7 BI embedded analytics trends to construct your 2021 data strategy

According to Gartner, embedded BI analytics provide you insights within your natural workflow, without the need to toggle to another application. Embedded systems are also narrowly deployed around a specific process such as to optimize a marketing campaign, or to generate leads through a sales cycle or to manage an inventory cycle, or even for financial budgeting (source).

To use the power of business intelligence your enterprise doesn’t entirely have to build a BI solution from scratch (although, there is no harm in it if it is a part of your overall business transformation strategy). There are many BI embedded analytics tools that you can incorporate within your existing IT infrastructure. Knowledge of tools
through any Power BI Certification can make your work much easier. There are many enterprises looking for a quick integration across their existing ecosystem so that their operations are not interrupted.

Hence, major business intelligence software development services and consulting companies provide both turnkey software development solutions as well as strategic enterprise integration services.

What are embedded BI analytics systems?

Embedded business intelligence analytics systems mean integrated BI solutions within your existing processes, applications and portals. They give interactive dashboards, reporting capabilities, data analytics, visualization features and predictive analytics within your existing systems. These are not standalone applications. They become a part and parcel of your existing IT environment, augmenting its capabilities from within.

There are many advantages of using embedded BI systems rather than using standalone applications including

• Easy access to relevant data.
• Higher user adoption.
• Greater relevance.
• Value addition.
• Reduced IT costs.
• Better reporting with relevant contexts.

In standalone BI systems, the data exists in silos. In embedded solutions, users can create intricate reports combining data from multiple data streams to fit their exact needs. This way, business intelligence can be made a part of regular decision-making rather than an isolated process. Such embedded systems can also be made a part of your general workflow. They are like add-ons that extend the capabilities of your existing systems.

Of course, every emerging technology has its own specific trends. The field of business intelligence software solutions is no exception. Exploring the possibilities of embedded BI analytics solutions for your enterprise? What trends must you keep in mind for 2021? Let us examine some options.

1. Greater expectation for self-serving embedded BI components

Nobody likes bloated software. There is a constant shift towards minimalistic software applications whether it is Google products or the latest design changes in Windows 365 Office Suite. You must have observed that the features that you use often begin to appear on the toolbars, and the features that you don’t use appear less or completely vanish.

Users expect to use embedded BI analytics system tools based on their needs. They want to be able to pick and choose. The same is going to be the case with your employees and your customers. Hence, if you’re going for an embedded BI system for your upcoming data strategy, make sure that the end users are not bombarded by an array of BI features and they get to use only those features that they need.

2. Mobile first approach to embedded BI analytics systems

When you think of business intelligence systems, often the images of intimidating mainframe servers with blinking lights and the ominous buzzing sound, come to your mind. But technology these days has enabled one of the most advanced applications to seamlessly function on mobile phones and tablets, and this is what your end users or your employees expect, in most of the cases, by default. 

From buying diapers for their babies to buying complete security systems and IoT-connected appliances, they are buying everything from their mobile phones. They want a BI-enhanced experience on their mobile phones. Your executives want complex decisions on their fingertips. They want data visualization that seamlessly fits on their mobile screens. When they are making presentations, they should be able to generate forecasts and projections on the fly, using their phones and tablets. Hence, a mobile-first experience must be at the top of your priorities for your data strategy in 2021.

3. Cloud-native BI analytics systems

Data and the software processes that drive intelligence out of the data, both must exist in the cloud. The increasing trend in 2021 is towards creating cloud-native BI solutions that are accessible through APIs or direct calls by mobile and web applications.

Most of the enterprise-level businesses are already using SaaS-architectures. They are fast moving away from software applications that are installed on local machines. Hence, for your upcoming data strategy, you need to keep in mind that all your embedded BI analytics solutions must be cloud-native.

There are multiple benefits. Existing and new software applications and mobile apps can easily access the BI features remotely, on the go. Enterprise-wide implementations can be carried out without the worries of redundancy. Development costs can be lowered considerably. New features can be adopted almost immediately. Existing cloud properties can be leveraged instantly.

4. APIs are the name of the game

APIs keep your data and software separate. They are also secure. Current applications can be used instead of building everything from scratch. APIs allow applications to connect with each other and exchange critical information without disturbing each others’ integrity. Once the connection is made, your existing mobile apps and web applications can issue BI commands to get the needed results.

Businesses and technology companies have been using APIs for decades now, but with the advent of cloud-native applications and embedded BI systems, they are more widely being used these days for greater security and faster data processing and analysis.

5. Collaborative business intelligence systems

Every BI system, whether it is embedded or stand alone, derives its main strength from its ability to communicate with data originating from different stakeholders. Managers and workers have different needs than customers and clients, but the insights obtained from these data needs may solve a common purpose. Hence, various processes must be able to interact with each other without digital barriers, but at the same time, maintaining the integrity of individual processes.

Collaborative BI is an emerging trend. It is a combination of tools such as social media, chat bots, email, and instant messaging tools. It uses natural language to analyze interactions and then make predictions and forecasts.

6. Hyper- automation in embedded BI analytics systems

Hyper- automation means automating low-level digital tasks that are repetitive but are performed with great frequency and predictability. An example could be using advanced OCR to capture data on paper documents, automatically, without human intervention.

This way, instead of spending time on tasks that can be performed by the machine, employees can focus on high-level tasks such as decision making, interpretation, structural analysis and business growth. Hyper- automation is one of the biggest advantages of business intelligence systems, especially when it comes to generating big data for analytics purposes.

The good thing about embedded BI analytics systems that come with hyper- automation is that they can sit atop your existing platforms and at the same time provide startling results. The features may include business process management, integration services and insight engines.

7. Artificial intelligence, machine learning and natural language processing

Frankly, very few business intelligence solutions these days come without AI, ML and NLP, nonetheless, there is a chance of overlooking them when talking to a BI development provider.

Up till now, computers have been incapable of interpreting intelligence contextually and heuristically. What has so far separated human brain from computers is the ability of the human brain to use past experiences to make current decisions and predict future events.

In computers, this drawback is fast being solved by machine learning and natural language processing. 

Computers these days can learn from the past feedback they obtain from the users as well as the computations that they make themselves. They can also chart out highly complex behavioural patterns to processing natural nuances in the language people use when interacting with each other. 

Hence, these capabilities are at the core of every embedded BI analytics system.