{"id":44210,"date":"2025-04-10T12:23:11","date_gmt":"2025-04-10T11:23:11","guid":{"rendered":"https:\/\/www.intelligentcio.com\/apac\/?p=44210"},"modified":"2025-04-23T11:08:08","modified_gmt":"2025-04-23T10:08:08","slug":"three-things-that-data-teams-need-to-do-before-implementing-ai-chatbots-for-business-intelligence","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/apac\/2025\/04\/10\/three-things-that-data-teams-need-to-do-before-implementing-ai-chatbots-for-business-intelligence\/","title":{"rendered":"Three things that data teams need to do before implementing AI chatbots for business intelligence"},"content":{"rendered":"\n

Avi Perez, cofounder and CTO, Pyramid Analytics, says that in order to unlock the value of business intelligence AI, data teams need to first prepare their databases, their approaches to AI governance, and the way they interact with other stakeholders.<\/em><\/p>\n\n\n\n

AI chatbots that deliver business intelligence capabilities are soaring in popularity right now. Every department is eager for more accurate and reliable forecasting, along with faster and richer insights to support their decision-making and strategic planning.<\/p>\n\n\n\n

It\u2019s almost an arms race among enterprises: whoever gains meaningful insights first gets a head start at mitigating emerging risks and seizing nascent opportunities.<\/p>\n\n\n\n

There\u2019s barely a use case that doesn\u2019t benefit from them: sales teams track product demand trends, marketing teams access campaign performance metrics, customer support teams automate simpler customer inquiries, HR teams monitor employee engagement and productivity, and manufacturing teams optimize process efficiency.<\/p>\n\n\n\n

According to research by BARC, AI-enhanced BI is expected to drive faster time to insight and a reduced workload, among other advantages. It\u2019s not surprising that this shiny new tool is on everyone\u2019s wish list.<\/p>\n\n\n\n

But implementing an AI chatbot for business intelligence requires more thought than ordering socks from Amazon. If you rush into the project without laying the necessary groundwork first, you\u2019ll risk encountering significant challenges.<\/p>\n\n\n\n

We\u2019re talking anything from unreliable outcomes that skew your business decision-making, to serious penalties for non-compliance with privacy regulations and industry standards. So before you implement an BI chatbot, check that you\u2019ve covered these three critical bases.<\/p>\n\n\n\n

<\/a>Get your house in order in terms of data quality<\/strong><\/p>\n\n\n\n

Data quality is the single most important ingredient for successful generative BI. Because AI tools rely on vast volumes of complex data to generate answers, there\u2019s a higher likelihood of poorly-defined data polluting the entire pool. What\u2019s more, small errors in a training dataset can be amplified as they are copied across the system. In extreme cases, this can result in model collapse.<\/p>\n\n\n\n

You need to ensure that data governance policies are not just in place but enforced. Implement robust rules controlling data collection and storage, data management, and data ownership and stewardship. Only clear data trails can assure data integrity and deliver reliable data that\u2019s been cleaned correctly and processed consistently.<\/p>\n\n\n\n

In a similar vein, keep an eye on inter-departmental differences around data enrichment and how they calculate the metrics they rely on. Different teams have different objectives, so it makes sense that they\u2019ll take varying approaches to enrichment and metrics. They might start off with the same datasets, but apply different weights, labels, and sources as they process it, resulting in multiple versions of truth that eventually lead to inconsistent and unreliable outcomes.<\/p>\n\n\n\n

Make sure that third-party AI services don\u2019t have access to your full data<\/strong><\/p>\n\n\n\n

When you set up your infrastructure for a BI chatbot, it\u2019s tempting to connect a third-party large language model (LLM) directly with your business databases. After all, if the LLM has access to the most up to date data, you\u2019ll get more accurate insights, not to mention a seamless system that\u2019s faster to respond.<\/p>\n\n\n\n

But that would be a big mistake, primarily because of security. There\u2019ve been many headlines about public GenAI models leaking proprietary data from prompts, and even if nothing links, it\u2019s possible for unauthorized parties to draw conclusions based on sensitive information thanks to the queries that users put in.<\/p>\n\n\n\n

Some LLM developers are open about utilizing user inputs to use for model training, which increases the risk of data breaches and leaks over time.<\/p>\n\n\n\n

Even if your data uploads are siloed, giving third-party AI services access to your proprietary information is asking for trouble. Many regulations, including HIPAA, GDPR, and CCPA, have stringent requirements around cloud server usage and access to sensitive customer data. Simply connecting a database with an externally hosted AI tool would be considered non-compliance, inviting penalties and fines.<\/p>\n\n\n\n

<\/a>Prepare your data team to become enablers<\/strong><\/p>\n\n\n\n

Bear in mind that your systems, data, and infrastructure might all be ready for BI chatbots, but that your employees may not be. One of the big benefits of chatbots is that they enable self-service insights, but ordinary, line-of-business users who don\u2019t have a data analytics background are unprepared for this step.<\/p>\n\n\n\n

It\u2019s not just that they don\u2019t know how to code their queries, because a chatbot overcomes that by allowing them to ask natural language questions. It\u2019s that they lack a data analytics mindset, and sometimes even lack basic data literacy. They may not know how to turn questions into queries that deliver useful answers, how to word them in ways that chatbots can understand, or which visualizations to request in order to gain the insights they need.<\/p>\n\n\n\n

That\u2019s why your data team has to be ready and waiting to support their line-of-business colleagues. They are going to have to step out of their usual role to serve as guides to BI thinking. Prepare them to educate your other employees, correct their mistakes, and explain how to get the results they\u2019re looking for.<\/p>\n\n\n\n

They\u2019ll also need to supervise the chatbot for hallucinations and flaws and train their colleagues to be aware of the ways that inaccuracies and bias can creep into AI-powered BI insights.<\/p>\n\n\n\n

<\/a>BI chatbots require careful preparation<\/strong><\/p>\n\n\n\n

Implementing a BI chatbot can be a smart move for your company. It can speed up time to insights, support new products and services, and drive better business decision-making. But you\u2019ll only be able to harvest those benefits if you lay the groundwork first. Making sure that your data and employees are ready, and maintaining strict standards for access, are vital for successful BI chatbot adoption.<\/p>\n","protected":false},"excerpt":{"rendered":"

Avi Perez, cofounder and CTO, Pyramid Analytics, says that in order to unlock the value of business intelligence AI, data teams need to first prepare their databases, their approaches to AI governance, and the way they interact with other stakeholders. AI chatbots that deliver business intelligence capabilities are soaring in popularity right now. Every department […]<\/p>\n","protected":false},"author":58,"featured_media":44510,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7027,44],"tags":[1108,8248,7973,8249],"class_list":["post-44210","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-intelligent-technologies-ai","category-top-stories","tag-ai","tag-ai-chatbots","tag-expert-opinion","tag-pyramid-analytics"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/posts\/44210","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/comments?post=44210"}],"version-history":[{"count":2,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/posts\/44210\/revisions"}],"predecessor-version":[{"id":44499,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/posts\/44210\/revisions\/44499"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/media\/44510"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/media?parent=44210"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/categories?post=44210"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/tags?post=44210"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}