By Charles Caldwell, VP of product management, Logi Analytics

Organizations are collecting and processing data at a rate unlike we've ever seen. With so much access to collected data compiled from the user journey, the data industry has largely failed to transform it into meaningful analytics that the workforce can utilize. Right now, polished analytics tend to be provided through tools that generally cannot be accessed easily through the operations workflow. To allow for more strategic, data-driven decisions within an organization at large, all employees need access to dynamic analytics within their general workflow.

To accomplish this, organizations should first determine what kind of data is meaningful to them and can be utilized to support sales and marketing efforts. When collected, it's essential to ask yourself: What data is relevant? How can this data be presented clearly? Will users interact with this information, and how? These are questions that can be addressed by using products that assist customers while enjoying a sleek user experience complete with positive interactions with their data. To resolve these concerns, users look to business intelligence (BI) tools that collect and organize data, showing helpful information that can assist in making effective business decisions.

Even though traditional BI tools and embedded analytics are different aspects of BI, they offer varying features and specs to users. In understanding how both of these implementations of BI are used in business operations, we must break down each approach and its essential purposes.

Traditional BI - Useful or Outdated?

Traditional business intelligence, or traditional BI, creates top-down reporting and data analysis based on the business systems and organization. While it has supported large-scale deployments of several thousands of users, the top-down data method requires IT to closely monitor heavily-governed data warehouses.

The BI industry is undergoing a massive transformation. Business teams are constantly searching for strategies to collect precise data and information to be used in decision making. Traditional BI struggles to meet these needs due to inefficiencies. With costs rising quickly, lower efficiency and failure to provide relevant data that customers require, traditional BI has become somewhat stagnant.

Some additional issues are surrounding traditional BI; namely, it has become an outdated approach. It's generally too complex for the everyday user and often does not deliver the benchmark ROI due to continual costs and upfront investments. Traditional BI forces users to leave their current workflow and navigate an overly complex full-stack and largely inflexible modeling structure.

An existing traditional BI consumer might be dissatisfied with a system that primarily relies on past results and historical data to achieve an analysis. Comparatively, embedded analytics are specifically geared towards business users without the IT expertise needed to quickly make decisions based on a wide variety of data samples.

Traditional BI tools are not designed to be embedded. Nor were they  built for architecture, user design, security and additional features to be scalable or integrated easily in the future. This rigid approach does not leave much room for customization or self-service functionality for most business users.

The absence of embedded analytics within traditional BI means that collected data is not presented in an actionable method for decision-makers. When comparing traditional BI and embedded analytics, the latter supports and plugs integration directly into the user workflow while improving user engagement and creating a more well-rounded experience.

Embedded Analytics - Creating a Modern Solution

Embedded analytics is an enhanced method of using collected data within an application. It's capable of displaying necessary data dashboards in an application where needed. Using information from dashboards, previous data history and reports within a streamlined workflow makes for a more comprehensive decision-making process.

To easily distinguish between BI and embedded analytics, users should leverage the term ‘business intelligence' when producing a dashboard, report or pivot tables for an analyst or executives. Use ‘analytics' when moving beyond base-level BI functionalities and implement information and data to help customers complete tasks efficiently.

Embedded analytics require specified solutions based upon use cases rather than retrofitting an older BI tool to complete tasks and support embedded functions. Frequently collected application data can be gathered and presented nearly automatically, ensuring that data workflows guide employees.

Unlike its outdated alternative, embedded analytics alleviates the need for heavy IT interactions, which can frustrate users and lead to low production of timely relevant reports. The minimization of costs means that small to medium-sized organizations will cut down on upfront costs and open up the capability to train employees on how to use the analytics platform. Embedded analytics provides many other opportunities for organizations to excel. Built with flexibility at its forefront, data modeling does not have to be unyielding with embedded analytics. Unlike traditional BI, it lets users remain within the application and adds functionality to existing workflows. Lastly, using embedded analytics on an ongoing basis heavily reduces long-term costs and shortens implementation cycles to maximize ROI.

Time after time, traditional BI has failed its adopters. However, the appeal of fully comprehensive embedded analytics will continue to draw more users in, making them more data literate and creating a data-forward business. Embedded analytics is proven to be highly reliable for delivering substantial advantages to organizations and software vendors.

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ABOUT THE AUTHOR

Charles Caldwell is the vice president of product management at Logi Analytics, which empowers the world's software teams with the most intuitive, developer-grade embedded analytics solutions. He has more than 20 years of experience in the analytics market, including more than 10 years of direct customer implementation experience. Charles writes and speaks extensively on analytics with an emphasis on in-app embedding, optimizing user experience, and using modern data sources.