Wikipedia defines Embedded Analytics as “the integration of analytic content and capabilities within applications, such as business process applications or portals.” That’s a pretty straightforward definition, from a techie point of view. Stripping back the definition even further, we’re left with the definition of embedded: “to fix (an object) firmly and deeply in a surrounding mass”; and analytics: “the systematic computational analysis of data/statistics.” Therefore, think of embedded analytics as meaning to work with and integrate software that collects, condenses, and displays data for your business and marketing outlooks.
Data analytics has long been a part of many industries, from finance to technology, and many other businesses as well. These organizations are constantly collecting and analyzing data to improve any number of services (including customer satisfaction, product management, tailored marketing strategies, etc.). Over time, the means of collecting this data have grown, in both quantity and sophistication; and technology has had a big impact on that growth.
More and more companies are investing in technology that helps them gather and analyze data. After all, the more data they have, the more informed all their decisions can be. Handling that much data can be tricky, though; some companies have multiple sources that are always tracking, adding, and updating their numbers. As a decision-maker, it’s tough trying to manage these sources and make sense of the data spread in front of you. How do you know which numbers are most relevant to the choices you need to make at any given moment? That’s where embedded analytics and statistics for big data swoop in.
Embedded analytics and statistics for big data allows a business, or user, to customize and congregate the data they need into one location. No longer is there the need to shift and search through vast quantities of data; embedded analytics performs and displays this analysis right into the software application of a company’s choosing. And that—all the information one needs to make the best-informed decisions, in one place—can lead a company or business to greater efficiency and success.
Working Smarter, Not Harder
The one thing to remember about embedded analytics is that it incorporates your company’s data in real time. That’s kind of its defining feature. Whereas other models of business intelligence act as fixed layouts to influence policies and decision makers, embedded analytics allows fluid guidance for companies and individuals by streaming data—which might be better for a continuously updating business climate.
If you are looking for a new way to efficiently deal with previously analyzed data, or if your company is entirely new to the embedded analytics concept, know that you are making a move toward two core best business practices:
1. Higher customer satisfaction: In the digital era, users and customers are accustomed to technology making their lives easier. Embedding analytics adds a greater (and, these days, often expected) convenience towards quickly accessing the right information.
2. Better use of company resources: When the world has this much technology on-hand, companies and businesses expect their workflows to be faster, cheaper, and better. They want to save time and money, two of the most important resources in any industry; and embedding analytics can certainly help in these ongoing endeavors.
Choosing Embedded Analytics
One might be looking to use embedded analytics for their company if you work with large quantities of data that you would like to consolidate for in-house use, or feature publicly as a marketing strategy (think of companies that use embedded analytics to track and display product reviews and ratings). Maybe that data is fixed, meaning that it’s a finalized set of numbers; or maybe that data is dynamic, meaning your company would benefit more from streaming data as new information arrives.
Additional decisions regarding the scope of embedding analytics include:
- Should your business be building these analytical tools in-house, or outsourcing? Going to an outside company for these services, who may be a more experienced in blending and visualizing complex data, might save you time and money. By keeping the development process in-house, though, your business may have better control and customization of the platform you wish to use.
- How is your business using its resources, in terms of time and money; and will these resources be more efficiently used by adopting embedded analytics? This is important to note, because developing and using this technology isn’t cheap. Also, the amount of time taken to build, train, and start the system can be substantial (which can also lead to higher costs).
- And, finally, will this investment be worth your time in the long run? The ways that technology is used, and technology itself, are constantly being updated. From software patches to full hardware overhauls, bringing embedded analytics into your company may end up being a short-lived boon. It’s on companies to properly evaluate their business model, and the amounts and types of data that they sort through. A year from now, will the cost-benefit analysis of buying into embedded analytics have a positive reflection on your company, your investors, and your customers and users?
Succeeding with Embedded Analytics
In making the greatest success out of one’s implementation of embedded analytics, it is important to focus on the user. In business, keeping your customers as a top priority will bring greater profits, recognition, and overall success. When it comes to employing embedded analytics, though, providing the best user interface becomes about more than mere aesthetics and convenience.
With the customization allowed in embedded analytics and statistics for big data, it’s important to research and focus on the roles and responsibilities of the current and potential users involved. Once you have identified the users, you should also identify the specific set of problems that embedding analytics would solve. Phrased another way, how simple or complex of an interaction are your users expecting with the company’s data (or, on the simpler end, perhaps they interface with the data in a fixed, pre-prepared way).
It’s encouraged that the individual(s) investigating these queries take the time to answer them thoroughly; after all, in choosing to go with embedded analytics, you are making a significant investment in the amount of time and financial resources being put towards elevating your business and users’ experience. To be successful with embedded analytics, you need to know who your audience is, and how your users can best interact/interface with the data being presented.
Even after you’ve identified and matched these areas, know that this information will continue to update and change as time goes on. Staying ahead of this influx of newer users, the learning curve pre-existing users will experience, and the continuously improving technology environment we live in, will keep embedded analytics benefitting you.
