Technology advancements in Business Intelligence have given rise to Augmented Analytics. In this blog, we will cover the basics of Augmented Analytics while trying to investigate what role the combination of Machine Learning (ML) and Natural Language Generation (NLG) play in making the implementation of Augmented Intelligence in business a profitable decision.
To understand Augmented Analytics better, we must start with its origin. According to a report published on Gartner on Jul 27, 2017, the concept of Augmented Analytics is defined as “an approach that automates insights using Machine Learning and Natural Language Generation.”
In another article published in 2019, Gartner included Augmented Analytics in the first position as one of the top 10 data and analytics technology trends. Where Gartner mentioned Augmented Analytics as “the next wave of disruption in the data and analytics market.”
What does Augmented Analytics mean to modern businesses? How to does it matter to business analysts? Is Augmented Analytics the future of Business Intelligence? Let’s find the answers to these and many such questions.
What is Augmented Analytics?
Augmented analytics is the use of Machine Learning (ML) and Natural Language Generation (NLG) techniques to enhance data analytics, data sharing, and business intelligence.
Augmented Analytics enables data democratization and allows enterprises, no matter what their size is, to extract meaningful insight from their data sources. The deployment of Augmented Intelligence in business makes it possible for companies to adapt to a data-driven approach.
Is Augmented Analytics poised to take BI a notch higher?
The recent developments for BI and analytics tools include Augmented Analytics which is all set to produce a distinct experience than the tools traditionally used. By integrating AI elements into BI and analytics processes, the use of Augmented Intelligence in business analytics helps users prepare data, obtain new insights, and comfortably circulate them with everyone in the organization.
This new trend offers a fully transformed user experience across the entire Business Intelligence process as Augmented Analytics leverages the strengths of Artificial Intelligence and Natural Language Processing. Data ingestion, insight discovery, identifying patterns, and interacting with the platform will all become more streamlined and powerful.
What are the major roadblocks in the implementation of augmented analytics?
With automated data preparation and pattern discovery, Augmented Analytics enables users to gain deeper and faster insights into data. However, Augmented Data Discovery Tools come with their own set of obstacles. Let’s discuss the major challenges associated with these tools.
Cost Overruns
Processing data at scale can add to massive costs. Where cognitive services providers offer easy-to-integrate tools with their APIs, by charging separately for each execution of the service, they run a risk of cost overrun for you. To solve this problem, you can choose only the databases of unstructured data to analyze, instead of running the computation repeatedly on every piece of data.
Building Trust
Companies using Augmented Intelligence for business analytics need to invest efforts in building trust in the obtained output and ensure that employees do not lose their jobs. As most of the analytical work is going to be performed and automated by algorithms, rather than humans, adapting to ML-generated recommendations is something managers might find challenging.
The idea of losing their job due to the deployment of powerful technology or decreasing their worth in the company can motivate staff to slow down the progress. The executives in the leadership role must provide reasoning by explaining how Augmented Analytics supports employees by simplifying decision-making, rather than replacing them.
Danger of Pseudo-Insights
The huge step of withdrawing insights from the underlying technical aspects to enable users to generate deep, valuable insights is also a danger. The high-power Augmented Data Discovery Tools can be exploited if handled with negligence or used maliciously on purpose to produce pseudo-insights and harmful recommendations.
Robust Data Governance Strategy
A robust data governance strategy must be in place while implementing Augmented Analytics Tools. Unreliable data can generate undependable insights, which in turn can make the results unreliable. Relying on algorithms for data preparation and modeling processes can negatively impact the decision-making landscape as the insights may foster biases caused by the person creating the model.
Best practices to overcome this issue may comprise managers incorporating a semantic layer for binding disparate data sources together. In addition, the data should be augmented with auxiliary telemetry, user and system information, for example.
What are the noteworthy Benefits of Augmented Analytics?
The use of Augmented Intelligence in business can produce a myriad of businesses benefits, creating opportunities to extract maximum value out of data and business intelligence. Let’s have a look:
· Deeper data analysis
Augmented Analytics performs all the heavy-lifting tasks for you in analyzing exhaustive data combinations. It helps you identify the factors that influence your output.
· Faster results
The implementation of Augmented Intelligence in business enables users to find solutions to their problems without the help of data analysts, in a matter of seconds. They no longer need to wait for data experts or IT to fill the gap. Augmented data discovery reduces the time and complexity of deriving valuable insights from new data sets, especially unstructured ones. Cognitive capabilities are often leveraged by Augmented Intelligence Solutions to scale more efficiently than manual processes.
- Optimum use of resources
When data analysts aren’t bombarded with questions, they have more time to invest in deeper research that machines yet fail to support, providing you with more worth from your resources.
· Actionable insights
Essentially, Augmented Intelligence solutions make the data analysis process simpler than ever, enabling you to gain crucial insights into your data so that you can better strategize your objectives.
- Real-time view of Business’ Health
Augmented Data Discovery tools provide users with the ability to examine real-time conditions and act in accordance with them, which was earlier possible with very expensive and noninteractive dashboards only. With Augmented Analytics, users can visualize, understand, and act on live IoT data or live spatial data. It also allows them to acquire a live view of business transactions in just moments, making way for real-time commerce and customer engagement.
- Act on Insights Faster
The current era’s BI tools are good at pinpointing minor adjustments to correct the course of action. Augmented Analytics puts your data to even better use by enabling you to discover deeper insights that could lead the way to more radical and impactful changes.
- Ability to deal with unorganized data swamps
To cater to scalability, flexibility, and democratization needs of data, most progressive organizations are making a shift towards cloud services. However, it has created possibilities for data to grow out of control due to the absence of a clear goal, data management architecture, or governance strategy. Augmented Analytics uses AI to make an algorithmic sense of unorganized data swamps.
In Summary:
Augmented data discovery is an emerging BI capability for automatically preparing and organizing enterprise data for self-service BI. This is particularly challenging for unstructured data from sources like email, social media channels, IoT feeds, and customer service interactions.
Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. When implemented using the best practices, Augmented Analytics can create a pathway for the key executives to obtain real-time, actionable insights for simplified, data-driven decision-making.
