7 Key Components of the Global Multi-Channel Data Strategy

As global channels converge and connectivity creeps into more aspects of our lives, companies must use a) Technology, b) Data and c) Insights to reach customers in a seamless way. True seamlessness is not about being everywhere all at once, but being at the right place at just the right time, and delivering the perfect message.

For most Global organizations, the answers to business questions lie in Multi-channel Data – the massive volumes of structured and unstructured data generated across many marketing and business channels both inside and outside the company. Being able to analyze all of this data in a meaningful way can be a daunting task if the proper Global Enterprise Data Strategy is not in place, and if you don’t have the means to process multi-channel data from multiple sources quickly and effectively. And once you have processed it, it’s a whole other battle to make it meaningful globally to the people in your organization who need to understand it and use it on a daily basis, optimizing customer experience and driving the business forward.

To help Global organizations build the right Multi-Channel Data strategy, here are seven key components they should consider:

1. Establish a common Global Data Model. Ensure that all of your data is centralized in a Common Data Model to provide a single accurate view of the global business. The Common Data Model establishes conventions such as data fields, naming, attributes and relationships so that everything is aligned across customer, transactions and other internal and external data sources.
2. Establish Global Data & Analytics COE. Many data & analytics efforts are connected to IT organizations. Leading companies have begun creating Data & Analytics COE, staffed with data analysts, data scientists and IT partners, to oversee disparate data projects. Companies typically design the Data & Analytics COE to drive widespread use of analytics across the enterprise, standardize data sets, tools and platforms, and confirm data accuracy and consistency.
3. Focus on Global Scalability and Open Data Standards. When global teams across the company format their data in the same way, technologists can build tools that scale across regions at a fraction of the effort and cost. Open data formats streamline the process of finding and using data across the company. By using an open-standards platform, organizations can leverage existing systems while reducing IT costs and gaining flexibility in terms of serving the business. Also, data standards help global marketers get the information they need to make the best decisions possible for their customers. Every day countless global marketers make hard decisions about where to buy advertising, how to reach a particular customer segment, and where to spend their money effectively. With standardized global data, marketers anywhere can receive unprecedented guidance on the decisions they face.
4. Start Small and Scale. Do not hoard multi-channel data. Don’t make the mistake of trying to gather all imaginable global data to feed a multi-channel analytics project prior to its start. The main danger in hoarding data: by the time you have gathered the data you think you’ll need, you’ve run out of budget and time to do anything with it. An effective approach would be for multi-channel data strategists to source data according to a specific marketing strategy and business questions.
5. Create Globally - Consume Locally. Today, information can be accessed on almost every mobile device, and from cloud-based netbooks to in-store portals. Organizations need to ensure a common infrastructure for producing and delivering global enterprise reports, scorecards, dashboards, and ad-hoc analysis while empowering global users with real time access to self-service BI, mobile BI, and the ability to create their own BI content and personalized dashboards using a simple, easy point & click interface.
6. Use the power of External Data. Truly capturing meaning from Multi-Channel Data means effectively integrating foundational data from internal data sources with external data from third-party environments (i.e. vendor data, social media, and demographics). The platform must be able to harness information in multiple ways, from structured databases and distributed predictive analytic systems, to mining unstructured data. But don’t overdo it! When you think about what external data you need for your data warehouse, the same rule that applies to internal data in your warehouse is just as applicable to externally sourced data - make sure that your analysis and decision making will have true business value before you go through the trouble of analyzing, transforming, storing, and making available all this external data.
7. Provide Global Users with Actionable Insights. It is almost a cliché to say that data should be actionable. Books and data professionals like myself usually say that so people will avoid measuring, and reporting, on stuff that has either very little value to the business, or that is very hard to impossible to influence. Multi-Channel Data Users need to be able to act on information without leaving the application and opening another.  This type of closed-loop, cross-domain analytics ensures that Multi-Channel Data will have an immediate informative and beneficial impact on day-to-day operations. For example, if the company buys media via agencies – media optimization platforms need to be connected to EDW in a real time empowering real time media optimization and optimizing customer experience.

Establishing the foundation for leveraging Data across the company is worth the effort.  When global business users can take action right from the analytics dashboard, the impact on operations and multi-channel customer experience is immediate.