Big data is critical for businesses looking to thrive and gain a competitive edge over their competition. The concept of data management involves best practices, processes, and policies that ensure the quality and consistency of raw data derived from various sources.
What’s more, the demand for data management skills has increased in recent years. Data-centric organizations rely on the meaningful insights gleaned from big data analytics to inform timely, strategic business decisions that’ll give them an edge over their competitors.
If you plan on becoming a data scientist, you’ll need specific skills for effective data management. This article explores some of the essential skills needed.
Data Analysis
Effective data management requires an in-depth understanding of data analysis. Data managers and analysts must know how to analyze data and find patterns that drive positive business outcomes. Essentially, you should know how to inspect, clean, and model a dataset. You should also establish a set of procedures for manipulating and interpreting the insights gained.
Through data analysis, analysts can effectively manage data and solve complex business problems. More importantly, data analysts must know how to determine the relationships among variables using an algorithm or mathematical model. Several data analysis methods include cluster analysis, cohort analysis, regression analysis, neural networks, data mining, and factor analysis.
Structured Query Language (SQL)
SQL refers to a standard database language that analysts use to create, maintain, and access a relational database. It’s a must-have skill for every data scientist or analyst. The importance of structured query language in data science is often understated. Big data requires a mastery of SQL to derive meaning out of it for strategic decision-making.
The upside is that SQL is relatively easy to learn due to its simplicity. In the end, a data scientist will know how to use SQL to query and manipulate datasets. Furthermore, SQL integrates with scripting languages like Python and R.
Data Integrity
In today’s business world, keeping data accurate and secure is increasingly important. Data managers must be proficient in data integrity analysis. This involves monitoring company data security to mitigate data loss, accidental errors, and data breaches. As an analyst, you should know who’s accessing company data at specific times to protect the data lake or data warehouse. To ensure data integrity, business users must update the firewalls and security systems to keep up with the ever-changing techniques of data miners and unauthorized users. Try to stay abreast of all potential threats facing data security.
Data Virtualization
Data virtualization incorporates different data sources into one virtual place for effective management. Business users should know how to integrate data virtualization software with a data management platform to virtualize raw data from multiple sources, including cloud data, data lake, data warehouse, and big data projects.
You should be proficient in creating a secure abstraction layer to give end-users more visibility and control over data operations. Having data virtualization skills puts you on the food chain, given that you would be saving the company a significant sum on IT operational costs.
Business Intelligence
Today, many companies are investing in business intelligence solutions to keep up with changing market trends. Having a background in BI tools sets you apart from others in the industry. You must know how business intelligence works and its application in data management.
With business intelligence, one can gain insights and identify trends faster to make timely business decisions. Essentially, BI involves using data visualization to identify patterns in a given data set. In addition to generating actionable insights more quickly, BI skills improve the reporting efficiency in an organization. Business leaders can leverage customer data analytics to boost product value.