This is part of Solutions Review’s Premium Content Series, a collection of reviews written by industry experts in maturing software categories. In this submission, Casey McGuigan, Product Manager at Infragistics, provides integrated analytical definition and context, and ways to refine your data-driven decision making.
If you’re involved in the business intelligence world, you’ve most likely noticed the increase in the use of embedded analytics over the past couple of years.
There is a key reason why integrated analytics is becoming increasingly important across all industries. Companies from manufacturing and finance to healthcare, retail and insurance are no longer willing to accept decisions based on intuition or intuition to guide less than optimal decisions. Integrated analytics systems allow organizations to embed analytics capabilities and visualize reports and dashboards into their existing applications to put real-time insights into the hands of their decision makers. The popularity of embedded analytics stems from its ability to refine the decision-making process based on hard facts.
Definition of embedded analytics: what exactly is embedded analytics and what can you use it for?
Integrated analytics systems provide real-time reporting, interactive data visualization, and/or advanced analytics, including machine learning, directly into an enterprise business application so business users can easily access data and use them. The main purpose of the built-in analytics is to provide up-to-date business information in the easiest way possible so that any user can understand it and act on it.
The capabilities and tools that come with an integrated analytics system can be used in different ways and for a variety of purposes. The two main uses of embedded analytics are to improve data-driven organizational decision-making and to complement existing software products.
The power of data
Built-in analytics brings the power of data to all your users so they can make faster, data-driven decisions in-app without switching context. Using a single application ultimately saves time, increases productivity, and provides a better scanning experience.
Another reason to consider embedded analytics is that these systems come with self-service capabilities. This means that regardless of users’ area of expertise, capabilities and technological skills, everyone can independently analyze data and create dashboards and reports.
And of course, one of the biggest benefits of embedded analytics is the fact that when you integrate analytics into your existing application, you increase revenue opportunities. When you increase customer satisfaction, app usage, and empower your customers to make faster, smarter decisions based on data, more sales will follow.
Using embedded analytics in the decision-making process
Various industries – including healthcare, finance and banking, retail, manufacturing, education, etc. – use the built-in analytics for internal and business purposes. For example, cybersecurity innovator Sensato reports that after integrating analytics into their system, they were able to speed up their visualization time-to-market by a factor of 10.
The company struggled to make sense of massive amounts of data and analyze it so security experts could develop more sophisticated protection strategies.
“Allowing a customer to view all of this information is critical to understanding what happened during the attack – or may still happen – and to enabling our customers to strengthen their cybersecurity emergency responses,” says John Gomez, CEO of Sensato. “Our engineers didn’t need to become visualization experts, we didn’t need to do all the testing of the visualization platform, and we didn’t need to think about its evolution or worry about its reliability.” Another company in the database productivity market, Atanasoft powers its two main products – DataGEM and AtanaSuite – with built-in analytics to put data into context and to maintain and share multiple versions of their dashboards.
“We wanted to add true visualization capability on top of the ability to create charts. We were looking to move from the comprehensive data retrieval and analysis capabilities of AtanaSuite and DataGEM to dynamic information presented in dashboards defined by the user,” said Jim Richardson, President of Atanasoft. “That’s what makes data analysis effective. Dashboards are in our products, so they’re visible; they’re there when where the customer needs the information, so they can get information and make decisions.”
Other well-known global companies that use embedded analytics to guide their decision-making include Netflix and Spotify, for example, which collect data on songs, movies, artists, and more. that their users listen to; This data helps shape their upselling and engagement strategies by determining relevant series/song recommendations that they know their users will love.
Today, the amount of data collected has never been greater or more complex. A data-driven organizational culture can help companies make smart decisions at the time of impact, which in turn can improve results. Data-driven decision-making leaves no room for experimentation, so risks are minimized.
When companies realize the true power of analytics and the full value of their data, everyone in the organization – whether you’re a sales manager, human resources specialist, product developer, designer, marketing manager or business analyst – is empowered to make smarter decisions with data, every day.