Here and Now Analytics
Part 2: Embedded Analytics – the benefits and use cases
Here and Now Analytics
In our previous article, we discussed the role of Gen-AI in helping to bridge the gap and contradiction between a single version of the truth with having the right information accessible to you in real-time, Gen-AI will drive the quality of decision-making to levels not seen before. In part two of the blog we cover the popular use of Embedded BI as part of your business and customer solutions alongside its use as an internal tool.
A bit of context:
Data visualisation tools have delivered a better understanding of our data but effective decision-making goes beyond visualisations. Innovations and business intelligence made it easier for business users to engage with their data, just a few words on some relevant themes:
Self-service BI -Enable users to create their own reports and analytics, rather than relying on a centralised reporting team.
AI-driven, augmented analytics - Combine the power of artificial intelligence and machine learning with traditional data analysis techniques to surface data insights without having to search and drill down to find the right data.
GenBI - The convergence of generative AI and analytics to enable business users to talk to their data and derive more value – they know what they want, but sometimes building a report or analysis can be just too hard or time-consuming.
One theme that encapsulates all of the above is embedding analytics into your applications
Embedded Analytics
Embedded analytics is the integration of data analytics capabilities, directly into software applications, websites, or workflows, providing users with insights and data-driven decisions within the context of their daily processes and functions.
The benefits of embedding a specialised capability into an application are not new and are driven by reducing complexity, time to market and using best of breed approach to functionality. The complexity when it comes to data application encompass both the UX elements and factors like security, performance and supportability in the equation.
Adding an analytical capability that natively compounds the application data is a powerful thing, it provides all the benefits to the end customer and relies on a data platform that is moving and enhancing at a fast pace its platform and capability which you and a business inherit whilst keeping focus on your core application and customer base.
Most applications will have an element of reporting and data handling, natively,
However, when it comes to providing self service analytics, business intelligence and the flexibility needed to achieve that, most will do a pretty bad job at it.
Applications serve a purpose and are successful because they are doing something well, unless they are applications that are data analytics application, their focus and knowledge will always be on their core purpose, therefore should probably rely on a best of breed modern data analytics platform that focus on being the best in that and have the capabilities and mission to embed into other core applications. Let’s outline a few cases to deduct from:
Manufacturing and The Internet of Things (IoT): Embedding analytics into Industrial IoT devices and machinery enables real-time monitoring of production processes, predictive maintenance, and quality control. By analysing data from sensors and equipment, manufacturers can optimise operations, reduce downtime, and prevent equipment failures.
Finance and Banking: Embedding analytics into banking and financial applications allows users to analyse financial data, detect patterns, and make informed decisions about investments, risk management, and compliance. This can enable end users to have the analytical capability they need in a self-service context, without the added complexity of creating an additional platform to the core application. In todays world end customers expect more than just the data but the ability to interegate and ask questions of it in a simple and friendly way.
Human Resources: HR software often includes embedded analytics for workforce planning, talent management, and employee engagement. HR professionals can analyse data on employee performance, turnover rates, and skill gaps to identify areas for improvement and make data-driven decisions about recruitment, training, and retention strategies.
Retail and E-commerce: Retailers embed analytics into their e-commerce platforms to track customer behaviour, optimize pricing and promotions, and improve the shopping experience. By analysing data on sales, inventory, and customer preferences, retailers can personalise marketing campaigns and recommend products to individual shoppers.
Education: Educational institutions use embedded analytics in learning management systems and educational apps to track student progress, identify learning gaps, and personalize instruction. Teachers and administrators can analyse data on student performance and engagement to improve teaching methods and curriculum effectiveness.
Reporting and analytics bundled as part of a business application is a growing trend and helps solution vendors add further levels of differentiation. They provide targeted out-of-the-box functionality, typically based on the application's single data source, and answer the basic requirements. As users our decision-making processes will go beyond the basics, merging and combining multiple data sets and information and a level of ad hoc analysis that supports our train of thought, here and now.
Providing more than the obvious from your data
Embedded analytics and the convergence of generative AI can accelerate time to value, improve the user experience, reduce development costs for data applications, it will deliver the following benefits to your users and customers:
Seamless Integration: Users can access analytical insights directly within the applications they already use, eliminating the need to switch between different tools or interfaces. This seamless integration enhances user experience and productivity.
Contextual Insights: By embedding analytics within specific workflows or applications, users can gain insights within the context of their daily tasks. For example, sales representatives can access customer analytics within their CRM system, allowing them to make data-driven decisions while interacting with clients.
Improved Decision-Making: Embedded analytics provide access to relevant data and insights at the point of decision-making. This leads to more timely and accurate decisions, resulting in better outcomes for businesses and organizations. It can include competitive analysis, market information and history.
Increased Efficiency: Users can perform data analysis and gain insights without relying on data analysts or IT specialists, reducing the time and effort required to access and interpret data. Your help desk size doesn't need to increase to address this requirement and can be addressed via data driven automation.
Personalized Experiences: Embedded analytics allow for the delivery of personalized insights tailored to individual users’ needs and preferences. For example, e-commerce platforms can recommend products based on customers’ browsing history and purchase behaviour, enhancing the shopping experience.
Real-Time Visibility: Users can access real-time data and analytics within their applications, enabling them to monitor key metrics and performance indicators as they change over time. This real-time visibility enables proactive decision-making and quicker responses to changing conditions.
Enhanced Collaboration: Embedded analytics facilitate collaboration among users by providing a centralized platform for sharing and discussing data and insights. Teams can collaborate more effectively on projects and initiatives, leveraging data to drive consensus and alignment.
Scalability and Flexibility: Embedded analytics solutions can scale with the needs of the organisation, accommodating growing data volumes and user requirements. Additionally, they offer flexibility in terms of customization and configuration to meet specific business needs and use cases.
Competitive Advantage: Organisations that leverage embedded analytics effectively can gain a competitive advantage by leveraging data-driven insights to innovate, optimise processes, and deliver superior products and services to their customers.
Embedded analytics partnered with Gen-BI as part of the natural business workflows empower users with the tools and insights they need to drive better outcomes, improve efficiency, and stay ahead in today’s data-driven business landscape.
There are a few technical considerations that needs consideration ahead of going into an embedded project, with pros and cons between doing it yourself vs embedding an existing platform. we will cover the technical aspects in the next blog. .
About Camden AI
At Camden AI we understand getting bogged down in the flood of data can be costly and time-consuming, and it can be challenging to find meaningful insights if you cast too wide a net.
We also understand the benefits of delivering insights at the point of need, inside portals, and workflow applications, which makes it possible for users to take immediate action without needing to leave their day-to-day work.
We deliver innovative data-centric solutions focused on analytics and AI. Dedicated to providing customers with innovative tailored solutions and attentive service.
Simple to get started and a quick delivery path to business value – talk to us about how we can help and make a difference.