9 Embedded Analytics Trends for 2019

Introduction

2019 promises to be another exciting year for analytics and BI technology as the fast pace of innovation persists into a new year. Spearheading this innovation is the increased adoption of embedded analytics, which is changing the way we gather, manage and analyze data. Even more significant, embedded analytics changes the way we learn from and interpret data, as well as how we act and react to what we learn.

In 2019, embedded
solutions will go beyond
the capabilities of
conventional analytics.

We have entered a technological phase in which intelligent machines and powerful analytics can go beyond analysis and visualization into action. They can teach and advise users on what decisions to take. And they can achieve this in ways that are suited to a wider, non-technical, business audience, empowering organizations to make faster, smarter, data-driven decisions without inflated budgets. We can expect embedded analytics to grow and become the norm throughout this year and with it, new approaches
to data management, new benefits, and new challenges.

Chapter 1 Trends for Embedded Analytics 2019

Bringing Analytics Everywhere Means Embedding It

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 nearly all the software we use. We’ll be able to analyze the huge volume of data produced by our digital worlds and the software we use and package 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 in all of
our workplaces: products,
processes, and places.

As an extension of this, companies will increasingly seek out vendors who offer them “white label” platforms, so they can allow their own customers to build dashboards in workspaces so customized that you can’t tell who designed the engine and who’s powering it.

Chapter 2 Trends for Embedded Analytics 2019

Analytics Becomes Increasingly Actionable

Embedded analytics will shift from a purely advisory role to an actionable role. We’re approaching a future where dashboards and standalone analytics will be part of operational processes. Innovations such as Sisense BloX will allow users to learn valuable lessons from analytics and carry out new functions straight from their dashboards.

It will be easier than ever to create customized, actionable analytic dashboards by accessing libraries of templates to quickly create functions and visualizations, connect with apps, and integrate application functionality directly into your dashboards.

Embedded analytics
will expedite the
process of turning
insights into action—
faster, more effectively,
and more responsively
than ever before.

Embedded analytics will drive this trend, enabling and empowering non-technical users to take direct action from insights with less or no need for IT or development specialists to intervene.

The simplicity of function and implementation will accelerate business processes and make organizations increasingly responsive.

Chapter 3 Trends for Embedded Analytics 2019

Augmented Business Intelligence Will Enhance Decision-Making

Organizations’ use of artificial intelligence is already improving many operations and enhancing users’ product experience. AI algorithms can find, visualize, and present relevant data to drive greater insights. When AI and machine learning technology is applied to business intelligence, and distributed via embedded analytics, the power of data insight and visualization tools evolves into augmented intelligence and ultimately actionable analytics.

Augmented intelligence
is the evolution of AI
that enables embedded
analytics to help real
time decision making.

What does this mean? Technology is now available that not only analyzes and presents data but can also learn over time from the data that it’s gathering. It can provide unprompted but relevant updates itself that can help inform decisions. With embedded analytics, the time between analysis, the creation of dashboards, and action will get smaller, because users will be able to act directly on findings gathered in data and presented in dashboards. Business intelligence will become a more proactive process than ever, decision-making and converting insight to action will accelerate, thereby expediting outcomes that meet an organization’s objectives.

Chapter 4 Trends for Embedded Analytics 2019

Dashboards Will Become Data Applications

Embedded analytics won’t just be about providing insights. Now you’ll have a way to act on those insights directly from your dashboard without leaving the application.

There will be an increasing move towards embedding, customization, and integration with other business apps and software. Dashboards will increasingly have the capability to connect to applications so that users can act almost immediately on insights presented in the dashboards. No longer will business users have to exit a business application to view results, analyze performance, and view recommended actions. They’ll be able to do this all from one place, creating a seamless and efficient experience.

Embedded analytics
will push the
capabilities of your
BI dashboards to new
directions in 2019,
as actions will be
implemented directly
from dashboards.

In short, you’ll be able 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 click a button within the dashboard to create and launch a new location-specific marketing campaign to boost sales in that area.

Chapter 5 Trends for Embedded Analytics 2019

Analytics at the Edge Will Improve Performance

The IoT’s escalating number of connected devices has put increasing pressure on networking bandwidth, as more and more data travels back and forth over communication networks between the cloud and data-producing devices. This can slow down analytics and visualization capabilities.

The solution to this problem is to decentralize the analysis and processing of data away from the cloud so that analytics is performed much closer to where the data originates (e.g. sensors, motors, healthcare devices, wearables, and smartphones). Embedding analytics in these locations, at the far reaches of a network (what’s known as the “edge”), cuts down the need to transfer data between the cloud and devices and improves processing speed and reliability. Analytics embedded at the edge has the potential to change the way data is consumed, as content can be pushed out to where people want and need to consume their analytics, instead of logging into a dashboard.

