What is Augmented Analytics?
As its name suggests, augmented analytics is augmenting or supplementing human intelligence and expertise with machine intelligence to facilitate the accelerated and effortless transition of raw enterprise data into valuable insights for agile and astute business actions. Augmented analytics leverages advanced technologies like machine learning and natural language processing to refine each and every step of your business intelligence process, starting from data preparation to sharing imperative information among stakeholders in simple semantic language.
The research firm Gartner coined the term ‘Augmented Analytics’ in their 2017 report ‘Gartner Hype Cycle for Emerging Technologies’.
According to the report: “Augmented analytics is an approach that automates insights using machine learning and natural language generation, marks the next wave of disruption in the data and analytics market.”
To dive deeper into the above statement is to understand how augmented analytics, since its inception, has been influential in simplifying and radically changing analytics and business intelligence processes.
This article aims to throw light on the overarching aspects of augmented analytics including its best practices, importance, uniqueness, and even challenges. However, before divulging further information, it is advisable to first understand how business intelligence evolved over the years.
Evolution of Business Intelligence: From Traditional BI to AI Powered BI
The term Business Intelligence finds its roots in the book published by Mr. Richard Miller Devens on a banker, Sir Henry Furnese, who successfully banked on information or ‘train of business intelligence’ to stay ahead of his competitors. While earlier business intelligence process was carried out on pen and paper, advent of computers saw a new approach to analyze business data.
Traditional Business Intelligence
First generation business intelligence era saw the IT department as complete owner and manager of enterprise data. The focus was mainly on developing and sharing static reports and KPIs. The system was dominated by Extract, Transform, Load (ETL) tools to integrate data from different sources onto a single database or data warehouse, and Online Analytical Processing (OLAP) tools to analyse data. However, the supporting tools were mostly the sole prerogative of IT experts with extensive knowledge of queries.
Self Service Business Intelligence
The reliance of traditional business intelligence on IT staff saw a detrimental effect on businesses, especially with lengthy and inefficient process to answer important business questions. Advent of different visualization and data discovery tools soon eclipsed the first -generation business intelligence with a more agile version supporting self-service analytics.
Self-service business intelligence would empower business users to access their data, exploit different visualizations to discover latent patterns and outliers, and conduct an ad-hoc analysis of data from multiple sources. This emancipation from old school data extraction, transformation, and modeling process paved the way for data democratization and data literacy among enterprises.
Machine Generated Business Intelligence
The proliferation of smart and IoT devices have exposed business users to real-time data with a focus on dynamic and contextualized analysis for remaining agile and astute. Chronic storage of skilled data scientists with requisite analytics skills to convert raw data to insights has paved the way for organizations looking out for more sophisticated technologies. Modern-day business intelligence solutions are now being driven by artificial intelligence and machine learning augmenting processes like data preparation, insight generation, trend detection, etc.
Gartner in its latest data and analytics report says “Augmented analytics is changing how less skilled users find insights and interact with data. It enables ML and AI techniques to automate tasks such as data preparation, insight discovery, insight sharing as well as machine learning model creation and deployment.”
Augmented Analytics Best Practices
According to Gartner’s latest report: Even though augmented analytics will be pervasive by 2022, only 1/10th of overall consumer population will capitalize on it. Factors affecting widespread adoption of augmented analytics is lack of technological and organizational maturity, low level of data literacy, inhibition to changes, and data availability concerns.
With machine learning and artificial intelligence, it is now possible to query data in natural semantic language while discerning patterns and outliers to augment one’s analytics capabilities. However, the process of adopting augmented analytics is not flawless and requires companies to follow below given best practices:
- Promote data literacy in the entire organization to increase data comprehensibility and optimization. Data literacy includes appropriate reading of charts and graphs, drawing accurate inferences from data, and identify data quality issues.
- Make sure to carefully orchestrate addition of augmented and NLP driven skills to existing analytics and BI tools through regular evaluations.
- Foster trust by including every concerned stakeholder in the new analytics initiative. Evangelize the potential of this new technology to users such that it ensures maximum business impact, opportunities, and success.
- Look out for vendors who provide explainable features in their platforms, showing the key elements influencing autogenerated recommendations, insights, or models. The vendor should also have a robust training and support system to empower citizen data scientists.
- Prepare your organization for the next wave of big data analytics by deploying smart technologies like data cognition engines to convert vast amount of data to sheer neural networks facilitating agile and subtle analysis.
Adapting to newly developed technologies is not necessarily a step by step process but should be a mature progression. Instead of carving for success overnight by implementing augmented analytics to your overarching business processes, start simple with small changes, check how they impact your business processes and contribute to business objects.
In order to propel an organization’s data and analytics program catalyzed by augmented analytics, companies should include the latter in their portfolio, providing a wide range of capabilities in the perpetual analytics sphere.
