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For decades, the value of data and the importance of robust analytics have shaped products and businesses. Today’s product teams fully grasp the potential of their data, and their success relies on their ability to monetize it at scale and speed, extract insights, and provide value for their customers. 
Amidst the increasing necessity to future-proof solutions, a pivot toward the adoption of flexible, modular, composable solutions is taking place in the market. A recent study from <a href="https://www.gartner.com/account/signin?method=initialize&amp;TARGET=http%3A%2F%2Fwww.gartner.com%2Fdocument%2F4023010%3Fref%3DsolrAll%26refval%3D378391207%26">Gartner</a> predicts that businesses will prioritize modular offerings that deliver UI- and API-first functionalities in order to keep up with the pace of innovation and change. And much of this race to innovation relies on APIs' ability to abstract complexity for developers and requires robust analytics platforms that can seamlessly integrate with development and CI/CD ecosystems. 
Sisense is thrilled to unveil Compose SDK for Fusion, a new step in our continued mission to empower app builders to embed analytics everywhere. It’s a flexible and modern development toolkit that enables the embedding of context-aware analytics in a code-first, scalable, and modular way. Compose SDK is essentially built as an innovation engine for product teams, accelerating productivity and time to market, and lowering development costs. This new addition expands Sisense’s no- and low-code embedded analytics platform with pro-code tools, and allows us to support various use cases—from Product Managers seeking a no-code experience to pro developers crafting bespoke embedded analytics experience—meeting product teams where they are. 
Sisense’s success in the embedded analytics space
Reflecting on Sisense's 15-year journey fills me with immense pride. From our humble beginnings as a small, driven team, we've evolved into a profitable analytics powerhouse thanks to our teams’ relentless innovation and unwavering commitment to our customers.
In the past half-decade, our focus has been on perfecting embedded analytics through our flagship offering, Fusion. In coworking closely with our design partners and customers, we've heard time and again that dashboards are not “dead". Our clients deeply appreciate and rely on the flexible power Fusion brings to the creation, customization and embedding of artifacts like dashboards, widgets, AI, and code, and the ability to embed these elements into their applications at scale.
The API-fueled era of embedded analytics
As the technology landscape continues to evolve, Sisense is set to adapt and grow alongside it. In this flourishing "API economy," and with data’s growing role in monetization, developers are seeking greater control and integrations that align with their unique development practices and CI/CD pipelines. This search has ignited a surge in demand for “developer-first analytics.”
Now, the vast majority of organizations are naturally focusing on their core competencies to stay ahead of the market, and do not have the time or resources to build and maintain this complex analytics solution in-house. Today, they’re looking outward for solutions that can abstract this complexity via APIs—just like Twillio did for communication and Stripe did for payments.
However, the traditional BI analytics space hasn’t adapted fast enough to this new market need. Many BI vendors offer 'embedding,' but often as an afterthought or satellite feature. Developers have little use for the restricted, non-secure, monolithic iFrame or simplistic API integrations offered by traditional BI tools. They require a  robust solution, designed for and tailored to their needs, that can harness the full potential of modern technologies, including Generative AI.
The promised (and limited) land of open-source 
The growing shift towards open-source tools is a testament to developer’s struggle to find such solutions. But while open-source tools may initially seem appealing for in-app analytics, their luster often fades when challenges arise. Developers must be able to navigate efficient query performance, diverse data source management, and intricate integrations. Additionally, CTOs are tasked with ensuring data security, meeting compliance requirements, managing updates and support, and coordinating integration with existing systems. The initial allure of open-source analytics gives way to a complex web of tasks, potentially jeopardizing project timelines and straining resources. Ultimately, open-source tools aren't able to provide the scalable, enterprise-grade, and secured capabilities organizations require.
