Data Science is the New Oil Industry
The field of data science has seen huge growth in recent years. As both the technology and data collection required for data science becomes more sophisticated, data scientists are seeing unprecedented results from data science techniques such as machine learning and predictive analytics. Still, data science is a relatively new field for most businesses. There remains much to be discovered and many challenges—both for data scientists and at a societal level—to overcome. The Economist has likened this data science boon to the early days of the oil industry, which was similarly lucrative, fast-growing and unregulated. Now that “data is more valuable than oil,” the data science industry should be given similar scrutiny.
Those on the frontlines of the data science field are data scientists. It is the data scientist who must execute increasingly complex data science projects, who must adapt to new technologies, and who must be mindful of shifting best practices and regulations. Given their value to the continued advancement of data science, it’s no surprise that there has been “a 56% increase in [data scientist] job openings in the US over the past year, according to LinkedIn” and that TechRepublic calls the role of the data scientist “the most promising job of 2019.”
The 80 Percent Problem in Data Science: Data Preparation Drawbacks
Of the many obstacles that the data scientist faces, there is one that is perhaps rather unsuspecting: data preparation. As data volumes grow, so too does the time required to prepare this data for analysis, which can include anything from scrubbing duplicates to standardizing values to addressing missing or incorrect values. Data preparation consumes up to 80 percent of a data scientist’s time, which limits data scientists from focusing on work that is worthy of their niche skillset.
The German Stock Exchange, aka Deutsche Börse, is one of the many organizations that recognized their data scientists were overly burdened with time-consuming data preparation tasks. First, they attempted to solve this data science problem by outsourcing it. Deutsche Börse hired a consultant dedicated to preparing a particularly gnarly dataset instead of the usual internal data scientist. The dataset drew upon both internal and external sources with varying codes and data sources, and it took the consultant roughly nine months to make reason of the data. Next up was a solution that combined intelligence and automation: data preparation platform Designer Cloud. By using Alteryx Designer Cloud, the data science team at Deutsche Börse was able to prepare that same dataset in just three weeks, allowing data scientists to focus on projects more aligned with their skillset Today, Alteryx Designer Cloud continues to empower the data science team at Deutsche Börse to focus on the problems they were hired to solve—of which there are many, and counting—instead of data preparation.
Designer Cloud: The Secret Weapon of a Data Scientist
Alteryx Designer Cloud is routinely named the leader in data preparation, with the ability to improve data scientists’ efficiency. Its intelligent data preparation platform, powered by machine learning, accelerates the overall process of preparing data by up to 90 percent, which makes a huge impact on the time-to-value for any data science project. Here’s how it works: with every click or swipe you make in Designer Cloud, the platform will automatically suggest a list of transformations that you can select from or edit. At the same time, Designer Cloud’s visual interface surfaces errors, outliers, or missing data for data scientists to fix.
Designer Cloud is a data scientist’s secret weapon because it allows them to accelerate the precursory work to data science projects so that they can spend more time solving actual data science problems. The data science field will continue to change and grow more complex; in order for organizations to succeed, their data scientists shouldn’t be bogged down with data preparation. To see how Designer Cloud can make an impact on your data scientists and entire data science team, click here to schedule a demo with our team.