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Augmented analytics: Are you ready?

Wanna become a data scientist within 3 months, and get a job? Then you need to check this out !

Augmented analytics will be a game changer so you need to start preparing now

Augmented analytics is arguably one of the most exciting emerging technologies that currently exists. After all, it not only makes sense of data (i.e. giving you facts and figures) but it also tells you why that data is important to your business and the actions you can take based on the data to improve performance.

So, if the data is telling you sales are down in a particular geographical area, with augmented analytics, you will know the cause of the sales drop. The ultimate goal of augmented analytics is then to also make recommendations on what you should do about it.

But wait – augmented analytics is a future trend

The above potential of augmented analytics sounds fantastic, doesn’t it? The problem is, augmented analytics solutions do not currently exist.

Here’s a roundup of where things currently stand:

  1. Data is available to your business from a wide range of sources – yes, you have this
  2. You can collect and collate this data easily – yes, you can do this too
  3. The data can then be cleaned and presented to you in a digestible form – yes, you can do this manually and there are comprehensive tools, such as data dashboards, which can do this automatically
  4. You can learn the ‘why’ from data, i.e. why the data is what it is, what happened, etc. – in a way, you can do this, yes, but it is almost exclusively a manual process that requires specialist data science skills. It is also very time consuming
  5. You can get recommendations on the actions you should take based on the information from the data – again, only manually with the help of a data scientist

So, we are a bit away from augmented analytics being a reality.

Starting on the augmented analytics journey

Just because full scale augmented analytics solutions don’t yet exist doesn’t mean you should sit back and wait for them to become available.

First of all, development in this space is advancing at a rapid pace. According to Gartner, augmented analytics is currently the number one trend in the data and analytics sphere.

There are also multiple companies working to develop augmented analytics solutions. This ranges from startups to long-established tech corporations such as Microsoft, Oracle, IBM, SAP, and Salesforce.

Secondly, there are things you can do now to ensure your business is in the best possible position to take advantage of the opportunities and benefits that augmented analytics presents.

Below are four practical steps you can take now to make sure you are augmented analytics-ready.

1) Evaluate your data

Like everything else in relation to data, augmented analytics is only as good as the data it is working from. If it has poor quality data, it will produce poor quality results. Plus, for augmented analytics, poor quality results are a massive deal.

After all, the ultimate goal is that an augmented analytics solution will make data-based recommendations on what your business should do to remain competitive, improve profits, increase sales, and so on. Poor quality outputs from augmented analytics mean inaccurate and/or incorrect recommendations that could be very costly to your business.

So, you should take steps now to ensure the data you have is high quality. This includes ensuring you are getting data from good sources and also ensuring it is being properly cleaned.

2) Ensure you have data oversight

Augmented analytics is not about eliminating the data scientist, although there are some that think this is the ultimate goal. However, a more realistic view, at least in the medium term, is that augmented analytics will make it possible for data scientists to concentrate on the real value-adding aspects of data.

So, instead of spending their time on manual and highly repetitive processes like data collection and data cleaning, data scientists can spend their time overseeing the system and making sure it is delivering maximum benefit to the business.

You need to make sure you have this oversight in place either through in-house data scientist resources or using an outside vendor.

3) Improve data literacy in your business

A fully operational and effective augmented analytics solution will permeate through just about every part of your business. In other words, it won’t just be the data experts and the senior decision makers who will be involved.

Others will have direct contact with the system and/or they will be directly impacted by the actions and recommendations that result from its implementation.

Therefore, augmented analytics will require everyone in your business to be data literate. You need to start educating them now.

4) Look for early adopter opportunities

Finally, start looking for early adopter opportunities from emerging augmented analytics vendors. Specifically, identify opportunities to engage in sandboxing or to operate a free trial. This will let you see the challenges of implementation as well as how augmented analytics can complement your existing processes with a view to automating them in the future.

Don’t wait for augmented analytics to become the norm before taking notice. This is a technology that will be highly influential in a short period of time. The risk of being left behind is great, so start now.


Because I am a believer in augmented analytics I have worked the last few years on delivering the best augmented analytics solution that can be: Datalyst. Datalyst combines many different techniques, methods and heuristics, in order to deliver easy-to-understand models and results. It is not a tool for data scientists, but rather a tool for those who want to work with data scientists or data.

Some use cases for this product include:

  1. Understanding the value of your data, before you hire a data science team.
  2. Create models that can help inform policy.
  3. Create data products, without having to hire data scientists.

Feel free to reach out to me if you have any questions!

Wanna become a data scientist within 3 months, and get a job? Then you need to check this out !