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Table of contents
Identification of data types and sources
Business process modifications or new process creation
The technology impact of big data workflows
Finding the talent to support big data projects
Calculating the return on investment (ROI) from big data investments
What is big data in an organization?
Why is big data important for businesses and organizations?
Key Takeaways
Last Updated: Mar 27, 2024

Applying Big Data within Your Organization

Author soham Medewar
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Master Python: Predicting weather forecasts
Ashwin Goyal
Product Manager @


Looking at the numerous strategies for putting big data to work for your organization is the greatest way to understand the economics of big data. While specific expenses may vary depending on your company's size, purchasing power, vendor relationships, and other factors, the expense categories are pretty consistent. 

The following aspects of big data economics should be investigated:

  • Identification of data types and sources 
  • Business process modifications or new process creation 
  • Technology changes or new technologies for big data 
  • New talent acquisition and upgrades to existing talent 
  • ROI potential of big data investments


Given the rise in popularity of big data, it's best to look at the economics from two angles: starting up and steady-state. We examine these topics in order to comprehend the economic implications and benefits of big data.

Identification of data types and sources

You'll need to examine new and evolving data kinds and data sources as big data evolve. Some of them you may be able to control, while others will have some control over your actions. The most crucial choices you'll have to make in terms of categories and sources are:

  • What information will you need to solve your business problem?
  • Where can you get the information?
  • What can you do with the information you've gathered?
  • What is the frequency with which you must engage with the data?
  • Who is in charge of the data and work products?
  • How long do you have to store the information?
  • Can you rely on the information and its source?


Now consider an illustration to assist you to grasp the practical aspects of linked economics. If you work for a consumer goods company, you're probably interested in using big data to better understand your customers' requirements, habits, purchasing patterns, and loyalty. You'll need to find data on experience, usability, sentiment, competitive alternatives, and so on to meet these objectives. Some of this information will be available in more traditional formats, such as CRM systems and current data warehouses. This brand manager will most likely be seeking more than typical data and will need to know where to find alternative viewpoints.

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Business process modifications or new process creation

After the brand manager in the above example has verified the data sources, she must determine which processes are impacted. For example, Identifying new clients from big data sources and adding them (as prospects) to current customer databases will have little impact. In other circumstances, you may need to develop new procedures to understand how large data sources may be used to generate new insights about your brand or to drive deeper insights into consumer loyalty and retention. In any instance, it is critical to model the costs associated with changing existing work processes or creating new ones. The true economic impact of big data will have to weigh the costs of these changes against the possible benefits.

The technology impact of big data workflows

So far, the brand manager has recognized different types and sources of big data, as well as the necessary improvements to business operations. She must now comprehend the technological implications of her discoveries. When big data is applied to processes, it should be able to use many existing technologies and applications. However, it is far more likely that new technologies will be required to get the greatest economic benefit from big data investments. If a brand manager has to collect data from multiple social sites, each with a distinct data type, she will need to collaborate with the IT teams to determine which technology best meets the business and financial requirements.


For example, if a MapReduce engine is required, can Hadoop be used, or is a commercial solution more appropriate for the tasks? Is it necessary to implement Apache Hive, or may current data warehouses be used? Is it possible for an RDBMS to handle huge data, or will an alternative data store be required? To capitalize on this hot new trend, you will almost surely see product implementations that include components of Hadoop and Hive. It is safe to say that new technologies will be necessary as big data is introduced into work contexts. Existing technologies are too fragile, or they are too basic or underpowered to handle the stress of big data applications since they are developed for a specific goal. This means that the costs of moving your organization to the next level using big data will be incurred. This is why your economic analysis is so crucial.

Finding the talent to support big data projects

In this case, the brand manager's requirements will result in the development of new processes and technologies. Each of these requirements, in turn, will drive the need for new talents and the updating of existing abilities in a variety of disciplines, but most obviously in the IT and business analyst fields.


Data scientists will be needed to supplement the ranks of business analysts. This can be performed through consulting arrangements in the early stages but should be transitioned to permanent staffing as the direction and benefits become clearer. Unless you work in a small to medium-sized firm or organization, a single data scientist is unlikely to provide the solution. Creating a team of data scientists entrusted with uncovering big data sources, analytical techniques, and business process impacts will yield the greatest leverage.

​​Knowledge of new big data technologies will need to be introduced to existing team members through training and mentoring for the IT team. As your firm reaches a steady-state, it is reasonable to expect that fresh talent will be required. Consulting resources should be used to assist your organization in getting started with big data efforts.

Calculating the return on investment (ROI) from big data investments

In this situation, the brand manager must develop an ROI case for big data in order to better understand and predict new ways to build the consumer base. All of the costs that have been addressed must be evaluated against the potential outcomes of the investments. How long will it take to return the cost of a big data initiative investment? The answer, like many others, is "it depends." If the brand manager develops a solution that is exclusive to her area of responsibility, the ROI may be less appealing than developing a generic strategy to use big data across many parts of the organization. If additional brand managers, customer service representatives, or salespeople can benefit from the enhancements, the ROI can be highly appealing, if not convincing.

The most significant aspect of developing the ROI model is fully baking in the economics across all of the categories discussed previously in this article to provide more comprehensive coverage and improved predictability of the outcomes.


What is big data in an organization?

Big data refers to massive, difficult-to-manage volumes of data – both structured and unstructured – that inundate enterprises on a daily basis. However, it is not only the type or quantity of data that is significant; it is what businesses do with the data that is important.

Why is big data important for businesses and organizations?

Big data analytics enables businesses to harness their data and use it to discover new opportunities. As a result, smarter company decisions are made, operations are more efficient, earnings are higher, and consumers are happier. Businesses that employ big data and sophisticated analytics get value in a variety of ways, including cost reduction.

What is an example of big data?

Big data is a phrase used to indicate a large collection of data that is growing rapidly over time. Examples of Big Data analytics include financial exchanges, social media sites, jet engines, etc.

Key Takeaways

In this article, we have discussed different techniques to apply big data within your organization to increase the productivity of the organization.

Want to learn more about Data Analysis? Here is an excellent course that can guide you in learning. 

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