Imagine a world where data analysis is no longer a laborious and time-consuming task, but a realm of possibilities fueled by the incredible power of artificial intelligence and machine learning. In the past, data analysis required manual effort and countless hours of human work. Now with AI Analytics, it is easy, fast, and better to make sense of data and let the AI provide us with useful insights and information.

AI analytics is a branch of business intelligence that uses AI and machine learning to automatically analyze data. It helps businesses gain quick and valuable insights from their data, leading to faster and better decision-making.

A Good Example is SCIKIQ using ChatGPT in the process of Data discovery and Data preparation. This frees up a lot of tasks as well like cleaning, and formatting while integrating data from different sources. Companies also use AI analytics for data mining to discover hidden patterns and trends in data which we might miss by people.

The AI analytics market is projected by Gartner to reach $238.5 billion by 2025, growing at a rate of 22.5% from 2020. This growth is fueled by factors such as the rising volume and complexity of data, the need for real-time insights, and the wider availability of AI and ML technologies.

According to the recent Voice of the Enterprise: Data Platforms and Analytics survey by 451 Research, a significant number of enterprises recognize the importance of AI and ML in their data platform and analytics initiatives. Out of all the respondents, two-thirds agree that AI and ML play a vital role in their data-driven efforts. However, this percentage jumps to an impressive 88% among the most data-driven companies that base almost all of their strategic decisions on data.

AI analytics Drastically improves Data Analytics

While AI may not introduce wholly novel concepts, its profound impact lies in its ability to significantly improve existing processes. Through advanced algorithms and machine learning techniques, AI empowers organizations to achieve unprecedented levels of speed, accuracy, and personalization in their data-driven endeavors. Moreover, it unravels elusive patterns and anomalies that may have otherwise remained hidden, illuminating valuable insights that propel industries forward. As a result, the immense potential of AI holds the promise of bringing about a monumental shift in the realm of data analytics, permeating various sectors and driving innovation at an unprecedented scale.

Mckinsey predicts that AI techniques have the potential to create between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries. This constitutes about 40 percent of the overall $9.5 trillion to $15.4 trillion annual impact that could potentially be enabled by all analytical techniques.

A recent study conducted by the McKinsey Global Institute sheds light on the tremendous value and potential of AI in improving analytics across different sectors. By analyzing over 400 use cases, the study highlights the wide-ranging applications of AI and its ability to enhance existing analytics processes.

According to a study, Two-thirds of the opportunities for implementing AI involves improving existing analytics use cases. In 69% of the analyzed use cases, AI could suggest improvements. 16% of the use cases are considered “greenfield” cases, The remaining 15% of use cases show limited additional performance from AI.

A recent study by Google and Aible found that AI can be used to improve data management in a number of ways. The study found that AI can reduce the cost of data analysis by a factor of 1000, improve the accuracy of data analysis by up to 50%, and speed up data analysis by up to 100 times. The study also found that AI can be used to improve data quality, make data more accessible, and protect data privacy.

Changing the Data management landscape with AI Analytics

Changing Data Diversity and Complexity are asking for increased use of AI/ML or AI Analytics in the Data Analytics space. Gaurav Shinh, CEO of DAAS LABS and SCIKIQ Said, “The kind of request for data analytics, integration, and management has changed drastically. Data management & Analytics has evolved beyond just relational and structured data. Various forms of data like data include images, Documents, PDFs, email text, CSV files, sensor readings, audio recordings, Social media, and video files, each requiring different approaches for analysis and interpretation. ” He talks more about what’s happening latest on Data analytics on his Data insights blog.

Artificial intelligence (AI) is changing data management in a number of ways, including:

• Automating tasks: AI can automate many of the tasks involved in data management, such as data cleaning, data preparation, Data discovery, Data integration, and data analysis to a great extent. This is a great help to data engineering teams who took a huge amount of time checking the accuracy of data at each step.
• Improving accuracy: AI can improve the accuracy of data management by identifying patterns and trends that would be difficult or impossible to detect by data engineers. This can lead to more accurate data and of course, time saved.
• Personalizing insights: AI can be used to personalize insights for users. This can help businesses to provide more relevant and engaging content to their customers. This invariably leads to contextualizing of data for the users.
• Detecting fraud: AI can be used to detect fraud by identifying patterns of suspicious activity. This can help businesses to protect themselves from financial losses and to maintain the integrity of their data.

Fusing AI and Blockchain for Data Integrity

In addition to all the promising advancements, one aspect that is reshaping data management is the fusion of AI with blockchain technology. With blockchain’s immutable and decentralized nature, combined with AI’s analytical prowess, data integrity, and security could reach unparalleled levels. Companies such as IBM are investing heavily in the fusion of AI and blockchain. They believe that by integrating these technologies, they can address the challenge of data traceability, ownership, and authenticity, making data sharing more transparent and secure.

Embracing Contextual Data Management

Gartner predicts that by 2025, CDM will become the new norm for businesses worldwide. Contextual data management (CDM) is another frontier that AI analytics is helping to explore. This new form of data management revolves around understanding the context in which data is used, enabling a more sophisticated and nuanced approach to data analytics.

Context is the surrounding circumstances that help us understand something. It can be the setting, the people involved, or the events that have happened. Context is extremely powerful if Data managers can implement it. AI models can be developed to offer deeper insights, understand customer behavior better and more accurately.

Edge Computing: AI on the Periphery

Another trend influencing the data management scene is edge computing, where data processing takes place closer to the source of the data – at the edge of the network. This approach minimizes latency, reduces bandwidth use, and ensures quicker response times, leading to real-time insights.

AI’s role in edge computing is twofold. First, it can help in determining which data to process at the edge and which data to send back to the central system. Second, it can also carry out preliminary data analysis at the edge, making decisions in real-time without relying on a distant data center.

Data Democratization: Power to the People

In an era of increasing digitization, data is no longer confined to technical experts. Businesses are pushing towards a democratization of data, allowing employees across roles and departments to access data and draw insights. By integrating AI into the process, complex data analysis tasks can be made accessible to non-specialists, boosting data literacy within the organization and fostering a culture of data-driven decision-making.

To conclude, as AI technology matures and is integrated with other emerging technologies, the scope of its applications in data analytics and management is likely to expand even further. Businesses that embrace these changes and invest in AI-based data analytics tools stand to gain a competitive advantage in an increasingly data-centric world. The future of data management is indeed inextricably linked with AI and the promise it holds for improving efficiencies, insights, and decision-making processes.

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