Sponsored Content
Sponsored Content
The Power of Data Fluency
By Adina Sapp
Data Science and Decision Support
Though the role of the data scientist barely existed five years ago, it has now risen to the top of the job market in terms of demand.1 Data scientists can interpret data to create meaningful stories, forecast trends and generate information that drives organizational improvement.

Research shows that data-driven organizations are three times more likely to report significant improvements in decision-making2, and many that have not already invested in data analytics are looking to do so. For some, this means hiring data scientists and investing in big data tools that collect, aggregate, integrate and analyze data from multiple systems.

Because data scientists typically command a base salary of more than $100,000, however, not all organizations can afford to hire these professionals or create a dedicated data science team.3 The organizations with the resources to do so must determine the most effective data team structure for their company.

Regardless of the industry or department you manage, working with data will soon be an essential part of all our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning or artificial intelligence.
Many organizations have one centralized team where data analysis primarily resides. With this model, multiple challenges can arise, including bandwidth limitations and information hoarding. Additionally, there are lost opportunities when the rest of the organization cannot contribute their uniquely valuable subject matter expertise to the decision support process.

DataCamp advocates that data skills should be “democratized” among the entire organization. When data fluency and skills are shared, organizations are able to make better business decisions.

Difficulties of the Centralized Data Model
In the centralized data model, a data team typically works on requests following a tedious or politically driven prioritization process. This can create a bottleneck, which is a problem when information is needed quickly.

MIT estimates that only half a percent of data is analyzed.4 Creating a silo of data tools and restricting access to a narrow group of employees places too heavy a burden on a single team and limits the value of the results.

Communication between the centralized data team and the rest of the organization can also be problematic. Data scientists may have difficulty explaining their analysis to a general audience or use different tools that are not accessible in other departments.

Additionally, if data teams have trouble understanding the context of a request, they may not be able to properly fulfill it in a timely manner. The team asking the question always has the best context on the question they are trying to answer, and this context can be lost in transfer from one team to another.

The Power of Data Models
Benefits of the Democratized Data Model
Fortunately, companies can provide the necessary data tools, skills and responsibility to all staff, not just to a dedicated team.5 Harvard Business Review notes that “companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire crossfunctional, agile teams that include data engineers, data architects, data-visualization experts and — perhaps most important — translators.”6

The democratized, decentralized data model encourages collaboration, leads to better solutions and incentivizes learning.7 This is good for employees and employers because it empowers staff with valuable skills and yields a fuller utilization of the available data.

“The organizations that succeed are those that can quickly make sense of their data and adapt to what’s coming. The best way to enable deeper insights is to adopt a decentralized model where data science expertise is dispersed across an organization,” according to DataCamp.8

Most inquiries are relatively simple requests that anyone with basic training could fulfill.9 But even in the centralized data model, a data-fluent team makes better requests, as they can communicate their request to the data science team much more effectively, yielding better results from the data science team.

Upskilling with Data
No matter where you are in your career or what field you work in, you will need to understand the language of data.10 “Working with data will soon be an essential part of all our jobs, if it isn’t already.

This could take the form of basic data analytics, data science, machine learning or artificial intelligence,” according to DataCamp.11

Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp, will soon be leading a webinar addressing how data can impact your employees’ work, what they need to know and how to go about educating them. To register, visit webinar.clomedia.com/what-your-employees-need-to-learn-to-work-with-data-in-the-21st-century-0.

1 Columbus, L. (2017). “LinkedIn’s Fastest-Growing Jobs Today Are In Data Science And Machine Learning.” Forbes.
2 PricewaterhouseCoopers (2016). PwC’s Global Data and Analytics Survey 2016.
3 Paysa. Data Scientist Salaries.
4 Democratizing Data Science in Your Organization. DataCamp (2019).
5 Goldman, J. “Whatever You Do, Don’t Hire a Data Scientist (Here’s Why).” Inc.
6 Henke, N. Levine, J., and McInerney, P. (2018). “You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role.” Harvard Business Review.
7 Democratizing Data Science in Your Organization. DataCamp (2019).
8 Ibid.
9 Democratizing Data Science in Your Organization. DataCamp (2019).
10 Datacamp.com.
11 Democratizing Data Science in Your Organization. DataCamp (2019).