No two industries are alike. No two companies within the same industry are the same. But virtually every organization in every market faces two related problems: 1)what to do with all the information pouring into data centers every second of every day and 2)how to service the growing number of users who want to analyze that data.

Users are lining up outside the CIO’s door to request access to the incoming flood of data for analysis. Plus, when they get access, they want response times to their queries to match what search engines like Google deliver—a near impossible task especially when you consider that most data science projects take weeks or even months. Getting the information and the answers now is not merely a trend or an expectation, it’s a requirement for the business.

Why SKYLARK Chose SAP BusinessObjects Predictive Analytics

The Japanese restaurant chain, SKYLARK, was looking to turn billions of records into more customer visits. But it was looking for a solution that could operate at the pace of its business— rapidly iterating through one-to-one marketing strategies, delivering insights in days without requiring a large team of data scientists. That’s one of the reasons they chose SAP.

“SAP BusinessObjects Predictive Analytics, with its quick analysis speed, is a perfect match for a company like ours that introduces measures in short cycles,” says Yutaka Sera, director of the insight strategy group. “It is designed to be highly accurate even with the standard settings. Operations from data processing to data mining can be executed simply, and the accuracy and speed of analysis are ensured regardless of the skill level of the staff in charge.”

Now mere human beings are too slow for some markets. For these enterprises, it’s machine-to-machine speeds that’s in demand. Predictive analysis is being applied to business processes for financial transactions executed by computers, which are the only things capable of working as fast as the data is moving.

How Monext Benefited from Predictive

With millions of credit card transactions every week, Monext, a European e-payment service provider, needed a better, more targeted way to predict transactions that are potentially fraudulent. With the help of SAP BusinessObjects Predictive Analytics, Monext was able to:

  • Build predictive models that were optimized for each credit card provider
  • Reduce the number of false alerts for each of their various types of cards.
  • Embed these models into the transaction process ensuring that every transaction is analyzed for potential fraud within milliseconds of occurring.

Tackling the Big Data problem only solves half of the analytics challenge in most companies. Embracing an ever-expanding user community is also essential. Luckily, purpose-built analytics architecture can meet the challenge. And not just on a whiteboard in theory. But in the field every day.


Cisco Empowers Sales Department to Improves Customer Insights and Sales Conversion

Cisco wanted to equip its sales organization with customer insights to improve its overall sales strategy as well as execution within individual accounts. To help improve customer sales conversion, they wanted to tell sales reps what Cisco solutions would be most relevant to a given customer based its profile and purchasing history.

By using SAP BusinessObjects Predictive Analytics on SAP HANA, Cisco was able to accomplish this with the analytics team in its sales department rather than relying solely on Cisco’s data science center of excellence.

Bottom Line

Generating more insights, faster is a no brainer for any organization wanting to tap the steady flow of data. All you need is a tool that turn out models in days, not weeks using data science experts or analysts with a data science mindset.

Learn More

Download “The Forrester Wave: Predictive Analytics And Machine Learning Solutions, Q1 2017

Read the rest of the blogs in our Predictive Thursdays series for more on all things predictive.