Big data analytics is the driving force helping industrial organizations manage the large volume of information from connected assets and sensors today. As critical infrastructure and manufacturing organizations become fully digital operations, asset management technology will ensure they perform at the high level demanded by customers and investors.
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While technology is advancing at a rapid pace, humans are still a critical component in the data cycle. Behind the final reports and dashboards is a data scientist who draws strategic insights from calculations about how to improve asset performance and reduce risks. It’s no surprise then that this career is in high demand nationwide.
In fact, data scientist currently ranks number one in top jobs in the United States, with a 4.4 job satisfaction rate according to Glassdoor.
So what makes this role so crucial? In ever-changing industrial markets, identifying efficiencies and realizing cost savings from data helps organizations remain competitive and improve their bottom line. Gartner estimates that poor quality of data can cost companies an average of $13.5 million per year.
As data scientists become one of the most sought after hires across industries, aspiring data pros must be aware of specific considerations that could help further their careers. Before embarking on this career — or in the midst of changing roles — future data scientists should consider the following in order to succeed.
Math is what counts
Did you pay attention in math class? Those with a knack for mathematics and who are skilled in statistics make for strong data scientist candidates. Add soft skills, such as analytical thinking, communication and trend spotting, to the equation and you’ve got the reason for a high demand of data scientists. C-level execs actively seek candidates who can both understand the data sets and clearly communicate the findings.
But traditionally, many school curriculums have not emphasized statistics and the quantitative skillsets required to analyze such complex volumes of data. Thanks to a stronger focus on STEM education — now starting as early as preschool — these skills are being taught at earlier stages, prepping those for the vast job market that has been made possible by technology.
Beyond math skills, data scientists must be able to think creatively and develop context around the data in order to tell a story. Not only should data scientists be talented with numbers, they must also excel at problem solving. Being able to take qualitative phenomenon and quantify it in a meaningful way is a challenge; however, the ability to look at data sets and develop strategic insights from a business mindset is the very thing that makes a data scientist so valuable to companies.
IoT is spiking demand
From smart home devices, such as overhead lights that switch on when detecting movement, to self-driving cars, the internet of things is all around us. While some may say the massive growth of connected devices has made our lives easier, it has also created an abundance of “messy” data, or data that is of lower quality or disorganized.
Take fitness wearables for example: You’ve completed your run and have a clear sense of distance covered. But there are many other factors at play such as the type of trail, weather conditions, age and weight. It’s these types of elements that help improve the data quality and make for a more complete story about your workout.
To apply that to enterprise-level initiatives, data science teams take on the challenge of identifying and developing ways to produce valuable outputs from data of variable quality originating from various different sources with different classifications. Business leaders typically want to see the high-level insights presented in an accurate, clear way. In the desire to see whole numbers, users do not always understand the importance of also looking at the statistical certainty around data measurements.
As a data scientist, you’re responsible for taking statistical validity into account when evaluating metrics for both data quality and performance benchmarking. Data science teams will mine the through mounds of data in order to create and measure benchmarks for tracking improvement efforts and identifying trends or growth opportunities.
Applying data science to the field
Data is an important element that companies can use to draw strategic insights around how to improve asset performance and reduce risks. For example, leveraging data to identify efficiencies helps energy companies to realize cost savings that will keep many businesses afloat.
Many organizations have identified the need for a data science team, though few have been able to fill these types of roles. But the role is crucial; in fact, it’s the very thing that makes trains more efficient or helps mining companies minimize unanticipated, and dangerous, failures.
In a world that’s becoming increasingly complex, it’s no longer simply what you know, it’s what you can do with what you know. Now more than ever, it’s crucial for those in the field to be equipped with skills that will enable them to evaluate and the solve problems that could have a tremendous impact on the world.
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