Composability as a core strategy
<a href="https://www.gartner.com/account/signin?method=initialize&amp;TARGET=http%3A%2F%2Fwww.gartner.com%2Fdocument%2F4023010%3Fref%3DsolrAll%26refval%3D378391207%26">Gartner</a> predicts that 60% of new SaaS designs will support both UI- and API-first access by 2025—making preparedness for composability a common cloud application trait—and that 60% of new custom business applications will be built using reusable business services via shared curated component libraries or marketplaces. 
Composability isn’t just a buzzword; it's the frontier of modern application development. I believe that this new era of composable modern apps is fundamentally changing how applications are developed and delivered: Agile and modular, these products seamlessly integrate insights data and operations at their core, allowing developers to create innovative experiences tailored to the specific needs of their users.
For example, a beloved navigation app runs on a robust analytics engine fueled by big data, real-time user inputs, and complex algorithms behind the scenes. AI enriches this capability, powering features like "leave home now," which uses machine learning to analyze real-time and historical data for optimal departure times. This intricate system not only informs core navigation features but also integrates into users' broader "systems of action," such as sending real-time traffic alerts or "leave home now" notifications to email or phones. But building and maintaining such complex analytics in-house is a gargantuan task most organizations can’t afford. Turning to APIs for embedded analytics would yield a much higher ROI by enabling developers to rapidly adapt to change and create new value for their users. A composable modern app would drastically lower development costs, accelerate time to market, and allow businesses to thrive in the gen-AI speed age.
Compose SDK: Expanding our dev-first offering
With the launch of Compose SDK for Fusion, Sisense is meeting the moment and answering the modern developer’s needs. The excitement we've received from our customers and partners so far has been overwhelming. Designed with a developer-first approach, our solution is built to accelerate the development process, reduce maintenance overhead, and save valuable time. Compose SDK currently supports native React and Typescript, promotes a code-first methodology, and is delivered as NPM packages. This enables developers to integrate Sisense data and visualizations directly into their application code, harnessing the potential of Generative AI to create sophisticated, dynamic, and intuitive analytics experiences.
But we're not stopping at meeting immediate demand. Compose SDK is our pledge to revolutionize the embedded analytics landscape, and the first step in our mission to empower developers across the globe. By providing developers with unrivaled capabilities to build analytics into their apps and monetize their data with full flexibility, we’re envisioning a world in which the boundaries of creativity and innovation exist on entirely new heights.
“We switched to Compose SDK and it has allowed our team to focus on building better data models rather than dashboard design,” says Hive CTO Dmitriy Karpman. “It allows us to envision a world of possibilities which puts our users in control. The most exciting thing about the developer experience has been the fact that Compose SDK is built with modern, high-quality tooling that makes it a joy for our developers to work on top of.
Embracing a holistic low- or no-code, light-or limited-code, and full-code approaches, Sisense is now uniquely positioned to serve a full, use-case agnostic spectrum of development teams—regardless of their analytics maturity.
A heartfelt salute to our incredible Sisense team who made this possible—your dedication and drive continue to inspire me. 
To our cherished customers—your enthusiasm and support are inspiring. Our exploration of this new embedded analytics era has just begun, and you can expect to hear much more from us in the next few months. We are thrilled to have you with us on this journey.
Onwards,
Ariel Katz, CEO of Sisense.
<a style="color: #005fa5;" href="/webinars/how-to-build-data-products-with-insights/">Sign up today! Learn how to build the next-gen data product &gt;</a>

Ushering in a new era of embedded analytics

How to Meet the Demand for Analytics Across the Organization

<h2>Introduction</h2>
Launched by OpenAPI at the end of November, ChatGPT has changed the perception of what’s possible using Artificial Intelligence. A wide range of industries and use cases are adopting ChatGPT for its ability to reduce manual effort and accelerate operations. Not only can ChatGPT summarize information, synthesize new content, and assist development, it can also supercharge your analytics. In this white paper, you will learn how to use it to save time and effort in your operational analytics processes and gain more insight.
