What is Embedded Analytics?
Embedded analytics is the seamless integration of analytical capabilities, data, and visualization in a platform, software product, or existing workflow. The analytics are fused into the platform/software used by the stakeholders in a business so that it’s readily available as a feature or core functionality and not as a separate tool. This approach leverages the stakeholder’s familiarity with the platform/software, making the analytics more useful and assimilable in the regular workflow or routine projects.
Embedded analytics is a deployment classification and focuses more on how analytics within an organization or system are deployed rather than how they are visualized or performed. The term was originally introduced by Howard Dresner, considered the father of Business Intelligence (as it’s defined today), in 2007.
Components of Embedded Analytics
The core components of embedded analytics are not different from conventionally deployed analytics, but they have to be handled/approached from an embedding perspective.
Data (Sources, Types, and Cleansing/Processing):
The data component of embedded analytics focuses on where data is coming from, what data types are coming into the business, how it's pre-processed/cleaned for evaluation, and how raw data is displayed/accessible to the stakeholder (if it is). The platform where the data has to be embedded may not be able to handle data-related requests natively, and accounting for this limitation should become part of the development and embedding strategy.
Analysis (Methods and Techniques):
How data is analyzed, which algorithms and analysis techniques are applied to it, and how much control a stakeholder has over analysis methods and protocols are factors that should be considered when developing or deploying the analysis component of embedded analytics.
Identifying various visualization components needed by the various stakeholders and how they can be embedded/incorporated into the platform/software they use to connect with business can streamline development and deployment. It becomes a challenge in environments where stakeholders have devices that cannot assimilate modern data visualization elements.
Another aspect of embedded analytics is navigation, drill down, drill across as you navigate between application/process and the analytics/BI.
How much customization a stakeholder needs and can perform are two different questions that need to be asked and answered when developing or choosing embedded analytics. A lot of customization access may be difficult to integrate, especially in a relatively restricted platform or software like the ones used by financial institutions or defense institutions.
Ethical and Regulatory Checks:
These checks can be applied at various levels, including the data collection sources and gateways that accept the data into the primary platform/software to be used by embedded analytics. Their placement should be decided before deployment.
Benefits of Embedded Analytics
Like conventionally deployed or used analytics available as a separate tool or software product that various stakeholders may use parallel to their primary platform/software, embedded analytics can be crucial to business intelligence. Business Intelligence is the software, platform, or tool that helps a business use its data and analytics to make informed/intelligent business decisions. Data, in its raw form, is akin to a useful unmined resource. Analysis makes what's inside the "mine" usable and useful. Finally, business intelligence helps you determine where and how it should be used.
However, they offer multiple benefits over traditionally deployed analytics.
- Embedded analytics accelerate the adoption of both analytics and business intelligence. Once the analytics are available in a familiar environment and more accessible, it's easier to leverage in business decision-making.
- Embedded analytics empower and encourage stakeholders in a business to use the data/analytics available to them in more comprehensive and inventive ways. If the analytics data is to be ported from a separate source, it becomes harder to assimilate it into the natural workflow, which results in limited use cases. But if it’s integrated into the workflow and familiar software/platforms, stakeholders may use it for every applicable instance.
- Embedded analytics can eliminate/significantly reduce human errors if the data is plugged into the routine environment manually. They can also reduce friction instances if analytics data is connected to the primary platform/software through the Application Programming Interface (API).
- Embedded analytics and integrated BI capabilities can also lead to better, more well-informed, and data-driven business decisions, which help act on the data directly from the visualizations.
- Embedded analytics can also make analytics and business intelligence data more accessible to a wider variety of stakeholders. If it's customized for their workflow and they are authorized to access it, analytics can be made available to virtually anyone within the industry (or to outside stakeholders) by embedding it into the platform/software they use to connect with the business.
These benefits are most apparent/prevalent in use cases where there is virtually no separation between business intelligence and embedded analytics, and the two are collectively integrated into the workflow, software, or platform typically used by the stakeholders (assuming it's not the business intelligence platform itself).
