How to understand augmented analytics and the future of Excel
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As the world of data analytics grows vaster and more complex, what role will Excel serve? Will it become obsolete in an AI-powered data ecosystem? This post explores the emergence of augmented analytics and the role Excel will play in this transformation.
Data is getting more complex
In 2017, the International Data Corporation (IDC), a market intelligence firm, predicted a remarkable tenfold increase in the amount of data in existence from 2016 to 2025, totaling 163 zettabytes or a trillion gigabytes.
With the overall quantity of data on the rise, it’s also expanding in variety. According to AI services provider Taiger, 80% of digital data was unstructured by 2020 — a figure that has likely escalated with the emergence of generative natural language processing products like ChatGPT. In addition, real-time data has gained significant importance, with IDC estimating that streaming data will comprise 30% of all data by 2025.
Analytics is getting more sophisticated
The explosion of data, characterized by research advisory Gartner as volume, velocity, and variety, has necessitated the use of advanced analytics methods. Data science has helped organizations uncover relationships and insights in the data using a variety of computational and statistical methods, while machine learning and artificial intelligence (AI) enable computers to learn and simulate human intelligence. These techniques have allow businesses to automate decision-making processes, identify trends in real-time, and create personalized experiences.
This revolution is here to stay: 94% of business leaders responded to business advisory Deloitte that AI is critical to success over the next five years, and the Bureau of Labor Statistics has projected a 36% increase in the number of data scientists employed over a decade, from 113,000 in 2021.
Excel and the legacy of self-service BI
Self-service business intelligence (BI), enabled by tools like Excel, has revolutionized decision-making for businesses. It allows individual users to access and analyze data independently, without relying on IT. However, self-service BI is limited in scope and sophistication. Data must be structured to work with Excel, restricting analysis to descriptive and diagnostic analytics, and lacking the ability to perform advanced algorithms and machine learning models for predictive or prescriptive analytics.
To make more strategic decisions, businesses need to complement self-service BI with more advanced analytics tools and techniques like data mining, machine learning, and artificial intelligence. Combining these tools provides a more comprehensive view of data, enabling informed decisions in today’s rapidly evolving business environment.
What is augmented analytics?
To bridge this gap, a new approach called augmented analytics is emerging. Augmented analytics leverages AI and machine learning to assist with data preparation, analysis, and visualization, making it easier for business users to access advanced analytics techniques without requiring specialized technical knowledge.
With the help of AI, augmented analytics platforms can automatically detect anomalies and inconsistencies in data, helping to ensure that the insights generated are accurate and reliable. Additionally, the use of natural language processing (NLP) allows users to query the data with everyday speech, making it easier to explore data and generate insights.
Another important concept in augmented analytics is the use of easy-to-use, accessible coding languages like Python and R. These programs make it possible for “citizen data scientists” to create light scripts that can be used to extract insights from data. This democratizes data analytics and makes it accessible to a wider range of users, without requiring them to have a formal background in data science.
In conclusion, augmented analytics is an exciting new approach that is bringing modern data science and AI into traditional self-service BI. With its use of easy-to-use coding languages, automation, AI-powered data processing, and NLP-driven data querying, augmented analytics platforms are empowering more users to generate insights from data, leading to better decision-making and a more data-driven culture within organizations.
Excel for augmented analytics
So how does this play out for the future of Excel? With augmented analytics, it can serve as a bridge between self-service BI and data science/AI, enabling organizations to make better use of their data and stay competitive in a rapidly evolving business landscape.
Currently, Excel can be used for augmented analytics in several ways. First, the program’s built-in machine learning capabilities, such as the ability to create forecasts and perform regression analysis, allow users to apply advanced analytics techniques to their data without requiring specialized technical knowledge. Additionally, Excel supports the use of plugins and add-ins that can integrate with external data sources, enabling users to perform more complex analytics tasks.
Looking ahead, Microsoft is actively developing new features that will further enhance Excel’s capabilities for augmented analytics. For example, the company has announced plans to integrate AI-powered insights directly into the Excel interface, making it even easier for users to extract insights from their data. In addition, Microsoft is improving the use of NLP to make it easier to query data in Excel using natural language.
Another possible area of development for Excel is the integration of Python. This will enable users to write Python scripts directly in Excel, allowing for more advanced analytics tasks to be performed. This will make it easier for citizen data scientists to create more sophisticated scripts and Python-powered workflows without the need to deploy elaborate development environments.
In conclusion, Excel is an important tool for augmented analytics, and its use in this field is only expected to grow. With the addition of new features such as AI-powered insights, NLP-driven querying, and the integration of Python, Excel is poised to become an even more valuable tool for data analysis in the years to come.
Modern Excel and beyond
As data continues to grow in complexity and volume, businesses need to adopt new technologies that enable them to leverage this data effectively. Augmented analytics is a promising approach that leverages AI and machine learning to assist with data preparation, analysis, and visualization, making it easier for business users to access advanced analytics techniques without requiring specialized technical knowledge.
“Modern Excel,” with its popular tools like Power Pivot and Power Query, has been instrumental for self-service BI. However, the limitations of self-service BI mean that Excel must evolve to become more flexible and compatible with new technologies such as AI and Python coding to remain a valuable tool for augmented analytics. With the addition of new features such as AI-powered insights, NLP-driven querying, and the integration of Python, Excel is poised to become an even more powerful tool for advanced analytics, helping organizations stay competitive in a rapidly evolving business landscape.
What questions or predictions do you have about augmented analytics and the future of Excel? Let me know in the comments.
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