Data scientists are, first and foremost, problem solvers. But new problems can’t always be solved with old tricks.Currently organizations in every industry are experiencing overwhelming challenges, many of them emerging from shifts to digital, the need to instantly deliver new service models, or better meet changing customer priorities. This demands a commitment to lifelong learning, […]
From a business perspective, data literacy is a critical topic, one that continuously arises as organizations strive to become more digitally-driven. A foundational enabler for organization-wide digital transformation, for example, is enhancing data literacy amongst different teams and roles.
Naturally for a data scientist, this is a top priority. Uncertain markets, dynamic business environments, increasing data growth, and the need to enhance innovation and agility all becomes linked to data science. After all, businesses need their data scientists to help them not only survive these challenges, but transform them into data-driven decision-making, opportunities, and competitive advantages.
In recent years, there’s been more responsibility placed on businesses and organizations to encourage skill development in data literacy and help employees across every discipline learn to analyze data.
One of the key reasons behind this is because as more companies adopt new technologies embedded in data platforms, they require more business users to understand how to utilize these systems to help promote stronger data governance, security, and privacy.
Data literacy also encompasses:
For a data scientist, data literacy is a skill that’s often taken for granted. But the ability to read and understand data in a variety of contexts is truly a transformative capability, particularly in an era of constant disruption and evolving technologies.
A common challenge in many organizations is figuring out which data combinations are most useful, addressing data management gaps, and getting rid of data that doesn’t offer much value or use. This optimizes and speeds up data sourcing and organization for analytics processes. Yet many data scientists or IT engineers who manage data don’t always perform this valuable role.
This is one area where a data scientist can tighten up their skills through continued education and solve a distinctive challenge. One option is to take a course in data curation and strengthen data management capabilities and knowledge with resources like the DataFlux Data Management Studio tool offered by the SAS Academy for Data Science.
It’s important to note that data literacy isn’t the same as being tool proficient. A data scientist with advanced statistical skills isn’t going to generate meaningful insights if they don’t connect business context or incorporate specific domain competencies. For example, a data scientist might be able to successfully read and understand data, but not have an understanding of where it originated.
The optimal balance for data literacy lies at the junction between individual skills and capabilities, tools, and data. Lifelong learning can help data scientists close the gaps across these elements so they can better prepare for fluctuating business and market dynamics, and continuously produce reliable results from analytics.
Let’s take a closer look at a couple of industry challenges to demonstrate how data literacy can help overcome specific obstacles.
Healthcare
One of the fastest evolving industries today is healthcare, where factors growing demand for value-based care, and need for faster drug discovery cycles are leading to rapid adoption of artificial intelligence (AI) and machine learning (ML) solutions.
In medical imaging, AI is used to enhance diagnostic accuracy, augment physicians and radiologists, and improve patient care delivery. Certain obstacles can arise though from both the models and industry-specific challenges. Factors like uncertainty, interpretability, false associations, over-fitting, and system barriers impede the potential use of these AI-based solutions in real-world clinical environments. Addressing these kinds of challenges could involve continued study in computer vision, deep learning (DL) algorithms, and convolution and recurrent neural networks to help optimize image recognition capabilities and extend conventional solution programming.
eCommerce and Retail
Businesses must be able to rapidly fulfill customer needs through solutions like optimized Inventory Management, which ensures product availability for sales and monitors things like warehouse stock quantity.
These businesses need to forecast demand levels and effectively manage fluctuating demand according to anticipated patterns in sales and make correlations between trends and relationships across the supply chain. During crisis or uncertainty, this becomes even more challenging. Working out some of the modeling challenges in a virtual modeling studio, such as the Model Studio in SAS Viya, can help data scientists improve ML models for forecasting.
A key aspect of data literacy is preparing for the future of work. Demand for technological skills, both interacting with technology and coding, is projected to grow more than 50%, while complex cognitive skills will be increasing by a third.
Interestingly, the future of work calls for workforce changes like reskilling and upskilling to help people adapt to either new roles or gain new capabilities for their current roles. For example, operationally and asset intensive domains like transportation and manufacturing, and roles like claims processing and maintenance, are facing more aggressive automation-induced changes than many other sectors, which affects skill requirements. This is something for industry and business professionals to keep in mind as they consider different options to take to intensify their data literacy skills.
Now, to explore some other options to enhance data literacy according to different interests:
Many of these options are available in the SAS Academy for Data Science, where data professionals can acquire or enhance their skills with tools like SAS, which works with many open source tools like Hadoop, Python, Hive, and other programs, making it a versatile skill for practicing data scientists.
Every area of life is impacted by data. The world is only becoming more digitally and virtually dependent, and data literacy skills must flourish alongside these changes so that data can benefit every cornerstone of daily life, regardless of the context.
Data literacy opens new doors, potential, and opportunities, and is a skill that should be enriched, whether that’s data applications, data techniques, or data communications.
By Ronald van Loon