The different between Data Driven Business vs Organization – Iris

Data-driven Marketing

Today many companies are striving to become data-driven. And there are different ways organizations can be driven by data. So what is different between the concept of Data-Driven Business and Data Driven Organization. Whether a business can transform into a data-driven business or not?

You’ll find it out soon through this article.

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1. The concept of Data Driven Business and Data Driven Organization

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In the word of digital marketing, there are those that are completely data-driven, others that use data to drive a more conventional business, and still others that use data to enhance or optimize their business.

1.1. What does Data-Driven Business mean exactly?

More than just installing the right tools and applications. Becoming data-driven is about making data and analytics part of the business strategy, systems, processes and culture. It’s about creating a mindset in which analytics form the basis of all fact-based business decisions, and are embraced by all levels of the organization.

1.2. The definition of Data Driven Organization

Any organization that tends to adopt data driven is considered as Data Driven Business.

For example, your organization can put data driven like a business function. It can help your employee in company understand and believe about the importance. They also can use data to make more informed decisions. It’s also mean that every company or organization might become an Data Driven Organization as long as your teams places data at the heart of the organization.

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2. Data Driven business classifications

According to SAS, your businesses can be driven by data easily by the ways below:

  • Completely data-driven businesses create revenue out of data.
  • Data-infused businesses disrupt their markets as they use data to significantly boost efficiency and gain advantages over competitors in the industry.
  • Data-informed businesses recognize that there's a need for improved data management. So they can continue to be competitive.

In general there are three models of Data Driven Business: Fully data-driven businesses, Data-infused businesses, Data-informed businesses.

2.1. Fully data-driven businesses

Each organization that is totally data driven contends exclusively based on changing data into a monetizable asset. These organizations can create revenue streams effectively through their platforms to adapt information sharing in a methods for value exchange. They can advantage by extracting some kind of charge layered over the essential expenses of an exchange.

2.2. Data-infused businesses

Data-infused business is a different class of data-driven business. This kind of data driven business manages and sells from an inventory of products (and possibly services). And manages the end-to-end sales process, but uses information to drive marketing to increase sales.

An obvious example of a business like this is Amazon. Amazon has developed a website driven by recommendation engines that make product recommendations. Its massive supply chains are driven by predictive analytics. It depends on the sharing economy (like Uber) by outsourcing delivery to independent drivers who use a similar marketplace application.

Another example is Netflix, which transacts with content providers (like movie studios) for material that can be streamed. It charges a monthly fee to customers to generate revenue. While Netflix doesn't own the content, it uses its platform to broker content delivery.

2.3. Data-informed businesses

The third class of data-driven business is data-informed business. This kind of model includes more conventional companies that are adapting data technologies to fit their existing business models.

The example of this kind business is John Deere. John Deere is an equipment manufacturer that's embracing Internet of Things (IoT) devices and embedding them into its newest models of equipment. This business generate data that can be used to monitor equipment performance. It also help the purchaser maintain the equipment, identify opportunities for improving designs. As well as finding the best ways to market products to customers.

(*Three model of Data Driven Business is according to SAS article)

3. Conclusion

At last, because of the excess of information arrangements, numerous organizations today are attempting to locate a solid match for their particular case.

Recognizing the best-fitting advancements and executing them effectively stays one of the best difficulties headed for information driven significance.

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