Key challenges of augmented analytics
The credibility of augmented analytics in taking accelerated, agile, and precise business decisions has been obvious since the past few years. Augmented analytics, with the combination of more sophisticated and complex analytics has become more strategic and important for organizations to ascertain their position in the post COVID world of uncertainty.
However, this increasing complexity has paved way to potentially damaging challenges such as lack of transparency and trust in ML and AI models, threat to job security, and many others which have increased resistance to application of augmented analytics. Some of these are mentioned below:
- Hesitation on the part of business executives to adapt to changing analytics scenario, and instead relying more on intuition and on traditional decision-making exercises.
- Risk of exposing valuable data to users with malicious intentions who can exploit the high-power tools to promote fake insights and damaging recommendations.
- Fear of losing jobs with the onset of automation in every aspect of analytical operations, with revamping of workloads and refurbishing of work processes.
- The prevalent ‘black box’ image of augmented analytics tools that they are not transparent and trustworthy in terms of decision making.
- Lack of data literacy among the wider workforce population in leveraging augmented analytics to access valuable insights.
- The risk of contriving bias into data models created by citizen data scientists, which can skew results of machine learning models, and also demand extra effort from technical experts.
- Lack of trust on the displayed recommendations and insights unless and until a strong explanation is provided.
- Misconception that augmented analytics is linearly progressive and implemented only when a robust data foundation is established.
It would require a lot of efforts and measures from data and analytical leaders to overcome the above-mentioned challenges. Organizations need to rejuvenate themselves and expand their working scale to adapt to the changing culture.
How is augmented analytics unique?
The uniqueness of augmented analytics comes from the fact that it encompasses artificial intelligence technologies working in the background to learn and improve results. Compared to a typical business intelligence model, an augmented analytics system is more:
- Comprehensible with the use of natural language generation to display insights in simple semantic language.
- Faster to process any kind of data with the use of advanced machine learning algorithms.
- Unbiased to include every type of influencing factors before drawing conclusions.
Figure below shows how different organizations perceive the outcome of implementing augmented analytics on different factors:
Why does augmented analytics matter?
Despite the availability of modern-day analytics and business intelligence platforms, organizations still find it difficult to generate or consume insights, develop data models, and share findings. Businesses are still struggling to identify the most pertinent datasets to analyze, explore, and convert to actionable insights.
This is where augmented analytics comes into the picture, exposing business users to the most important insights at an accelerated rate and less time, without relying on expert data scientists. In fact, by automating many aspects of data science and machine learning solutions, augmented analytics actually enhances the productivity of data scientists and the IT department, allowing them to focus on more strategic issues. Also, it facilitates the creation of less-skilled roles like citizen data scientists allowing for more accountability and empowerment.
Augmented analytics capabilities along with business monitoring and natural language interfaces are now being leveraged by many vendors to provide innovative user experience with dynamic and autogenerated data stories.
In fact, predefined dashboards with usual point and click authoring and explorations are now being replaced by dynamic and context based personalized data stories.
Gartner says: “75 percent of data stories will be automatically generated using augmented analytics by 2025”.
Augmented analytics along with other accelerants are facilitating stronger clustering and links between data and analytics governance, investments, practices, and processes. This collision has rejuvenated the ecosystem of business processes, enabling the transition from data to actions in an end to end workflow structure. A full-stack augmented analytics solution would provide the following capabilities on a single platform:
- Data science and machine learning enabled analytics and BI capabilities.
- Data warehousing, data integration, data profiling, and data modelling capabilities.
- Business process management, process automation, and decision modelling capabilities.
- Insight sharing capabilities allowing direct flow of discovered outputs into decision engines, while tracking prospective changes.
Gartner says: “Augmented capabilities are blurring the distinction between analytics and BI capabilities and DSML capabilities”.
Augmented Analytics coupled with conversational analytics capabilities are empowering users with the expertise of both data analyst and data scientist, allowing a smooth transition from dashboards with predefined KPIs to dynamic personalized context-based insights.
As Gartner’s Rita Sallam says: “Don’t Wait for Augmented Analytics to Become Mainstream — Start Today!”
No wonder, it predicts: Augmented Analytics will be the driving force behind purchasing of A&BI platforms, DSML platforms, and of embedded analytics by 2021.
What is Augmented Data Discovery?
Traditional methods of acquiring and processing data involves a lot of excruciating efforts by data scientists in an organization, especially in integrating a single data source to the analytics pipeline. While extracting data from internal sources means struggling with data silos in different departments, external data sources involve a lot of time and expense for negotiating the transaction and legalizing it.
To add to the agony, the data scientists still have to go through a cumbersome process of cleaning and preparing the data for production. This in turn results in a wide number of potential data sources being unexplored as much of the time and money is spent on limited number of data sources. And this is where machine learning automation steps in, in the form of augmented data discovery.
