Artificial Intelligence (AI) is the buzzword of our generation. Tech Giants, aka the Googles and OpenAIs of the world, are competing to gain an edge in the AI space. OpenAI recently launched its new GPT-4o model. The one with multi-modal capabilities with the ability to reason across text, audio and video. In the wake of its competitor, Google released “Project Astra” at Google I/O 2024. An initiative to create universal AI agents to assist in all possible life perspectives. AI agents are the backbone of AI based data science/analytics.
The Buzzz: AI Agents
Corporate space, from large firms to budding startups, is also rapidly changing and evolving. While the usage of AI agents has skyrocketed in the software-building space. It is yet to have been used extensively in the data analytics space.
- The expectation is for automatic dashboards and the ability to get answers using plain English. But the translation for complex requests to SQL queries aka straightforward answers is yet to be available. Though, with the use of AI agents, a business user can write complex queries faster.
- The future is not complete automation but assisted automation. Thereby, the future has a machine/agent executing the task with guidance from a human. For edge cases, monitoring and ensuring correct outputs.
- With AI and corresponding tools/agents. Data analysis and the ability to query etc, is a lot more accessible now. This essentially means we still need people with skills, but they can perform much better than before and everyone’s skills are much more potent.
Data-Driven Businesses
With the growth of data and the advent of AI agents, the world of data has seen a massive transformation, at least in terms of perception. The expectations are sky-high and the promise is of a world without labor. Thus, the ability to create great things at a much faster pace and lesser resources.
This has led to not just the need for useful AI agents and tools. But also good data practices, infrastructure and pipelines to run your data models on. So, the need for AI agents is only going to improve the need for better data infrastructure and trying to get it right.
The ability to do this well and then use automation to build these pipelines fast is going to get extremely critical going forward. Those with the ability and experience building these data models and data infrastructure, such as data lakes and warehouses, know that it’s not a cakewalk. The real goal is to build a good data foundation so you can leverage and build agents, etc.
Couple this with the need for good analysts, engineers, and data scientists, you will end up with a robust team.
The growing competition and human needs demand more and more complex data to be analyzed for better decision-making. The AI analytics market is projected to grow at a rate of 22.5% from 2020, reaching $238.5 billion by 2025. Two-thirds of the sample survey of 451 enterprises by S&P Global Market Intelligence agree that AI plays a vital role in their data-driven efforts. While 88% of the most data-driven companies that base almost all of their strategic decisions on data agree with this fact.
The AI PR!
Not able to believe in the above data? Picture the importance of AI through the following examples:
- Global IT services, including Tata Consulting Services (TCS), have been using AI-enabled analytics for optimizing transport operations, creating hyper-personalized customer experiences. And thereby evolving services as a part of data-fueled sustainability.
- Microsoft uses AI workplace analytics to optimize employee productivity by efficiently relocating offices, resulting in a 100-hour workweek savings and $520,000 annual savings.
- Coca-Cola leverages social media data and AI-fueled picture-matching technology to deliver 4 times more effective targeted ads.
All these stories are quoting current existing ML based solutions/approaches as AI. It’s almost like rising tides lift all boats! The rise in AI agents will result in the growing adoption of ML and other traditional-based approaches.
Let’s understand “Where” and “Why”
The proliferation of tools and positive intent behind the adoption of ML/AI etc, will mean the key part is going to be what problems to solve and why these. The ease of building new projects and tech will make the ability to prioritise and choose the right initiatives to be the key! Those with this ability will keep getting better.
WHY?
AI based Data Science/Analytics, while in use. Has yet to evolve into a stage where it becomes the key IP behind a firm’s success. One of the most prominent ones is TikTok, whose recommendation engine is supposed to be the best in the market, propelling its growth and helping it become one of the best social media companies in the world. So much so that the US had to ban the firm from operating in the US with foreign ownership!
Financial trading firms are the ultimate data analysis experts who store, process, and make decisions on massive data sets in a short time. Have incredible teams of small but very smart folks who propel the companies. The future for other industries is similar, with small but skilled teams expected to drive most of the value.
WHERE?
The versatility of AI/ML-based decision-making is what makes it useful in various sectors.
- As it can be understood from the above discussion, AI based science/analytics revolutionises businesses by enabling personalisation, data-driven decision-making, predictive analytics, evolved fraud detection, versatile data analysis and equipment fault detection.
- Real-life examples like Netflix’s customer segmentation, PayPal’s fraud detection, and Walmart’s supply chain optimisation, and the advancements in them in recent times, show the impact of AI’s transformative ability.
From Finance to Retail, Healthcare to Telecom, every sector has utilised the power of AI and has been part of the ship sailing over the large waves of AI transformations (Everyone may remember the winter of 2022, when the world was introduced to the power of GenAI, in the form of OpenAI’s ChatGPT).
The use cases will only rise, and the IP creation will be a key prerequisite for company and value generation. The edge in businesses will come from their ability to work with data and corresponding strategy!
Conclusion
The advancing AI space has a profound impact on data analytics in the corporate world. And as it continues to evolve, it becomes essential for organisations to leverage these advancements and explore the full potential of AI in data analytics.
- Implementation of AI-driven data analytics comes with several complex challenges that can be tackled with proper considerations or solutions.
- Though Data Analytics is a very old concept, several businesses, especially budding startups, find it difficult to utilise it to improve real-life outcomes.
- The added need to use AI Analytics to handle complex data poses the challenge of skill gaps, training, integration complexities, data security concerns, etc, in front of the enterprises.
Startup Analytics, as the name suggests, is about taking the startup steps behind building your analytics layer correctly so you can focus on AI and ML-based initiatives next. We help firms to smoothen out their journey in “terms of” and “by using” data analytics techniques. The added ease of using AI analytics to solve the previously mentioned challenges can make any startup grow! “RELAX AND SAIL THE WAVES OF DATA”
Reach out at admin[at]startupanalytics.in
