In today’s highly competitive business environment an executive within an organisation cannot wait for period end reports to drive decisions. Very often, these multi-functional reports and dashboards require information to be collected from a number of applications across the organisation such as the ERP/accounting system, separate Information Systems, other ‘business critical’ spreadsheets, external data feeds and also social media analytics to give a real view of the state of play.
Real-time Data and Analytics using Business Intelligence reports and dashboards showing the status and performance of various parts of the organisation are becoming increasingly indispensable. When implemented successfully, BI offers management the ability to get the answers from routine statistics as well as to ad-hoc questions on the spot. Data Analytics can also help you perform various what-if scenarios. Proactive prediction of scenarios based on defined models can also drive interventions in pricing, promotion or launch timing.
While information has always been an important ingredient to driving business models, now, it is frequently the most important ingredient that talent and algorithms transform into the company’s primary product or service design. As ‘Big Data’ and more sophisticated analytics become a real business differentiator, organisations should reframe their strategies to maximise market impact – and the capabilities they need to do it effectively.
The following Information Economics concepts describe how the paradigm of production, distribution and consumption of products and services is dramatically altered by Big Data and predictive analytics.
Information-driven feedback loops: Companies exploiting information-driven positive feedback loops generate more value for customers by providing access to a valuable network of connections, knowledge and associated services, which incentivises customers to contribute more data, thereby increasing the value of the network. Social network and financial payments companies use their customer populations to create information feedback loops that dramatically reduce the cost of production, distribution and consumption of data, allowing them to create numerous information-based products and services.
Information marginal costs and benefits: Business models that reduce the marginal cost of accumulating and managing information and maximise benefits from continuously using additional information to make better decisions or create new products and services have the power to continuously monetise their information assets.
For example, a leading consumer financial services software provider allows customers, at a low marginal cost of adding additional data, to consolidate their login credentials and financial data in one place. They then create greater marginal benefit for customers through services that help them understand how ‘customers like them’ save and/or spend their money.
Better data and new analysis techniques make proactive decisions more possible and cost-effective
As the company learns more about the customers, it can integrate new customer information at a low marginal cost that expands marginal benefits by adding additional customer services such as projecting tax impact on their income or recommending financial products.
Information enhanced learning curves: Business models that accelerate learning through more targeted, faster exchange and application of information create new types of value. For example, industrial products companies are using sensors to monitor and accumulate information about potential equipment risks related to material quality and reliability under specific manufacturing conditions. Quickly expanding their knowledge with better information helps them learn how to quickly adjust risk management and safety programmes to mitigate hazards, reduce insurance costs, and avoid supply chain disruption.
Information aggregation/decay ratio: In situations where we cannot access the right information quickly enough or to gather it from multiple sources simultaneously to make good decisions the value of the information can be seen to decline over time. A decision may have to be taken later leading to a sub-optimal result. As an example, the longer it takes to get medical test results from providers, the less effective the remedial care could be for the patient. Similarly, the effectiveness of pricing changes or special offers decreases rapidly in a competitive environment.
Better data and new analysis techniques make proactive decisions more possible and cost-effective.
Organisations can improve decision making by systematically building analytics and information capabilities to monetise more of their data assets.
First, companies can build a capability to quickly and cost-effectively source and aggregate more data from open, commercial, and proprietary information sources into a mineable format.
The second step is to go beyond aggregating data, and explore novel combinations to invent new data with new potential uses.
Third is to build processes and architecture to rapidly develop and test new analytic models driven by new data, increase processing speed through better model coding, improve insights over time and quickly launch them into production.
george.sammut@mt.pwc.com
christian.azzopardi@mt.pwc.com
George Sammut is a PwC Advisory Partner and Christian Azzopardi is a manager in the PwC Advisory team focusing on technology solutions for better business performance.