6 Benefits of Augmented Analytics

by Dhiren Patel, Chief Product Officer & Head of Customer Excellence

6 Benefits of Augmented Analytics

by Dhiren Patel, Chief Product Officer & Head of Customer Excellence

Traditional data analytics products deliver static reports and dashboards. These products are expensive from a TCO perspective, difficult to use for end-users, time-consuming to implement, and limited in capabilities. Businesses have spent millions of dollars on legacy platforms with very little adoption by end-users. Business is asking for faster insights. Business is asking for more meaningful and actionable insights. In the backdrop of these challenges emerge the promise of augmented analytics platforms.

As the name suggests, augmented analytics platforms augment the analytics experience for everyday business users. As it will become clear, augmented analytics platforms have the promise of delivering a true paradigm shift for businesses.

1. Enhanced data management

Quality of data is an ongoing process for most companies. However, the goal of delivering advanced data analytics can not wait for data to be perfect. Modern augmented analytics platforms help companies with data quality, and governance. Companies that have a centralized view of their data, which is gathered from various sources across the organization, can act faster and address business problems. Their data-driven strategy pays off because they can offer a better customer experience while also increasing revenue.

Data Catalog

Before the end-user starts using the platform, augmented analytics platform delivers a data catalog with relevant attribute dimensions. Data catalog gives visibility into data and allows users to incorporate relevant business context at the time of set up. Additional metadata about data gets created that is helpful in generating deeper insights for end users.

Data Quality

Augmented analytics platforms run a set of rules and algorithms to deliver data quality insights at a table, column, and workspace level. Most businesses don’t have an objective view of data quality for their use cases. Enterprises know that poor data results in poor decisions, but even though data quality has become part of every enterprise’s data strategy, it remains elusive. Insights into data quality allow businesses to determine the validity and usability of certain data attributes. This helps improve the end-user experience with such platforms.

2. Streamlined data analysis experience

Current data analytics landscape is plagued by scattered workflows and point solutions. Augmented analytics platforms deliver an end-user experience that integrates key business requirements. For example, end user workflow can be streamlined with intelligent search being at the centre stage - in other words, additional advanced insights are integrated into a simple, intuitive search interface. Business users get an augmented user experience leading to better adoption and usage.

3. Highly accurate insights

Intelligent Search

Intelligent search technology for data analytics is a giant improvement over static reports and dashboards. Augmented analytics platforms provide a simple, intuitive intelligent search experience powered by NLP. Users can now converse with data, ask questions and build their own reports & dashboards.

Instant Answers

Intelligent search experience will help users get answers to questions instantaneously, in seconds. More importantly, users will get answers to questions that they have not even thought about. In fact, augmented analytics platforms typically learn from user searches and based on that learning, start delivering advanced insights in the form of interactive audiovisual stories autonomously.

4. Data democratization across your organization

Augmented analytics platforms are not just built for analysts. They are built for all users. They are specifically built for business users with no technical expertise. This helps everyone converse with data. Data democratization demands a strong data strategy and culture, as well as the right infrastructure to enable it.

Organizations must examine issues such as speed, future requirements, cost, and the types of workload expected when selecting a deployment model. Although it is a critical process for businesses to make this decision after they’ve established their data strategy – to have it functional in theory requires a robust Augmented analytics infrastructure. This helps everyone get advanced AI-powered insights while hiding all complexities. In other words, this helps companies democratize data across the entire organization.

5. Lower investment cost

Some augmented analytics platforms do not replicate or duplicate data. This helps clients save money on redundant storage and maintenance services for data. Legacy platforms required clients to maintain a large army of technical resources for support activities. Augmented analytics platforms reduce this need by more than 90%. Finally, business users don’t have to wait for weeks and months for new insights and reports. Considering all of these dimensions of ownership, augmented analytics platforms can help clients save anywhere from 50 to 80% on a TCO basis.

6. Better data governance and more control over your data

For legacy data analytics platforms, data governance was an afterthought. Most of these platforms create data islands for different use cases. From an information security standpoint, it is quite chaotic to manage who has access to what kind of data. This creates a significant risk for clients. On the other hand, augmented analytics platforms are built with data governance as a key feature allowing businesses to control who sees what data at a table, column, and row-level.

A few common mistakes to avoid

Not all augmented analytics platforms are built alike. Deeper analysis and evaluation is required to distinguish benefits in key areas like intelligent search, root cause analysis, end user experience, data governance & quality, and deployment architecture. Most analytical platforms also duplicate and cache client data - this has significant implications from a TCO perspective that must be considered and evaluated upfront.

MachEye’s SearchAI integrates the powers of an Analytics Copilot to offer intelligent search, actionable insights, and interactive stories on your business data. MachEye empowers every user with intelligent search, interactive audio-visuals, and actionable insights. Unlike traditional analytical platforms that only provide answers to "what" questions on data, MachEye offers a comprehensive solution that enables users to answer "what, why, and how" scenarios for everyone in the organization.

Through a user-friendly interface that includes Google-like search and YouTube-like audio-visual experiences, decision-makers at any level can receive actionable insights and recommendations. MachEye adds a new level of interactivity to data analysis with its actionable "play" button feature.

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