Augmented data discovery is one of the biggest innovations in data-driven businesses, leveraging machine learning to scour huge amounts of internal and external datasets for perfect matches or relevance. It involves smart algorithms to not only find data but also join existing data with a wealth of external data while matching different ontologies across the organization. Augmented data discovery relies on augmented data preparation and automated pattern discovery to facilitate self-service business intelligence.
According to Gartner, augmented data preparation streamlines schema detection through effective data profiling, metadata classification, and data enrichment, thus improving data discovery.
Another important element of augmented data discovery is augmented data catalogues which involves augmenting data finding, annotating, tagging, and sharing to facilitate smooth data inventorying.
Gartner says: “Augmented data discovery is a next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users.”
Figure below shows how BIRD’s inbuilt machine learning algorithms helps users gain access to instant insights by automating the following tasks:
- Data profiling to help examine data from vastly disparate sources.
- Feature engineering to extract features of raw data through data mining, and boost ML algorithms.
- Data modeling to build a machine learning model by selecting the required parameters.
What is Augmented Decision Making?
The analytical power and speed of smart technologies like artificial intelligence, machine learning, etc. have not only helped ease the process of data preparation, discovery but also enabled business leaders to take more agile and smarter decisions. Contrary to popular belief, augmented decision-making is unlike artificial intelligence which promotes automatic digital decision making.
Rather, it enables human intelligence to augment computer systems in strategic thinking, planning, and other decision-making ventures. In fact, augmented decision involves human computer interaction to accelerate and improve decision making process.
Even though the entire raw data to action recommendation process is automated by the involvement of artificial intelligence and machine learning, the decision making is entirely the prerogative of human beings. The recommended actions are shared to relevant stakeholders through various interfaces like email, text, voice, etc., who optimize, quantify, and rank them as per various factors.
Augmented decision making encompasses the amalgamation of powerful computing technologies with human expertise and experience in taking data driven decisions. Having said that, optimization of augmented decision making comes from the following elements:
- Consciousness of humans in monitoring and assessing the data being used for analyzing and extracting insights by artificial intelligence.
- Experience of humans to recognize significant patterns and important insights along with accuracy and credibility of algorithms in analyzing and drawing meaningful insights
- Human Expertise to strategize inferences drawn from the automated analysis by different algorithms and tools.
How Business Benefit from Augmented Analytics?
Augmented analytics has revolutionized the way businesses are leveraging data and analytics to keep their position intact in this high paced competitive world. According to Gartner, with the impact of data and analytics rising high during this pandemic; significance of augmented analytics in contributing to business model innovation is inevitable.
Over the past few years augmented analytics has been successful in manifesting the collaboration of continuous intelligence and artificial intelligence in supporting decision-making processes and solve impending operational problems for every business owner – whether big or small.
Regardless of being an entrepreneur or an experienced CEO, every business leader can now cherish the benefits of augmented analytics. In fact, every stakeholder; a non-technical user, a data scientist, a marketeer, or a salesperson can gain from augmented analytics as given below:
- Data scientists are now emancipated from the tedious tasks of running repetitive queries and reports, instead focusing to solve more complex issues.
- Non-technical employees can get instant answers to any kind of business questions without having to depend on IT experts, thus improving strategy and performance.
- Marketing executives can now easily optimize marketing strategies by capitalizing on opportunities and reducing customer churn rates.
- Marketing researchers can now easily assess the company’s performance against price, products, promotions, and place.
- Digital marketers can build personalized web experience for their users by effectively synchronizing digital traffic with advertisement data.
- Salespersons can get easy and accelerated access to detailed information about sales pipeline, performance metrics, opportunities, win-loss, etc.
Apart from the above-mentioned instances, numerous aspects of your organization can benefit from augmented analytic, allowing you to gain access to next-level insights with profound agility and astuteness.
The figure below shows some facts and figures on how augmented analytics is making it easier for organizations to maintain agility and competitiveness by infusing machine learning and cognitive intelligence into existing analytical systems.
However, despite this acknowledgment of how businesses benefit from augmented analytics, CIOs are still not prepared to leverage the complete potential of this technology to rejuvenate their business models. This is where the credibility of full-stack augmented analytics comes into the picture, a functionality effectively offered by selected platforms like BIRD.
Examples of Augmented Analytics in action
Now that you have an overarching view of augmented analytics; its definition, challenges, uniqueness, benefits, and best practices, let us have a quick glance at a couple of real-world augmented analytics solutions in action.
Factors influencing business duration in days
The figure below shows an example of a smart analytics dashboard for an IT helpdesk displaying how a number of business days are affected by factors such as a number of reassignments, days since the update of incidents, hours of business, etc.
As evident, the dashboard also effectively portrays the correlation between business duration and other elements in both numerical form as well as in text using simple comprehensible language.
Predicting Nifty Close Index using Forecasting Model
Figure below shows a smart insights dashboard showing predicted Nifty Close index for a period of 5 months, based on historical data. The model also shows variance, seasonality, and randomness for the target variable.
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