Purpose-Built for Embedding Self-Service BI Keeps Analytics in Workflow
Businesses rely on accurate and relevant insights based on the data they collect. End users need to gain that insight at the point of decision, but their companies often segregate the data by department or into data silos. Compounding the problem has been the widespread use of separate business intelligence and data analytics platforms across the organization. The decision-making process remains inefficient as users lack accurate, up-to-date and complete information. This fundamentally flawed process results in unfavorable outcomes.
But a software company can provide key insights at the point of decision if it integrates embedded analytics into its business application. End users get access to accurate, up-to-date information that’s appropriate for their roles directly inside their workflows. Embedded analytics enhances a company’s ability to recognize and respond to an emerging trend while empowering team members to take action based on the data analysis.
Embedded analytics provides software companies with extensive and even transformative benefits. The seamless incorporation of embedded analytics into an end user’s workflow opens the door for visual explorations and analyses, ultimately leading to the realization of new insights that may have otherwise gone undiscovered or unnoticed.
Benefits of Embedded Analytics and Self-Service Business Intelligence
Informed, data-driven decisions require enabling the end user by implementing self-service BI at the point of decision. Embedded analytics are designed to encourage accurate, informed, and data-driven decisions, providing a substantial benefit.
The competitive advantages of embedded analytics extend far beyond the ability to make instantaneous decisions based on the most relevant and updated corporate data available, and those additional benefits are worthy of exploration.
The seamless incorporation of embedded analytics enables end users to enjoy greater operational efficiency. An embedded analytics solution keeps end users from having to exit their workflow to create and use reports and dashboards to gain business insight. The ease of accessibility, therefore, encourages data-driven decisions while preserving or enhancing operational efficiency.
Software companies substantially benefit from the simple fact that any system viewed as disruptive to the end user’s workflow is less likely to be utilized and will, therefore, suffer from a poor user adoption rate. The inherent convenience and accessibility of embedded analytics thus provide mutually beneficial outcomes for the software company as well as the end user.
Embedded analytics increases the likelihood of trend recognition and enhances the speed with which a particular data insight leads to an appropriate action or response. Embedded analytics offers further benefits, including the ability to tailor the user’s dashboard by role to complement the end user’s normal workflow. This level of customization ensures the data is properly contextualized, which, along with visual data exploration, encourages the kind of ad hoc analyses that reveal new, valuable insights that the end user can then act upon.
Potential Challenges: Ensuring the Successful Incorporation of Embedded Analytics
Any operational change brings about challenges often caused by nothing more than a lack of familiarity. It is, therefore, critical for software companies to understand the specific needs of the end users of their product. During the setup and customization process, embedded analytics can be specifically tailored according to the needs and role of the end user, ensuring the seamless integration of embedded analytics within the end user’s regular workflow. If the end user’s workflow is for example, by having to navigate to a separate analytics UI, or by using one with a design unlike their application, the likelihood of adverse consequences increases, lowering adoption, engagement and renewal rates.
Businesses find that a forward-looking approach that considers the end user’s future needs essential. This should revolve around the incorporation of “right-sized analytics” that account for the current and future needs of various users and their business along with the potential issues that might arise from further empowerment.
To properly incorporate right-sized analytics, software companies must determine the ideal amount of self-service analytical capability integrated into the application.
Right-sized analytics ensure the user is not overwhelmed with data that is not relevant to their role or the workflow. To assure adoption by everyday users, report and dashboard designers cannot be overly complex
An initial launch of analytics containing prebuilt reports and dashboards, using business-relevant names for fields ensures the most valuable and relevant data is embedded in a way that stimulates greater efficiency and improved production within the workflow. Once the end user is comfortable and familiar with the embedded analytics, additional metrics and more complex report and dashboard design capabilities can be incorporated on an ongoing basis.
It is also worth noting that the end user should contribute to determining the “right-sized analytics” for an application’s workflow and their role. Soliciting input from the end user is helpful, but it is perhaps more efficient to allow the end user to customize the workflow of the application through the creation of their own reports, dashboards and data visualizations. This requires an intuitive self-service analytics component that must be integrated into the application.
Robust embedded analytics tools should include functionality that lets users share new reports and dashboards, and perhaps even provide metrics on report usage for management.
Adoption, engagement and renewal can be further encouraged through additional strategies. Software product teams, business leaders, and business process analysts can host can host meetings, webinars and training sessions to address the concerns of end users. Business process analysts responsible for their application’s workflows can also meet regularly with team members to promote analytics best practices.
