LinkedIn

Twitter

GitHub

Resource Library

For many businesses interested in offering intelligent analytic solutions, it isn’t readily apparent how critical a role proper data protection and consumer privacy practices can play. When working with personal information, organizations must ensure that any third-party vendors with access to the data will remain compliant with the privacy laws governing their jurisdictions.
That’s why Sisense places such a heavy emphasis on deploying state-of-the-art software and adopting industry best-practices that are specifically geared towards ensuring data privacy. The latest step Sisense has taken to further this goal is through its recent certification with the ISO 27701 for PII Processors.
This significant achievement marks a new chapter in Sisense’s ability to offer best-in-class analytics implementations that bolster clients’ personal information protection. Here is why an ISO 27701 certification matters and what it means for Sisense’s existing and future customers.
What Is ISO 27701?
For decades, the <a href="https://www.iso.org/home.html">International Organization for Standardization</a> (ISO) has developed and published standards on a wide variety of topics and industries. From net-zero environmental impact guidelines to quality management regulations, the ISO is a globally respected resource that keeps businesses accountable to their customers and each other.
In the case of ISO 27701, organizations need to specifically implement, maintain, and continually improve on a <a href="https://www.isms.online/privacy-information-management-system-pims/">Privacy Information Management System</a> (PIMS) in order to be eligible for certification. In general, PIMS represents a set of procedures and policies that organizations develop to ensure compliance with privacy and data protection laws. With clearly defined <a href="https://www.isms.online/iso-27701/#:~:text=Privacy%20information%20management%20covers%20the,comply%20with%20regulations%20like%20GDPR.">collection, processing, storage, and deletion of personal information policies</a>, companies that implement PIMS are better positioned to handle personal information and communicate all relevant operational procedures to customers and internal stakeholders.
Certification in ISO 27701, then, means that the organization’s PIMS was modeled after some of the strictest laws and regulations related to the protection of personal information currently applicable, including the <a href="https://gdpr-info.eu/">General Data Protection Regulation</a> (GDPR) and California Consumer Privacy Act (CCPA). This adherence, in turn, should give customers, investors, and consumers the comfort that personal information is being handled appropriately.
How Sisense Became ISO 27701 Certified
The PII Processors certificate subsumed under the ISO 27701 best reflects how Sisense processes personal information. The auditors, who audited our policies, procedures, and practices for processing personal information in accordance with ISO's requirements, have determined that these meet the ISO 27701 requirements.
As a <a href="https://commission.europa.eu/law/law-topic/data-protection/reform/rules-business-and-organisations/obligations/controllerprocessor/what-data-controller-or-data-processor_en">data processor</a>, Sisense provides <a href="/platform/privacy-and-data-protection-center/">transparent documentation</a> around all its data policies that help customers understand how their privacy and data protection obligations are met. The Sisense <a href="/platform/privacy-and-data-protection-center/">Privacy and Data Protection Center</a> contains several resources for customers including a:
<ul>
	<li><a href="/platform/security-and-trust/gdpr-compliance/">Q&amp;A page</a> for modern data protection laws that Sisense complies with, like the GDPR and the CCPA.</li>
	<li><a href="https://pages.sisense.com/rs/601-OXE-081/images/Data-Processing-Addendum.pdf">Data Processing Addendum</a> (DPA) that goes into further detail about the data protection and security measures in place to protect the personal information of customers (and, subsequently, their own customers).</li>
	<li><a href="/legal/service-providers/">List of Sub-processors</a> that Sisense has assessed to ensure they have adequate security measures, certifications, and data practices before working with them.</li>
	<li><a href="/legal/government-requests/">Government Requests Principles guide</a> that describes Sisense’s approach to government agency requests for customer data.</li>
</ul>
What ISO 27701 Certification Means For Your Data Privacy
The ISO 27701 certificate serves as a third-party, independent assurance that Sisense gathers, stores, and retains personal information in accordance with some of the most advanced and strict data privacy regulations on the international stage. This in and of itself is a significant achievement and one that visibly reflects Sisense’s internal commitment to safeguarding customer information.
Existing Sisense customers and prospects looking for an analytics solution should view an ISO 27701 accreditation as a symbol of Sisense’s ability to protect personal information. The typical due diligence process that assesses a company’s security and data protection capabilities can add time to identifying and signing a contract with the right third-party partner.
Standards like the ISO 27701 greatly expedite stakeholder buy-in and shorten implementation timelines. Rather than submitting time-intensive questionnaires, engaging in interviews, and reviewing dozens of documents in an effort to estimate Sisense’s level of compliance with privacy and data protection laws, businesses can now choose to rely on Sisense’s ISO 27701 certificate as a third-party assurance for the level of protection that Sisense applies to their personal information.
When it comes to providing secure and adaptable analytics solutions, Sisense offers a credentialed, intelligent platform that can convert data into revenue. With AI-powered processing, Sisense supports embedded analytics platforms based on pro-code, low-code, and no-code development techniques. ISO 27701 is just one more proof point for the value Sisense can bring to product teams wanting to flesh out their offerings with accessible, relevant analytics tools.
For more information on how Sisense can elevate your product line with our embedded analytics program, contact us <a href="https://www.sisense.com/">here</a>.

Sisense Achieves ISO 27701 Certification for PII Processors: What That Means For You

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. 
&nbsp;

Resource Library

‌

‌

‌

‌

‌

‌

‌

‌

‌

‌

‌

‌