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How artificial intelligence in data analytics can help visualise business data?

In an ultra fast-paced digital world, businesses of all sizes produce huge amounts of data that are challenging to keep up with. Such data carries much promise when it comes to analyzing them. Recent technological advances have changed how enterprise analytics perform. There are still some challenges to using data and analytics in many aspects of an organization. However, when using artificial intelligence in data analytics, businesses can produce outcomes far beyond what they can do manually, both in terms of speed and accuracy.

Analytical approaches comprising predictive models have now begun to shift merely to descriptive approaches, which is already beneficial for many users and continues to be valuable. Descriptive analytics has evolved much, making greater use of visual analytics. Despite this, making use of data and analytics to interpret and envisage significant phenomena in businesses is difficult.

Predictive models capitalize on past data and a reasonable amount of expertise to create and predict outcomes. However, the use of past data here limits how and when they can be deployed. Existing data analytics approaches have historically been a bit narrow. They are focused on particular functions or units, even though many business problems and issues cut across functions and units.

Data Analytics Influenced by Artificial Intelligence

Powered by automation and artificial intelligence, the next-generation of enterprise analytics is emerging. Apart from this, the innovation relies on connections across existing information systems and role-based assumptions about what decisions will be made on data and analytics. AI-enhanced software has the potential to assess data from any source and deliver meaningful insights. It can analyze customer data that can be particularly revelatory and disrupt product development while improving team performance and enabling businesses to know what works and what doesn’t.

Artificial intelligence typically refers to the field of data science. It leverages advanced algorithms to power computers to learn on their own. By integrating AI into their data analytics processes, businesses can be able to automatically clean, evaluate, explain and visualize their data.

In an article, Tom Davenport and Joey Fitts wrote that automation in analytics, often called “smart data discovery” or “augmented analytics”, is reducing the reliance on human expertise and judgment by automatically pointing out relationships and patterns in data. The systems, in some cases, even recommend what the user should do to address the situation identified in the automated analysis. Together these capabilities can transform how people analyse and consume data.

Artificial intelligence and automation have made significant advancements in data analytics that were inconceivable a few years ago. Enterprises these days are realizing the benefits of these technologies and using them to examine their data to derive fine-grained insights. AI is now creating new methods for data analysis. Historically, data engineers or analysts have had to use a query or SQL when it comes to analysing data. However, as the significance of data continues to grow, multiple ways to excerpt insights have emerged. Artificial intelligence emerges as a crucial technology, becoming the next step to query or SQL.

Earlier, data and analytics have been discrete resources that needed to be fused to accomplish value. This also required extensive knowledge of what type of data was apt for analysis within an organization. Most data analysts lacked such knowledge in a broader context. However, AI-powered analytics can increasingly provide context. Many key vendors are already using these capabilities in their transactional systems offerings, such as ERP and CRM.

In conclusion, this is just the beginning of data analytics powered by artificial intelligence. As the advances in this technology will continue to evolve, the potential of AI-driven data analytics tools will be striking.

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