Our core positioning as an Embedded Analytics vendor is at the same time the closest we can think of to our customer base reality and also confusing in a way because there are so many word overlaps and massive differences in the data analytics world that can’t always be perfectly captured in simple words.
The vocabulary problem is not just with the ‘Embedded’ part of Embedded Analytics. It’s the nature of integration with the host system, from Branding to Data Security to Process Automation. It’s the “Analytics” part that can mean a lot of things, particularly given its adjacency with “Analysis”.
The goal of this article is to pull these words contextually together and at the same time draw differentiation lines between them to help you - a company having a product that needs to integrate analytics - navigate this space. We don’t intend to be exhaustive but to open up your mind to the similarities and differences between all these words.
Covered terminology
- Data Visualization: graphical representation of data, i.e. charts, tables, widgets, gadgets, etc.
- Data Semantics: modeling the hidden business logic inside the data model to allow expressing business queries
- Business Intelligence: data analytics results presented on a digital reporting format
- Analytics - computational analysis of data or statistics
- Actionable Data / Insights: execution of a computational action based on data analtics and/or data visualization.
- Product Analytics: analysis of the performance and usage of a software
- Data Science: mathematical techniques to analyze data, i.e. statistical methods, machine learning algorithms, etc.
- Big Data: capture, storage and analysis of a large volume, rapidly changeling and diverse data
- Artificial Intelligence: near-human intelligence exhibited by machines
- Data Security: protection of digital data and segregation according to areas of responsibility for the end user.
And finally, Embedded Analytics: integration of Analytics and Data Science results into a software/application/system through Data Viz and Actionable Insights end users can consume, while ensuring Data Security.
Software Industry Terminology
The software industry focus around data is not a new one. Back in 1958 IBM started talking about Business Intelligence systems. See the full history of BI. As the industry evolved, niche methods and applications emerged all with their own terminologies. Let’s explore some of them here.
Data Semantics
From the article Semantic data model - Wikipedia we read:
A semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models.
Between the source data in its various forms and shapes and the analytics and visualization, a layer must define the logic that makes sense between the data element. An example for this task is to use OLAP-based analytics, see also the same topic from another angle also in our own article about it here.
Example: icCube Semantic Layer
Data Visualization
From the article Data and information visualization - Wikipedia we read:
Data and information visualization (data viz/vis or info viz/vis)[2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount[3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.
For all activities starting from massive data and ending in emerging understanding the act of analytics is to reduce complexity without losing meaning in the process. Visual representation is ideal to balance meaning and complexity whether though the use of reports, dashboards or widgets is up to the specific use case.
Example: icCube Dashboard
Business Intelligence
From the article Business intelligence - Wikipedia we read:
Business intelligence (BI) consists of strategies and technologies used by enterprises for the data analysis and management of business information.[1]Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.
This is a broad umbrella term covering almost any product that starts from the data and ends up at the presentation level - visual or otherwise.
Analytics
Reading Analytics - Wikipedia we learn:
Analytics is the systematic computational analysis of data or statistics.[1] It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.
So here we don’t narrow the vertical silo application but the software layer used to contribute to the end result. We talk about implementations of algorithms based on mathematics to extract the meaning from the data. The real life implementation can be B2B, SaaS, on-premise, fully automated, with or without visualizations.
Actionable Data / Insights
We can read in Actionable Insights - TechTarget:
Actionable insights are conclusions drawn from data that can be turned directly into an action or a response. The data informing the insights can be structured or unstructured, quantitative or qualitative.
So here we move beyond the meaning extraction and visualization to driving the end user or even an automated process directly into taking action as and when needed.
While actionable insights provide the impetus for an action, people or processes are needed to execute the actions.
Example: Embedded icCube with actions in the Host System
Product analytics
From the article Product Analytics vs. Embedded Analytics we read:
Product Analytics will help you monitor your SaaS usage and product engagement internally, while Embedded Analytics will bring the value of your SaaS as analytic capabilities for your customers
So in this case we jumped from a broad umbrella term to a narrow niche application of the broader products, technologies and methodologies.
Example: Google Analytics and similar tools
Science and Technology Terminology
Underneath most useful data centric software modules is a theoretical framework based on simple or extremely complex mathematical methods. Let’s explore some of the terminologies used.
Data Science
Reading Data science - Wikipedia we learn:
Data science is an interdisciplinary academic field[1] that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.[2]
So this is the general field of study researching meaning mining from data.
Example: Multiple Linear Regression
Big Data
Big data - Wikipedia tells us the following:
Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity.[4]
So it’s Data Science on huge data sets with challenges around quality, consistency and rate of change.
Example: Big Query data source and similar
Artificial Intelligence
Reading Artificial intelligence - Wikipedia we see:
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and uses learning and intelligence to take actions that maximize their chances of achieving defined goals.[1]Such machines may be called AIs.
At the first glance this doesn’t seem to be related to the other terms but when we think about it, asking questions to a system that understands the topic and the contextual data to provide an answer is exactly what the ideal dream analytics is. Artificial intelligence is an extension of data science and Big Data whether from a natural language processing angle or a meaning extraction angle. See our more details in LLM : Have an Intelligent Business Discussion with your Data!.
Example: icCube AI Chat Bot
Data Security
Looking at Data security - Wikipedia we see:
Data security means protecting digital data, such as those in a database, from destructive forces and from the unwanted actions of unauthorized users,[1] such as a cyberattack or a data breach.[2]
From an analytics perspective, the data level access rights and how users can or cannot see a slice of reality is hyper-important both because of regulatory and company policy perspective. This is a key point in Embeddable systems where the Data Analytics layer needs to be at all times in sync with the host application’s security - on the fly, at all times. We can also note that when doing analytics across multiple databases, having a semantic layer underneath it allows for the alignment of the naming conventions and data relationships across the board between the host and the embedded parts.
Example: icCube Schema Permissions
Considerations for your Embedded Analytics Project
Features you need
When you are exploring your own Data Analytics capabilities, you’ll be needing to create value out of your - or your customers’ - data generated in your SaaS platform, you will need data, semantics, meaning generation and a way to use the results. This is true whether you do product analytics or want to go for actionable insights. (Big Data and/or AI not mandatory but possible)
Skills your team needs
Your team needs a wide variety of skills from understanding your customers’ businesses, their processes and (analytic) use-cases, to understanding what end-customers want to do with their data as well as extracting actionable meaning, present data and drive processes (in your SaaS) at scale. This doesn’t necessarily mean that you need PhDs in Data Science or deep learning experts in your software group but be clear about who you need to create the value you want to deliver.
Conclusion
From a product/technology, data science, user and company stance, business intelligence, data visualization, actionable analytics, product analytics, Big Data, AI overlap. It’s the context in which they are used that makes them different. The features you will need for each use-case sometimes overlap and sometimes differ. Let’s make sure you don’t drown in the lingo and that the features you need will be covered by the solution(s) you choose.
How can we help you?
At icCube we develop and help B2B SaaS solution providers integrate a fully embeddable platform that starts with your data, allows you to implement your Data Science, extract meaning, visualize the result as needed and allows the end users to take safely and securely to take action directly in the host application to correct problems or move situations along… and completely disappears seamlessly for the end user. With years of experience we can help you make the right choices for your specific use case and we are ok if that choice is not us at the end of the conversation.
Curious about how we can help you evaluate the options that will make you successful? Reach out to us — we'd love to talk!