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Putting a Value on Data Introduction Putting a value on data Introduction The value of information assets has never been greater. According to the European Commission, by 2020 the value of personalised data – just one class of data – will be one trillion euros, almost 8% of the EU’s GDP1. In a rapidly evolving data landscape, more and more organisations are looking for ways to create value using their or someone else’s data (see Figure 1). Those that take the time to properly diligence and understand the value of their data will unlock significant value. Those that don’t risk wasted investment in costly implementation programs with little or no upside. In a recent paper2, PwC’s Data and Analytics leader, Neil Hampson, talked about strategies for creating value from data. In this paper we help organisations understand how to value data and some of the drivers that impact value. Figure 1 – Organisations are looking for value in a rapidly evolving data landscape Organisations are starting to Organisations are getting requests from 1 understand the value of being viewed 2 others to use/buy their data as data-centric • Increasing use of data analytics to inform • Valuation multiples of data-driven firms decision-making and expand capabilities tend to be significantly higher than • Growing dependency on third party other industries data sets • Valuations of data-driven firms within • Pace of change requiring new data sets the same industry also tend to be and insights to meet evolving conditions higher than their peers 2 (e.g. more sophisticated targeted 1 marketing) 3 6 Organisations have rightly 3 Organisations are continually on started to fear big data players 6 Data landscape the look-out for new revenue • High concentration of data opportunities aggregators; fragmentation of • Pressure to diversify revenue suppliers and consumers. streams and grow margins • Increasing potential for • Traditional customer acquisition/ disruption from major data 4 retention methods are becoming players (e.g. Facebook, Uber, 5 less effective Amazon, Google) • Monetising data from IoT sensors is becoming an essential element of many new businessmodels Organisations are witnessing the success of others Organisations are finding technology is improving 5 with strong analytics capabilities 4 while costs are dropping • Companies with good data analytics capabilities • Use of Open Source software for data storage are twice as likely to be in the top quartile of and processing (e.g. Hadoop) is growing performance within their industries • Cloud storage costs are only cents per GB per month Technology and data trends Mobility, connectivity, automation, big data, cloud computing and the proliferation of the internet of things enable new capabilities and data-driven opportunities 1 https://www.weforum.org/agenda/2017/09/the-value-of-data/ 2 Creating value from data, March 2019 2 | Putting a value on data | PwC What is a data asset? In simple terms data comprises – ‘facts and statistics collected together for reference or analysis’2. There is a lot of it about. The world is must start by identifying and classifying 3. Highlight key issues which might producing about 2.5 quintillion bytes of what it has – i.e. performing an inventory of hinder its data strategy (such as legal data per day, with 90% of all data having its data. By doing so it can: or regulatory restrictions over use of been produced in just the last two years3. the data4) 1. Assess the quality of its data With this explosion in the sheer volume of 4. Think about ‘use cases’ for the data (i.e. data being generated, an organisation 2. Identify gaps in the data (which it may what are the possible applications for its wishing to understand the value of its data wish to fill by buying data or partnering data) with others) The process of classifying the data will give an organisation a common understanding of the data it has across its different business units and functions (see Figure 2 as an example), but that doesn’t mean the data is valuable. Figure 2 – Illustrations of data source and data category dimensions – an inventory of data Data source Data category Sub-categories Illustration Authored data Master data Customer data Customer address • Typically created through • Describes people, places, Supplier data Contact details some kind of creative, and things that are critical to human process. a firm’s operations. Product data Products, Features • E.g. Architectural drawings, Employee data Employee name photographs User Provided data Transactional data Sales data Customer purchase history • Data purposefully provided • Describes an internal or Payment data Payment date by users into a system external event of transaction. without any expectations. Touchpoint data Call record • E.g. Social media, Geospatial data Current Location ecommerce reviews Captured data Reference data Jurisdictions Provinces • Recorded from events • Information that is used Control data Holiday calendar occurring in the real world solely for the purposes of or in software. categorising data. Currencies Currency codes • E.g. Financial transactions, Industry standarddata Country codes Web browsing logs Derived data Metadata Descriptive data Author, Abstract • Generated by combining, • Characterises other data, Tables, columns Type, Relationship aggregating and otherwise making it easier to retrieve, processing other data. interpret, or use the data. Lineage data Modifications • E.g. Credit scores, Audit trail data Accesses, Changes aggregated transactions Unstructured data Audio data Recordings • Data lacking a consistent Text data Reports format or syntax to describe Video data Surveillance footage objects and attributes. Picture data Social media postings For the data to have value it must satisfy some basic premises: It must be identifiable and It must promise probable future It must be under the 1 definable – a data asset may be 2 economic benefits – the data 3 organisation’s control – the made up of specific files or specific asset must have useful organisation must have rights to use tables or records within a database applications which are capable of and exploit the data in a way which driving incremental future cash is compliant with applicable laws flow either for itself or others and any contractual licensing arrangements, whilst also protecting the data and restricting access to it by others. 2 Oxford English Dictionary 3 https://www.weforum.org/agenda/2017/09/the-value-of-data/ 4 Significant regulatory responses to widespread use of data – General Data Protection Regulation (GDPR) for example – have prompted many organisations to define what data they have and think more carefully about how it can be used. PwC | Putting a value on data | 3 Data value drivers Assuming an organisation invests the time to create an inventory of its data, the value of that data lies in its ability to allow an organisation to generate future economic benefit (‘data monetisation’). Some examples of data monetisation strategies are shown in Figure 3 below: Figure 3 – Typical data monetisation strategies Strategy 1: Strategy 2: Strategy 3: Enhance current business Enter adjacent businesses Develop new businesses Leverage enhanced Generate new White-label Create new data Create new offerings data for core insights capabilities and business infrastructure • Seek opportunities • Understand deep • Monetise existing • Partner with • Develop new sets of to enrich existing client insights analytics adjacent players analytics and data service through • Enhance marketing capabilities via across the business products (e.g. new data sources campaign ROI and white labelling to value chain benchmarks, tools) • Develop and conversion clients and other • Identify new • Develop new leverage new partners across the sources of data products that platforms value chain (e.g. unstructured) benefit from • Deliver enhanced • Commercialise to combine with enhanced data and services (e.g. in real infrastructure to sell existing data sets analytics (e.g. time) platforms as a • Monetise new sets real-time net asset service of data value, active non-disclosed exchange traded funds) Netflix provides a good example of Strategy to change a little. This use of granular data Some of these value drivers define the 1 – using data to enhance its current to customise the individual user experience quality of the data (Completeness, business. Unlike many traditional makes subscribers sticky and reduces the Consistency, Accuracy, Timeliness), others broadcasters, it uses individual subscriber costly risk of producing or commissioning could either render the data valueless or data to tailor its content offerings based on unpopular content. create unique and valuable competitive your particular preferences. You can see advantages for the owner or rights holder this whenever you let a family member use Delving one layer deeper, the extent to (Exclusivity, Restriction, Liability, your Netflix account profile to watch films which data can drive future economic Interoperability). you’d never consider – you’ll notice the benefits for an organisation will depend on streaming service’s recommendations start key characteristics of the data – data value drivers such as those shown in Figure 4. 4 | Putting a value on data | PwC Figure 4 – Data value drivers • The more unique the dataset, the more • The more timely and up to date the data valuable the data is 1 2 is, the more valuable it is • The extent of the impact of exclusivity on • However, timeliness is a relative measure, Exclusivity Timeliness value is dependent
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