Ultimately, even the end users who expressed any level of doubt or expressed initial resistance will rely upon the data insights made available through embedded analytics.
Strategies for Deriving Value from Embedded Analytics
There are a number of practical strategies for deriving value from embedded analytics available to both software product teams and end users. The following strategies are particularly effective in gaining substantial short- and long-term value from embedded analytics:
- Cross-database reporting
- Data preparation for reporting
- Ensuring data quality and management
- Establishing a semantic layer
- Fostering information discovery
- Incorporating visualization tools
- Limiting data access according to user roles
Organizations often collect data in multiple data sources, including popular databases such as Azure SQL, MSSQL, MySQL, Oracle and PostgreSQL. Cross-database reporting is when an analytics platform retrieves data from multiple data sources to use in a report. The analytics tool should be able to set up cross-database joins and queries so that they represent a unified logical data model to end users.
The analytics software should use cross-database reporting instead of depending on an ETL process to consolidate data into one database. For example, end users can create a report using tables from an Oracle database and from MSSQL without the need to understand the underlying repositories. The end users get access to data in real-time, instead of viewing stale data or waiting for the conclusion of nightly ETL processes.
Data Preparation for Reporting
Businesses seeking to optimize the use of analytics need to gather, combine, structure and organize their data. Pulling together data from different systems requires integration, consolidation and cleansing to ensure end users get valid results in their reports from BI and analytics platforms. Data preparation resolves problems, including, for example missing values and inaccuracies. Database systems use different formats that have to be reconciled as well.
Ensuring Data Quality and Management
Ensuring the quality of a company’s data and managing that process is the key reason for preparing data for reporting. Collecting, storing and processing that data requires technical expertise. Organizations cannot afford to make decisions based on bad data. Proper data governance gets rid of unclean data to keep it out of the analytics process.
Bad records and duplicates need to be removed and incorrect records need to be cleaned. Organizations should develop procedures and assign management responsibilities to ensure data quality management becomes part of the data life cycle.
Establishing a Semantic Layer
A semantic layer for your analytics solution displays database fields and categories using business-friendly terms. Field names and terms that work well within the database may make little sense to the business end user, and multiple data sources may use different words for the same type of information or abbreviations that don’t match business practices.
An appropriately configured semantic layer insulates end users from unnecessary technical details.
Fields that are not relevant (such as primary and secondary keys) or sensitive (social security numbers, salary figures) should be hidden from the end user. A semantic layer also helps create the self-service BI and analytics model, all while allowing end users to create queries using common business terms. An embedded analytics solution with effective self-service will enable business users to prepare data for reporting with a user interface.
Limit Data Access According to User Roles
Responsibilities vary among the individual departments and users at a business. Each end user needs access to the right data for the department’s needs along with the ability to share reports and dashboards with the right group of fellow users.
Overwhelming users with irrelevant data disrupts the analytics process. Failing to restrict access to only the appropriate data for each user can lead to compliance issues. No one outside of HR should get access to personnel records. The same is true of health records of any kind falling under HIPAA privacy rules limiting how providers discuss or access patient information.
Fostering Information Discovery
Creating a data-driven culture takes preparation and requires ownership and support for the transformation by company executives. Embedded analytics are considered one of the best ways to encourage the use of analytics: The use of embedded analytics eliminates the disruption caused by having to exit one application to open up a standalone BI platform, successfully keeping users within the workflow of the business application.
Embedded analytics solutions take on the look and feel of the application, creating a seamless environment. Solutions Review reported that “Embedding analytics into existing workflows helps business users gain access to the capabilities they need without having to go outside of the environments they use daily to do so.” If it’s easy to use, the analytics platform will gain acceptance by end users.
Dale McIntyre of Pharos Systems International shared three pieces of advice on CEO.com about fostering information discovery. He suggested that a business clarifies its purpose and include the open sharing of ideas. End users need the opportunities to make their own discoveries, which embedded analytics can help supply. Managers need to promote a discovery mindset by giving team members time for creative exploration. And by all means, eliminate those barriers to employees’ curiosity so that they can exercise their creativity. McIntyre warned that policies intended to create operational efficiency may be the root of resistance to change. That rigid mindset sets a barrier in front of innovation.
Incorporate Visualization Tools
It’s been said that a picture is worth a thousand words. Nowhere is this any clearer than when a mass of numbers in a table are presented as a data visualization.
