Achieving Successful Data Science Projects with Automation

by March 16, 2020 0 comments

automation

It has always been hard for a human to evolve with rising trends yet it’s in their nature. With the commencement of the fourth industrial revolution, we noticed that people are becoming more and more agile to embrace new technologies instead of fearing the rigorous changes they would have to go through. Such a welcoming gesture and enhance acceptability has made large scale innovations possible. In the case of automation and data-technologies, businesses and their humanly assets have been sport to incorporate and excel with such advancements. Automation and data science has always been complementary to each other. The former is a must for driving data-enabled decisions and culture as well. Automation unlocks new edges of data that were not explored before.

However, not all businesses are keen to understand the potentials of automation in delivering more value to data science projects. Nick Elprin, CEO and Co-Founder of Domino Data Lab, said, “Sixty percent of companies plan to double the size of their data science teams in 2018. Ninety percent believe data science contributes to business innovation. However, less than 9% can actually quantify the business impact of all their models, and only 11% can claim more than 50 predictive models working in production.”

Ryohei Fujimaki, Ph.D., founder, and CEO of dotData said, “We’ve seen studies that report only 4% of companies successfully implement business intelligence (BI) and artificial intelligence (AI). dotData is a company that focuses on data science automation for enterprises. “It naturally makes you wonder what the other 96% are doing,” he added.

“There is a great deal of business interest in this,” said Fujimaki. “Data science is key to business growth if you can unlock its potential. You can predict new products and costs, and even customer churn. The insights that data science can generate cuts across all industries, whether it is pharma, aerospace, manufacturing, retail, finance, or other.”

However, the problem lies with the work is that companies take an average of two to three months to complete a single data science project.

“Data science is difficult for enterprises because it requires an interdisciplinary team to be successful,” said Fujimaki. “First, you have company ‘domain experts’ who know particular areas of the business and can assist in defining important business use cases. Data science talent is also difficult to hire. Then, you have to collect, clean, and prepare data, which can consume more than 80% of the project time. You then must define different data models, algorithms and visualizations and try them out in an iterative mode, knowing that not all of them will work. Finally, when you get a strong project that meets a business case, you have to migrate the project into production. This often impacts business processes.”

As the entire process becomes too time-consuming, to achieve a successful AI project, a number of companies are migrating towards adding machine learning to get even more out of the initial AI work. However, adding machine learning can take another 20 to 30 percent of project time. “Again, you must continually test and retest, to ensure that data is accurate and that you are realizing your business case objectives,” said Fujimaki. And this where automation enters the picture.

In order to go beyond the AI and ML techniques with a fast pace, companies can further automate ML processes. “With this capability, you still need business domain experts, data scientists, and engineers, but you can automate many of the statistical and mathematical operations of data science,” said Fujimaki. “This makes data science more sustainable in organizations, and it enables companies to cover more ground because they can provide data science products faster.

He further continued, “There are many elements in this process, but data science automation can help. In addition to enabling your enterprise to complete more data science projects and get products to market sooner without having to do all of the data science operations yourself, a kind of ‘democratization’ of data science begins to occur in organizations. Now, many people who might be business domain specialists can also use automation without having to become full-blown data scientists.”

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