Data science essentially uses a variety of algorithms and processes to analyze certain aspects of data. It has established itself as a key component for companies focusing on business growth, however, more often than not, businesses are unsuccessful in doing so.

As a matter of fact, Ryohei Fujimaki, CEO of dotData, stated in accordance to his personal findings, “We’ve seen studies that report only 4% of companies successfully implement business intelligence and artificial intelligence.

It naturally makes you wonder what the other 96% are doing.” Implementing new systems and processes into one’s business can be a complex and convoluted process.

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Regarding the implementation of data science methods into a company, one of the major problems is the considerable amount of time it typically takes to complete a data science project. Although implementation can improve certain facets of your business such as customer churn and predictive analytics, it can take months to fully realize the benefits.

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There are several steps in the process of partaking in data science projects including data preparation, personnel hiring/training, testing different visualizations, launching project production, and more.

On top of that, many companies look to incorporate machine learning to enhance their initial AI work, which can end up adding roughly 20%-30% of project time.

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A heavily supported solution to the current data science complications is data science automation.

Essentially, this allows companies to automatically perform many aspects of the data science process, such as analysis and preparation, with minimal internal resources. It expands a company’s potential for success through increasing agility and productivity.

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Rather than performing a single data science project every few months, automation enables a company to perform ten times faster.

It encourages companies to increase the pace of current production and operations without compromising reliability. Data automation should not be looked at as a replacement for personnel but rather a strategy to streamline processes and increase efficiency.

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Employees, however, such as data scientists, engineers, and domain experts, are still needed to run data science projects through monitoring implemented systems and exploring different outcomes. If anything, automation simply allows a wider variety of people to use data science processes without being experts in the field.

All in all, data science, integrated with data automation, has the potential to revolutionize the way businesses operate. These processes are vital to company growth and have the potential to make or break the success of a company.

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Automation, incorporated with data science, can advance our current processes to levels we have not breached before. In a time where technology is advancing more rapidly than it ever has before, companies should look to take advantage of available resources in hopes of adapting to societal trends.