Article | May 2015 | Kanvic Grey Matter
Before starting the Big Data journey executives want to clarify the opportunities and outline the right path to follow. In this context, the questions to address are: how will their organisation build its data assets and how will they be monetised?
Big Data is an emerging phenomenon that is still unfamiliar territory for many business executives. While embarking on a Big Data journey they are often faced with the challenge of deciding where to start. They want to clarify the opportunities and outline the right path to follow. In this context, the first questions to address are: how will their organisation build its data assets and how will they be monetised?
We have developed a useful framework to help executives clarify the possible Big Data opportunities and build winning strategies around them. Companies can potentially leverage their Big Data assets in four opportunity areas. The choice a company makes will be based on how executives decide to approach two fundamental issues: data acquisition and data monetisation.
This practical framework is designed to help executives think clearly about their Big Data game-plan. It will drive strategic discussions in the right direction and help decision-makers understand what type of data will be valuable to end- users, what capabilities will be required, and how resources should be allocated.
We have given a short description of each opportunity and illustrated them through examples to explain how companies can generate value from Big Data:
1. Data consumer: These companies rely primarily on publicly available data to generate useful insights for their business. They generate value not by capturing their own data, but from their capacity to turn available information into useful business insights. A good example of this is consumer goods companies that record and analyse social media conversations about their top brands to monitor customer sentiment.
2. Data broker: These companies compile publicly available data from multiple sources and sell it to other businesses. They generate value from their ability to cross data from a large variety of sources and neatly package it into specific data sets. The multinational company Acxiom, for example, aggregates customer data from social media, civil records and other public sources. The resulting output is sold to businesses that are looking for a particular piece of information. For instance, data packages that give the profiles of new home owners in a given geography can be bought by furniture or consumer durables businesses.
3. Data supplier: These companies have privileged access to certain information and generate value by selling it to outside parties. This model is widely used by smartphone applications that often generate revenues by tracking a customer’s movements and selling that information to advertising companies or city planners for example. Similarly, some retailers that capture data about shoppers’ purchasing behaviour also supply the data to consumer brands whose access to such transactional information is more limited.
4. Data owner: These companies rely on their own data and use it to gain useful insights to improve their business. The value of this model lies in gaining unique insights over their competitors. Some utility companies for example use predictive analytics to identify which components of their infrastructure should be serviced in a preventive manner. These companies send their maintenance teams before a pipe breaks-down or is damaged enough to require costly repairs. This way they avoid interruptions in service and optimise their maintenance efforts.
To determine which opportunity is most attractive for their organisation, decision-makers need to investigate a number of critical issues.
Some organisations are already data rich, having effective systems to capture, store and retrieve information about their supply chain, sales and financial performance for example. In this case, business leaders can start by understanding how the information that already exists internally could be turned into useful insights for their company, as well as for other businesses.
This requires executives to get a full picture of their existing data assets and to collaborate with Data Science experts to visualise what type of insights could be generated from them. Decision-makers in data rich companies should also clarify whether their business would gain more from keeping its data proprietary or from selling it to outside parties. In the first case, retaining unique insights can provide a competitive edge, but in the second case the cash generated from the sale of data can be deployed to grow core areas of the business.
If the business only has a limited amount of data to start with, executives need to determine whether they are willing to develop new capabilities to build such data assets or whether outsourcing is a better option. Significant resources must often be allocated to generate, capture, store and process data internally. If the business is not ready for this kind of commitment, executives should consider limiting the scope to publicly available data and outsourcing key analytics and technology capabilities to outside providers. However, publicly available data will enable mostly generic insights focussed on market trends and consumer behaviour. By contrast, if executives decide to build their own data assets, company specific insights can then be obtained to track customer behaviour, supply chain performance, or marketing ROI for example.
Finally, decision-makers need to decide how Big Data will be integrated in their organisations. They should determine wether they want to leverage Big Data as a part of their existing organisation or as a company spin-off. If the data is used internally this decision will be based on how disruptive Big Data will be to the existing business. For example, the US bank Signet started using analytics to discriminate between customers and adapt credit terms to different profiles. Historically, the company had only offered standard rates for every customer, so price discrimination through analytics was a major change in both operating model and marketing strategy. The data driven side of the business was eventually spun-off as Capital One which became a leading player in the financial services industry.
Decision-makers should also consider the option to spin-off in cases where data is sold to other businesses. For example, a retailer wishing to sell its proprietary customer data would likely have its brand and sales capabilities tuned for B2C operations. But to become successful at selling data to B2B buyers, a new brand and sales-force might be required. Because both businesses are fundamentally different in this example, operating them separately might be preferable.
In conclusion, the first step in realising the Big Data opportunity is to determine how you will turn your data into monetisable assets. This choice depends on a number of elements revolving around how your organisation should source its data, generate insights and create value for final users and customers. Answering these questions will give executives the initial foundation on which to formulate a winning Big Data strategy.