Leveraging GenBI can empower CIOs to unleash the potential of GenAI and Business Intelligence for their organization while keeping their customer data safe. Let us look at how.
Generative artificial intelligence, or GenAI, has been a transformative force in many different business fields since it appeared on the scene in 2022. According to McKinsey, GenAI could bring savings opportunities of up to $2.6 trillion across various operational functions.
However, many business leaders hesitate to adopt it due to security fears. IBM reports that 96% of executives see adopting generative AI as increasing their organisation’s chances of experiencing a security breach within the next three years.
If data is the new oil, it’s only useful once it’s been refined. GenAI could refine it faster and to a higher standard. However, it’s also a precious resource that must be safeguarded, and large language models (LLMs) have been known to compromise that safety. How can business leaders balance these two conflicting considerations?
Enter GenBI, the new generation of business intelligence
GenBI aims to resolve this dilemma by marrying GenAI and business intelligence (BI). GenBI delivers on the unfulfilled promise of self-service BI (SSBI) tools to make BI truly accessible to non-technical users.
Touted as revolutionary a decade ago, SSBI solutions intended to take data insights out of the preserve of data scientists and put them within reach for every stakeholder. However, people generally don’t know which graphs, charts, or visualizations to ask for or how to discover initial data to prepare data for their dashboards. Even with SSBI tools, data scientists still do most of the busy work, and users who need data insights are still stuck in a line waiting for them to arrive.
In contrast, GenBI allows users to ask queries in natural language and explore data more naturally. These solutions “understand” your needs and automatically choose the best formulation for your data.
Additionally, many SSBI platforms only produce static images and simple charts. GenBI can generate complex, dynamic visualizations that you can manipulate, zoom in and out, or continue investigating a particular subset of data.
The security headache of GenBI
That said, the same security concerns that surround GenAI are extant in most GenBI solutions. The LLMs that power GenAI tools often store the data that’s used in queries or for training, and use it for their own purposes, such as improving the LLM.
This raises the serious risk that an LLM could reveal sensitive proprietary business information. The Cisco 2024 Data Privacy Benchmark Study revealed that 48% of people admit having entered private company information into GenAI tools – and the true percentage may be much higher. Over a quarter of organizations have at least temporarily banned the use of GenAI tools due to privacy and security issues.
At the same time, business users worry about the precautions a GenBI solution takes to secure data. If it isn’t hosted on your infrastructure, you can’t be as certain about its security posture. Additionally, consumers are nervous about the privacy and security of GenAI tools, so deploying them could mean damaging customer trust.
This comes alongside a worrying lack of transparency that raises concerns about bias, hallucinations, and unreliable results. What are developers doing to address these issues? Let’s look at three GenBI solutions and their approaches to resolving security concerns.
Amazon Q in QuickSight
Amazon AWS offers Amazon Q in QuickSight as a GenAI assistant that helps users create and manage data insights. The solution responds to natural language text prompts to build dashboards and automated contextual summaries that help explore the data. With Amazon Q in QuickSight, every user can generate interactive data stories, without waiting for BI experts or data scientists to update the data and produce new dashboards.
Amazon Q addresses security issues by guaranteeing that it understands and respects the roles, permissions, and governance identities that you establish.
The platform promises not to use your data to improve its underlying models, and won’t permit any user to apply Amazon Q to access data that is outside their permissions credentials.
Pyramid Analytics
Pyramid Analytics is a GenBI solution designed to empower business users to access and explore data independently. Users can ask queries about data using natural language, such as voice or text prompts.
The solution can turn even vaguely worded questions into logic to produce complex visualizations, charts, and dashboards that best answer the query. Every response is fully dynamic, so users can manipulate it further to investigate specific aspects or subsets of the information, all using natural language questions.
Pyramid safeguards data privacy and business security by providing a protective layer between the LLM of your choice and your data. The solution scans your data sources to create context-informed metadata, which it sends to the LLM along with your query. It then performs the recommended data manipulations within its secure platform, so the LLM never accesses your data. Pyramid can guarantee that the LLM will never store your data, leak it, or use it for unauthorized purposes because it never has access to it in the first place.
Tableau Einstein Copilot
Einstein Copilot is a GenAI assistant which enables Tableau BI users to explore data through natural language prompts. The copilot works alongside stakeholders to help them to manipulate data more effectively.
Users can ask for advice on the best way to visualize data or interpret results, and receive dashboards and formats that help them gain the insights they need.
Salesforce, the company behind Einstein and Tableau, promises that the LLM won’t record or save your data, the prompt you use, or the response that it produces. It scans data sources to build a context summary that it sends to the LLM, applying data masking to hide sensitive PII. In a workaround that’s similar to what Pyramid has built, this ensures that the LLM has no access to your proprietary business data.
GenBI can deliver GenAI capabilities without the risks
There are many valid reasons to be cautious before applying GenAI tools to your business data. It’s important to maintain strong data security, ensure all your employees comply with data privacy measures, and carefully vet every solution that may access your data.
However, these three GenBI solutions demonstrate that it’s possible to benefit from GenAI without risking a security breach. Business leaders don’t need to allow the fear of a security incident to hold them back from unlocking the full power of their data. The right GenBI solution can bring true self-service BI insights to everyone who needs them, guiding better decision-making and improved business strategizing.
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