Analytics will go to the
very edge to maximize
effectiveness and
benefits, bringing AI
analytics to very small devices
with very limited
processing power.

As embedded analytics has become more prevalent, it’s logical that 2019 will see more data analytics capabilities migrate to edge computing, meeting the challenges of processing petabytes of data for real-time insights. Although the technology is relatively nascent, many varied industry sectors have already adopted this approach. More will inevitably follow.

Chapter 6 Trends for Embedded Analytics 2019

Hyper-Personalization

As BI and analytics can be embedded at the edge, the capability grows for more specific data analysis, which results in more individualized, personalized results. We will begin to see more hyper-personalization, or hyper-targeting of everything as information on the individual use of devices and consumption of specific data can be gathered and analyzed.

This trend will be particularly conspicuous in a smart household, automotive and healthcare devices, to give three big examples. We can expect car manufacturers to enhance their safety technology by embedding analytics within their vehicles that gather data on their drivers so that vehicles can respond to their drivers’ habits to minimize errors. And in the development of autonomous vehicles, this technology can be applied to sensors that see beyond the line of sight and to virtual road-testing.

Data can be gathered
and shared at an individual
level to offer tailor-made,
hyper-personalized
insights for each
customer or user.

Healthcare devices can collect and relay data to be shared with loved ones and care providers, enabling more individualized monitoring, diagnostics, and treatment. Within the home, sensors in lights, floors, and other fixtures and fittings even have the potential to gather data passively (such as weight, blood flow, blood pressure, etc.) to detect physiological or behavioral changes that signal problems or issues.

Chapter 7 Trends for Embedded Analytics 2019

A Shift to Hybrid Analytics

More edge analytics typifies a wider shift towards an increasing multiplicity of data sources. Users will be empowered to work with both live and cached sources for analysis, on both current and historical data. Increasingly, we’ll see a turn to incorporating live connectors to analytical databases (such as Redshift, Snowflake, Google BigQuery) in the process, in order to combine this data into complex and complete data models within the same dashboard.

Embedded analytics
shared with customers or
colleagues will increasingly
include data sources from
cached and live data in a
hybrid manner.
In 2019, both data types will
be brought together, analyzed
and presented in one place.

Technological evolutions like these will mean you can embrace lightening-speed, live connection access to data in any analytical database, making ad hoc and complex queries easier and more accurate than ever. This will fuel the trends of hyper-personalization and edge analytics.

Chapter 8 Trends for Embedded Analytics 2019

Enhanced Accessibility, Accelerated Decision-Making and the Rise of Self-Service Analytics

Analytics offers more value when embedded, as it provides deeper contextual data that creates more thorough insight. Previously, analytics required specialists to use analytics applications and visualization tools, and then provide data and reports to their internal customers; a cumbersome process. Embedded analytics removes this necessity. It is becoming an integral part of enterprise solutions, broadening the scope, the accessibility and therefore the value of the data it generates, and accelerating decision-making that arises from this data. It achieves this faster than traditional solutions by automating processes and it boosts the capabilities of business users by offering enhanced functionality and ease of use.

Embedded analytics
empowers users to explore
more data themselves,
increasing the
value of the products and
services they use by
extracting data from them.

This means that analytics is becoming more usable and comprehensible to business users, removing the burden on IT to crunch the numbers, conduct analysis, and produce reports. Consequently, users are increasingly empowered to explore data themselves without help from technical support. The result: more self-service analytics, leading to more insights; and with embedded analytics, more dashboards, and reports evolving from the edge in response to customer behavior and needs.

Chapter 9 Trends for Embedded Analytics 2019

Usage Analytics in the Subscription Economy

Embedded analytics will increasingly contribute to usage analytics employed to laser-target actions. This will gain more traction and importance in the growing area of subscription business that has seen the likes of Unilever acquire businesses like the Dollar Shave Club for $1 billion in recent years.

Turning customers into subscribers requires businesses to have a much greater appreciation of customers’ needs, interests, lifestyle and engagement with trends. This most effectively requires real-time data and accurate analytics. And when your business relies on this data-driven dialogue with subscribers, you need to be sure that your analytics is genuinely adding value, both in terms of what you learn about your subscribers, and to ensure that your business is using analytics most effectively.

Embedded analytics will
feed usage analytics with
enough data to understand
customer behavior in a
subscription economy.

So, it’s important to know that the data you’re gathering from your products and processes, and the dashboards with which you’re visualizing them, are being widely used by your customers and those within your organization to help their decision-making and increase their satisfaction. Then you can be sure that these tools are effective and worth the investment. Embedded analytics is an engine for usage analytics that gives you a clear picture of engagement with your products or services.

Summary: Sisense Embedded Trends for 2019

Summary

Staying on top of these developments means collaborating with an analytics 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 them 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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