Even today, both data and data science is an unexplored expanse waiting to be discovered with all the beneficial statistics that it holds within
It is essential for organizations to harness the power of data to increase their profit margins! Yes, unbelievable but true. Numbers hide facts that only an experienced analyst or scientist can unearth. Why spend time reading the numbers, and spend hours poring over data?
Because it is estimated[S1] that data science could enable retailers to increase their profit margins by an astounding value of 60%. In a bid to achieve this figure, collecting, collating, and storing data has become integral to business operations.
Quantum of Data
The quantum of data collected by businesses has exploded over the years. Let us check out some interesting facts to validate this argument:
- It was estimated that the digital universe had 44 zettabytes of data during the beginning of the year 2020.
- Soon, every individual would be creating approximately 1.7MB of data every second.
- In a single day, more than 2.5 quintillion bytes of data are created.
Data, along with data-science, has created a world of unexplored expanse. This world is waiting to be discovered along with the exciting acumen that it can deliver.
The 4 W’s & 1 H of Data Science
Let us get the idea of Data Science with the help of 4 W’s & 1 H
What is Data Science?
Data Science is an emerging field that utilizes scientific techniques and algorithms to find essential information from a massive stack of data.
Who handles the data?
Data scientists play with data. They analyze both structured and unstructured data to gather essential insights for every single industry.
Where is Data Science used?
Data Science is used in more than 90% of industries, including telecommunication, automobile, entertainment, healthcare, and retail.
Why is Data Science used?
On completion of data extraction, it helps in adding value to your business through enhanced decision-making like helping senior staff members, hiring new candidates, and better-informed business decisions. Learn more about the applications of data science.
How is Data Science Beneficial to Businesses?
It has become precious for every business to harness the power of data through Data Science. Whenever you think about any business problem, you can find better solutions if you go through data analytics and understand it.
Various businesses are utilizing the power of Data Science to make business decisions, enhance customer relationships, and promoting employee training.
Let us have a look at some of the key benefits that Data Science offers to every business.
- Defines the audience for every industry and their habits to improve sales.
- Enhances the efficiency of every organization.
- Utilizes best practices to identify and then solve issues.
- It makes the business decision-making process more effective.
- Redefines the goals of every business by adjusting them as per the market trends.
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The route from Data to Decision
Organizations need to create a comprehensive process that can help them transform data into a viable decision-making tool. Let us check out how this occurs:
- Defining the goal
The company goes through its business requirements and comes up with a goal.
- Collection of the data
Data is collected from different sources through both online and offline methods.
All the obtained data is then segregated and bifurcated in different groups dedicated to specific goals.
- Analysis of the data
Data is analyzed by keeping in mind the company’s requirements to obtain all the relevant insights.
- Strategizing using business psychology
Experts come up with a plan for the betterment of the business by understanding the insights.
With the help of obtained insights of data, appropriate decisions are taken for achieving the defined business goals.
- Review and iteration cycle
After the successful execution of the decision, the user experience is analyzed and an improved plan is created to enhance the results. This process continues until satisfactory results are achieved.
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Is data science really important for businesses?
Using data in businesses is not really a new concept. But, the term 'big data' has turned out to be a widely adopted phrase in businesses to refer to large datasets that organizations collect. Businesses can determine the market wants by looking at the yearly trends, which also helps to analyze the sales.
Business data can be obtained from different sources such as eCommerce sites, CRMs, spreadsheets, surveys, social media, and several other platforms. Customer feedback is considered to be the most important data for every business. This is the reason why data science has turned out to be the most important thing for every business in all sectors.
Businesses are not only able to improve their sales, but they also see exponential profits by understanding their customers in a better way.
How does data science help in decision-making?
Decision-making is the key for all the companies that are running efficiently even after facing plenty of obstacles in the run. With the help of data scientists in the company, it has become pretty easy for executives to make better-informed decisions. Data scientists filter out the data that the organization has collected to create actionable insights that can help in making profitable decisions for the organization.
Other than that, data science also has the capability of automating various complicated processes involved in decision-making. You will also receive solid directions to make the right decisions by understanding all the available possibilities based on the obtained data. Data science also allows you to know the outcomes and impacts of decisions that are yet to be made.
How do you make a data-driven business decision?
Before you make any business decisions, it is necessary to have a plan of action on how you will be obtaining the data and also interpreting it for making the right business decisions. Once you are done with laying down the foundation, then you need to follow this five-step process:
1. Understand all the objectives and prioritize accordingly
2. Explore and represent only relevant data
3. Draw out conclusions from the data
4. Plan out a strategy
5. Analyze success and repeat the process