5 models for Big Data business success

Can data be used to create growth? At least Ronald Reagan believed so, when he said that information is the modern world oxygen. Information has, of course, always been important in business, but the Big Data phenomenon is forcing companies to think more about how they could use information to drive their business.

Big Data and company competitiveness

Gartner defines: Big Data are high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Big Data's core is therefore not the data nor its high volume, but the company's innovative way of using various sources of available information for successful business building. More and more enterprises are already operating this way: a recent survey found out that 80 % of companies see that harnessing data would make their business stronger.

From a company point of view, sources of data can be divided into three, partly overlapping categories:

• Firm internal data or the one available through business operations: for example, customer click data at company web pages, data collected by customer services or data provided by equipment and systems delivered by the firm.
• Data obtained from other companies or entities, in most cases paid data: for example, aggregated data related to potential customers or machine use -related or environmental data provided by firms generating or aggregating them.
• Public data: Data that has been made available by government, often free-of-charge, for example, various public records, weather and map data.

Data volumes are not large, they are huge - last year 2.6 zettabytes (10 to the power of 21) of data was created in the world, and that number is expected to grow by 40 % annually.

Source of data has a significant impact on businesses. This can be analysed by looking at the simplified value chain of Big Data that consists of four stages, see also the attached picture.

Each stage of the value chain sets different types of requirements to be competitive:

• Data generation: Opportunity to get access to proprietary data by means of own business; understanding of what is essential in data and how it can be generated.
• Data aggregation: Understanding of where and how data can be acquired; understanding of how data should be combined and stored so that it can be further processed and made useful.
• Data analysis: Analytics and algorithm skills; ability to interpret the importance of data from use point of view.
• Data usage: Ability to present data to users in a readily comprehensible form; understanding of user needs.

Familiar theory that explains how early-stage vertically integrated industries are gradually transforming into specialised ones, ie. horizontally integrated, can also be used in Big Data context. As industry competition is constantly in motion, companies need to shape their own strategy and positioning on the value chain by looking at their competitive advantages and capabilities.

Big data advantages, at their best, reinforce competitiveness of a firm, resulting in profitability and growth. In order to really achieve results with planned activities, Big Data projects need to be anchored in the improvement of competitive position. Assessment of own capabilities gives a realistic picture of what value chain positions are sustainable in a long run. Because overall business benefits are the most important ones, it is feasible to outsource the stages, where competitive advantages cannot be defended.

Big Data growth engine

This section deals with development of Big Data business models and especially ways to achieve growth. Big Data strategic benefits, such as ability to make faster, better or more proactive decisions, or efficiency improvement factors, such as improvement of capabilities and processes, are not discussed in detail here.

Independent Business

Independent Big Data based product businesses can be built by established companies or startups.

For established companies, it is convenient to develop new offerings for existing customers or take advantage of the data that the ongoing activities are generating. In the first case, understanding of customer needs and good customer relationships are typically key competitive advantages. In the latter, preferential access to data gives differentiation. For example, McKenney, a mechanical contractor of buildings, developed a service to reduce maintenance costs by collecting actual building data over the years. If a company position in customer interface or in data collection is strong enough, it is possible to partner with other players to complement the value chain.

Startups are usually missing most of the established companies' competitive advantages and resources in utilisation of Big Data, so they have been more active in the development of tools and know-how. There are many areas, however, where new, disruptive business models have been created, such as job search and recruitment firm Bright, Climate Corporation in crop insurance or Ambiohealth in remote health. Acquiring a startup can offer an established firm a head start to create a new business.

Improved access to public data has reduced the barriers that startups and other actors are facing when bringing new services to markets. For all have access to the same information, successful companies need to continuously improve user experience or add new data sources to stay competitive.

Sales of tools and know-how

In particular startups are actively developing new tools and know-how, in order to sell them to other businesses and organisations. These are exemplified by DataSift and Enigma in data aggregation, Cloudera and Cloudant in data storage, Cloud Physics and Gravity in data analysis and Icimo and Platfora in data presentation. Sensinode, recently acquired by ARM, is an example of Finnish Big Data startups, focusing on data collection technologies.

Support to other businesses

Activities that support well-established businesses will be hugely important as sources of business growth. Also startups are important as outsourced innovators of established firms: they are able to develop and test new ideas and when enough traction has been created, an innovation often ends up being part of the purchasing company's activities.

Support activities aim to maintain and improve competitiveness of company´s current business operations. Life cycle cost reduction of company products through proactive maintenance, cost reduction of distribution through route optimisation and customer satisfaction increase by utilising customer data are good examples of that. The key thing is how Big Data affects on overall revenues and expenditures, not costs and potential income of Big Data initiatives as such. GE and Konecranes are great examples of companies investing in so called industrial Internet - it is believed that Big Data strategy differentiates products and brings significant additional income. Amazon and Walmart are prominent firms utilising Big Data for customer relationships.

The core of the data used for support purposes is mostly in-house, but it can complemented, where appropriate, with purchased and public data. For example, weather and traffic data can be used to refine equipment use or website behavioural data.

Data sales

If a company cannot find meaningful ways to use available Big Data in its own business, opportunities to sell the data to other players should be explored.

Companies such as Bloomberg, Experian, and Affecto are already selling their data and also offer views and analyses based on them. These actors, however, are vertically integrated with operations primarily based on their own data. It is expected that new players, who generate or aggregate unstructured data originating from multiple sources, will be given a wealth of new opportunities.

This will provide Big Data generators and aggregators opportunities to sell data with various maturity levels; either to specialised broker firms or to other firms that make use of this data in their own businesses.

Marketing tool

Big data can also serve as a tool to attract customer prospects to become clients of a company. The trick is to provide free or low-priced information that is important and valuable to potential customers. Use of information creates a relationship of trust with the information provider and reduces barriers to buy full-priced products. Earnings impact is generated indirectly, through increasing customer numbers and revenues.

For example, an investment services company could use social media and the Internet to make sentiment analysis of listed companies for anticipating stock price movements and to provide this service free-of-charge for potential customers. Similarly, an equipment manufacturer could create thought leadership in its industry by developing an easy way to track factors that affect on raw material or semi-finished product prices, important for its potential customers.

If the used data are company proprietary, it is usually easier to functionally combine them with the products sold to customers. There are no reasons, however, that would prevent from developing this kind of offering based on acquired data only.