Augmented Analytics: How Does it Work?

26 October 2020

Big Data first entered the scene way back in 2005. The term was coined by Roger Mougalas, and captured the imagination of industries across the globe. After fifteen years, the world is now looking towards the next frontier in data, Augmented Analytics.

They say that data on its own has no value for a business. Let's say your company's data reveals that sales figures have decreased by 15% per month. This type of data alone can't determine the true cause of the sales drop unless you take a deep dive to figure out the reason.

And if you should come to the realisation that your paid ads require a new marketing approach, that’s an actionable insight that tells you precisely what to do.

The lesson? Data needs to be interpreted to be useful.

Data analytics projects involve numerous stages such as data aggregation, pattern analysis, extraction, insights generation, cleansing and much more. The process itself isn't that complicated, the tough part is getting the right insights.

How does Augmented Analytics work?

Augmented analytics fuels a business engine at an accelerated rate by automating an important chunk of the insight generation process. Automating repetitive and mundane data cleaning and organisation tasks gives data scientists more time to dedicate to strategic analysis and decision-making. Plus, it minimises the risk of human error in the data.

Powered by Augmented Analytics, Smart Data brings the whole picture together. It is necessary for solving problems and identifying areas for improvement because decision-makers must be able to view how the engine works as a whole to produce an effective solution. By putting data points into a unified system, they can track everything on one platform.

1. Data discovery and preparation

Most existing augmented analytics technologies lie here. The algorithm's primary job is to automate data preparation tasks such as collecting, cleaning, labelling etc. Business stakeholders can use machine learning to detect, visualise and narrate relevant findings automatically without building complicated models.

2. Signal detection

The analytics algorithm is able to detect true signals in data with an excellent measure of reliability, but this cannot connect the discoveries with business situations or actions. It needs assistance from data scientists to convert discoveries into concrete business insights. The time that they must spend on each insight is decreased drastically.

3. Insight generation

The algorithm will use its knowledge of previous business cases to its full advantage in order to connect trends in the data with a wider business context. It can then go further, offering concrete action steps based on its insights. In reality, the engine can also track the implementation of actions and offer additional insights to the business for optimising its effectiveness.

What are the benefits of using Augmented Analytics?

Increased accuracy

The larger the volume of data, the greater the possibility of error. Using machine learning shrinks the room for such mistakes.

Better speed

Augmented Analytics speed up the time to completion of projects by requesting process immediately and using AI to cull appropriate data.

Reduced bias

Machine learning takes a more thorough and infallible approach that is not subject to inherent bias. Augmented Analytics can identify the right insights by quickly analysing massive amounts of data combinations.

More resources

Augmented Analytics allows people to focus on high-value tasks.

How can marketers benefit from Augmented Analytics?

Lower costs

Augmented Analytics empowers marketers to take charge of data thus allocating resources in an efficient way by opening the doors of insights to everyone. Augmented Analytics tools also allow marketers to reduce workload and extract the maximum value from the data scientists they recruit.

Cutting-edge insight

Augmented Analytics allows marketers to organise data and run an analysis using a combination of machine learning and natural language processing. They can uncover patterns, trends, and anomalies while interacting with huge quantities of data.

Effective performance measurement

Augmented Analytics gives enables you to measure the outcomes they drive. Marketers can evaluate progress and measure performance via analysis of audiences and channels. This implication is very powerful because it makes marketers aware of what's working and what's not so they can make adjustments in their campaigns and reallocate their resources accordingly.

Actionable insights

Once the patterns have been identified, the results must be communicated to executives and this data can be simplified by Augmented Analytics. This tool can read a report and translate with ease. Organisations can participate in data-driven decision making with actionable insights.

Conclusion

With the discovery of Augmented Analytics, organisations are now able to explore all the possibilities from the collected data and automate a great deal of data science tasks. When working together with data scientists, data insights will become democratised which means, it will be available to a wide pool of business users. The quicker you leverage this tool, the faster you’ll reap its benefits.