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Supports users & use-cases Life-cycle from data to insight with Self-service analytics Interactive dashboards Conversational analytics Custom embedded analytics
Embedded analytics software gives an additional sheet of advanced data analytics and visualization on top of a business’ existing software tools to assist users in simplifying their business intelligence (BI) needs and achieving advanced dashboard and reporting capabilities within existing applications like Client Resource Management, Accounting, and HR.
Embedded analytics is the coordinating and integrating platform for analytic content and capabilities within business process applications. It offers useful information and analytical tools designed so that clients can work efficiently, with smarter and more effectiveness thru the apps they use on a usual basis.
Influential embedded analytics projects have more than appealing data visualizations. Embedded dashboards and reports should create new insights and actions. The most common analytics abilities have :
Dashboards and data visualizations: Charts and graphs displaying performance metrics
Static and interactive reports: Tabular viewing of data without or without parameters and scheduling abilities
Self-service analytics and ad hoc querying: Enabling users to ask their questions of the data by exploring a set of data
Benchmarking: Comparing performance data against best practices from external data
Mobile reporting: Ensuring interactive functionality on mobile devices and taking every advantage of abilities specific to mobile devices
Visual workflows: Incorporation of transactional capabilities directly within the analytic user interface, sometimes termed as write-back
Predictive analytics: An area of machine learning and artificial intelligence giving users a platform to answer the question, “What is most likely to happen based on my present data, and what can I do to change that result?”
One of the questions often asked is how embedded analytics software is different from traditional business intelligence software. The shortest reply is, “It’s all about context.”
Business intelligence is a set of independent systems technologies, processes, people, etc. that is aggregated from data from multiple sources, preparation of the data for analysis, and then providing it for reporting and analysis on that data from a central viewpoint. It is the perpetual way for supporting managerial level decisions that need highly aggregated reviews of information from across a department, function, or entire organization. These systems are specifically developed for operating within the platform of a person who solely performs data analysis.
Embedded analytics, on the other hand, is a set of capabilities that are tightly coordinated and integrated into existing systems. CRM, ERP, marketing automation, and/or financial systems that have additional awareness, context, or analytic capability to support decision-making related to particular tasks. These jobs may need data from multiple platforms or aggregated reviews, but the output is not what should be called a centralized overview of information. It is a targeted solution backed by knowledge to assist a decision or action in the context in which that decision or action takes place.
In a way, business intelligence is a map that the client uses to coordinate the route before a long road trip. Embedded analytics is the GPS navigation inside the car that navigates the journey in real-time.
While traditional Business Intelligence has its place, the fact that Business Intelligence applications and business process applications have solely separate interfaces forces clients to switch between multiple apps to get insights and take action. They were switching from one form to another to gain business insights may wastes up to two hours per employee each week.
Unlike Traditional business analytics software, is an embedded analytics platform gets information and insights inside the applications people utilize daily. While advanced analytics capabilities are embedded in existing software, clients get a high-end application experience and can be more productive by combining insight and action in the same application.
Salesforce: If the user works for a company which has a sales team, chances are user is familiar with Salesforce.com. When users think of embedded analytics in a B2B context, they are usually thinking of the Salesforce model, where analytics are sprinkled throughout the application in a few critical places like the homepage and an analytics tab. Salesforce has two tags – Dashboards and Reports – where users can consume information and create their reports all within the platform. Users can customize the homepage to show key performance indicators like quarterly sales and leads against quota. While this is a good start, we’d like to propose that embedded analytics can be more than this such that it’s integrated into the core workflows of the application, not just the UI.
Amazon: Amazon is the Gold Standard for providing relevant analytics to encourage on-site conversion. Fundamentally, Amazon exists to sell books. They have an excellent procedure to support the eCommerce experience with fast shipping, low prices, and Buy Now with 1-Click. But Amazon also satisfies customers’ informational needs by providing product ratings, video reviews, and suggested products. Amazon has added tremendous value by delivering relevant analytic information at the point of the transaction, creating a superior customer experience.
Data modelling: An embedded analytics tool can upgrade existing business applications by collecting, defining, organizing, and visualizing data to study past trends and predicting future activity.
Affordable and comfortable to use than full BI solutions: Embedded analytics software generally costs less than a robust Business Intelligence solution. The software coordinates and integrates with an existing software by supporting user unique data analytics requirements, making it easier to use and train employees on how to utilize one single integrated application.
Improving Productivity: Embedded analytics helps in improving the user experience while increasing end-user adoption and growing revenue. Unlike business intelligence software, embedded analytics allows users in the applications they are usually using every day and gives valuable data in their hands that they can use to make decisions or get insights quickly and easily.
Dashboard: Displaying key business indicators and essential data points using textual and visual tools on a single screen.
Interactive reports: Creating customizable reports by filtering data points and adjusting report details to adapt the requirements of multiple departments and stakeholders.
Data blending: Importing and combining data from multiple sources into a single functioning data set for extracting data insights.
Data visualization: Representing analyzed data information using graphical tools like charts, diagrams, and pictures.
Coordinating with third-party apps: Connecting embedded analytics tools with third-party applications for exchanging data.
Self-service analytics: Performing analysis with the least support from IT teams.
Most products available in the market are priced on a “per user, per month” basis, and can be divided into three pricing tiers based on their starting price. A premium product is priced higher and has unique features like big data analytics, advanced administration, OEM, and data stories.
Compatibility and functionality: Before buying, the user must determine whether the embedded analytics solutions on the shortlist are compatible with user business’ existing software tools. From there, ensuring that the level of analytics provided by the shortlisted solutions meets user functionality needs. Don’t invest in software that can’t integrate with the tools already use, or pay for functionality user don’t require.
The total cost of ownership: In addition to the buying price, the cost of embedded analytics software can have other factors like training and support service fees. Many solutions may have low upfront costs but need ongoing payments or maintenance fees over time. Get a complete thorough cost evaluation of the shortlisted products, and after that, go for picking the solution that makes the most sense for the budget with the system’s entire life cycle.
Artificial intelligence (AI) is transforming embedded analytics into proactive analytics: AI-enabled integrated analytics solutions have live dashboards and dynamic analytics capabilities. These advanced solutions provide more exact predictions. They are not limited to showing historical lows and highs and can understand from historical data, to help detection of job flow anomalies in real-time.