Deploying and Scaling Embedded Analytics
Embedded analytics is an increasingly popular way for enterprises to incorporate data into more areas of their organizations. Garner defines embedded analytics as “a digital workplace capability where data analysis occurs within a user’s natural workflow, without the need to toggle to another application.”
Essentially, embedded analytics involves grafting a useful tool or application somewhere that makes it more seamless for people using it. There are a few reasons this can be advantageous. First, it’s a waste of time for employees to be constantly switching between different programs. Compiling everything they need in one place makes their work far more efficient.
Further, you don’t want everyone to have access to all data. Embedded analytics is a way to ensure users have direct access to data pertinent to their roles, but nothing more. This is crucial from a productivity standpoint; but moreover, it’s much more secure. Security needs to be a top priority for enterprises in today’s world of application vulnerability.
Now that you understand the importance and purpose of embedded analytics, let’s look at best practices for deploying and scaling it.
Look at the Needs of Your Enterprise
No two organizations are exactly alike, so you can’t just follow the lead of others when deploying and scaling embedded analytics. There are three temporal elements to consider when making all decisions: past, present, and future. These play a major role in determining your best course of action for embedded analytics incorporation.
Most enterprises have some kind of legacy analytics program. But is what’s been used in the past right for the present and future? Oftentimes, the answer is “no.” Businesses—and the tools available to them—are in a constant state of flux. That’s a big part of the reason why deployment and scalability are so important to embedded analytics: Organizations need to know what it’ll take to shift gears.
When it comes to looking at the present and the future, your company can’t afford to settle. The competition isn’t sleeping. Your organization needs to think long-term when planning and putting together a new analytics architecture.
Consider Architecture and Design
Some enterprises will want to play an active role in determining the specifics of their analytics design and architecture. Others will feel more comfortable adopting embedded analytics tools from a platform provider. When going with this second option, it’s critical to pick an analytics provider that offers flexibility, along with what you need to get the job done right.
ThoughtSpot is a unique cloud-based analytics company in that it uses artificial intelligence to improve usability. Embedded analytics is often going to be utilized by people without a deep data background. Keeping these employees on-track and productive without needing lots of assistance can be a game-changer for enterprises.
How Much Will You Use the Cloud?
There’s a seemingly endless debate over whether on-premise or cloud networks and applications are a better choice for enterprises today. While there are some valid reasons for both, cloud applications have some distinct advantages when it comes to deployment and scaling.
Cloud applications don’t require massive infrastructure investment in order to get online. Services are run through remote servers and monitored by experienced IT professionals. You also only pay for the services you’re using at the moment when you choose cloud applications. The cloud is a perfect medium for scaling services up and down. Enterprises looking for quick, affordable deployment with the option to seamlessly add or remove functionality should carefully consider cloud options.
Refining Analytics Features
Once things are up and running, your data team can start refining the embedded analytics programs in place. This involves a process of testing new applications and features with limited data pools.
Taking a gradual approach to refinement, enterprises can eventually end up with the exact analytics tools they need. Embedded analytics that fulfills their desired purpose can be a powerful tool for organizations, as this improves operating efficiency, while also providing essential information.