It was in 2005 that Roger Mougalas coined the term Big Data. Ever since, it has captured the imagination of industries across the spectrum, across the globe. After more than decade, the world is now staring at the next frontier in data – Augmented Analytics.

It was back in 2017 Gartner predicted Augmented Analytics to be the future of data, and in 2019, it already is the number 1 trend in data analytics. As per a research published by Allied Market Research this year, the global market for Augmented Analytics will reach USD 29.86 billion by 2025.

Data On Its Own Holds No Value

Data-driven insights are no longer just good-to-have, they are crucial for staying ahead of the curve. Most future-ready organizations today have embraced data analytics to deepen their understanding of customers and drive bottom-line growth. The problem is, their ability to leverage the power of data is severely limited, leading to a failure of data analytics projects. According to Gartner estimates, an incredible 60% of big data projects fail!

Data, by itself, holds no value for a business.

Say, a company’s data reveals that sales figures are dropping by 5% every month. But what does that really mean? This decline could be attributed to a failure of advertising methods, industry trends, or something entirely different. There is no way to figure the cause out unless you take a deep dive into the issue to uncover the real reason behind the decline in sales. For instance, you may come to the realization that your paid ads are less effective and need a different approach. Now you have an actionable insight that tells you exactly what to do.

The lesson – you need actionable insights, not simply informative data.

A data analytics project involves a number of processes – data aggregation, extraction, cleansing, pattern analysis, insights generation, to name a few. While the process itself isn’t so complicated, the tricky part is generating the right insights. Because data scientists are in short supply, other than being an expensive resource, companies need an advanced yet affordable tool for analysis at scale.

By harnessing the power of AI and ML, Augmented Analytics offers freedom from the tedious process of processing, aggregating, and visualizing data. Naturally, it is the next big disruptor in the world of business intelligence.

What is Augmented Analytics?

Augmented Analytics (AA) presents a novel solution to businesses to make sense of swathes of chaotic data. Augmented Analytics combines Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to automate the process of insight extraction from data.

The AA-driven tools organize, manage, filter, and analyze datasets to produce actionable insights, speeding the process of turning data into digestible information. Owing to the reduced manual involvement and dependence, businesses can rapidly analyze data at scale, and easily obtain patterns and trends.

Augmented Analytics reduces an organization’s dependence on data scientists and other manual processes by automating this crucial process with little to no supervision from a technical expert. Essentially, it cuts down on the human intervention part, weeding out less relevant insights automatically. Thus, the risk of missing important insights or making errors is vastly reduced, resulting in a streamlined and reliable data analytics process.

Augmented Analytics is set to create a new standard for business growth as organizations consume and generate massive streams of data from multiple sources but face challenges in making the data readily usable. Let’s see how.

How Does Augmented Analytics Work?

A business’ engine needs data to fuel growth. By automating a crucial part of the insight generation process, Augmented Analytics fuels this engine at an accelerated rate. When repetitive data cleaning and organization tasks are automated, data scientists will have more time on their hands for strategic analysis and decision-making. Additionally, this shrinks the scope for human error.

Smart data, fueled by Augmented Analytics, brings together the whole picture. When an organization’s data is siloed i.e. distributed across several different platforms, it presents a hurdle in smart decision making. To solve problems and identify areas for improvement, the decision makers must be able to view how the engine works on a whole, not how different parts work separately. By integrating data points into a unified system, decision makers and CMOs can track the entire picture on one platform.

In a paper published this year, Gartner outlines the different facets of augmented analytics:

• Data preparation and discovery
  Currently, most existing augmented analytics technologies lie in this stage. Here, the algorithm’s primary job is to automate data preparation tasks such as cleaning, labeling, collection, etc.

  At this stage, business stakeholders can use machine learning to automatically detect, visualize, and narrate relevant findings without having to build complicated models or algorithms.
   

• Signal detection
  At this stage, the analytics algorithm is able to detect true signals in data with a good measure of reliability. But, it cannot connect the discoveries with business actions or situations.

  So it still needs assistance from data scientists to transform such discoveries into concrete business insights. The upside is that the time they must spend on each insight is reduced drastically.
   

• Insight generation
  This is where the augmented analytics engine can directly interface with executives with almost no input from a data scientist. The algorithm will leverage its knowledge of past business cases to connect trends in the data with the larger business context.

  Then, it can go further and offer concrete action steps based on its insights. In fact, the engine can also track the implementation of such actions and offer additional insights to the business for optimizing its operational effectiveness

Benefits of Augmented Analytics

Augmented analytics takes the benefits of business intelligence to the next level, with unprecedented efficiency and accuracy:

• Improved accuracy
  When data scientists manually work on datasets and prepare them for analysis, there is room for error owing to the human element. Statistically speaking, the larger the volume of data, the greater the possibility of an error. By leveraging machine learning, the room for such mistakes shrinks greatly.
   
• Increased speed
  With standard business intelligence tools, the time required to manually prepare data and the wait time for related parties to respond to requests delay the completion of projects. Augmented analytics speeds this by immediately beginning request processing and leveraging AI to cull appropriate data – at the speed of a machine, not a human.
   
• Reduced bias
  When data scientists work with analytics tools, there is scope for blind spots and biases to creep in, leading to missed insights. Machines, on the other hand, take a more thorough and infallible approach, with no inherent bias. By quickly analyzing exhaustive data combinations, augmented analytics can identify the right insights.
   
• Greater resources
  Instead of reducing the need for human data scientists, augmented analytics can increase their value by freeing them from manual labor, allowing them to focus on more high-value tasks i.e. creating richer, deeper insights.

