As technology takes up a more significant role in our day-to-day lives, the scope and depth of the data gathered by businesses, service providers, and governments continue to grow. Every organization now wants to tap into the data they collect to generate insights that power their decision-making. This is the reason data science is gaining widespread adoption across industries.
As a business leader, there are some things you should know about data science. We have highlighted the most significant below. But first, let’s understand what data science entails.
A Quick Overview of Data Science
Data scientists combine several disciplines, including probability, programming, statistics, cloud computing, analysis, etc., to derive value from their data. As a data scientist, you must be an expert in nearly all the data science subfields to work in this vast and rapidly expanding field.
Most businesses today know the value of a data-driven business strategy and the need to develop, nurture and maximize cutting-edge technologies. They are keeping up with the latest data science trends and technologies that can help them extract the most precise insights from the steady stream of gathered data.
Types of Data
Now that you know what data science entails let’s look at the holy grail of data science – ie, the data itself. Leaders looking to transform their organizations must understand the different types of data and how to collect and use them to gain a competitive advantage. Below are the various types of data.
Big Data refers to a sizable amount of data that cannot be stored or processed using standard data storage or processing tools. The enormous amount of data generated by human and machine activities has resulted in such complex and vast data that neither humans nor a relational database can help analyze. However, when properly analyzed with modern tools, these vast amounts of data will offer your organization beneficial insights that aid in improving your operations. Big data are often described using Volume, Velocity, Variety, Veracity, Value, and Variability.
A large amount of data is produced as the internet and technology expand. This data can be categorized into structured or unstructured data. Structured data has a high degree of organizational properties that make it simple to access and use for analysis. An example is a relational database. The data has a defined structure which is helpful to analysts thanks to the tables, columns, and rows.
On the other hand, unstructured data refers to datasets that are not well-organized and do not follow a predetermined data model. The majority of big data is made up of unstructured data. The amount of unstructured data is growing due to the images we post to Facebook and Instagram and the videos we consume from YouTube and other platforms. You can learn more about data forms by exploring structured vs. unstructured data. Data types such as XML, CSV, and HTML files fall into a group known as semi-structured data.
Open & Proprietary Data
You can further categorize data into open and proprietary data. This is based on defining the data’s availability, ownership, and usage rights. Open data is freely accessible to everyone. These datasets are regularly used in Kaggle competitions and other training environments. On the other hand, proprietary data is owned by a person or business. It is shielded by copyright laws and perhaps other legal measures.
Data Sources and Repositories
Data can also be categorized and even analyzed based on the source. For instance, you can analyze social media data and transactional data separately. Data can also stream from field machines, user gadgets, IoT devices, etc. Proper labeling of the data sources can help boost data utilization and accuracy.
Data repositories are sets of data that have been isolated for data reporting and analysis. Another name for a data repository is a data library or data archive. Examples of data repositories include; data warehouses, relational databases, data lakes, and Data marts.
The Bottom Line
Data science’s primary contribution to an organization is its facilitation and empowerment of decision-making. If you use a data-science strategy in your business, you are more likely to do better than your peers.
When implemented right, data science capabilities allow for greater profitability of your business, enhanced performance, operational effectiveness, and improved workflows. You can invest in your data science capabilities by hiring the best data science talents and providing them with the right tools and technologies.