4 March 2015

Data-Driven Business Models (DDBM): A Blueprint for Innovation

“A Blueprint that can be utilized by established organisations to create their own business models that rely on data as key resource”

We live in a world where data is often described as the new oil. Just as with oil, 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. For today’s businesses, effective data utilization is concerned with not only competitiveness but also survival itself. In some industries, such as publishing, big data has spawned entirely new business models. For example, after a movement towards a digitally oriented distribution model and dwindling advertising revenues, certain publishers began to accumulate data relating to their online users – users whose demographic was particularly attractive to advertisers. This data could then be sold, enabling targeted and more effective advertising.

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 DDBM. The DDBM blueprint and the corresponding six fundamental questions of a data-driven business will allow existing businesses and start-ups to follow a step-by-step process to construct their own DDBM centred around the businesses’ own desired outcomes, organization dynamics, resources, skills and the business sector within which they sit. We are presenting an integrated framework that could help stimulate an organization to become data-driven by enabling it to construct its own DDBM in coordination with the six fundamental questions for a data-driven business.

The DDBM Blueprint suggests that creating a business model for a data-driven business involves answering six fundamental questions:

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. Often an organization understands the potential value and benefit associated with data but fails to determine a specific aim before undertaking a time-consuming and costly data acquisition and analysis process. Seven key competitive advantages are attained; shortened supply chain, expansion, consolidation, processing speed, differentiation and brand. For example, the fashion retailer Zara aimed to achieve close to real-time customer insight into fashion industry trends and purchasing patterns so that it could better align itself with its customers, resulting in increased retail sales volume. Zara aimed to utilize a shortened supply chain to gain competitive advantage and incorporated near real-time sales statistics, blog posts and social media data into its analytic systems, to rush emerging trends to market.

2. What is our desired offering?
A business must decide in what way the DDBM construct will benefit the company’s current offering or, alternatively, create an entirely new one. Established businesses have a tendency to utilize data to improve or enhance their current customer offering, which is often called a ‘value proposition’.. A company can offer raw data that is primarily ‘a set of facts’ without an attached meaning. When data has been interpreted it becomes information or knowledge. Typically the output of any analytics activity attaches some insight or application.  For example, the mobile phone service provider AT&T increased the positive public perception of its brand after evaluating a customer sentiment analysis based upon both internal (current users) and external (potential users) data sources. This insight enabled AT&T to improve its product and service offering in areas considered most important to its potential and actual customers, thus maximizing the derived benefit from the investment.

3. What data do we require and how are we going to acquire it?
Data is obviously fundamental to a DDBM. 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. 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. For example, the fashion retailer Topshop combines customer-provided data, free available data from fashion blogs and social media, and existing data within its own databases when running predictive and descriptive analytics protocols to determine emerging trends within the highly competitive retail clothing industry

4. 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’. 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. In the financial services sector, where finely-tuned predictive analytic modelling influences business decisions, Goldman Sachs plans years in advance to ensure it has the capacity, hardware, processes and employee skill sets available to utilize increased data volumes and new technologies. In fact, approximately 30 per cent of all Goldman Sachs’ employees work in technology and development.

5. 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: 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.

6. What are the barriers to us accomplishing our goal?
Interestingly, our research and analysis revealed clear links between specific inhibitors to the implementation of a DDBM. Established businesses are experiencing cultural issues, personnel issues, and internal value perception obstacles to implementing a DDBM. Our study suggests that issues with personnel may be the most severe DDBM implementation inhibitors experienced by both new and established businesses and may be linked to a variety of other obstacles to a business becoming data-enabled.

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