October 24, 2022

What is Embedded Analytics?

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According to a recent study by Harvard Business Review, some analysts are actively switching between 1,200 different applications and websites on a daily basis. While many enterprises have already solved this issue with an embedded analytics solution, others are still in the dark.

But, what is embedded analytics? More importantly, how can it help boost your team’s productivity?

Generally speaking, embedded analytics consists of various analyses and statistics that are contained in one specific application or service. This stands in contrast to traditional or stand-alone analytics, which collects data and performs analyses from various sources.

The 5 Levels of Embedded Analytics

Embedded analytics can be implemented on five different levels. Each level represents a greater degree of scale or sophistication, and individual enterprises will find more value in one level or another.

  • Level 1: The most basic form utilizes embedded analytics on the web, usually through HTML or JavaScript coding.
  • Level 2: This level uses secure custom portals to provide data security, end-user personalization, and enterprise branding.
  • Level 3: Enterprises that require two-way interactivity, row-level controls, and interactive dashboards will likely find the most value in this level.
  • Level 4: Those requiring real-time connectivity and interactivity, including enterprises using programs like SAP or Salesforce, should opt for this level.
  • Level 5: The highest level of embedded analytics is typically reserved for enterprises who use notifications, alerts, and thresholds to trigger automated systems.

While the different levels can be confusing, your choice ultimately depends on the type of software and services you’re currently using — or planning on using — to manage your enterprise data.

Driving Your Business With Embedded Analytics

Embedded analytics can be used to drive your business in various ways, but you must first decide whether you want to buy your embedded analytics solution or build your own. There are arguments for and against each option, so you’ll want to do your research before deciding on one or the other.

Building your own embedded analytics

Unique enterprises that require embedded analytics for a specific use case will likely find more benefit in building or coding their own embedded analytics. This is a great way to ensure analytics are targeting the right datasets, but there are some drawbacks to consider:

  • A dedicated team of programmers and developers is required to code and maintain in-house analytics.
  • Proprietary analytical solutions often require significant time to develop, resulting in a longer time to market.
  • The amount of scalability will be limited to the capabilities of the development team.
  • Technical support might not have an immediate solution to unknown bugs and undiscovered issues.

Buying embedded analytics

Many enterprises buy their embedded analytics from a third-party analytics or business intelligence (BI) vendor. Since these are often standardized solutions, they’re user-friendly, accessible, and scalable. However, there are some drawbacks to consider here, too.

  • A lack of customization means you might not receive all the features you need.
  • Standardized solutions often include features that you don’t need.
  • Your third-party vendor could go out of business at any time, leaving you without an embedded analytics solution.

Advantages of Embedded Analytics

Whether you decide to build your solution in-house or pay for a third-party service, modern embedded analytics provides a plethora of advantages to today’s enterprises. Some of the top benefits include:

  • Offers easy integration with other apps, services, and software
  • Provides customizable and highly accessible dashboards
  • Facilitates improved data security, governance, and control
  • Monetizes data through premium products and services
  • Creates a company culture based on verifiable data and statistics
  • Strengthens the overall customer experience

Embedded Analytics in Action

There are plenty of success stories involving embedded analytics in action. Whether you decide to position your analytics internally, publicly, or via a third-party BI vendor is ultimately up to you, and there are specific use cases for each option.

Internal embedded analytics

Embedded analytics works great when used within internal dashboards and web portals. This approach allows your team to pull records from all of your organization’s datasets, which can later be injected into your customer relationship management (CRM) software as needed.

This ultimately means that your team won’t have to leave your chosen CRM in order to access the actionable insights provided by embedded analytics and business intelligence in general. If you’re trying to save time or recoup lost productivity, this might be your best option.

Public embedded analytics

Some organizations use their embedded analytics within a public space. This is often achieved through blog posts, websites, and online reports.

This is a great way to keep your customers informed of any news or events surrounding your company, products, and services. By using interactive visualizations that automatically refresh on a regular basis, you’ll be able to show your customers — both current and potential — the exact datasets you want them to see.

Third-party embedded analytics

Enterprises also use embedded analytics within third-party applications. Again, this results in greater productivity by containing all of the important data in one place. However, you’ll need to find an embedded analytics solution that is compatible with your enterprise resource planning (ERP) or CRM software.

Embedded analytics as a career path

Those who want to explore embedded analytics as a career path will often do so through the field of business intelligence. Since it’s common to see embedded analytics in business processes and BI today, there are plenty of opportunities available for those with the right skill set. Some of the necessary skills include:

  • Data Familiarity: BI and data analyst roles require familiarity with modern data, including data discovery, preparation, management, and quality control.
  • Analytics: Successful candidates in the BI field are comfortable using various forms of basic and advanced analytics, including descriptive analysis, predictive analysis, prescriptive analysis, and more.
  • Computer Software: Finally, most BI and data analyst roles require strong familiarity with computer software, including SQL and business intelligence software like Microsoft Power BI or Tableau.
  • Communications: Data analysis is only part of the role. The other part involves communicating results through various reports and presentations.

Making Embedded Analytics Work for You

If you find it difficult to keep track of your various data sources, apps, and services, or if your team is losing valuable time by switching between different BI platforms and solutions, embedded analytics is right for you. Not only will you strengthen productivity across your entire enterprise, but you’ll find it easier to monetize your data and maximize your revenue, too.

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