How is Augmented Analytics Transforming Business Intelligence?
Data is the ultimate means of making excellent decisions, and why not? It helps determine the problems and challenges as they come our way, highlights opportunities, and helps see transformations, so we can work towards the desired goals.
As data becomes more convoluted with every passing moment, managing it and extracting valuable insights using traditional BI systems is not efficient. Addressing this challenge is Augmented Analytics.
In this blog, we will see how Augmented Analytics is transforming business intelligence. Additionally, we will also discuss use cases that benefit technology.
Gartner, the technology research and consulting corporation, coined the term Augmented Analytics. Augmented Analytics can be expressed as the next step in analytics’ evolution. The technology enables data scientists and business users to use the technology of today, such as artificial intelligence (AI) and machine learning (ML), to find and visualize information from unstructured data.
Data scientists can employ augmented analytics to analyze data without bias or previous views about how variables in the data are related. It eliminates the necessity for a specialist in the creation and management of advanced analytics models. It allows data scientists and developers to integrate ML/AI into applications that provide data science and machine learning content. Data scientists with evolved skills get better options to dedicate themselves to innovative creation and construct the most relevant models.
How Does Augmented Analytics Work?
While comparable to other forms of BI in its analytical workflow, augmented analytics enhances data analysis using ML, NLG, and AI furthermore. Here’s how:
Data preparation is all the work done on data for query and analysis. It includes the collection, filtering, connection, and validation of datasets. And usually, it demands the expertise of developers and data scientists to conduct.
However, this process can be automated with augmented analytics tools. Data preparation and streamlining integrations of all your data sources can be run through automation systems — including data warehouses, cloud platforms, web service tools, and analytics platforms.
Once the data (and metadata) has been added to the pipeline, everything from data filtering to dataset unification is done by the automating systems for you. This opens up time constraints for your data scientists, engineers, and developers to focus on creating new analyses to deepen insights.
Insight discovery is the part of the data analytics process where the algorithm analyzes the data via the curtains of a predefined model to discover answers to questions, such as quarterly revenue or customer acquisition rates. However, since models traditionally have to be developed by data scientists manually, insights can be blind in the specificity of metrics.
With augmented analytics, insight discovery is both uncomplicated to initiate and thorough. Queries can be set up using natural language and voice inputs rather than hyper-specific keyword entries. Machine learning algorithms can drill through all of your data (no matter how many rows there are) to uncover detailed, targeted insights to find answers to your questions question.
How Augmented Analytics is transforming Business Intelligence (BI)?
Using powerful AI and ML algorithms, Augmented Analytics helps businesses reduce their dependence on manual processes and/or data scientists by automating the insight-generating process. It also reduces overlooks and inconsistencies because of human errors while generating insights. However, it is essential to make decisions in such a way as to provide a clear image of the situation, which is crucial for the system to work as intended. Revolutionizing how consumers engage with data, consuming it, and turning insights into action can all be automated.
Augmented Analytics is changing key phases of Business Intelligence, which are currently still being conducted manually and are prone to human error, as discussed above.
The automation offered by augmented analytics has transformed traditional business intelligence (BI) into self-serving business intelligence. While traditional BI used to be an uncommon tool that was majorly managed by the IT team, self-serving BI can be operated by business users who are the end-users in most cases.
The significant disadvantages of traditional BIs are that it requires highly skilled data analysts and has a lengthy time-to-insight period with poorer quality of data compared to augmented analytics. Modern self-serving BI solutions powered by augmented analytics give us user-friendly graphical interfaces that end-users can understand. These solutions can handle an extensive amount of data from multiple unstructured sources quickly and efficiently. Intelligent BI also makes data security, governance, and access control simpler for the entire organization. They also help reduce the involvement of the IT team to manage business analytics.
Some of the many advantages of using BI powered by augmented analytics include:
- Deeper data analysis: Analysis of exhaustive data combinations and efficient discoveries of all the factors influencing your business are now possible.
- Quicker results: Since there is no manual scanning of data, you get quicker results.
- Better use of resources: When you automate a significant part of your analytics process, more complex and deeper research can now be addressed by your team.
- Actionable insights: By streamlining the data analytics process, you get access to key insights that can help you make better data-driven decisions.
Does your organization plan to adopt Augmented Analytics BI in the future? If yes, then how will it benefit an organization. Feel free to share your thoughts in the comments section.
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Payal is a Product Marketing Specialist at Subex, who covers Augmented Analytics. In her current role, she focuses on CIO challenges with data management, and potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.