Along with the growing demand for data scientists and data specialists around the globe, there is an increasing number of myths associated with the role of data scientists. Thanks to its predictive modeling capability, a data scientist is considered skilled enough to be able to build predictive models that can predict the future buying behavior of online customers. However, a common myth, particularly among non-technical professionals, is that a working day in the life of a data scientist purely comprises of building predictive models only.
So, is a data scientist simply the Nostradamus of the 21st century or is more of a “Creator” who creates useful business insights and solutions from the available data? In reality, data scientists are not just involved in creating efficient prediction models but can also provide strategic solutions and creative insights to businesses based on the data predictions. As an example,DataRobot enables its retail customer to use an Artificial Intelligence or AI-enabled predictive model resulting in an increase of $400 million (in retail profits) in just 3 months.
Through this article, we aim to burst the myth about how data science is not just a task of predictions but a tool to drive flourishing business solutions. In the following sections, let’s discuss 5 core skills of a data scientist that makes them a valuable asset to any business enterprise, followed by real-life solutions (driven by data science) that is transforming a variety of industries.
Core skills of a data scientist
First, let’s start with 5 core skills that every good data scientist must have:
1) Problem-defining skill
Every data scientist must be able to define and formulate a business problem. Through asking relevant questions to the subject experts, they must be able to simplify a complex business problem into simpler parts that can be easily categorized. This process requires a certain level of curiosity and hypothesis building.
At an intuitive level, data scientists know how to approach a particular business problem and typically follow them up with the following actions:
i) Identify the main features of a particular use case.
ii) Frame the right questions that would provide the desired responses.
iii) Decide on the various approximations to be applied.
iv) Consult with the right subject experts.
2) Technical skills
Once the business problem has been defined, data scientists require technical programming and statistical skills to extract the necessary data. While data scientists use a variety of programming languages, they must be familiar with the following software:
i) R, an object-oriented programming language that is used for data visualization, predictive modeling, and statistical analysis.
ii) Python programming language that is fast, powerful, and an easy-to-learn tool essential for data science.
iii) Structured Query language (or SQL) that is used for managing structured data in database systems.
iv) Hadoop, an open-source framework that facilitates distributed processing of huge volumes of data sets across computer clusters.
3) Analytical skills
After completing the data extractions, data scientists must possess theanalytical skills to manipulate the data sets and extract value from it. As a data analytics company, Tableau offers products that work well with data science tools like R and Python. The Tableau tool works great for data exploration and analysis.
For example, Tableau Public can be a tool that can unleash creativity skills with its collection of rich data sets that can be used to create creative and engaging visualizations.
4) Visualization skills
As data visualization is an effective mode of presenting data in the form of visually-appealing dashboard tables, graphs, and even images, data scientists must be able to correctly interpret the data results and connect them back to the original business problem.
A great example of effective data visualization is that of Andy Kriebel’s dashboard that reflects a financial statement using some cool visuals.
5) Communication and influencing skills
Data scientists must be able to build a compelling data-driven story that can influence decision-making by connecting a business problem to actionable insight. Along with story-telling skills, they must have effective communication skills that can convey their data insights to both technical and non-technical users.
How Data science can influence innovative solutions?
So, how is data science enabling innovative problem-solving solutions in the market today rather than just being a prediction tool? Here is a look at its impact on the following industries.
Government agencies
Data insights are driving the empowerment of government agencies thus providing indicators on their mission goals. Operating since 1790, the U.S. Census Bureau is adopting innovative modes of data collection with tools such as the American FactFinder, a database that stores data retrieved from various public surveys. The U.S. government is also praising the easy data access policy (followed by the U.S. National Centre for Health Statistics) that provides easy access to health records without compromising data security standards. The U.S. Department of Veteran Affairs (in association with students of the University of Virginia) have created a data-based hackathonthat aims to improve healthcare access for U.S. veterans.
Image Source: https://cdns.tblsft.com/sites/default/files/blog/bluewaternavyveteransact_uva_hackathon.png
Retail industry
Be it a start-up or a large retailer, adoption of the latest technology remains the leading trend in this industry. Retailers continue to use AI and machine learning in data analysis to improve their marketing strategies and customer support. Business insider estimates that AI adoption will increase profit levels in retail by 60% by the year 2035.
Through its physical “Amazon Go” stores in the U.S. and Britain, online retail giant, Amazon is enabling its customers to shop for products without having to queue at the checkout line. With its AI-powered Amazon Go mobile app, the technology can automatically scan and detect the products being taken by the customers and charge the amount directly to the customer’s Amazon account.
Apart from the success of AI technologies in online retail, even brick-and-mortar retailers like Sephora and Stitching Fix are using AI to personalize their in-store customer experience. For example, the AI-enabled Color IQ beauty product (launched by Sephora) can recommend beauty products based on the skin tone of the shopper.
Besides personalization, data science-enabled AI and machine learning are being used to optimize the retailer’s bottom-line revenue through accurate inventory management and pricing.
Climate Change Mitigation
Data science is enabling the collection and analysis of climate-related data that is required to protect major cities and people from the devastating impact of climate change. For example, “Neighborhoods at Risk” is an interactive data tool that provides city planners the latest updates about climate-related risk factors such as heat and flooding to the local demography.
Big data and predictive analytics are playing a crucial role in providing real-time analytics about climate change. An example of this is the Global Forest Watch tool that analyses over 100 local and global data sets to collect data about forest conservation, land use, and deforestation.
Opioid Control
Opioid addiction and abuse are among the leading causes of overdose deaths in the Western nations particularly in the U.S. In the year 2017 itself, there were over 72,000 deaths in the U.S. caused due to opioid-related overdoses.
The U.S. Food and Drug Administration (FDA) plans to reduce opioid abuse through the use of data analytics. Through a large-scale data warehouse, FDA plans to use machine learning algorithms and predictive analytics to assess vulnerability factors among opioid users and identify trends that contribute to the opioid epidemic.
The above 5 industry case studies are an eye-opener to how data science is playing an effective role in providing real-life solutions rather than just being a predictive tool.
Conclusion
Apart from being used as a predictive tool, data sciences encompass business functions such as business problem definition and creation of data-driven solutions that can improve customer experiences and revive environment and health-related problems. As a data scientist, you can build accurate data-driven predictive models along with tackling business-related problems with your technical and analytic skills.
Data science is not just confined to prediction tasks but envelopes a lot more capabilities and can be leveraged in almost every business - be it a tech giant or a startup.
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