Augmented Analytics is a term often mentioned in the data science circles today. The exponential growth in data generated around the world has made Data Scientists one of the most valuable assets in the job market. With that surge in demand, they also became one of the most expensive resources for organizations to acquire.
According to Gartner, A scarcity of data scientists will no longer hinder the adoption of data science and machine learning in organizations by 2025.”
What is the miracle solution that will allow organizations adopt data science and machine learning (despite the widely known shortage in ‘Guru’ data scientists)?
Could adopting Augmented Analytics powered by “Citizen data scientists” be the solution to restore the balance? Could Augment Analytics be the way for a wider Data Science adoption across Mature and less Mature Organisations?
Augmented Analytics: A New Wave in Data Science
Augmented Analytics are solutions that:
- Automate data exploration and provide relevant statistics without requiring deep domain knowledge,
- Create easy visualizations through dashboards and intuitive design
- Build advanced analytics models: Algorithms find patterns in data, auto-select features/Variables/Models that are best suited to datasets (e.g. Genetic Algorithms),
- Provide robust predictive models without a single line of code.
- Deliver prescriptive analytics to end-users quickly and easily.
In a nutshell, it is automating machine learning to do the ‘dirty’ jobs that would take 60% to 80% of typical analytics’ project timeline. In other terms, it enables the removal of bottlenecks that data scientists keep facing when performing analytics’ projects. This will save substantial time that could be reinvested in digging further into the results and devising better recommendations to the business.
In the context of remedying to the shortage of data scientists, the legitimate question that one may ask is:
Can augmented analytics be an alternative to hiring data scientists?
The answer is yes if it is powered and led by a Citizen Data Scientist but let’s first define the concept of ‘Data Scientist’.
Citizen Data Scientist and the Optimization of Data Science
A Citizen Data Scientist (CDS) is someone who can create predictive and prescriptive models without necessarily being in the organization’s analytics department. CDSs are not expected to be experts in statistics, machine learning or IT. However, a broad knowledge of these domains enables them to produce advanced analytics using augmented solutions.
CDSs are close to business analysts, only some training in the main concepts of the field can transform the latter to the former.
The advantage of CDS is that they are embedded in the data business. Therefore, they are aware of the daily challenges and the changes in the emergency scale when it comes to prioritizing business requests.
Hence, augmented analytics are likely to offer them the required flexibility to use machine-assisted models. Such models are used to solve business questions promptly & efficiently, and attend to the evolving needs of the business when it comes to analytics.
Augmented Analytics solutions aim to optimize and automated the early stages of data science projects. That leaves more space to the data scientists (Expert or Citizen) to focus on the savviest part of the analytics’ project (insight and recommendations). This will in turn allow to add a significant value by automating the bottleneck jobs like data cleaning and preparation. It’s more apparent especially that unstructured data environments like data lakes are increasingly adopted. Augmented analytics algorithms detect schemas and catalogs data and even recommend enrichment.
Are data science and Machine learning skills going to be the same for the foreseeable future?
As clear as it can be, a paradigm shift in the analytics technologies field is happening. This implies that organizations need to take strategic moves to take the most out of it.
It seems that a new ‘breed’ of data scientists is evolving to meet the needs of this era. This is what we call Citizen Data Scientists that are now gaining more and more notoriety. Additionally, they seem to be the perfect match for harnessing the power of augmented analytics.
But where do organizations have to look for Citizen Data Scientists?
Companies should consider upskilling existing employees or fresh graduates who have educational background in Physics, Mathematics, Finance, Computer Science, Economics… In other terms, anyone who is able and willing to perform quantitative work and who is aware of core business issues.
A combination of citizen data scientists and augmented analytics seems to be the winning combination. In order to be successful, organizations need to prepare the adequate ecosystem. That is ac by raising data literacy and providing access to data use and most importantly to ‘speak’ data across the organization.
By Oussama Mabrouki and Ramla Jarrar