Embedded analytics is the future of analytics

Digital transformations have taken over corporate America. Over the last few years, businesses of all sizes have discovered much of their success now relies upon the ability to quickly interpret incredible amounts of data. While the business intelligence space grows exponentially, traditional BI tools still struggle to keep pace with the need for quick, decisive interpretation of this information surge.

Embedded analytics has inserted itself into this dilemma as the nimble, robust solution.

Interpreting the Data

Modern tools available to businesses make them adept at gathering intelligence about customers and prospects. Yet, organizations are challenged to dissect the intelligence, manage and disseminate it to leverage this knowledge into comparable levels of growth. They question: "what is all this intelligence telling us?"

It’s a common plea among businesses and their staff who are burdened with rigid software solutions yet are not IT professionals.

While traditional BI software boasts the strength of being data-rich, its major drawback is that it’s wholly beneficial only to the most technologically savvy users. Those with technical degrees ultimately get comfortable using traditional BI data solutions. At the same time, the novice, advanced and even experienced analysts struggle to develop the sophistication needed to navigate a data software platform as designed.

Even if a user-analyst develops into a master of the technology, there are limitations inherent to its design that will ultimately restrict business output. The obstacles are numerous and can include:

• Users must leave a workflow to obtain data
• The tool is too complex for ordinary day-to-day operations
• Rigid data modeling structures
• Long implementation time
• Low value for general users incapable of producing insightful data
• Limited return-on-investment due to expense

The architectural design of these traditional tools makes them difficult to integrate or scale for the future, too. Their one-size-fits-all features deliver on the original goals set by their developers. Still, they leave little room for the end-users to create customizable functions that suit individual business goals. And users’ business goals are fully expected to fluctuate over time.

Staying on Task

Without embedded analytics, collected data often isn’t presented in ways that are relevant for prompt decision-making and doesn’t mature with business needs. Conversely, embedded analytics makes an immediate impact by integrating data directly into the workflow. It accelerates decision making by drawing upon information accessed directly from dashboards, reports and previous history.

Using traditional BI to generate a report or pivot table can be beneficial, but embedded analytics moves well past such borders. Beyond its attribute allowing users to remain within their workflow, other benefits include:

• Flexibility in data modeling
• Shorter implementation cycles that result in reduced costs
• Analytical functionality created and controlled by users
• Enhanced user experience
• Users remain in the application

Additionally, if businesses adopt embedded analytics into their data toolset, they unleash user-analysts’ potential to obtain up-to-the-moment intelligence, resolve business problems and better predict future outcomes.

With embedded analytics, users can create specific user profiles that reflect their roles and responsibilities within the organization. User-analysts can readily qualify and quantify business values to the data through these profiles, thereby enhancing problem resolution.

Alternatively, establishing personas that access data fitting individual users’ general needs can suffice and ensure that data is kept secure outside their need-to-know realm. Embedded analytics can also match functionality with users’ needs and experience, allowing them to work smarter. Features grow to match user talents, roles and queries relating to their responsibilities. And with embedded analytics, businesses can choose the depth of data integration obtainable to each user or persona.

Organizations that adopt these functional characteristics to their data analysis are better suited to developing project requirements and prioritizing project phases. The self-service and personalization built into embedded data tools allow users to increase their analysis proficiency. And as their talent level grows, more meaningful intelligence is within their grasp, making for quicker and more informed, forward-thinking business decisions.

Evolving Needs

Many businesses commit themselves to traditional BI solutions then, later, are forced to adapt how they draw upon the intelligence when the software proves deficient to their needs. This happens across the enterprise, between teams or case-by-case among individual users. Regardless of scope, the BI solution forces people to change how they work with the data rather than improve how they get and use the data for decision-making.

On the other hand, fully embedded analytics retains its luster by continually evolving to serve users in their workspace, making them data-savvy and decision-driven. As businesses become even more data influenced, neither analysts nor the organization will need advanced degrees to discover what its intelligence can reveal.

Analytics are becoming infused in all software applications. Just as consumers demand seamless service in interactions with businesses, organizations are demanding more of themselves, and in B2B interaction, to deliver timely decisions from the intelligence they compile.

Embedded analytics addresses demands for self-service. Its aid extends well beyond tech-savvy analysts to include non-technical users who need access to intelligence within their business applications. This support brings user experience to the forefront, putting analysis and decision-making into more hands than just a few power users. As businesses set aside many long-standing traditions for newer, more agile operations, embedded analytics will lead the charge of transformation.

Image credit: Olivier26/depositphotos.com

Charles Caldwell is vice president of product management at Logi Analytics, with more than 20 years of experience in the analytics market, including a decade of direct customer implementation guidance. He writes and speaks extensively on analytics, emphasizing in-app embedding, optimizing user experience and utilizing modern data sources. Caldwell earned his MBA from George Washington University. On LinkedIn and Twitter.