(Mr. Squid/Shutterstock)
Steve Jobs once compared the electric motor to the democratization of new technologies. When the electric motor was invented, it could only be manufactured on a large scale in factories. They had great applications, but required significant resources to maintain, from elaborate belts and pulleys to a team of skilled mechanics. It was impossible to create a real lasting impact. At some point these large electric motors were reduced to fractional horsepower electric motors and proliferated to the point where we now easily have more than 50 in the average household.
We are at a crossroads with generative AI, where we will see this emerging technology proliferate to a point where it will be applied to almost every possible use. A Alteryx investigation found that nearly 80% of respondents already see the benefits of generative AI in achieving their organizational goals, while predicting that their organization’s use of generative AI would increase from 32% to 53% over the next Next 3 years.
Generative AI is considered the solution to many business challenges. Companies looking to democratize data and analytics at scale are finding that generative AI is proving extremely successful. The reason is that AI improves business analytics tools by improving their usability and then synthesizing the information in a consumable way.
Here we’ll explore how generative AI helps unlock the full potential of business analytics. To do this, it’s important to understand the obstacles many organizations face when it comes to democratizing analytics.
Identify the barriers
(Peshkova/Shutterstock)
Achieving enterprise-wide adoption of analytics is critical to empowering staff, especially those in the line of business who typically do not have deep data skills. Every industry leader must make highly informed decisions to succeed in an increasingly competitive market. It’s easier said than done. For the most part, these challenges can be summarized into three key challenges:
- People: Companies typically do not have enough data scientists, AI or analytics experts to meet the demand for information needed across the enterprise.
- Systems: More often than not, businesses are limited by siled, legacy systems that struggle to answer modern business questions that don’t fit neatly into any single business system.
- Data: The growing complexity and volume of data makes it much more difficult for businesses to maximize its value. Using data meaningfully often becomes a slow, tedious and inefficient exercise, leaving an untapped goldmine of usable data untouched.
Leveraging Generative AI for Increased Efficiency
The good news is that generative AI can play a transformative role in overcoming these challenges. It can be leveraged to enable more people, in all areas of business, to use analytics in their daily decision-making.
(SomYuZu/Shutterstock)
Generative AI makes analytics tools easier to use with its ability to integrate natural language interfaces, essentially allowing users to perform complex tasks using basic English as a basis.
“coding language”. Years ago, analytics tasks could only be performed with code – a skill that requires specific technical expertise that can take years to truly master. Then, visual tools made analysis more accessible. Now, generative AI is changing this paradigm even further by allowing users to easily ask natural language questions to perform analysis tasks.
We’re also seeing huge progress in improving the quality of automation across the data analytics lifecycle. AI tools can translate much more than just natural language. By mastering a wide range of coding languages and data formats, AI can be a powerful automation tool by quickly translating expressed business expectations into execution systems, without having to navigate the intricacies of their manual instrumentation.
Balancing generative AI with a unified analytical approach
Like any emerging technology, generative AI also presents several challenges, risks and limitations to its large-scale adoption. This includes concerns about data privacy, exorbitant costs, and hallucinations or the generation of false information.
The key to balancing the benefits and potential challenges of generative AI is to find a solution or platform that integrates different mechanisms that can control these challenges and follow the principles of responsible AI. Examples include rapid engineering techniques that make AI results trustworthy and reliable, as well as data and metadata management capabilities that ensure data governance. Additionally, analytics is the ideal domain to practice responsible AI because the presence of a domain expert analyst who understands the shape of the organization’s data is always there to interrogate the outcome. With enterprise-grade guardrails like these in place, businesses can truly harness the potential of generative AI to create new heights of value from data.
The impact of generative AI on scaling and optimizing business analytics is clear. When combined with a governed enterprise solution and a holistic approach that prioritizes democratizing access to data and analytics, businesses can better leverage generative AI and unlock its full potential to achieve better, more informed business results.
About the Author: 
Like a Whillock is vice president and general manager of machine learning and artificial intelligence at Alteryx. Asa’s 30 years of experience spans market-leading companies such as Intel, Macromedia, Adobe and now Alteryx. His passion is incubating new companies, having founded 13 in the areas of machine learning, analytics, platforms, video streaming, communications, privacy, security and client experience. Asa has also been awarded 37 patents and received the 2016 Technology & Engineering Emmy and the 2017 Adobe Founders Award.
Related articles:
Linux Foundation Promotes Open Source RAG with Launch of OPEA
What’s holding back GenAI’s return on investment?
Why a bad LLM is worse than no LLM at all
