Businesses are becoming more inclined toward adopting augmented analytics tools as they simplify data management and analysis for data scientists as well as other business users, making it quite desirable. The entire DSML & BI space is evolving with augmented analytics tools offering pros of each market across all the use-cases in data management, BPM, and decision-making. We have explored the world of augmented analytics in this blog. We also look into how it is emerging as the next big wave of disruption in the field of data analytics and BI and changing the face of this field.
“No one ever made a decision because of a number. They need a story.” – Daniel Kahneman
In times when there is a huge influx of complex business data, using Analytics and Business Intelligence (BI) is becoming particularly important to make sense of large amounts of data and take the right decisions.
When we look at the Data Analytics and BI space over the years, we can spot some significant changes. For example, low coding platforms largely replaced complex coding-based platforms; and data visualization tools gained immense popularity. These shifts have completely changed the way businesses organize and analyze data for decision-making. One such disruptive shift in the field is that of Augmented Analytics.
Augmented Analytics is basically an upgrade to the capabilities of existing data analytics tools powered by AI, ML, and Natural Language Processing (NLP) which automates the tasks of data analysis. One can easily get the desired results by merely typing what they want, and the tools automatically perform analysis based on the business needs.
According to Amandeep Singh, Analyst at Quadrant Knowledge Solutions, Augmented analytics, through the use of modern technology, improves the users’ ability to interact with data at a deeper level for obtaining personalized results. Augmented analytics enables businesses to deal with the complexity of large volumes of data on a daily basis through data preparation, management, analysis, and reporting, enabling insight-driven business choices.
Companies are becoming more inclined toward adopting augmented analytics tools as they simplify data management and analysis for data scientists as well as other business users, making it quite desirable. We have explored the world of augmented analytics in this blog. We also look into how it is emerging as the next big wave of disruption in the field of data analytics and BI.
So, what is Augmented Analytics?
Simply put, it is an approach to data analytics that employs the latest technologies like AI, Machine Learning, and Natural Language Processing to automate the analysis tasks and produce meaningful insights for the decision-makers.
Traditionally, it was the job of data scientists and analysts to dig out stories from the data and equip the decision-makers with actionable insights and reliable information to make the right choices. However, with advancements in technology, data analytics tools integrated with augmented intelligence are becoming more prevalent now. These tools automate the process of data preparation, discovery, generation of valuable insights, and visualization. The result is that humans can easily navigate the data and interact with it on a deeper level. Moreover, the users need not know complicated query languages or require coding skills to interact with the software since technologies such as NLP/NLQ (Natural Language Querying), ML & AI eliminate the need for it. Hence, augmented analytics builds more efficiency in the BI analytics software and makes the decision-makers self-reliant with the following:
- Augmented Data preparation and Data discovery
- Context-specific powerful insights with visualizations
- AI-driven intelligence to help identify connections in the data points
- Coherent and smooth collaboration as well as presentations
Why is it becoming Popular?
The final goals of such augmented analytics tools are to make predictions and optimization of data as well as generation of actionable, trustworthy, and intelligent insights for better decision-making. Critical features such as Data Preparation (Automation and transforming of all aspects of data preparation such as data collection, data enrichment by cleaning, profiling, manipulating, and modeling of data), Anomaly Detection, Predictive Analytics & Forecasting, Conversational User Interface/Natural Language Interface, Data Visualization, Analytical Insights, Security, and Governance help realize these goals.
In fact, businesses are now developing newer versions of augmented analytics technology solutions equipped for functions such as automatic data identification, data source connectivity, data catalog, storytelling advanced statistical techniques, and natural language interactions that are making augmented analytics increasingly advanced. Combined, all the above-mentioned functionalities along with such advanced features can streamline and automate nearly every aspect of data management. With such advanced features in augmented analytics, businesses are reaping the following benefits:
Accelerated Real-time Insights
Businesses get real-time insights and can make faster decisions when they adopt augmented analytics into their BI framework. These tools automate data management and the process of insights generation that refreshes in real time. Hence, users get faster and more customized insights with the help of machine learning algorithms, AI, and NLG in augmented analytics tools. All they have to do is ask a question and hit enter. The AI will dig through billions of data points in minutes and its ML models along with other integrated technologies will generate comprehensive insights with visualizations and reports in a way that makes it easy for the users to find answers to their questions. These real-time insights enable businesses to take advantage of time-sensitive opportunities and make optimum informed strategies.