Built on top of GPT–3.5 as a Generative Pre-training Transformer language model, ChatGPT responds to a broad range of user inputs. As a result, ChatGPT can support a massive array of use cases, from automating coding, creating customer service responses, providing language translation, and more. It’s even poised to change how we regularly get answers to simple questions that we currently use Google for today. 
ChatGPT is starting to change the way we work, but it is already clear that AI models can reduce costly operational overhead. For example, organizations routinely collect a wide range of ‘write-once, read-rarely’ profile information, such as company size, industry segmentation, or funding details. This type of information is generally available from multiple sources and only valuable when it’s needed. Plus, It usually requires significant curation and reconciliation.
Automating enrichment and augmentation of analytics with new fields, segmentations, and classifications is a challenge that analytics professionals and operations face daily. They must choose between spending costly hours grooming information that will likely never be used or waiting until it is required, when the lead time to gather it from external sources is prohibitive. 
By operating as an on-demand source for augmented and ancillary information, ChatGPT has the potential to eliminate much of the pain and effort associated with traditional data preparation and, in the process, enable teams to segment and slice data in new ways, on-the-fly.
However, applying an on-demand model is only feasible if ChatGPT is tightly integrated with your data and analytics tooling. Without it, the ease of self-service analytics is quickly lost. Seamlessly integrating ChatGPT to supercharge your analytics is now possible. It’s easy to do, and we think you’ll find it’s a game-changer.
<h2>Why you need to integrate ChatGPT with your analytics tooling</h2>
Teams often spend way too much time in manual data prep and enrichment today. Estimates range as high as business and data analysts spending 70 to 80% of their time getting data ready for analytics, a large portion of that spent enriching and labeling data and creating new attributes to aid analysis. Adding new fields to the data sets for analysts to slice and dice by is often painful, onerous work. For example, consider a seemingly simple query:
<blockquote>
What’s the breakout of my customers by industry?
</blockquote>
It’s the kind of question critical to making decisions about entering new markets and launching new products. But what if the thousands of customers in the data set were not segmented by industry beforehand? What if Industry isn’t a field in the data or data model? As an analyst, it almost certainly usually means you’re out of luck without investing significant time and effort.
It typically means going back to the data, adding external classification information to the data set, and reloading it in your data warehouse, and that’s incredibly time-consuming. In many cases, it requires manually matching with and reconciling third-party data sets, repetitive Google searches to tag customer records manually, and often painful convoluted IF () and VLOOKUP () formulae in Excel or custom Python script. Not to mention updating your data model. 
Worse, if teams subsequently need to classify and label data in a new way – such as by zip code or county, then it’s back to manually prepping the data again.
<h2>Gains from a tight integration of ChatGPT with your analytics tooling</h2>
ChatGPT is exceptionally well-suited for this kind of work, and it can do this kind of search, extraction and reconciliation automatically and quickly. However, selecting the right moment to weave ChatGPT-sourced information into your analytics experience is critical to make it truly useful. For example, collect this information during ETL, and both storage and query costs will stay high; collect it once requested and you may struggle to insert it into the data model to enable the user's expected BI functions.
The optimal approach collects the requested data on-demand and uses ChatGPT to recommend the ideal insertion point in a model, providing a highly-responsive experience for the user. With this approach, users receive their own AI-powered always-on personal data prep and data analyst bot as part of their analytics experience. They can extend and augment their data and data model by simply asking ChatGPT. 
Achieving it creates a massive opportunity for teams, freeing them from manual work prepping and augmenting data while ChatGPT does the lifting, to spend time on more value-added and strategic work. But more importantly, beyond freeing up time, there’s transformational value to be had―enabling teams to be much more fluid in their analytical queries, segmenting data in new ways, with ChatGPT doing the work behind the scenes. 
<h2>Sisense + GPT-3: A new way to supercharge your analytics</h2>
A reference implementation of this strategy is available for Sisense Fusion, and is a perfect example of how to incorporate GPT models into analytics processing. This implementation leverages the uniquely flexible capabilities of Sisense Fusion to inject dynamic queries to GPT-3 as part of the query process, automatically generating new database tables and relationships in the existing data model while leveraging guidance from GPT-3. 