Different facets of the data being analyzed get represented by the color, size, shape, shading and motion of visual objects. Visualization types may include bubble charts, heat maps, tree structures, scatter plots and more in addition to the standard pie, bar and line charts. Users find it much easier to spot trends with these visualizations instead of just comparing numbers within cells of a table. But they can still drill down into the underlying data.
Follow Strategies to Ensure Value of Embedded Analytics
These strategies are specifically designed to yield positive outcomes ensuring end users and service providers derive substantial value from the adoption of embedded analytics. The ability to limit access to data based on individual user roles, for example, not only ensures that security concerns are properly addressed, but it also reduces the likelihood of overloading users with too much data or too complex of a report, dashboard, or data visualization design experience.
Embedded analytics enables leveraging database relationships and queries against multiple databases. A key factor to this strategy’s success is giving end users the ability to explore data without needing to use or understand SQL. This enables them to glean vital insights that would have otherwise required a level of literacy in database architecture.
This vast range of functionalities provides business users with the agility necessary for continued growth and expansion. Software companies stand to benefit from helping end users develop business agility, as any tool that fosters growth and expansion is sure to enjoy exceptional adoption, engagement and renewal rates as a result.
Evaluating and Selecting an Embedded Analytics Solution
A software company creates an evaluation process based on its needs and those of its customers. Any organization should find the following questions beneficial as part of the evaluative process:
- What technology will be required?
- Is a separate database server setup necessary?
- What are the costs associated with an embedded analytics solution?
- What is the licensing model?
- How can embedded analytics be monetized?
- How does the solution implement security? Does that solve compliance issues?
- How will ongoing administration and growth be managed?
- Is there adequate support for multi-tenancy?
- What track record does the vendor have with embedded analytics?
Since there are significant differences among each organization’s individual needs – enterprises, for example, are likely to weigh certain considerations much differently when compared to a software company looking to OEM – a thorough evaluation process helps ensure the selection of the ideal embedded analytics solution.
Careful consideration of these questions in relation to an individual organization’s needs gives the software company a chance to optimally design and integrate embedded analytics for the organization and its end users. Proper setup, along with appropriate guidance and the availability of ongoing support, makes a difference between embedded analytics capable of delivering an array of substantial benefits and embedded analytics that go entirely unused.
Key Takeaways for Ensuring Optimal Outcomes with Embedded Analytics
End users and software companies stand to reap significant rewards through the incorporation of embedded analytics, including substantial improvements arising out of the immediate availability of key insights at the point of decision. Seamlessly integrated embedded analytics empower team members to not only take immediate action based on accurate and up-to-date data but also encourage end users to engage in deeper analyses that ultimately reveal insights that might have been otherwise overlooked and allows end users to benchmark and then optimize their daily activities.
Customizing and tailoring embedded analytics according to the unique needs and preferences of the end users is the key to ensuring the realization of an optimal outcome. As long as embedded analytics are set up in a way that complements the end user’s workflow and begins with “right-sized analytics,” both the service provider and end user stand to experience a broad range of short- and long-term benefits.
Definitions and Relevant Terms
Embedded Analytics – In platforms and other technologies central to various business operations, embedded analytics is a term referring to built-in, or embedded, analytical functions, tools, and capabilities that include, but are not limited to, data management and report and dashboard creation.
Business Intelligence – Business intelligence broadly refers to the technological process for analyzing data and presenting information to help executives, managers and end users make informed business decisions. Organizations utilize BI tools and applications for data analysis, including functions pertaining to data collection, prep it for analysis, run queries against the data, and create reports, dashboards and visualizations to reveal trends and other insights.
Service Providers/ISVs – A term referring to the software companies and developers of SaaS or other applications (sometimes referred to as independent software vendors, or ISVs) in which embedded analytics provide beneficial outcomes for both the provider and end user.
Self-Service Business Intelligence – This enables business users to have direct access to data, streamlining the critical decision-making processes that lead to appropriate action. End users can create reports, dashboards and data visualizations to gain insight from the data and make the best business decisions.
Definitions for Key Metrics
Adoption – Adoption metrics measure the rate at which end users incorporate newly available technologies.
Engagement – Engagement metrics define or measure the value or extent of the benefits realized by the end user following the implementation and utilization of a new technology.
Renewal Rate – Renewal rate metrics measure end-user satisfaction as defined by the percentage of users who continue to utilize a particular technology relative to those who elect to cease using the technology.
Implementation Time – Implementation time metrics measure the rate at which a specific technology completes the architectural, customization, and deployment processes necessary for implementation.