In what could be a glimpse into the future of AI integration in Business Analytics, MicroStrategy on Tuesday announced a new addition to its platform that simplifies access to business analytics data within organizations.
MicroStrategy Auto is a customizable AI bot that the company says provides a faster, easier way to deliver helpful information to everyone in an organization.
Auto is the latest enhancement to MicroStrategy AI, released in October 2023, a solution for quickly creating AI applications from trusted data.
Auto can be deployed as a standalone app or integrated with third-party apps, the company noted, and offers complete customization. Its appearance, language style, and level of detail can be adapted to the user’s specifications.
Since generative AI powers Auto, users can interact with the bot using natural language.
“We use GPT4 for the backend to determine what the user is asking and how to answer the question,” explained Saurabh Abhyankar, executive vice president and product manager at MicroStrategy.
“The difference between MicroStrategy and a large general-purpose language model is that in addition to the cognitive skills of the LLM. We add an analytical data structure,” he told TechNewsWorld. “So if you ask me how many hats I have in the store’s inventory. »
“You need both in a business analytics scenario because a chatbot like ChatGPT doesn’t have the context, business knowledge, security, and governance to answer a question like that,” she says. He adds.
Unlocking User Value Business Analytics
Using AI, Auto can remove barriers to fast, effective decision-making by making applications smarter and putting business analysis in the hands of users. regardless of their skill level or the application they are using. ‘they use. “They use it,” the company argued.
He adds that there is no need to use a complex dashboard to obtain information, and users can request information in a common language, making it easier to integrate business intelligence into business decision-making.
“We believe that using MicroStrategy AI will provide tremendous value by providing a variety of users with deeper insights that previously required more clicks and more granularity to understand. This is powerful for user self-service.
Nena Pidskalny, director of supply chain strategy and planning at Federated Co-operatives Limited, said in a statement.
“Giving more employees access to Business Analytics intelligence data can benefit a company by driving informed decision-making across departments, enabling agile response to market changes.
And promoting a culture of data-driven decision-making,” added Mark N. Vena, leader. . . and principal analyst at SmartTech Research in San Jose, California.
“However, easier access to business intelligence data can lead to potential harms such as data breaches, misuse of sensitive information, and jeopardize competitive advantage if not properly managed and protected,” he told TechNewsWorld.
Custom generative AI bots have advantages over general-purpose bots like ChatGPT, Gemini, and Claude, said Rob Enderle, president and chief analyst at Enderle Group.
A consulting services firm in Bend, Oregon. Capable of doing one or a few things well and potentially better,” he told TechNewsWorld. “They can also run locally because they use smaller libraries.
Enderle added that custom enterprise robots can also be safer than general-purpose robots. “They usually come from larger LLMs,” he explains, “but because they’re smaller and more focused, in theory, they’re less likely to do things you don’t want to do. »
Tackling Concerns About AI
Custom generative AI bots can also address businesses’ concerns about sharing data with large chatbots. “There’s always anxiety if you’re offering your private information or your client’s information to a tool that’s going to iterate on that data and may represent it in some way later,” said Duffield, a policy predictor at the Cato Institute in Washington. , DC think tank.
“Consumer-centric bots allow the companies behind them to use their conversations to improve the bots,” she told TechNewsWorld.
“This would not be the case with many of these commercial tools, as the way the information can be used will be contractually specified.”
“Companies don’t want to send all their data to a general-purpose LLM,” Abhyankar added. “They don’t want to train the LLM with their data because of the risk of that data leaking.”
With MicroStrategy, he explains, data is stored in the customer’s environment. Only metadata fragments are sent to our LLM, which is not trained on this data.
“We can do this because MicroStrategy does the calculations, and since the LLM doesn’t need to do that, it doesn’t need all the data,” he explained.
For this same reason, it is possible to prevent the LLM from having hallucinations. “LLMs, by their nature, are probabilistic,” Abhyankar said. “You can ask him questions, but you can get different answers to the same question. This is not ideal for a business scenario.
He argued that by running calculations at the MicroStrategy layer and performing them based on the business logic the customer has coded into our platform, we can avoid probabilistic problems.
“Therefore, the challenges of data sharing and hallucinations are largely eliminated because we use LLM only for cognitive skills and use customer data at the MicroStrategy layer reliably,” he stated.
Pumping Up Productivity Business Analytics
Making Business Intelligence more accessible to company staff can have productivity benefits. “This should enable decision-makers to make better and faster decisions, which would result in greater operational success,” Enderle said.
Data analysts, in particular, should see productivity gains from the self-service aspect of MicroStrategy Auto. “This makes data analysts more productive because they can do more simultaneously,” Abhyankar said. For them, this means increased productivity.
“When the end user can help themselves, the analyst realizes key benefits,” he continued. “They are free to focus on higher value things because they answer fewer questions and requests from end users.”
Sharad Varshney, CEO of OvalEdge, a
Data governance consultancy and end-to-end data catalog solutions provider based in Alpharetta, Georgia, noted that generative
AI technologies are profoundly impacting data analytics. Data at all levels.
“They simplify data discovery. Allowing teams like marketing or HR that don’t traditionally focus on analytics to use enterprise data assets easily,” he told TechNewsWorld.
“However,” he adds, “the data received must be managed precisely. Although a generative AI tool can quickly find and contextualize data, it does not consider data quality, traceability, or access.
“Once data is discovered,
policies must be implemented to ensure that the user requesting the data has the necessary access permissions to extract it,” he continued.
It must then undergo several quality measurements to detect duplicates, inconsistencies, and other factors before being classified and cataloged. Only then can it be analyze.
“Fortunately,” he added,
“there are tools available to automate these and other governance processes that greatly simplify data analysis and visualization.”
