By Satyendra Kumar
Artificial intelligence and data analytics seem like a perfect marriage. With AI, the need to painstakingly review spreadsheets and reports from various organizational silos to connect all the salient points and warning signs is reduced. In addition to saving valuable time, human error is also mitigated. AI allows for programming the right algorithms, accessing nearly unlimited data points, leveraging online storageand produce the most relevant information needed to identify trends and problems to correct.
The future is here, and organizations that quickly adopt this approach while providing the enhanced employee training needed will stay ahead of the competition.
Advocating for AI in data analysis
With the huge amounts of data that can be stored, especially as cloud services have become ubiquitous, traditional analysis tools may not be up to par. The inability to scale as demand increases and the lack of real-time, accurate analytics and insights derived from AI and machine learning (ML) through programmed algorithms are disadvantages in a competitive market. That’s why it’s essential to be prepared for the long term. The constant drive to collect more data points in the era of big data means that the demand for improved analytics will continue to increase, as will the need to use more AI and ML.
Rather than hiring a data analyst to spend hours poring over reports and spreadsheets, using AI frees up employees to focus more precisely on what an AI-generated report reveals in terms of patterns or trends. This leads to better decisions in real time when a course correction is evident, increasing productivity.
Actionable insights from AI-powered predictive analytics help drive future growth in a shorter time frame. Algorithms can be programmed to establish key performance indicators (KPIs), with built-in alerts when a specific situation or apparent anomaly needs to be addressed quickly. Both Snowflake’s AI Data Cloud and AWS’s Generative AI offer scalable, personalized services that help deliver better customer and employee experiences.
The Disadvantages of AI-Based Analytics
AI-derived data analytics are only effective if the algorithms programmed to monitor, report, and aggregate that information are effective. biased decision making Inherently biased training data or algorithmic bias can skew the analysis process. Skilled data architects and engineers are always in the loop, and data governance guidelines can help determine how algorithms are programmed to eliminate bias. There is also concern, especially among the next generation of data analysts, that a loss of critical thinking is occurring and that an over-reliance on AL and ML could lead to a loss of human oversight.
Reports that once took hours to assemble manually can be organized in much less time thanks to algorithms programmed to ask the right questions after accessing the ideal data points. As tempting as it may be, it is imperative that the human factor is always present when companies want to reduce IT overhead. Companies like IBM Cloud, Google Cloud, AWS, and Salesforce have been identified as the Top 10 AI-Based Data Analytics Companies for 2024Google Cloud was highlighted for its vertical integration model between data analytics, AI, ML and cloud infrastructure, which enables more seamless end-to-end solutions.
Establishing standards for how to use AI in data analysis
As with other emerging industries in the past, there are best practices, standards and government regulations around data access and privacy. Alliance for Data and TrustThe Alliance, founded in 2020 by some of the world’s largest players (American Express, Meta, the NFL, UPS, and IBM, among others), has been working to create data provenance standards “to help organizations determine whether data is fit and reliable for use.” The Alliance’s first data provenance standards were published late last year.
Another report According to a 2022 online survey, data professionals spent more than a third of their time preparing and cleaning data, more than creating reports and visualizing it. This is where AI could be able to reduce this preparation and cleaning time.
Harnessing Big Data for Future Growth
AI can be integrated into a modern cloud architecture and used alongside other reporting tools, such as PowerBI or Tableau, that analyze the data and create visual representations. As algorithms “learn” more about the scanned data, AI and ML offer the ability to remove some of the duplicate layers, leading to additional cost savings and improved analytics capabilities. Mastering big data in this way can result in faster response times, both internally and for end-user queries.
The demand for data collection, management, and analysis continues to grow. This demand is partly fueled by companies looking to differentiate themselves from the competition by managing their operations more efficiently (including automating decision-making through AI and ML) or spotting trends earlier, making the integration of still-emerging AI technologies very opportune.
These organizations span industries ranging from how professional sports are now using AI to analyze data to study trends in anticipation of an opponent to how to get an athlete back on track by looking at numbers to detect any deviations. Retail companies can spot consumer trends faster and send personalized offers based on their digital history.
While there is a learning curve for data analysts, algorithm programmers, and other IT professionals, organizations that master the dynamics of AI and data analytics to quickly identify “hot spots” and stay on top of the latest industry trends will have a head start on the future.
About the Author:
Satyendra Kumar is a Data Architect at Cogent Data Solutions and has over 15 years of leadership experience across multiple industries including pharmaceutical, financial, and manufacturing. He has helped organizations reduce IT costs and modernize IT infrastructure by collaborating with cross-functional stakeholders. Satyendra is an industry expert judge for Globee’s Technology Awards and a fellow of the IEEE Society for Technology Professionals. Connect with Satyendra on LinkedIn.
