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Make data work for you with the power of generative AI

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By Ramprakash Ramamoorthy, director of research at ManageEngine

Looking at current developments, there are signs that artificial intelligence (AI) looks to have a bright future ahead in the Philippines. Last December, the Filipino government launched the National Economic Development Authority (NEDA), which aims to create policies that accelerate digitalization by cultivating a tech-ready ecosystem in the country. Moreover, the government has also crafted a National AI Strategy in order to build a skilled AI workforce. These factors combined enable AI technologies, including generative AI, to flourish in both the business and social landscapes.

Generative AI is a crucial asset in answering employees’ questions as well as inspiring them with new content ideas. However, there are other areas that can benefit from the use of generative AI, such as data analytics. For example, generative AI can be used to help banks measure customers’ credit risk, which is a crucial factor in setting fair loan rates.

With organizations handling ever-increasing volumes of data, generative AI allows users to unlock a treasure trove of business opportunities that boost customer loyalty and improve operational efficiency. Below, we outline three ways in which generative AI can augment data analytics and allow users to drive better decision-making.

  1. Speed up data analysis
    Traditionally, businesses relied on batch processing techniques to process data for separate functions. The problem is that it takes too long to collect and store data, which makes taking advantage of real-time events a difficult endeavor. Generative AI can cut down processing time to a matter of seconds by having users simply enter text-based instructions. From there, the platform can quickly retrieve specific information that meets users’ requirements. These text-based instructions can range from easy requests like retrieving last month’s sales report to complicated commands like providing a detailed summary of the company’s revenue patterns during the last ten years. Deep insights such as this, delivered quickly, empower organizations to capitalize on real-time market trends or resolve time-sensitive issues before they snowball.
  2. Translate data into visual charts
    Organizations that handle large volumes of data may find it difficult to interpret the information into readable insights. However, by feeding data sets into generative AI, they can then translate them into easily digestible visual formats, such as charts, tables, and graphs. Moreover, generative AI can also help fill in missing information that can otherwise make it difficult for data to be interpreted accurately. With this capability, employees can get a complete picture of their current performance and behavioral trends. This makes generative AI a crucial tool for industries that require accuracy to ensure smooth-running business operations and plan for future developments, such as financial institutions.
  3. Reduce errors from manual analysis
    Relying on manual data processing techniques to uncover insights is a challenging feat, as employees may miss out on hidden insights that can otherwise help them achieve greater success. Furthermore, data analysts may inadvertently misinterpret the data, leading to organizations acquiring solutions that fail to meet business needs or creating marketing campaigns that are uninteresting to target customers. Generative AI can resolve this by automating analysis processes, allowing users to spot patterns and anomalies that are otherwise hard to identify manually. This gives employees more room to focus on making crucial decisions and monitoring their initiatives to ensure that they are achieving the desired results.

With great tools come great outcomes

No two generative AI tools are alike in terms of features, pricing, and complexity. Therefore, it is important that businesses sit down and carefully consider which AI tools can best meet their business needs. When choosing the best AI tools, it is important that organizations consider the following factors:

  • The first and most important step is for organizations to understand their data needs. This requires organizations to take stock of the volume of data they typically handle, the diversity of the information, and the need for accuracy in their operations.
  • Once that is done, organizations should then move on to assessing their business goals as well as their operational complexity. These details are crucial in identifying areas that can benefit from AI-driven analysis, whether it is customer service, fraud detection, or quality control. Besides that, this information can also help organizations decide between integrating a simple descriptive analytics tool or a deep-learning-powered predictive analytics platform into their infrastructures.
  • Assessing budgets is essential when choosing between open-source platforms and premium software for their operations. By doing this, organizations can equip their data analysts with the right set of capabilities without blowing a hole in their finances.
  • Choosing a tool with a user-friendly interface is preferable, especially if you have a team with different levels of technical expertise. With a user-friendly tool at their disposal, users will have an easier time processing data without having to spend time learning how to use the tool first. This, in turn, can lead to seamless employee experiences and improved business performance.

While data is crucial to understanding customers’ needs and improving operational efficiency, it is useless without the right tools to process and interpret it. By putting generative AI in the analytics driver’s seat, organizations can unlock new ideas that can help them make crucial decisions on improving their products and their operations. This, in turn, can help organizations boost their chances for success as well as build memorable experiences for their customers.

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