Data scientist time is a precious, expensive commodity. Do you truly understand what your data science talent works on all day? Are they spending way too much time researching data science theory, coding the same data preparation tasks over and over again, and maintaining scripts for model factories? Take a serious look at what your data scientists actually do. You’ll often find there is a misplaced focus that equates to a massive missed opportunity.If your data science team lives under the executive radar and is unknown to the business units they support, it is time to explore change. Take a deeper look at where data science time is invested and begin applying an "automation first" philosophy to new projects.
Expensive Data Science Inefficiencies
Most data scientists today work on long backlogs of random, simple machine learning projects that require an exorbitant amount of time manually preparing data, writing code, and debugging lengthy scripts. Job satisfaction is low. Turnover is high. Meanwhile, windows of opportunity close in the fast-paced digital era before your machine learning model can even get deployed.
Don’t let your competitors gain an advantage with new automated machine learning solutions while your data science team is lagging behind using outdated, manual approaches. The impact of doing things the old way may be much larger than you realize.
Becoming Ruthlessly Practical
“Companies don't need their data science teams to be good at data science theory; they need them to be ‘ruthlessly practical at delivering value.’” - Executive, Top Global Retailer
DataRobot’s approach to automated machine learning is a game changer. Our customers cite astounding bottom line impact from using an “automation-first” data science approach. In less than three months, a retail customer in Asia improved inventory forecast accuracy by 9.5%. That lift in predictive model performance and optimizing operations led to an estimated $400 million increase in profit. Other retailers achieved similar exponential levels of ROI from DataRobot inventory and pricing optimization projects.
Another DataRobot customer in the insurance industry, D&G, who already automated Python model factories reduced model development times from two months to two days. Historically, D&G’s data science team was unknown to business stakeholders. After DataRobot, the data science busy work was replaced by brain work. D&G’s data science team was able to spend much more time with the business, getting more projects done, and enhancing profitable business growth. Automation helped D&G’s data science team deliver far more value faster -- ultimately earning C-level recognition for helping successfully transform the business.
Yet another customer Dave Truzinzki, Chief Digital Officer at Crest Financial, publicly shared, “We were able to accomplish more in the first hour than we had built over the prior month.” There are countless other stories of initially skeptical data scientists seeing the light and embracing automated machine learning where it makes sense to make a transformational impact on the bottom line.
Benefits of Automation-First Data Science
Ideally, your talented data scientists should be engaged in the most strategic organizational projects delivering defined measurable benefits. Many simple machine learning projects may be ideal candidates for DataRobot automation, leaving only the most complex projects for manual scripting. By automating the time-consuming and repetitive tasks, your team can spend far more time solving problems and also delivering more value to your business. As your data science team becomes more involved in high profile projects and enhances model development productivity, the "automation-first" philosophy should also improve job satisfaction and retention of your top talent.
AI disrupts entire industries and markets. An AI-driven enterprise seizes every opportunity to automate processes, optimize outcomes, and gain a sustainable competitive advantage with data. Forrester predicts that “AI-driven companies will take $1.2 trillion from competitors by 2020.” The time has come for your team to begin adopting automation-first data science.
About the Author
Jen Underwood, Sr. Director Product Marketing, has held worldwide analytics product management roles at Microsoft and served as a technical lead for system implementation firms. She has experience launching new products and turning around failed projects in enterprise data warehousing, reporting, and advanced analytics. Today she designs products and helps analytics professionals learn how to solve complex problems with machine learning in the emerging citizen data science segment. Jen has a Bachelor of Business Administration – Marketing, Cum Laude from the University of Wisconsin, Milwaukee and a post-graduate certificate in Computer Science – Data Mining from the University of California, San Diego. Twitter: @idigdata.