Like any industry, data science has an abundance of buzzwords and hot topics that can be difficult for industry newcomers to parse. One of these is a concept known as “data culture.” By understanding the meaning of data culture and how it is being used, data science professionals can better understand how the industry is shifting.
What is a Data Culture?
Data culture is a workplace culture centered on using data for making decisions. Developing a “data culture” is a common goal for leaders who want to create products that are responsive to this current moment in time. Many of these professionals aim to leverage data in all aspects of the business, from the way that employees interact with supervisors to the decisions that are made about the future of the company or organization. A data culture gives credence to the power and accuracy of data to mitigate risk and assess the best possible outcomes.
While data culture is often compared to hierarchical cultures and consensus culture, it is also important to understand data culture as developing from other terms used to describe the influence that data science would have on society. The concept of a data culture was preceded by the interest and investment in data communities. Similar to the understanding of the world as a “global village” that was seen with the development of the internet, the concept of a data community informed people’s understanding of how data would bring people together through shared language and practices. Data communities acted as a response to working in virtual environments and on collaborative data science projects, leading to an investment in sharing information and data online as well as taking part in training programs and developing new skills.
The Costs and Benefits of Data Culture
Is data culture always a good thing? There are definitely some strong points in its favor. Developing data literacy can help community members unite in the context of organizational culture, as well as within the larger data science industry. The tenets of developing a data community can be used to prepare individuals within an organization to embrace a data culture.
While cultivating a data culture has benefits, it also creates a space in which the reliability and validity of data can trump the viewpoints of people who may rely on past experiences or case studies to prove their point. The reliance on data to run businesses and offices does not always take into account the inherent biases of the collection and analysis of data, especially when it comes to data on human subjects. It is important for conversations about data culture to not only privilege the power of information and data but to also critique how that power can be misused or abused by those in power.
Top Skills for Building a Data Culture
This shift towards a data culture creates a need for specific skills. Data scientists can respond to this trend by building and presenting their skills in areas such as data literacy, prescriptive analytics, and database management.
Data and Information Literacy
A typical goal when developing a data community and building a data culture is the inclusion of data and information literacy: the knowledge and understanding of how to communicate using information and data. Data and information literacy are necessary for interpreting and sharing data.
Within a data culture, these literacy skills require training in areas such as statistics and computer programming. They also require knowledge of industry-specific data science tools. When data literacy is high, it allows people working together within a data science team or company to have productive, effective discussions about how to complete projects and present findings to diverse audiences.
Predictive and Prescriptive Analytics
Data scientists who work in companies that embrace data-driven decision-making should understand predictive and prescriptive analytics. Predictive analytics involves analyzing historical data in order to make predictions about the future. Prescriptive analytics turn those predictions into scenario-based decisions.
In a data culture, predictive and prescriptive analytics are key when making decisions about a business or offering solutions to clients. They also allow businesses to analyze the performance of employees. Data science professionals in supervisory or managerial roles can benefit from learning analytics software and tools that help them understand the internal and external workings of an organization.
Database Management and Design
The culture of data relies on the collection and storage of big data. This is primarily because data-driven decision-making needs to pull information from large stores of data previously held by a company or market. Individuals within a data culture are best served by learning big database management and working within virtual or cloud-based environments. Automation and machine learning can be paired with knowledge of database design to make the process of querying, analyzing, and managing these data stores easier for individuals or data science teams. The move towards a data culture also reflects a need for new protocols and procedures around how data is managed and accessed by employees and stakeholders.
Want to stay up to date on Data Science Industry Trends?
Data culture is already influencing the type of skills and knowledge required from employees and managers in the data science industry. It is important for professionals in the field to stay up to date on the latest trends in data science.
Noble Desktop’s data science classes and certificate programs provide an introduction to key skills needed in the data science industry. The Data Analytics Certificate includes hands-on exercises and projects incorporating real-world data.
Additionally, Noble Desktop offers corporate and onsite training for companies and organizations to upskill employees. Any of these data science training courses can help develop the skills needed to create a thriving data culture.