Recently updated on August 8th, 2022

We dubbed the 1970’s as the onset of the Information Age. At its infancy, it was the promise of a new world of information at our fingertips associated with the proliferation of the transistor, a fundamental building block of digital electronics. While we are still in the Information Age, in 50 years we may look back and dub our current era as Data Science and The Age of Insight.

We have more information, or data, now than ever thanks to digital technology. But only until recently have we begun to categorize and explore that data effectively enough to gain significant insights that improve our daily lives and our businesses.

Data science has played a major role in transforming data into true business value and substantial competitive advantages.

What is Data Science?

Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning.

“As business leaders, we need to understand that lack of data is not the issue. Most businesses have more than enough data to use constructively; we just don’t know how to use it. The reality is that most businesses are already data-rich, but insight poor.”

Bernard Marr – Futurist, Author and Strategic Business and Technology Advisor

What is the Difference between Data Science and Data Analytics?

Data analytics is just one component of data science. It tells the story of what has happened and what is happening right now. Data science uses analytics to solve business problems based on current, relevant data.

Think of analytics as an investigation. You are trying to find out what happened. Once you understand the “what”, you then are trying to figure out the “why”.

The “why” is data science. Instead of simply plotting and examining the data, you are problem-solving.

Analytics is all about the current state of events. Data science uses that information to predict and understand future events. It takes organized data and critical thinking to accomplish this effectively.

There is no limit to the number of impactful insights that can be derived from your business’ data.

What are the Types of Data?

  • Structured: Data that has a high degree of organization for use in a database. It is typically easy to search, read and visualize.
  • Unstructured: Data that has no organization or pre-defined model. This information is typically text, numbers, dates and other forms of data. It is not ready for analysis. Around 80% of the world’s data is unstructured.
  • Semi-Structured: A form of structured data that does not obey a formal data modeling structure. Instead of being in a database, it contains tags that enforce hierarchies of records within the data.

What is the Business Value of Data Science?

Depending on the business and its unique needs, data science could provide business value in a variety of ways. Effective data science practices can improve revenue, reduce costs, aid in product development, increase business agility among many other valuable initiatives.

Some more specific examples are:

  • Using data to predict hardware failures increasing efficiency and preventing downtime
  • Analyzing consumer habits and sales data to improve products, its marketing or develop new ones
  • Identifying inefficient internal processes and then developing a business application to streamline that process
  • Using social media, corporate database and job website data to find talent that best fits within your organizational culture

There is no limit to the number of impactful insights that can be derived from your business’ data. The key is to make sure that your data is organized in a way that can be useful to your data science teams.

Data science teams need to be made aware of organizational goals in order to seek out ways to achieve those goals. In order to maximize the effects of your data teams, they should be embedded with your business units.

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