Although analytics is making it easier than before to extract usable knowledge from data, there are many challenges associated with it. For starters, it’s hard to prepare data for insights generation. And it’s only data scientists who can understand and prepare datasets for knowledge extraction. There are not many people with an analytical mindset who can understand complex data structures. And this makes data scientists scarce and expensive, which is why not every organization can afford them. Plus they are not business experts. However smart they might be at playing with data, they may not understand what’s best from a business point of view. Also, most data analysts’ time is spent on studying the reliability of sources of big data, preparing datasets, and cleaning them, which are of lower value than actual data analysis.
Augmented analytics makes data analytics accessible to even managers and business leaders who are not data experts. Managers and business leaders, with the help of augmented analytics, can easily extract information from data. Then they can place it into a business context to generate useful and actionable insights.
Augmented analytics is…
…a term coined by Gartner in their 2017 publication, “Augmented analytics is the future of data and analytics”. Gartner claims augmented analytics to be the future of data analytics. It refers to the use of ML and NLP to simplify data analytics and data sharing with business intelligence tools. The use of augmented analytics can automate data preparation and insight generation. Data in its raw form is almost useless to businesses. And to go from raw data to insights, businesses and data scientists have to undergo various technical steps. These steps include collecting data, cleaning it, conducting analysis, and then generating insights. These steps are very complicated to implement practically by everyone. Augmented analytics simplifies this process of insight generation and makes it easily understandable.
Augmented analytics can…
…automate data preparation. Data preparation is the first step in data analytics. It refers to cleaning and transforming raw data into a form that can be analyzed. It usually involves steps such as reformatting data structures, eliminating irrelevant data, and combining datasets. It also involves data cleansing. Data cleansing refers to identifying incomplete, inaccurate, and irrelevant data, and then replacing or deleting it. And today, where there is an abundance of data generated and available to generate insights, data preparation is a highly time-consuming process. Cleaning data and organizing it to prepare datasets is the most time consuming are the least enjoyable task for analysts. Augmented analytics with the help of ML and NLP can automate this time-consuming data preparation process.
Augmented analytics systems can understand human queries with the help of NLP to detect which datasets are required for a specific analysis. It can then combine datasets that can provide insights for the queries. Suppose a manager wants to know the sales of his company’s branches and other stores sales in the USA. Augmented analytics systems can combine datasets that have information about stores in the USA. This reduces the burden on data scientists to manually extract datasets that have information about USA stores and combine them. It can also match data from different datasets or within a dataset to detect and eliminate irrelevant and corrupt data to clean datasets that will be analyzed.
…generate proactive insights. Enterprise systems and other IoT devices if implemented can constantly generate data. But this data is used only when a manager or business leader wants to generate insights from it. Until that time, the data is just gathered and stored in servers. Augmented analytics will bring proactive insight generation to managers. Instead of just receiving data, storing it, and waiting for humans to analyze it, these systems will actively interweave data and assist humans. ML algorithms can self-learn and improve themselves. Hence, based on previous human queries, augmented analytics systems can understand relations between datasets, how they interact, and how to query them to extract insights that will be useful for businesses.
…eliminate dark data. Most of the companies are focused on generating insights based on customer analysis. And due to human biases, managers and business leaders end up finding only what they are looking for. This increases the amount of dark data that is often in the form of system log files, geolocation data, or network data, among others.
ML algorithms aren’t bound by any biases and provide results strongly based on training data. And since augmented analytics systems use ML algorithms for data preparation they will be free from any bias. This will help companies reveal insights that they would not have thought of.
…give voice to data. It might sound fictitious but NLP will literally give data a voice of its own. Augmented analytics systems will enable managers and business leaders to simply ask questions and extract relevant information from datasets. And with NLP, they will also be able to understand complex relations between datasets in a natural language format. Thus, augmented analytic systems can walk managers through datasets so that they can acknowledge and utilize the outputs of data analysis. Managers and business leaders can also question the outputs of augmented analytics to get a deeper knowledge of why and how the system reached a conclusion. This will help businesses to dive deeper into generated insights and assist them to make actionable decisions.
…democratize data. Augmented analytics makes it possible for everyone to visualize connections in the ocean of data. And this will enable everyone in an organization to easily interpret datasets. With data visualization, businesses can start drilling deeper into data and look for more insights immediately. When augmented analytic platforms will make data accessible to not just data scientists but all the employees, businesses can collect new ideas on how to use data. More minds will be put to think about problems, which will result in more ways of using datasets that might have not been previously thought of. For instance, businesses can assign a task to every employee in their organization to come up with a unique idea on how they will use data for facilitating business operations.
Real-world augmented analytics examples
Augmented analytics is a new concept and is still in infancy. There is no platform yet that can be referred to as a complete and advanced augmented analytics platform. But some platforms have started their journey to take augmented analytics from a buzzword to become ubiquitous. And some of these platforms are…
Microsoft Power BI
Microsoft Power BI is a business intelligence tool. It offers services like data preparation, data integration, and finding and sharing meaningful insights with the help of data visualization. It also helps businesses to understand relations between datasets through their dashboard. It also provides AI capabilities to classify data into different categories based on the source, type of data (structured or unstructured), and relations with other datasets. Such classification helps data scientists to easily extract datasets that are required to generate insights for different queries.
IBM Cognos Analytics
IBM Cognos Analytics platform is an AI-based analytics platform to create visual dashboards, uncover patterns in hidden data, and automate data preparation. It uses AI to find hidden patterns in datasets and present them to businesses in plain language to simplify data interpretation. It also offers AI-assisted data preparation to easily cleanse and combine multiple data sources in a few clicks. IBM Cognos Analytics platform also uses cognitive capabilities like natural language dialogue, which makes it possible for businesses to interact with data conversationally. It also helps to share actionable insights in an organization allowing easy access to insights to everyone.
Although there are not many advanced real-world augmented analytics examples available, the existing examples are yet impressive. And with enhanced research and development in the field, the real-world applications will surely improve. And the investment in augmented analytics will also play its part in the advancement of technology. The augmented analytics market was valued at $4,094 million in 2017 and is projected to reach $29,856 million by 2025, growing at a CAGR of 28.4% from 2018 to 2025. With such an impressive market growth, businesses will witness the growth of technology at an unprecedented rate.