Augmented Analytics: how Analytics and Business Intelligence Workflow will change

Analytics has become a strategic component, almost a necessety in same cases, of how value is created in most businesses. This last decade has been the one where people realised the real value of data and it has been the decade that brought about significant changes across business processes.

Now, stakeholders across each function like to do a fact check before making a big decision. That is where they rely on Business Intelligence or Analytics platforms to do the number crunching for them.

According to MarketWatch, the BI market is expected to grow at a CAGR of 26.98% over the years 2017–2025 and projected to reach 147 billion by 2025: we can say that the future looks bright.

However, BI & Analytics are at a critical inflection point. As data complexity increases, business people across the enterprise are awash in data, struggling to identify what is most important and what best actions to take.

To address this growing challenge, Augmented Analytics is rapidly gaining traction.

The International Data Corporation predicted that in the upcoming year, “40 percent of digital transformation initiatives will be supported by cognitive/AI capabilities, providing timely critical insights for new operating and monetization models”. 

According to Gartner, in their October 2019 research “Augmented Analytics Is the Future of Data and Analytics”, augmented analytics uses machine learning/ artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery and sharing. It also automates data science and ML model development, management and deployment.”.

Artificial intelligence will change everything about the analytics and business intelligence process, simplifying or eliminating some steps and radically changing and improving others.

Augmented analytics as part of analytics and BI platforms (Augmented ABI) enables machine learning and AI assisted data preparation, insight generation, and insight explanation to augment how business people and analysts explore and analyze data in analytics and BI platforms. With augmented analytics, business people and data scientists automatically find, visualize and narrate relevant findings (for instance correlations, exceptions, clusters, drivers and predictions), without having to build models or write algorithms.

A truly intelligent BI system based on ML/AI would start helping the user from the moment they begin interacting with it: data ingestion will be totally simplified. Within a few years, the AI elements will be able to surface datasets relationships via visualizations that allow human users to begin drilling down and looking for more insights immediately, or even to see if existing data fits within compliance rules and grant immediate access. Going smoothly from data ingestion to searching for insights will be a huge time saver.

Augmented Analytics Is Transforming Each Part of the Analytics Workflow

Currently in analytics, content authors such as analysts, citizen data scientists and expert data scientists perform the following three data-to-insight-to-action activities iteratively to find
meaningful insights:

  1. Preparing the data
  2. Finding patterns in the data and building models
  3. Sharing and operationalizing findings from the data

The augmented analytics paradigm accelerates the time it takes to get accurate insights for
business users by using ML algorithms to automate those three main sections of the data and analytics workflow.

An example of Augmented Analytics within Data Preparation

Concerning Augmented Data Preparation, Tableau Prep Builder from Tableau (Salesforce), a free product for Creator license customers, identifies if there is consistent pattern in data values in a field. It recommends ways to split and break out the field into useful ones. It detects that the field might have known semantics and then recommends changing the data field’s role to the one detected. Once a field is tagged with semantics, the system will identify invalid values and automatically fix them by mapping them to the closest valid value. Tableau Prep Builder also identifies some data quality issues, such as missing values, and provides the user with basic recommendations for how to deal with those missing values.

Conclusion

There are many benefits of augmented analytics for an organisation, for instance IT and data professionals are freed up to focus on strategic matters and special projects.
Since machines can efficiently analyse several data sources and combinations, augmented analytics allows for more in-depth data analysis.
Augmented analytics simplifies data analysis making it easier to get to actionable insights.
More individuals in an organisation become data focused and empowered by data since it becomes a part of their everyday activity and not just reserved for the data professionals.

Along with these improvements and benefits, business leaders should also need to take on the responsibility of educating their workforce, not just on a technical perspective, but also to inform their employees of tangible business objectives for data and how the BI software can achieve those objectives.


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