The automated insights generated by augmented analytics can thus be leveraged to assess business performance, identify growth pockets, and understand how a brand compares to the marketplace, thus contributing to a solid business strategy.

Ultimately, this results in cutting-edge insights driven by algorithms that would otherwise demand a huge investment of time and energy. This means data is democratized so that data scientists aren’t the only people in an organization that can make sense of the results.

Democratizing Data Analytics for all Stakeholders

Augmented analytics is fast becoming a popular data analytics tool, one that doesn’t need the involvement of data scientists, effectively collapsing the wall between asking questions and getting the right answers. One of the biggest advantages of embracing augmented analytics is the democratization of data.

Data scientists and analysts enjoy freedom from repetitive and low-value tasks like running routine reports. Instead, they can focus on solving complex queries and data science projects, offering critical business insights to the relevant stakeholders.

For small companies that don’t have the resources to build a team of expensive data scientists, augmented analytics will infuse accessibility and affordability into data-driven insights.

A bigger advantage lies for marketers like you. Augmented analytics is set to change how you make sense of customer data on a daily basis. Unlike earlier you don’t have to rely on an analytics team for in-depth research and reporting, a dependency that made your work time consuming and inefficient. With augmented analytics tools, you can regain control and track the entire customer journey, right from acquisition metrics to retention insights.

With augmented analytics, everyone in the organisation will hold the power to make informed and data-driven decisions, without having to depend on data scientists to furnish the required information. Naturally, this opens the doors for businesses to accelerate their growth at an exponential rate.

How can Marketers Benefit from Augmented Analytics?

Augmented analytics reduces the gap that existed between data scientists and other business users. The benefits of advanced data analytics are now available to every employee in every department, including marketing.

• Lower costs
  Because of a dependence on data analytics professionals, marketers faced a shortage and strain on resources. There is a high demand for the rare expertise of data scientists. However, hiring them is a lengthy and expensive process.

  By opening the doors of insights to everyone, augmented analytics empowers marketers to take charge of data and thus allocate resources in an efficient way.

  Moreover, Augmented Analytics also solves the problem of a waste of resources. Because traditional business intelligence tools demand grunt work (cleaning, unifying, entering, sorting data) out of data scientists before they actually get to the real job, they waste both time and talent.

  By automating the processes involved in data preparation and handling, Augmented Analytics tools allow marketers to reduce workload and extract the maximum value out of the data scientists they recruit.
   

• Cutting-edge insight
  For effective decision-making, marketers need granular insight at speed. Given that customers today expect personalized communications across channels, marketers need a real-time view of their individual needs and behaviors to offer what they’re looking for. Augmented Analytics tools allow marketers to do this without wasting any time.

  They quickly organize data and run analysis using a combination of ML and NLP. Interacting with huge quantities of data, they uncover patterns, trends, and anomalies – information that is gold to marketers.

  For instance, augmented analytics may identify the channels a specific audience segment engages with the most, and the types of ads that receive maximum response. With this insight, marketers know exactly where and how to reach customers to elicit a desired action. They can also minimize wastage by allocating resources to the channels with the highest probability of capturing audience interest.

  Additionally, augmented analytics can help with customer retention too. For instance, the data uncovered by Augmented Analytics tools can tell marketers not just where customers are spending their time but also how to appeal to them better. When marketers tailor campaigns to suit customer preferences and deliver relevant information, retention will increase over time.
   

• Effective performance measurement
  Augmented Analytics tools have the capability to measure the outcomes they drive. Marketers can, thus, evaluate progress and measure performance through an analysis of channels and audiences.

  This has powerful implications – marketers always stay in the know of what’s working and what’s not so they can adjust campaigns and reallocate resources.
   

• Actionable insights
  Once patterns have been identified, the results must be communicated to executives. The traditional ways of sharing data through reports and presentations is additional work for executives who are hard-pressed for have time to interpret data.

  Augmented Analytics tools can read a chart or report and translate the findings in a simple manner.

  Marketers can leverage the visualization abilities of augmented analytics to understand and share findings in a simple format with the C-suite. With actionable insights, the organizations can participate in data-driven decision making.

Businesses of all sizes have something to gain

Traditional data analytics platforms come with a major disadvantage. Before one can even get down to uncovering patterns, a great amount of manual labor is involved. While a business may onboard a data scientist to uncover insights, it may soon find that they spend most of their time cleaning and harmonizing data, not extracting insights from it.

Augmented analytics is on its way to transform the way businesses analyze data. Marketers, in particular, have much to gain. They can finally regain control of massive sets of data and meet customer expectations with personalized communication and experiences.

Ultimately, this can have a significant impact on the business’ bottom-line. By automating large-scale analysis and allowing marketers to generate insights, Augmented Analytics is paving the way for a more productive business landscape.

It is important that modern businesses understand the benefits of augmented analytics – speed, democratization, and insights. Armed with these, businesses are better equipped to anticipate what customers want, improve business processes, and lay the groundwork for success.

The Road Ahead For Augmented Analytics

In the present landscape, businesses are producing such a large volume of data that it has become impossible for data scientists to explore it on their own. Manual data exploration always runs the risk of missing key insights.

With augmented analytics, organizations have a tool to explore all possible hypotheses from the collected data and automate a great deal of data science tasks. When data scientists and augmented analytics work together, data insights will become democratized i.e. become available to a wide pool of business users.

There is no doubt that augmented analytics is here to set a new standard for business growth. The quicker you leverage this technology, the faster you will reap its benefits and be able to exploit growth opportunities.