Data Analytics in 2021: Trends, Use Cases, and Resources From Two Thought Leaders

If you’re considering a career in the field of data analytics, you have picked the right time. The Bureau of Labor Statistics estimates that data positions will increase by 16 percent between 2018 and 2028. Additionally, jobs in which data skills are applicable are expected to be among the most in-demand roles across a majority of industries by 2022, according to a report by the World Economic Forum.

The field of data analytics is always evolving, and it is important to stay up to date on the latest trends, advancements, and resources. Whether you’re preparing to enroll in a data analytics bootcamp or you’re just starting the job hunt for a position in the field, you should have an understanding of what’s happening in the industry.

To give you an inside look into the world of data, Trilogy Education Services, a 2U Inc. brand, recently hosted a Tech Talk titled “Technology Solutions Over the Years.” During the discussion, Tiffany Tram, Industry Engagement Manager at 2U, sat down with Mayur Patel, Software Engineer/Solution Architect at American Express and Anjali Samani, Director, Data Science, Data Intelligence at Salesforce.

Here’s what they had to say about the current state of data analytics and where it’s headed.

5 data trends to watch in 2021:

1. Data storytelling and visualization

According to Samani, “senior leaders rely on the value of … data.”. However, in order for that data to be useful, it must be converted into a format that is easily understandable. Through visualization and storytelling, data professionals can help a business’s key stakeholders (who may lack advanced analytical training) to identify patterns, understand complex ideas at a glance, and ultimately drive decision-making.

2. Data literacy for all

According to a recent report by Forrester, organizations with lower levels of data literacy in the workforce are at a competitive disadvantage. With that, we are seeing more companies implementing data literacy programs in 2021 to upskill their workforce. As Samani notes: “even if you’re not a data scientist, everyone is going to have to become data literate if they want to keep up with the changing trends.”

3. MATLAB gaining momentum

MATLAB is a programming language and multi-paradigm numerical computing environment for visualization, numerical computation, and programming. More businesses have started to utilize the language because it helps data professionals drastically cut down on the time usually spent pre-processing data and allows for quick data cleaning, organization, and visualization.

4. Reliance on DataOps

DataOps, or data operations, is where a lot of work is going, says Samani. While DataOps is a relatively new term, IBM defines it as the “practices that bring speed and agility to end-to-end data pipelines process, from collection to delivery.” According to Forbes, DataOps professionals are the new gatekeepers of efficient and effective business data, and the future of business insights will be heavily reliant on them.

5. Value of soft skills

There is a growing demand for data professionals that possess soft skills in addition to technical abilities. Research has shown that the seven top characteristics of success at companies like Google include soft skills such as communicating and listening well, critical thinking, and being able to work across diverse teams. Samani and Patel also emphasized the importance of skills like collaboration and lifelong learning.

Real-world uses of data analytics:

Samani and Patel also addressed the fact that data analytics is having an increasing impact in areas that many would never consider. As Patel notes, “If you want to work in data, you’re not limited to a handful of industries.” He further went on to say that he has been “blown away” by some of the real-world uses of data that he has encountered. Below are some examples.

1. Data to create positive societal change:

With an ever-increasing wealth divide in the U.S., many data professionals are wondering how they can create sustainable systems to use data insights for good. In a September 2020 panel discussion hosted by Trilogy Education Services, a 2U, Inc. brand, Samani and two other data scientists discussed how they’ve used their professional backgrounds to help organizations utilize data to benefit underserved communities. You can read more about their work here:

2. Data in the entertainment industry:

One of the most straightforward applications of data can be found in your own living room. If you use a streaming service like Netflix, Disney+, or HBO Max, you’ve likely discovered the service suggesting content based on your preferences, likes, and interests. These suggestions rely heavily on data science and unique algorithms to ensure accuracy. According to Business Insider, it is estimated that Netflix’s adoption of data-filtering saves them $1 billion per year.

3. Data usage in commercial agriculture:

Samani revealed that there is a growing adoption of data in commercial agriculture to strengthen environmental, social, and economic sustainability. This application of data is critical, as insufficient agriculture and food production must increase by 60 percent in order to feed the growing global population expected to hit 9 billion in 2050, according to the United Nations. Organizations like John Deere are leading this shift in applying data to commercial agriculture by launching data-enabled services that allow farmers to benefit from real-time data monitoring.

4. Data in the finance industry

According to Samani and Patel, there are many applications of data science within the field of finance. In fact, a 2021 Banking Industry Outlook report by Deloitte revealed that streamlining front-to-back data flows and deploying data analytics will be prerequisites to achieving efficiency in 2021. Banking institutions are using data today to prevent fraud, create better customer experiences, and even save lives, according to Accenture.

5 free data resources anyone can use

Here are five free resources, provided by Patel, that you may find useful if you’re interested in data science:

1. Free Ebook Foundation: This nonprofit organization is devoted to promoting the creation, distribution, archiving, and sustainability of free ebooks.
2. GitHub Developer Pack: This resource provides learners with free access to the latest developer tools in one place so they can learn by doing.
3. The Data Engineering Cookbook: If you’re wondering what to learn in order to become a data scientist, this free book is intended to be a starting point for you.
4. 10 Minutes to Pandas: This beginner-friendly resource is a short introduction to Pandas and is designed to help you gain a general understanding of the tool:
5. Library of Algorithms: This open-source resource is designed to help you learn data structures and algorithms and their implementation in any programming language.

About Mayur Patel:

Mayur Patel’s career spans over 15 years across IT delivery, business development, accountment, and more. Patel came to the U.S. in 2008 and began working as an ETL (extract, transform, load) consultant. A few years later, he started exploring big data technologies and began practicing POC (Proof of Concept) strategies. He would then go on to learn Rapid BI, helping him to drive business decision making. This diverse combination of skills and experience allowed Patel to enter the data field and start building data-driven products, grow business, and help with strategy for organizations like American Express.

About Anjali Samani:

Anjali Samani is a senior Data Science leader with 15 years of experience in multinational corporations, startups, and public-sector organizations in the U.S. and U.K. Her roles to-date have bridged technical data science and business to identify and execute innovative solutions that leverage proprietary and open data sources to deliver value and drive growth. Samani has extensive experience in managing and delivering commercial data strategy and data science projects. She has also worked closely with senior decision makers at organizations like Salesforce to enable them to define product roadmaps, develop data strategy, and execute data science projects.