Advanced marketing technology is quickly growing in popularity. Marketing spending is expected to surpass $32.3 billion by 2018. Technology plays an essential role in unlocking and analyzing accurate customer data, but it can go only so far if institutions cannot decode the findings and uncover rich customer insights.
Enter data scientists.
Historically, the amount of data and depth of learning that data scientists were able to use was limited by the underpowered machines allocated to them to perform their research. In today’s world, data scientists are leveraging parallel processing, enabling machines to break the data and corresponding tasks into bite-sized chunks, learn the ins-and-outs of each piece of data, and then report back what it learned to its centralized hub. With this, data scientists are now capable of analyzing data faster, and they can train computers to produce highly accurate results and increase the quantity of business solutions influenced through analytics.
It’s clear that data scientists and technology are valuable assets to any business looking to grow, but an analytics program can’t be successful with one and not the other. Businesses must utilize both the tools and craftsmen’s expertise in tandem to remain competitive in today’s data-driven market and drive ROI. Here are two crucial reasons why to develop a dynamic and effective data science team to work with your data analytics tools.
Machines Perform Advanced Mathematics, Humans Provide Industry Knowledge
We have seen many transformations in technology over the years, and technology is guaranteed to get better with time and focused research. But we still haven’t developed a machine that can truly understand its surroundings and learn for itself without well-defined instruction. Today’s analytics models, though impressive in their own right, do not have a grasp on industry or business needs.
Without the human touch of a data scientist, computers are just big calculators. And data scientists know the rules and underlying theories behind the data, but don’t have the time or capacity to manually crunch massive amounts of data by hand. But together, data scientists and machines make it possible to quickly perform thoughtful analytics with usable, accurate insights in a timely manner.
Data scientists with the deep knowledge of their industry enable machines to perform at their highest level of ability. Anyone with minimal data manipulation skills can plug data into an advanced analytics model, but only industry experts can recognize the model’s limitations, prioritize real-time analytics, and understand how to correctly stage, structure, and standardize data to extract accurate findings. Combining the tool’s powerful ability to analyze data and data scientist’s industry expertise is essential for success in any analytically driven organization.
Humans Guide the Tools to Analyze Reliable, Accurate Data
The data scientists who engineer an analytics solution play an important role in ensuring the machine output is accurate and relevant. An issue that is frequently encountered in the analytics space is data elements deemed unusable due to regulatory, ethical, or gathering/measurement concerns. On its own, a machine does not have the insight to filter these unusable elements, but data scientists step in to manage and guide the machine on the correct information and analytic processes to implement. Employing data scientists with a technical acumen and business knowledge helps organizations in a variety of industries to be the successful institutions they are today. For example, many healthcare institutions are using data to more accurately diagnose patients. This demands data scientists who have significant knowledge of the healthcare industry to ensure near 100 percent precision and accuracy when it comes to output.
Without the right talent and experience in executing and validating models, businesses will often overpromise and under-deliver, decreasing the company’s credibility – especially when it comes to important areas like healthcare and financial services. For example, many variables may be selected for use in a predictive analytics model, but if even a portion of the data is unreliable, the resulting model will be incorrect. Machines do not understand how the company and clients make money, so when providing analytics on their own without guidance from data scientists, the results will be inaccurate. These inaccurate models substantially increase the risk of delivering faulty advertising, missing out on opportunities, and negatively impacting ROI, damaging business or marketing department reputations.
Build a Diverse Data Science Team
Now that it’s a little clearer how machines and humans work together, how do you build a strong data science team? Some people say Ph.Ds. are required, but that’s not necessarily the case. While it’s extremely helpful to have individuals with data science-related education in your organization, diversity and understanding of the overall business model are key ingredients to a strong data science team.
Candidates from an array of backgrounds – financial services, healthcare, microbiology, and even the arts – will bring unique skillsets and perspectives to the analytics process, allowing institutions to identify patterns, trends, and outliers through various lenses. When building your data science team, look for people who have a high level of creativity and demonstrate intellectual curiosity. Ideal candidates should have a mind like a detective – snooping through data to find bread crumbs and artifacts that will lead to analytic and industry breakthroughs.
The marriage of tools and talent is a perfect match – and the courtship starts with a strong data scientist. If candidates don’t understand how the company and its clients make money, they will not be successful in their role. Without this knowledge, even businesses armed with the best tools and the richest data cannot build and execute an effective analytics program.
Keith Weitz is the Vice President of Data Strategy and Analytics at Segmint, where he is focused on predictive analytics, ETL, and data structure. Keith is a seasoned analytics innovator with more than 16 years of experience in the financial services and insurance industries.
Keith joined Segmint from KeyBank, where he directed a team of scientists responsible for analysis and statistical modeling for digital marketing of consumer and commercial products. Prior to his tenure with KeyBank, Keith held roles with several top 20 U.S. banks as an analytics executive, managing teams responsible for credit risk, marketing, and forecasting of consumer and commercial banking portfolios. Throughout his career, Keith also has served on Analytic Advisory Boards for major U.S. credit bureaus.
Keith holds a Bachelor of Science in Mathematics and Computer Science from Roosevelt University, and completed graduate studies in Statistics and Predictive Analytics at Western Michigan University and Northwestern University.
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