These benefits also make embedded analytics a viable alternative to the traditional approach to business intelligence, which can be siloed and available only to a limited number of stakeholders in an organization.
Embedded Analytics Deployment Challenges and Considerations
There are several factors you need to take into account when you are selecting the right embedded analytics solution for your business. This includes its compatibility with your existing business software/platform, your analytics needs, available resources, data literacy of stakeholders, etc. But if you make the right choice, the deployment may not be quite challenging.
In contrast, if you are developing and deploying embedded analytics, there are several challenges and considerations you need to take into account.
- Integration with inflexible and legacy infrastructure. Even if the embedded analytics functionality is state of the art, integrating it with an unoptimized infrastructure or platform will slow it down to the platform's level. The worst-case scenario might be the analytics embedding conflicting with the performance of the original platform.
- An inadequate understanding of your analytics needs. Many businesses opt for a solution-first approach to their analytics needs, i.e., choosing good analytics tools before they have fully identified their analytics needs and defined an analytics approach or practices. This makes it difficult for them to choose the right embedded analytics options (or build the right functionalities) that may not align with their business intelligence needs.
- Stakeholder digital and data literacy challenges. Technically, embedded analytics is a solution for making your stakeholders more "data-aware" and helping them integrate data and analytics into their decision-making and routine actions. But even it can be tricky without a healthy baseline of data literacy. Even if the business can educate its internal stakeholders about embedded analytics, the adoption may not be easier for external stakeholders, primarily customers/consumers.
- Transformations/fluctuations in the realm of analytics are the norm. Companies may lose access to various data streams due to operational changes or regulatory shifts (like phasing out third-party cookies). This may require a revamp of embedded analytics. Even if the data pipeline of a business doesn't change over time, analytical techniques and methods may change, and embedded analytics should be well-positioned to integrate the positive changes.
- The difficulty of embedding legacy analytics tools with the existing digital infrastructure. A wide range of individuals within a business are using a variety of analytics tools, even if they don't have a traditional business intelligence platform or embedded analytics. Embedding them into the company's existing digital infrastructure may not be possible or technically feasible, and embedding different analytics that the stakeholders may not be familiar with may introduce a learning curve and may cause adoption friction.
- Wrong analysis techniques or an inadequate approach to data visualization are just two of the many bad “analytics” habits that organizations may adopt en masse. That happens because individuals/teams/departments within an organization often adhere to tools and practices they are familiar with, even if it’s not the best fit for the job. This often translates into embedded analytics that underperform or confuse stakeholders instead of informing them.
- It's important to understand that most of the challenges are associated with the capabilities and practices of businesses adopting embedded analytics rather than the idea of embedded analytics itself.
- Data as a feature mindset is crucial for companies designing and deploying their own embedded analytics. Building these functionalities around the data pipeline and analytics needs can forestall a variety of issues down the line.
- Making data science implementations and deployment more accessible should be an embedded analytics goal. This ties into best practices like identifying the analytics needs of the business and the data literacy of various stakeholders.
- You have to take your cloud architecture into account before developing/deploying embedded analytics functionalities. A monolithic embedded analytics design may be difficult to integrate and may require you to make adjustments to your architecture, impacting existing functionalities. In contrast, a microservice and decentralized approach to developing and deploying embedded analytics functionalities may be easier to integrate.
- AI and ML needs and integrations of the business have to be taken into account. If all the analyzed data has to feed into a large ML training model or if AI supervision is to be added before or after a data preparation layer, addressing them in early stages can make deployment easier and may reduce future frictions.
- Embedded analytics are supposed to make things easier for users/stakeholders, but if the interface is too convoluted/complex for most users or becomes more difficult after the analytics are embedded, it may undermine several analytics goals.
- The inherent scalability of the native platform/software and that of embedded analytics is to be taken into account. If the native system is inflexible, the scalability needs of the embedded analytics have to be addressed before deployment, though it can be done on an ad-hoc basis if the system is flexible.
