The Emergence of Augmented Analytics & How it is Helping to Create a Data-Driven Organization?
In the last few years, the data flowing from various sources has changed the way organizations work and solve their business problem. Identifying potential data points, collecting data, and storing it in a highly secured place has been a need of the hour for many big companies across the industries. In this regard, Big data analytics practices are gaining more popularity; followed by the rapid adoption of AI/ML technologies (RL, DeepRL, NLP, etc.) in the workflow. Essentially these technological advancements are helping organizations to capture, store, and analyze data to convert them into valuable insights and solve business problems.
On the other hand, you would need to understand the current scenario of dealing with data, ensure the security and compliance factors, and select the right tools that suffice your data analytics prerequisites. But the challenge is how you identify the solution to your changing data needs? And what does all this have to do with augmented analytics? In this blog, we will discuss the Terminology of augmented analytics, What it offers to businesses, Global market projection, and many other touchpoints that hold the answers to these questions and help you navigate towards creating a data-driven organization.
Let’s start with the big picture!
The blend of different AI capabilities such as ML, NLP, and Computer vision (CV) with a few other advanced technologies like AR/VR are boosting the augmented analytics practices, especially in extracting valuable data insights. The graph of the pervasiveness of AI Vs. Instant/near real-time results shown in the above image is mere proof in this context. Thus, Augmented analytics brings all necessary ingredients with it to help organizations conduct more efficient and effective data analytics activities across workflow and create a hassle-free road map to be a data-driven organization; and form Citizen data scientists to solve new business problems with ease and unique capabilities.
Terminology of Augmented Analytics
Gartner- Augmented analytics uses machine learning to automate data preparation, insight discovery, data science, and machine learning model development and insight sharing for a broad range of business users, operational workers, and citizen data scientists.
In other words, it’s a paradigm shift that brings all necessary components and features to be a key driver of modern analytics platforms that program and integrate processes such as data preparation, creating models around data clusters, developing insights, and data cleansing to assist business operations and so forth.
What does it offer?
- Improved relevance and business insights: Helps to identify false or less relevant insights, minimizes the risk of missing imperative insights in data, navigates to actionable insights to users, and empowers decision-making abilities and actions.
- Faster & near-perfect insights: Greatly reduces the time spent in data discovery and exploration, provides near-perfect data insights to business users, and helps them augment the data analysis with AI/ML algorithms.
- Insights made available everywhere & anywhere: The flexibility and compatibility of augmented analytics expand the data reach across the workflow, beyond citizen data scientists, and operational teams who can leverage the insights with less effort.
- Enable less dependency on skill constraints: You don’t need to rely more on data scientists anymore. With the help of advanced AI/ML algorithms; augmented analytics fills the required skill constraints helping organizations to do more with technology than humans’ intervention in data analytics and management process.
The augmented analytics market is broadly classified into deployment, function, component, industrial vertical, and organization size. Later, the deployment category is further divided into the cloud and on-premises. Also, in terms of process and function, the market is segmented into operation, IT, finance, sales & marketing, and others.
Traditional BI Vs. Augmented Analytics
In the traditional Business Intelligence process, databases were analyzed to generate basic reports. The analysis was executed by a dedicated team of data analysts and access to the reports produced by these professionals was limited to certain teams. In a way, the regular business users were unable to use this facilitation due to complexity and security constraints. Hence, they were unable to make data-driven decisions.
In the latter days, the level of complexity was reduced with help of technological advancement. However, the manual data collection from data sources remained the same, where the data analysts clean up the data, select the data sources they want to analyze, transfer it to the platform for analysis, generate reports/insights, and share it across the workflow through emails, messages, or within the platform as shown in the above image.
In Augmented analytics, AI technology usage reduces the manual process of data collection and enhances the data transfer and reception across different sources. Once the data is made available from respective sources, the AI/ML-powered smart systems help users to select suitable datasets based on the relationships it has identified while bringing the data in for analysis. During the time of data analysis process, AI systems will allow user influence in the process, and also suggest different analysis combinations that human intervention would take loads of time to produce the same. Once the insights are generated, business users can leverage these insights across the workflow through in-app messaging, mobile apps, chatbots, AI assistants, and more.
Hence throughout the augmented analytics practice, AI empowers the data analytics process by simplifying the insight discovery activity and provides noteworthy trends and details without a specific user query.
With Augmented Analytics in place businesses can:
- Perform hassle-free data analysis to meet the business objectives
- Improve the ability to identify the root cause of data analysis challenges and problems
- Unearth hidden growth opportunities without investing additional efforts
- Democratize enterprise-wide insights in a BI perspective to enhance the business performance
- Opportunities to turn actionable data insights into business outcomes
The world is changing into a data world, and the data is now shaping up beyond big data. Countless devices are connected to each other and produce new data sets every passing day and minute. These data sets are processed and stored in a more complex form to create insightful information; hence businesses need to invest and start using robust analytical systems and AI assistance to make sense of their efforts in the data analytics journey. On the other hand, the need to democratize analytics and upsurge productivity; businesses need to innovate and change their legacy approaches. Augmented analytics is proving one such opportunity to uplift the existing and new business objectives to stay ahead in the race. Invest wisely and make the best use of Augmented Analytics to create a data-driven organization, ensuring success.