embedded
everywhere" … not only in
graphs and dashboards, but all
of our workplaces: products,
processes, and places.

As an extension of this, BI users will increasingly seek out vendors who let them “white label” their platforms, by allowing their own customers to build dashboards in workspaces so customized you can’t tell who’s powering it.

What’s more, embedded analytics will move from a purely advisory role to an actionable role. We’re approaching a future where dashboards and standalone analytics will be part of the operational process. More on that in a moment.

BI Will Mimic the Human Brain

With cognitive computing, structured and unstructured data from various sources is synthesized, with the system weighing context and conflicting evidence to come up with the most appropriate answers. In doing so, it provides a way to grapple with results and answers that may be ambiguous, uncertain, open to interpretation or highly context-specific.

Cognitive computing will be
one of the big buzzwords
of 2019. Merging AI and
machine-learning algorithms,
this approach roughly mimics
the behavior of the human
brain—enriching analysis in
the process.

When it comes to BI, this development is giving rise to data cognitive engines, or cognitive layers, that seek to organize unstructured data from sensors and other sources in more intelligent and useful ways. In the coming years, we’ll see more and more organizations harnessing cognitive computing to tackle problems stemming from monolithic legacy data storage platforms and outdated data capture methodologies from their products.

Cognitive layers help to ingest and process vast quantities of complex data in lightweight cloud platforms, rearranging this into an environment that works for swift and effective BI. Users will embrace it to help them put in place more and more effective data science or analytics projects.

BI Customers Will Get Conscientious

It’s not just today’s millennials who are getting woke. Organizations of all sizes are increasingly concerned about factors like their vendor’s culture, ethics, and diversity. In fact, 71% already say this matters to them.

This is why 2019 should be the year that you shift focus from “what you do” to “who you are” as a company. For BI providers, that means thinking about “data for good”—how you use data, what for, and how you handle ethical issues relating to storage and dissemination.

The culture, ethics, and
diversity of the company
are becoming even more
important in assessing a
BI vendor today.

It’s also a good time to review your non-technical offering. What is the customer experience like? How is the quality and availability of your support?

Dashboards Will Become Data Applications

It’s no longer purely about giving you actionable insights; you’ll now have a way to act on those insights, too—without leaving the application.
The big themes here are embedding, customization, and integration with other business apps and software. We’ll see the line blur between dashboards that show insights and the applications used to act on them, creating a seamless and efficient experience.

The capabilities of
your BI dashboards
will be pushed to
new limits in 2019.

This allows you to activate an organizational process based on insights displayed in your dashboard. For example, a dashboard could show you that inventory levels are low and include a button that activates an inventory order process directly from the dashboard. Or, you could see that sales are down in a specific location and then click a button within the dashboard that lets you create and launch a new location-specific marketing campaign to boost sales in that area.

BI Will Keep Going Mobile ... But Slowly

I know what you’re thinking: I’ve heard this one before! And you’re right, of course—industry observers have predicted a grand mobile BI takeover for years, but the rollout has been more cautious than expected.

That said, it’s growing steadily (up from 16% in 2013 to 28% in 2017) and will likely keep creeping in as analytics and BI vendors improve their mobile products in 2019. Engaged employees will begin to take their dashboards with them and make decisions from anywhere, on the go, to achieve better results.

BI companies are now waking up to the fact that they need a mobile-specific strategy—it’s not as simple as just letting you access BI content on a mobile device.

Trends for 2019 include:
improved security measures,
content adapted to screen
size and type so that all mobile
devices are treated the same,
and BI vendors taking advantage
of HTML5 capabilities.

Hybrid apps that merge native and HTML5 elements will also come to prominence as vendors look to create sophisticated tools that incorporate the benefits of native apps (knowledge generation, interactivity, NLP/NLQ integration, local caching) with the benefits of HTML5, a centrally-managed web browser that needs little tailoring to different operating systems. Providers will start to share Rich Internet Application (RIA) content across all types of mobile devices without relying on proprietary technology standards or putting up with their limitations.

Edge Analytics Will Bring Data Closer to Its Source

This means analytics will be performed much closer to where the data is actually created (e.g. sensors, motors, pumps or generators, healthcare devices, wearables, and smartphones), and cuts down the need to transfer data back and forth between the cloud and the data producing device.

In the coming years,
cloud computing
will evolve into
Edge computing.

2019 will see new technologies bringing data analytics to edge computing in real-time, meeting the challenges of processing petabytes of data at lightning speed for real-time insights.

There Will Be a Shift to Hybrid Analytics

In other words, users will be empowered to work with live and cache sources for analysis, on both current and historical data. We’ll see an increasing turn towards incorporating live connectors to analytic databases (i.e., Redshift, Snowflake, Google BigQuery) in the process, in order to combine this data into complex and complete data models within the same dashboard.

BI will increasingly
adopt a hybrid model
of cached and live data
in 2019, with both data
types being brought
together and analyzed
in one place.

Technological evolutions like these will mean you can embrace lightning-speed, live connection access to data in any analytic database making ad-hoc and complex queries easier and more accurate than ever.

Natural Language Processing (NLP) & Natural Language Querying (NLQ) will help BI systems to understand the information they ingest and make it easier for users to analyze data, as well as ask questions to find what they need. This reduces time to insight and learning curve while boosting adoption.

Meanwhile, BI will talk back. Natural Language Generation (NLG) serves up insights in easier-to-understand ways, allowing nontechnical users to read and interpret complicated charts and graphs, and explain relationships between datasets.

Alexa integrations and chatbots will bring the two together by letting users ask questions in natural ways—and get real-speech answers that they can understand in response.

In 2019, we’ll also see the growth of AI-assisted analytics that acts as a helpful assistant, doing much of the heavy lifting for us and even suggesting others in your team to share insights with.

It will be another impressive
year in the growth of Natural
Language Processing (NLP),
Natural Language Generation
(NLG), and Natural Language
Querying (NLQ). These will
have a huge impact on the
development of BI.

As a result, data prep will become more intuitive and visual, supporting the needs and experience levels of these non-data scientists. Again, AI will play a key role in helping these citizen data scientists to make smarter decisions about which datasets to mashup, which charts to use, and so on.

2019 will be the year of the
“Citizen Data Scientist”:
employees who use BI and
Big Data tools to perform
analyses, but aren’t trained
data scientists.

This is great news not only for citizen data scientists but for the team as a whole. Implementing the right technology to facilitate this self-service, AI-guided approach will take the pressure off a company’s actual data analysts and data scientists, allowing them to focus on complex projects without being called on for every routine analysis.

Summary

Staying on top of these developments means working with a BI vendor with the insight and innovative nature to turn cool ideas into genuinely useful, profitable tools at lightning speed.

Here at Sisense, we continue to lead the way in identifying approaching trends and incorporating these into our ever-evolving BI technology. Our vision is both grand and simple: mashup everything, empower everyone, embed everywhere, and incorporate AI throughout. You can’t lead the pack in 2019 with anything less.

See how we’re helping our customers tackle the biggest data challenges of 2019:

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