- You have to take the vendor/stakeholder ecosystem into account when designing or modifying the data management layer for your embedded analytics.
- How embedded analytics impact your existing security or how your platform/software (in which you are embedding analytics) may expose your data streams to new attack vectors or a broader/different attack surface should influence your development, selection, and deployment system.
Important Elements in Embedded Analytics
All Conventional Data Visualization Features
Even if embedded analytics have to work around the limitations of existing infrastructure, they should (ideally) include all the data visualization features the stakeholders are familiar with or use in dedicated/separate business intelligence solutions. This will make it easier for people to adapt to embedded analytics and use them more extensively within their business process. It will also ensure data across the different departments/stakeholder clusters.
If embedded analytics is limited in this regard, one business segment may rely on the existing features while others may revert to a separate business intelligence/data visualization solution, making internal reports inconsistent.
This is an important element if you are choosing an embedded analytics solution/platform instead of building one from scratch. Not all platforms are designed to easily integrate with all legacy systems, and if they are too incompatible, it may be reason enough to opt for a different solution. Deployment friction may throttle embedded analytics, preventing stakeholders from leveraging its full power. In contrast, embedding ease may significantly lower the time and cost associated with deployment.
Your stakeholders should have the option to customize their embedded analytics functionalities, reporting processes, dynamic dashboards, data-input sources, etc. It may be more challenging to accommodate embedded analytics than it is in a dedicated business intelligence solution, but it's crucial for easy adoption and comprehensive use. However, you have to balance it with permissions for various user bases. Not all of your stakeholders should have access to all data sources.
Full Data Pipeline Integration/All Data Types Should Be Accepted
Whether you are designing your own embedded analytics or opting for an existing platform that can be integrated into your existing platform/software, it's important that it can handle all the different data types that your business has access to. If the data needs to be converted, make sure the right tools/integrations, and protocols are available at the right layers/levels. Embedded analytics should also be able to take in both structured and unstructured data seamlessly, or its ability to run comprehensive analyses may be severely limited.
Data Profiling (AI/ML Powered)
Data profiling, i.e., the process of identifying useful patterns from raw data before it’s formally analyzed, can generate a lot of useful insights, especially if it’s enhanced with AI and ML models. Data profiling can be relatively basic, like identifying patterns from time-stamps of various data points, or more complex, like running sentiment analysis.
Embedded analytics should incorporate all the data sources and the entire data pipeline of a business. It’s an important consideration for businesses that have esoteric or legacy data sources that may have to be processed before being fed into analytics.
Embedded analytics may have more automation opportunities than conventional analytics, especially if the primary system/platform is built to accommodate that. Automation can help you get rid of repeated processes and make it even easier for stakeholders to incorporate analytics into their usual workflow.
Embedded Analytics: Custom vs. Pre-Built Tools
From a development and deployment perspective, there are three ways embedded analytics can be made part of your regular business - Building vs. buying vs hybrid model (building UI while leveraging BI capabilities). Both approaches have their own strengths and weaknesses that have to be weighed against each other by taking into account the analytics needs and resources of each business.
- Custom-built for a business's primary software product(s), business intelligence platforms, or analytics needs. They can be as simple as a simple API integrating real-time user data or market data to a platform or as sophisticated as a multi-layered analytics platform modified for and integrated into the user platforms of various stakeholders in the industry. Such embedded analytics can be precisely fine-tuned for the business needs and the data types the business has access to. However, the onus of maintaining, modifying, or improving them would be with the business's internal stakeholders.
- Pre-built analytics platforms/software that can be embedded into your workflow or primarily digital platform/software used by the stakeholders. They may offer a massive range of analytics functionalities that can be customized as per business needs/analytics use cases. Such tools are maintained and updated by the original developers, reducing the cost associated with maintenance and continuous development.
Embedded Analytics Resources
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