Evolution of BI Platforms
Tags: Augmented Intelligence, Analytics, Business Intelligence, Intelligence, Etl

We live in an era of Big Data. Around 1.7MB of data is created every second by every person. 2.5 quintillion bytes of data are produced by humans every-day. This sheer volume of data has become so huge, complex, and fast-moving, that to make sense of this vast amount of data is a challenge. So, Business Intelligence (BI) tools are used to analyze this humongous amount of data to uncover the insights that are crucial for the business. It makes data of any kind, easy to digest with stunning visualizations, detailed historical analysis, and customizable reports.

Over the decades, BI technology has evolved, and the market shows no signs of slowing down. While the inherent meaning has remained the same, but BI as a set of processes, technologies, and tools has changed a great deal, right from Traditional BI to AI-powered BI which uses Augmented Analytics. Before understanding how augmented analytics will change the analysis and business intelligence process, let us have a look at the evolution of business intelligence.

Traditional BI

The first generation of BI technology often referred to as “Traditional BI” was a centralized guardian tool for all enterprise data largely owned and driven by the IT and data specialists. Legacy deployments of multiple components such as data marts, data warehouses were technically complex and required extensive IT staff to maintain and manage it. The Extract Transform and Load (ETL) paradigm integrated data from disparate sources into a central repository for storage. Once stored, data was normalized and structured before it is further utilized to run queries and retrieve data for reporting.

Ultimately, the IT department generates and delivers static reports to the business owners. The analysis was usually descriptive and performed by specialized data analysts with restricted access to the reports. This entire process could take days, weeks, or even months to produce insights due to dependency on skilled IT staff. And thus, unable to make timely data-informed decisions. To make BI more accessible to business users, self-service BI became the next generation of analytics and BI.

Self-Service BI

The main drawbacks of traditional BI were the need for highly skilled technical analysts, lengthy time-to-insights, and poor quality of the data being analyzed. These drawbacks were overcome by a more agile approach that favored self-service capabilities: Modern Self-Service BI. This eliminated the technical stack designed for IT users and focused on providing data discovery and visualization tools to business users. It also provides business users the ability to conduct ad hoc analysis of data from disparate sources without any advanced technical skills. As compared to traditional BI, they can handle larger volumes of data drawn from multiple sources allowing for deeper analyses. They replaced the rows and columns of traditional data presentations with graphical pictures and charts.

In addition to historical reporting, it provides predictive and prescriptive reporting and insights in real-time. With these tools’ users get the information to make better decisions, with greater ease, and without having to rely a lot on data analysts and IT professionals. Modern BI solutions also make data governance, security, and access control simpler for IT teams.

Need for an AI-powered BI tool

Despite being more insightful and easier-to-use than traditional BI, self-service BI tools do have few limitations. As the volume of data rises, there is a requirement of data scientists to make sense of huge datasets. But the scarcity of data scientists and manual data preparation makes the process highly inefficient and prone to error. Also, the insights provided by self-service BI systems are limited to the type of queries made by business users. This is where the need for a new AI-powered BI i.e. Augmented Analytics BI system arises. It not only automates the data preparation tasks but also parts of data insights and the data discovery process.

Augmented Analytics BI

Augmented Analytics integrates AI into the analytics and BI process to help the user to prepare their data, identify relationships within the data, discover new insights, and easily share them with everyone in the organization. It reduces the dependency on highly skilled data scientists by automating insight generation using machine learning and artificial intelligence algorithms. Gartner states that more than 40% of the data scientists’ roles to be automated by 2020. Augmented Analytics BI tool can help less technical experts like Citizen Data Scientists to provide recommendations and suggestions based on their domain and primary skills to understand and gain insights from the trends and patterns. It will be free of human biases and reveal hidden insights crucial for the business. Also, the use of Natural Language Generation (NLG) can enhance the BI reporting process by allowing users to query the system and present the insights narratively.

Augmented Analytics will help move organizations beyond the dashboard paradigm to a new way of consuming insights i.e. data story. These will help the user understand just the insight and context that they need at the right moment to make the decision. By 2025, 75% of the data stories will be automatically generated using augmented analytics techniques.

Every organization will need an augmented analytics platform to create visualizations, aid in storytelling, and then help users to effortlessly share their findings across the entire organization. This will boost the adoption of Augmented Analytics BI across teams, especially among non-technical users. Augmented Analytics will change how users experience analytics and BI.

Does your organization still use Traditional BI and Self-Service BI? Do you plan to adopt Augmented Analytics BI in the future? If yes, then how it will benefit an organization. Feel free to share your thoughts in the comments section.

Author: Payal Paranjape