Two people looking at analytics

What is augmented analytics?

Your team is constantly in a state of growth, probably now more than ever. Whether through measurement or monetisation, your IT teammates may start looking into augmented analytics tools as a way to identify what your users are looking for, its value, and how knowing that data will help grow your business. Once coined “the future of data analytics,” augmented analytics will help provide the answers, understanding, and actions you’ll need in a fast-paced, ever-changing digital landscape.

Understanding augmented analytics

Augmented analytics helps your team transform large data sets into smaller, more digestible information through statistical and linguistics technologies. A combination of machine learning, artificial intelligence (AI), data insight, and augmentation explores how analytics can be built, consumed, and shared to your users.

Developed in 2017, there are three essential components to augmented analytics that businesses must understand:

  1. Machine learning based on algorithms that have the ability to learn from data without relying on rules-based programming.
  2. Natural-language generation (NLG) which translates machine findings into words and phrases that humans can understand, i.e., making data feel and sound more human.
  3. Automation insights, data-driven insights that help determine your business strategy.

These three components combined offer a deep dive into hidden patterns and provides a cohesive picture into what your raw data and insights can provide your users, as well as how your team should inform the business of what to do next.

The benefits of augmented analytics

Augmented analytics has become a big part of successful user experiences, most effectively combining the best aspects of machine intelligence and human curiosity. Some of the top benefits include:

  • Faster data preparation. Since augmented data preparation consolidates multiple data sources together faster, you can quickly detect repetitive actions, joins, accelerated insights, and higher productivity to build fully data-automated and quality recommendations which help personalise the user experience.

  • Reduced analytical bias. Bias caused by incomplete data sets and lack of context only hurts your users’ results. By enabling your machine to perform analyses usually saved for data analytic tools, augmented analytics can reduce potential bias by performing a larger range of data focusing solely on factors of statistical significance.

  • Elevated trust. Every time a user interacts with your data, it offers clues to your machine-learning algorithms and, over time, provides more relevant and accurate recommendations to your users. These suggestions help build your users’ trust since the provided data offers more relevant items like business context, intent, and more. As users are able to continuously see relevant selections, they’re able to trust your strategy, and then build trust in your business.

  • Increased data literacy. As you continue to gather data, your team and users have the opportunity to gain value from that data. By providing an automated analysis of your results, users can easily search for and visualise insights with their increased data literacy using minimal effort. This supports both your users and your organisation, as creating a data-literate workforce means accessibility is no longer an obstacle.

  • Extra time for team members. Since your IT teammates won’t have to spend time on collecting and analysing massive amounts of data sets and distilling them into actionable items, they’ll have more time to focus on high-level business strategies and special projects. This also helps empower your team as available resources become more data-focused and easily folds into their everyday activities instead of those tasks being reserved for data professionals.

Challenges of augmented analytics tools

Thanks to AI, machine learning, analytics, and business intelligence (BI) platforms, organisations are ascending to a new level of productivity and learning. Augmented analytics tools help businesses and analysts achieve better insights into how your users’ needs are constantly changing. And while there are many pros to augmented analytics, there could be some obstacles to adopting this process to your business. Some examples of things to watch out for include:

  • The quality of your training data. If your analytical models aren’t up to date, your insights will be pointless.

  • Accuracy and trustworthiness. Using comprehensive data that’s free of errors and updating your models periodically means your data stays current and users can trust you’re providing them the most recent and accurate information.

  • Relevancy. Filtering out irrelevant data is a hassle and time-consuming. Test out data regularly to ensure only relevant results populate for users.

  • Team performance and scalability. Depending on your platform’s capabilities and team’s skill level, implementing augmented analytics, as well as the volume that comes with it, into your process could slow down productivity momentarily.

Integrating augmented analysis for business success

Though augmented analytics has been around in some types of BI platforms, some businesses are now realising how beneficial implementing these learnings into their business can be. As your tactics shift from dashboard-centric to a more dynamic experience, as well as revolving more around dynamically generated data stories, you can anticipate trends and plan for them instead of being simply reactionary. Using business analytics tools to mine for augmented analytics, you’ll find new ways of interpretating data. It doesn’t just make it easier for users to analyse and act upon, but also gives your business a competitive advantage to make improved decisions and more revenue.

Simplify your users’ insights with Microsoft Power BI

There’s much to learn about your users that your team hasn’t even begun to tap into yet. By embracing automation, you can utilise data insights, machine learning, and NLG to give users a well-rounded experience and you a complete picture of your customers’ needs and wants.

Through augmented analytics, Power BI offers a way for IT and BI teams to refocus their processes to become more data driven without needing data professionals. Learn to simplify your business processes by creating faster insights, looking at data from different angles, increasing productivity, and offering more chances for better decision making.