Better Analytics with Automation
One of the most basic advantages of adopting augmented analytics for the business is automation of all the aspects of data analytics. This eliminates the need of manual presence in the tasks. With this automation of tasks, it offers the following benefits in terms of better analytics.
- Faster Data Preparation
- Reduced analytical bias caused by incomplete data sets, lack of context, and wrong choice of data points
- Deeper analysis at the most granular levels of data by automated querying
- Improved Accuracy with AI and ML models, especially in repetitive tasks and re-iterations
- Increased reliability of results
Better Business Decisions
Naturally, when generation of relevant real time insights and better analytics is taken care of by the augmented analytics tools, the users can focus on asking the right questions and focusing on the ‘why’ behind each question. By asking the most relevant questions for the given business problem and getting the right information at the right time in the easiest possible way, the users can make much smarter business decisions.
Moreover, it can track user behavior and provide personalized insights and action plans as per their needs. So, whether you are at the executive, senior, or managerial level, these tools customize themselves as per your needs to present the most relevant real-time insights to serve your end goals.
Democratization of Data
Another key advantage of augmented analytics is that its use is not limited to the data scientists or analytics. It grants everyone in the organization, regardless of their data or coding skills access to granular insights and reports in visual or other forms in real time. This democratization of data leads to increased efficiency and productivity in all areas of business, whether it is marketing, manufacturing, management, sales, HR, quality control, or research. Augmented analytics gives access to people working at any level in the organization, empowering them with right insights to make the best possible choices in their daily tasks as well as important decision-making.
All these key benefits along with many other advantages offered by Augmented Analytics tools act as motivating factors for the business to adopt these tools in their BI framework.
How is Augmented Intelligence changing the Analytics and BI space?
Some time ago, dealing with data was the forte of data scientists and analysts with special skills. This scenario is changing in current times. We see the market for Data Science & Machine Learning (DSML) and Business Intelligence (BI) evolving. Augmented Analytics is clubbing the pros of each market and it is emerging as a new market space. This evolution of Analytics and BI space is happening in all the different applications of DSML and BI, data management, BPM, and decision-making. The ultimate benefits of this merging of two marketspaces are accrued by all kinds of business users, regardless of their coding and analytics skills.
The evolving landscape of technology and augmented tools is driving changes in Augmented Analytics as well as the DA and BI field overall. Traditional dashboards are losing their relevance as BI dashboards offer more customized insights and business needs are becoming more intricate. Integration of technologies such as AI, ML & NLP is further advancing the capabilities of analytical tools.
The augmented analytics platforms are also evolving themselves to develop more role-based capabilities to give the best experience to the end-users as per their requirements. Even the developers are making the UIs to better suit various purposes of all kinds of users.
As Amandeep expresses, The AI-powered augmentation analysis expedites the efforts for gaining valuable insights by narrowing the search field, revealing important data to the right person at the right time. Furthermore, the augmented analytics platform vendors are focusing on virtual self-services for end users to capture detailed information about user behavior and deliver an impactful customer experience.
For marketing, access to real-time data helps in understanding the customers and creating personalized and proactive campaigns. By structuring and connecting various types of data in a common data storage, it improves efficiency in financial reporting with dynamic reports. Similarly, augmented analytics and its role-based capabilities enhance the work experience for sales, supply chain as well as procurement for the business. As the applications become pervasive across business functions, we can see decision-makers interact with the augmented analytics interfaces through workplace collaboration apps such as Teams or Slack as well as voice assistance software. In fact, the an idea that the concepts of gamification may be included in these platforms does not seem too far-fetched.
In a nutshell, by offering upgraded benefits and experience of DSML and BI, augmented analytics is making data analysis and management more interesting, easy, and user-friendly for users. Quadrant Knowledge Solutions is constantly keeping track of this evolution happening in this space with our in-depth strategic research and connectivity. To find out more about this evolving technology trend and the numerous opportunities it presents for technology vendors and buyers, visit our website or connect with us over a call.
Vaishnavi Dave is a Content Writer Quadrant Knowledge Solutions