Predictive analytics has become the key to helping businesses — especially those in the highly dynamic Chinese market — create differentiated, individualized customer experiences and make better decisions. Enterprise architecture professionals must take a customer-oriented approach to developing their predictive analytics strategy and architecture.

I’ve recently published two reports focusing on how to architect predictive analytics capability. These reports analyze the trends around predictive analytics adoption in China and discuss four key areas that EA pros must focus on to accelerate digital transformation. They also show EA pros how to unleash the power of digital business by analyzing the predictive analytics practices of visionary Chinese firms.

Some of the key takeaways:

◾Predictive analytics must cover the full customer life cycle and leverage business insights. Organizations require predictable insights into customer behaviors and business operations. Youmust implement predictive analytics solutions and deliver value to customers throughout their life cycle to differentiate your customer experience and sustain business growth.You should also realize the importance of business stakeholders and define effective mechanisms for translating their business knowledge into predictive algorithm inputs to optimize predictive models faster and generate deeper customer insights.

◾Make predictive analytics the core component of your big data architecture. Although predictive analytics and big data are not one and the same, big data technologies have become a key factor in improving predictive analytics. Big data platforms are still evolving and increasingly overlap each other but generally consist of five layers: infrastructure, virtualization, visualization, management, and analytics; the last one is where predictive analytics solutions belong. Put predictive analytics solutions at the core of your platform and create synergies between the analytics layer and the other four layers.

◾Focus on vertical coverage for predictive analytics solutions. Forrester sees three categories of ISVs for predictive analytics in China.Global players such as IBM, Microsoft, Oracle, SAP, and SAS provide predictive solutions for all major industries. Global predictive analytics ISVs such as FICO and RapidMiner target specific verticals. Local predictive analytics solution providers in China like AsiaInfo, BONC, Huawei Technologies, LongCredit, MeritData, MiningLamp, and Syntun are also building reputations in specific industries.

◾Learn from leading Chinese companies embracing digital transformation. China Mobile improved customer discovery for precise targeting of ads; Yihaodian optimized its marketing mix for effective channel interaction; ChinaUMS narrowed its prospect universe and drove ecosystem business growth; ICBC deepened its insights into the voice of the customer to improve customer satisfaction; and JD.com personalized digital experiences to maximize customer loyalty.

How do you architect predictive analytics? What business requirements are you targeting and which vendors are you using? Tell us how you’re embracing predictive analytics to accelerate your transformation into a digital business.

(About the author: Charlie Dai is a research analyst with Forrester Research)