Augmented analytics lets organizations connect disparate and live data sources, find relationships within the data, and create visualizations.
One of the latest data and analytics trends making its way to mainstream is augmented analytics. With a relatively brief history, it was first defined by Gartner in 2017 as using enabling technologies such as machine learning (ML) and artificial intelligence (AI) to assist with data preparation, insight generation, and insight explanation. Gartner went on to explain that it empowers experts as well as non-data scientists by automating many aspects of data science, including model development, management, and deployment of AI models.
Although still in the early stages, it has gained considerable traction as many organizations are realizing the importance of Big Data and its role in decision-making across the business. With the sheer volume of data now available to companies, they are finding it difficult to effectively interpret the data. The issue is expected to only get worse with the growth of the internet of things (IoT) connected devices. IDC predicts that by 2025 there will be 55.7 B connected devices worldwide, 75% of which will be connected to an IoT platform. IDC estimates data generated from connected IoT devices to be 73.1 ZB by 2025, growing from 18.3 ZB in 2019.
The roadblocks of AI and ML adoption for decision analytics
Although enterprises are eager to harness the promise of AI and ML technology, they typically encounter roadblocks such as demonstrating proof of value through nimble pilots, the absence of an integrated AI and data stack, and a lack of AI skills.
Across the data value chain, many processes remain largely manual and prone to bias; this includes managing and preparing the data for analysis, building AI and ML models, interpreting the results, and creating actionable insights. This manual effort often means that business users are left to find their own patterns and data scientists to build and manage their own models. The manual effort required by today’s processes typically results in users examining their own hypotheses, missing key findings, and ultimately coming to incorrect conclusions – which adversely affect business decisions, actions, and outcomes. The results of today’s approach are confirmed by Forrester Research determined that only 29% of organizations are successful at connecting analytics to actions.
Essentially, there are seven key roadblocks that organizations face when adopting AI and ML for decision analytics:
- Digital transformation has been evolving, albeit slowly. However, many executives don’t believe their company has the right technology to implement digital transformation and view digital technology as disruptive to their business model.
- As data becomes more distributed, integrating a large volume of data from different sources in disparate formats on legacy systems is challenging.
- Over the last few years, there has been massive growth and adoption of new technologies, such as AI, ML, data science, etc. As a result, organizations face a shortage of required skillsets.
- Legacy systems aren’t able to keep up with business demands because of exorable growth in data and the inability to manage multiple data formats across legacy storage platforms.
- Manual processes and workflows are no longer feasible for many organizations. This has resulted in automation initiatives that were put on the backburner now taking precedence.
- To address new and developing business challenges, organizations need to adopt emerging technologies, such as AI, ML, and IoT, in order to stay ahead of the technology evolutionary curve.
- Fostering innovation is now a priority for many companies; however, to keep up with business demands, new technologies and processes first need to be implemented.
Although there are seven key roadblocks, there is just one solution – augmented analytics.
Turn big data into big insights – faster
The adoption of augmented analytics removes roadblocks, eases bottlenecks, increases productivity and efficiency, improves accuracy, and delivers faster insights. It uses AI and ML techniques to automate the tedious manual data preparation process, insight data discovery, and sharing. In addition, augmented analytics automates data science tasks such as ML model development, management, and deployment. Although traditional business intelligence (BI) tools have supported basic capabilities for joining, manipulating, and transforming the structured data, augmented data preparation streamlines processes for data profiling, managing quality, cleaning data, modeling, and labeling metadata in a manner that supports reuse and governance.
Augmented analytics democratizes AI across the data value chain; it automates the data preparation process and aspects of data science, as well as narrates relevant insights using natural language processing (NLP) and conversational analytics. Companies that adopt augmented analytics require fewer technical experts – instead, they are able to leverage non-data scientists (citizen data scientists) with low or no-code abilities. These less business-savvy users are able to interact with the data to give recommendations for decision-making, which increases efficiency and productivity.
Similar to the traditional analytics workflow, the augmented analytics workflow consists of data management, data science, and data visualization. Augmented analytics differs in the techniques and technologies it leverages. Here are some of the benefits it delivers:
Data management
- Traditional workflow: 45% of the time is spent handling manual tasks, such as data cleaning, profiling, cataloging, etc., resulting in data scientists having less time to spend on more productive tasks, such as gaining insights from the data.
- Augmented analytics: Uses AI to automate data preparation, reducing preparation and discovery time by 50 – 80%.
Data science
- Traditional workflow: Since 34% of the time is spent on manual feature engineering model selection, and model training and deployment, less time is available for model validation, testing delivery, and operationalization.
- Augmented analytics: AI and ML techniques (AutoML – an AI-based solution, automates the process of applying ML to real-world problems), reduces data science task time by 40%, improves the accuracy of the models, and removes bias.
Data visualization
- Traditional workflow: Relying on the individual user to interpret the data, 21% of their time is spent using interactive techniques, such as filtering, pivoting, linking, grouping, and user-defined calculation.
- Augmented analytics: Use NLP for auto visualization of relevant patterns and automates data insights, reducing query to insights cycle time by 50%.
Augmented analytics democratizes AI across the data value chain, automating the data preparation process and key aspects of data science, while NLP enables users to gain insights faster. AutoML leverages techniques and relevant insights using NLP and conversational analytics, including:
- Augmented data preparation: Uses AI/ML automation to accelerate manual data preparation tasks, including data profiling and quality, enrichment, metadata development, data cataloging, and other aspects of data management, such as data integration and database administration.
- Augmented data science: Uses AI/ML techniques to automate key aspects of data science, such as feature engineering and model selection, as well as model operationalization, model explanation, and model tuning.
- Augmented analytics: A component of BI platforms, it embeds AI/ML techniques to automatically explore and visualize the data and narrate the relevant findings through conversational interfaces like natural language query (NLQ) technologies that are supported by natural language generation (NLG).
Augmented analytics enables organizations to truly democratize AI and turn data into reliable insights at greater speed and with more efficiency. It allows users – data scientists and citizen data scientists – to:
- Easily aggregate data from disparate sources
- Turn data into insights by building, interpreting, and tuning AI models
- Effortlessly share findings across the entire organization
The power of augmented analytics
This game-changing solution can help organizations augment ROI from analytics by increasing efficiencies across the entire data value chain. Organizations that have adopted augmented analytics are achieving quantifiable benefits, such as:
- 51% increase in decision-making confidence
- 50% increase in analytics efficiency
- 48% increase in analytics effectiveness
- 35% greater YoY increase in total customers
- 31% greater YoY improvement in employee retention
- 23% greater YoY increase in operational profit
The benefits of augmented analytics can’t be dismissed. As the amount of data continues to rise, enterprises and smaller businesses alike will need the capabilities it provides for quick access to actionable insights. With huge data running 10X to 100X faster, augmented analytics delivers faster time to insight.
Augmented analytics enables organizations to connect disparate and live data sources, find relationships within the data, create visualizations, and enable personnel to swiftly and effortlessly share their findings. With the ability to quickly provide insights, augmented analytics is changing how users experience analytics and business intelligence.
