Steve Lucas, executive vice president of Database and Technology at SAP, gave a presentation focused on extracting business value from big data. You can watch a replay of the session on the SAP Virtual Event site: Tune Into New Business Signals in the Age of Big Data (registration required)
The basic theme of the presentation was it’s great that you can now store “big data” – but that also you need technology that can help you “sense” the important signal from the ever-louder background noise.
Lucas claimed that even famous financier Ebenezer Scrooge would have been at home in a modern board rooms, because most organizations are still primarily run using income statements and balance sheets – tools for analyzing corporate performance that have existed for a long, long time. They remain as important as ever, but now should be supplemented with new measures, such as customer sentiment, to get a more complete, proactive picture of corporate performance.
According to Lucas, organizations need four things to be successful, as technology has developed over time
- 30 years ago: The ability to store information about what was sold: databases
- 20 years ago: The ability to ask questions about that data: analytics
- 10 years ago: The ability to plan for the future: predictive
- Now: The ability to know how customers feel about the purchase: sensing
Examples of signals that organizations can now track include:
- Brand sentiment – By capturing and analyzing customer comments on Facebook, Twitter, and LinkedIn in order to improve customer experience and optimize campaign performance.
- Predictive maintenance – By analyzing a continuous stream of machine data diagnostics, you can predict when the performance of machinery is degrading or even worse potentially about to break down.
- Insider threats – By looking for anomalies hidden in data about user behavior, you identify suspicious behavior and pinpoint potentially high-risk employees.
- Network optimization – By understanding usage patterns, and predicting customer trends, you can optimize your communications network.
- Propensity to churn – Maybe your business suffers from a customer turnover in a highly competitive market. How can you determine a customer’s propensity to churn, or in other words, a likelihood they will leave you as a customer so that you can offer new services or deals in order to keep them.
- Product recommendation – By analyzing customer purchasing history and online browsing patterns, you can build a profile of customer interests and make highly targeted product upsell recommendations.
- Fraud detection – Identify purchases or insurance claims that may have a high probability of being fraudulent by analyzing not only the transactional information, but also electronic documents.
- Risk mitigation, real-time – By analyzing financial transactions in real-time, you can identify high risk investments, sales, and deals before they actually occur.
- 360 degree customer view – By storing and analyzing all data about a customer, such as transactions, browsing history, customer profile, social media, and more, you can build a complete view of your customer across all channels.
- Asset tracking – Track high value assets and identify abnormal behavior that may put assets at risk of loss, or identify inefficient usage that is costing your business money.
- Personalized care – Use advanced analytics to create personalized treatments for patients, such as how Mitsui Knowledge Industry offers personalized cancer treatment based on genome analysis.
- Real-time demand / supply forecast – Maybe your business is very seasonal or varies greatly in each local market. How can you better forecast customer demand by market, or by season, or weather pattern, or market trend, in order to forecast supply requirements and optimize inventory.
As an example of what is now possible, Lucas invited Fred Samson to do a demonstration of CRM on SAP HANA, using an iPad:
A salesperson’s streaming view of current events, using data from inside and outside the organization.
A customer profile, with key indicators calculated and stored real-time:
An example of measures that can be provided from the cloud: customer sentiment
Predictive analytics, such as device per employee, in this case using a projection from last year’s figures
The application includes the ability to write back into the system – in this case, the sales person has sold the deal, and the data is updated.