Developing a data driven business model

Developing a data driven business model

The value contained within data is universally recognized. As the seemingly relentless march of big data into so many aspects of the commercial and non-commercial world continues, the practicalities of constructing and implementing data-driven business models (ddbms) has become an ever-more important area of study and application.

Capitalizing on this data explosion is increasingly becoming a necessity in order for a business to remain competitive, and is a modern twist to the old adage, ‘knowledge is power’.

The challenges are threefold:

  1. How to extract data,
  2. How to refine it
  3. How to ensure it is utilized most effectively.

Punch lines

“businesses and other organizations that fail to align themselves with data driven practices risk losing a critical competitive advantage and, ultimately, market share and the accompanying revenue.”

“for today’s businesses, effective data utilization is concerned with not only competitiveness but also survival itself. Many businesses are developing new business models specifically designed to create additional business value by extracting, refining and ultimately capitalizing on data.”

“data-driven businesses have been demonstrated to have an output and productivity that is 5–6 percent higher than similar organizations who are not utilizing data-driven processes (josh b, et al, 2015 university of cambridge)”

Clearly there is value associated with effective big data utilization, and the race is on for existing businesses, both large and small, to capitalize upon it. However, although big-data-oriented publications agree on the potentially positive impact of big data utilization, very few suggest how, in practice, it can be attained and none offer a research-based guide or blueprint that can be utilized by an existing business to help create and implement its own data driven business models

The six questions of a data-enabled business

  1. What do we want to achieve by using big data?

In order for a business to effectively utilize big data it is vital that its aims are clear and realistically attainable.

  • Competitive advantages
  • Shortened supply chain
  • Expansion
  • Consolidation
  • Processing speed
  • Differentiation and brand
  1. What is our desired offering?

A business must decide in what way the model construct will benefit the company’s current offering or, alternatively, create an entirely new one

  1. What data do we require and how are we going to acquire it?

Data is obviously fundamental to a data driven business model. Deciding which data is most applicable, and the nature of that data’s acquisition, is pivotally important to the success of a ddbm construction. Established businesses with a substantial number of customers, and therefore potential customer interaction points, are well positioned to effectively utilize customer-provided data within their ddbm, although this data is often combined with data from other sources. Customer-provided and acquired data was utilized by 80 per cent of the business organizations analyzed, with self-generated and existing data utilization slightly lower at 75 per cent. Free available data was the least exploited, with 60 per cent of the business organizations analyzed using this data source. This high utilization of all available data sources by established organizations is indicative that these organizations understand the value of data and orient themselves towards becoming data-driven.

  1. In what ways are we going to process and apply this data?

Methods of processing reveal the true value contained within data. Knowing which key activities will be utilized to process data enables the business to plan accordingly, ensuring that the necessary hardware, software and employee skill sets are in place. To develop a complete picture of the key activities, the different activities were structured along the steps of the ‘virtual value chain’.¹² to gather data, a company can either generate the data itself internally or obtain the data from any external source (data acquisition). The generation can be done in various ways, either manually by internal staff, automatically through the use of sensors and tracking tools (e.g. Web-tracking scripts) or using crowd-sourcing tools. Insight is generated through analytics, which can be subdivided into: descriptive analytics, analytics activities that explain the past; predictive analytics, which predict/forecast future outcome; and prescriptive analytics, which predict future outcome and suggest decisions.

  1. How are we going to monetize it?

Without the target of a quantifiable benefit to a business it is difficult to justify ddbm construction and implementation. Incorporating a revenue model into a ddbm is integral to its operational success. Seven revenue streams are identified by hartmann et al (2014): asset sale, giving away the ownership rights of a good or service in exchange for money; lending/renting/leasing, temporarily granting someone the exclusive right to use an asset for a defined period of time; licensing, granting permission to use a protected intellectual property like a patent or copyright in exchange for a licensing fee; a usage fee is charged for the use of a particular service; a subscription fee is charged for the use of the service; a brokerage fee is charged for an intermediate service; or advertising. Revenue models associated with a ddbm differ considerably from a standard subscription fee such as the new york times for advertising. These models vary considerably between sectors and within industries.

  1. What are the barriers to us accomplishing our goal?

If an established business organization does not have sufficient data-oriented and experienced personnel within its business then a company culture that is not conducive to constructing and implementing a ddbm is likely to emerge. This may also lead to the development of a negative perception of ddbm construction and implementation within the business.


As the advantages of big data utilization become continually more profound, organizations are forced to incorporate innovative data-driven practices into their business strategy or risk losing competitiveness, market share and ultimately revenue. The ddbm-innovation blueprint enables organizations to construct their own ddbm that is unique to their business and environment. Data has now become invaluable to business, so much so for most businesses with aspirations of growth or long-term survival that it should no longer be a question of whether they should become data-driven but rather how and when.

 Adapted from cambridge working paper 2015 “data and analytics - data-driven business models: a blueprint for innovation” by josh brownlow, mohamed zaki, andy neely, and florian urmetzer

Edited and simplified by nakala analytics

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