This reference architecture also demonstrates low-code app experience development, creating an interactive Q&amp;A app via Sisense BloX so users can tell GPT-3 how they want to augment the data set and model, and do so directly in their dashboards. Sisense and GPT-3 immediately handle the rest. 
How it all works can be demonstrated by answering the question posed earlier: “What’s the breakout of my customers by industry?”—when the customer attribute industry doesn’t appear in the original data model or data set. Sisense and GPT-3 will be used to fill the gap.
Below is the example data with a list of accounts. Ultimately, we’re looking for the percentage split of customers by industry even though the attribute doesn’t exist in the data.
<img class="aligncenter wp-image-741530 size-large" src="https://sisense.wpengine.com/wp-content/uploads/2023/04/chatgpt-whitepaper-4-1024x920.jpg" alt="" width="1024" height="920" />
To make it easy for the user to ask questions of GPT-3, Sisense BloX is used to build a simple interactive widget on a Sisense dashboard. This new UI assists in capturing what the user wants to request from GPT-3:
<img class="aligncenter size-large wp-image-741528" src="https://sisense.wpengine.com/wp-content/uploads/2023/04/chatgpt-whitepaper-2-1024x352.jpg" alt="" width="1024" height="352" />
In this example, GPT-3 is told the user wants the industry segmentation for a list of companies in natural language. GPT-3 is given the dashboard, widget, and column name, which ultimately contains the list of companies that need industry segment labels. 
The UI executes a Python script containing the question and the dynamically populated list of customer accounts formatted as JSON and submits it to GPT-3, which retrieves the industry for each customer.  Alternatively, if we want to add GPT-3 enrichment to the scheduled periodic data model refresh, simply executing Python rather than using the Q&amp;A UI, we can do that too. 
The result set is passed back from GPT-3 to Sisense Fusion as a JSON document. The Python script dynamically creates new tables in the origin database based on the GPT-3 result set and adds new relationships between tables in the model. Ultimately, it transforms hours or days of data enrichment effort into seconds.
In the example, GPT-3 returns each customer and their associated industry label in this example in JSON. Then, the script appends this as a new dimension to our data model and automatically joins our fact table of wins and losses with a natural join based on the account name. 
<img class="aligncenter size-large wp-image-741529" src="https://sisense.wpengine.com/wp-content/uploads/2023/04/chatgpt-whitepaper-3-1024x833.jpg" alt="" width="1024" height="833" />
With the augmented data added to our data model, it’s simple to continue our analysis. For example, we can quickly create two new visualizations using Sisense Fusion that show us the distribution of our customers by industry.
<img class="aligncenter size-full wp-image-741527" src="https://sisense.wpengine.com/wp-content/uploads/2023/04/chatgpt-whitepaper-1.png" alt="" width="859" height="353" />
The whole integration enables the analysis to be completed in a few seconds rather than waiting days or weeks for an operations team to prep and publish the new information. Using the interactive BloX widget, augmenting and enriching any other data, all using natural language, is simple when powered by GPT-3.
As an exciting and related use case, a software product can easily add GPT-3 supercharged analytics by embedding this BloX widget directly into the user interface. As a result, users gain flexibility that extends the value of the product and its data by enabling augmented or ancillary data to be added without any involvement of the product vendor.
This approach to on-demand data isn’t necessarily easy to implement in a business intelligence product. The Python compute kernel and the extensive API surface in Sisense Fusion allow direct query creation and dynamic updates to data models to get this kind of on-demand user experience. However, just about any ETL engine can at least augment data at ingestion using this technique. Sisense’s reference architecture for GPT-3 gives you a good starting point to try these techniques out for your own use cases. Here’s a <a href="/blog/how-to-make-bi-and-analytics-adoption-simple/">link</a> to an embedded example of the exact analysis we shared. 
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