Contextual analytics or embedding analytics directly into a business application’s transactional environment and core workflows, is the most sophisticated stage of business intelligence (BI) available to organizations today. It is a step ahead of the traditional integration of analytics- a standalone capability presented as a separate dashboard or reporting module.
Today, there is an increasing shift among organizations toward embedding transactional analytics into their enterprise applications. Since contextual analytics is the most advanced option in the market, there is also an increasing need for assessing an organisation’s analytics readiness first.
The fundamental difference of contextual analytics lies in how it offers analytics to the user to explore data or derive insight within their transactional environment rather than accessed as an external tool. This approach requires a deeper evaluation of the product’s analytical maturity level using a formal framework. Having such a framework can act as a deployment guide for your developers at every stage of embedded BI implementation, including contextual analytics.
But first, let’s delve into what makes contextual analytics so different.
Contextual Analytics Vs. Embedded Analytics
With contextual analytics, dashboards, reports and charts are no longer separated but are part of the main user experience. This integral change requires a closer look at your data and integration capabilities to align what it brings with all aspects of your software, including existing analytics.
Embedding these elements directly into core software workflows isn’t a replacement for any existing analytics, instead it is another big step toward making data and analytics a more seamless part of your end users’ day to day work. Do note that contextual analytics accommodates various levels of implementation and business requirements. Analytics can be embedded lightly - in the form of basic in-line charts and graphs users can see as they explore your software, or embedded deeper to offer AI-assisted insights and real-time alerts that trigger action from users while they work. It can be integrated with an experienced BI vendor partner or in-house if you possess the skill set internally.
Avoidable Pitfalls of an Improvised Approach
The need to assess your application’s analytical maturity and readiness for embedding analytics directly into the transactional workflow comes down to avoiding unnecessary BI roadblocks. For many organizations, BI and analytical capabilities are traditionally lower on the priority list when compared with other aspects of the software. Intensions to add BI features later are well-meaning and understandable, but moves the onus of these addition to developers leading to issues of visibility. Vendors typically lose sight of what their application’s current analytical capability, or what development needs to make the addition of a new feature not only technically achievable but also timely and cost-effective.
In a nutshell, implementing a basic dashboard module or data export feature and completely separate product features, is fundamentally different from fully embedded analytics in your users’ core workflow experience.
The Five Stages of Embedded Analytics Maturity
Based on our experience, there are five possible stages of product development, as illustrated in the embedded analytics maturity curve below.
Accelerating the implementation of contextual analytics comes down to your skillset, which will allow you to identify and evaluate your state of readiness accurately and determine a BI solution’s flexibility. If you do not have the internal expertise, implementing contextual analytics is easier with the help of specialized BI vendors that can help with assessment, and offer you a contextual analytics platform built around openness. We recommend you look for vendors and partners that allow for:
- Advanced APIs that lower total integration time and ensure that the need to rearchitect the software is minimal to zero.
- Customized experiences to make your desired contextual linkages, such as showing a user a relevant embedded chart when they click on a table of a client is possible.
- An open platform that allows for options for on-premises, cloud, multiple operating systems that guarantee long-term security, scalability and flexibility with your analytics.
- Rapid adaptation to your product development roadmaps with automated processes and administrative tools to lower total cost of ownership.
- White-labelling to make the rebranding and blending of the analytical solution and your native application components seamless.
Ultimately, having an awareness of your product’s readiness and taking the time to examine your application’s current state means you could potentially avoid costly integration mistakes, save time and ensure developers implement the best solution according to your exact needs now.