
As organisations and leaders wake up to the importance of data, we are seeing an increasing focus on data centricity and data-driven decision making. According to Forrester, insights-driven businesses are expected to grow from $333 billion in revenue in 2015 to $1.2 trillion in 2020.

However, the adoption of data science projects is often hampered by its excessive dependence on the availability of qualified data analysts and data scientists. Data analytics projects typically take months together to complete with even large teams is large and highly experienced folks. The primary reason for this is that pre-processing of data to execute analytics projects is an
extremely tedious process. It accounts for almost 60 per cent of the time spent by analysts.
Today, the volume of data generated is increasing steadily. Over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth. This data needs to be filtered, analysed, and maintained in a format that is retrievable. The process is highly inefficient and messy and places considerable strain on data analysts.
Automation as the Solution
Automation of processes such as data cleaning, finding the outliers, running filters and calculations can help greatly reduce the time spent on pre-processing. For instance, there are tools available that automatically adjust the hyperparameters during model training. Data analysts can access predictive analytics with visualizations with just a click of a button. What’s more, all this can be done without writing a single line of code and can be used as just a click and go solution.
Gartner expects more than 40 per cent of data science tasks to be automated by 2020. This automation will not only result in increased productivity, but it will also drive broader usage of data and analytics and help make more informed decisions. Citizen data scientists can play a key role in bridging the gap between mainstream self-service analytics by business users and the advanced analytics techniques of data scientists. With automation, citizen data scientists will be equipped to perform sophisticated analysis that would previously have required more expertise. This will enable them to deliver advanced analytics without advanced technical data science skills.
This process of automation will not only democratize artificial intelligence in analytics and make it more accessible to all, but it will also usher in the era of the “Citizen Data Scientist.” In effect, this means that users without knowledge of machine learning algorithms or R and Python coding can readily develop machine learning models. They can do this by simply uploading a dataset, choosing what they are trying to predict, and hitting the Start button. This trend will play a huge role in making analytics waaaaay easier. It will help data science move beyond the current realm of corporations and find adoption among SME’s as well. This leads us to the question

What Happens to Data Scientists?
The advent of AI/automation in data science is not a threat to data scientists and analysts. Instead, by mitigating the scope for errors in data pre-processing, it actually helps make a data analyst far more effective, thereby enhancing accuracy and work quality. Data scientists will still be extremely valuable especially when it comes to identifying and interpreting real-world scenarios and designing the right strategies. But automation will help reduce
the tediousness of their job and enable them to focus greater energy on real-world problems.
Therefore, automation in data science is a win-win situation for all since it will help broaden the appeal of data science while enabling data scientists to create far greater value.
About us
At Mate Labs we have built Mateverse, a fully Automated Machine Learning Platform, where you can build customized ML models 10x faster without having to write a single line of code. We make the jobs of Analysts and Data Scientists easier, with proprietary technologies vis a vis, Complex pipelines, Big Data support, Automated Data Preprocessing (Missing Value Imputation using ML models, Outlier Detection, and Formatting), Automated Hyperparameter Optimization, and much more.
To help your business adopt Machine Learning in a way that won’t end up wasting your team’s time in data cleaning, and creating effective data models, register here, and we will reach out to you.
