Startup leaders, consider these key data architecture decisions to enable an omnichannel strategy

The line between online and offline commerce is increasingly blurry. From Amazon’s move into the real world to traditional retailers like Walmart embracing the virtual one, storefronts now live across mobile, social media, the internet and around the corner.

Omnichannel strategy is the buzzy descriptor for creating a seamless, unified experience spanning these different venues. If you happen to spend your time listening in on quarterly earnings calls, you’ve likely heard this term more than once this year. In fact, during its most recent call with investors, Walmart trumpeted its own take on omnichannel and the importance of nailing this for customers.

Omnichannel is no longer optional

Retail is not the only sector embracing omnichannel. Healthcare, finance and many other industries all must meet customers where they live and transact, whether that be curbside or over Instagram.

Imagine a patient who asks a question of her primary care physician through an online patient portal when she’s not feeling well, collects data through a wearable device that is sent to the clinic, reviews the results from her blood work on a mobile app, then schedules an in-person appointment to determine a care plan.

Or consider a recent college graduate who uses the online interface for a large bank to handle bill pay and move money between accounts. He also uses that bank’s mobile app to send and receive money amongst his friends with Zelle and visits the local branch to meet with a loan officer ahead of buying his first car.

In both cases, the key to ensure a consistent level of support and customer experience throughout is having a cohesive strategy underpinned by technology.

Laying the groundwork for omnichannel

Companies need to plan for the technology components that enable these multiple points of engagement. This can range from designing mobile apps and online interfaces that support an omnichannel experience to integrating customer relationship management (CRM) software to track interactions across channels and surface personalized, appropriate responses depending on where customers are in the funnel.

Increasingly, companies are also interested in learning how artificial intelligence can help automate tasks such as responding to customer inquiries, power a chatbot, make product recommendations or prompt customers to do things like post a review.

But far and away, the biggest hurdle most companies face when designing for an omnichannel strategy is data collection, management and analysis.

A viable, scalable and secure data strategy can be challenging because the inputs are coming from multiple channels in real-time and must be collected, formatted and analyzed. Data must be gathered and moved to a database, cleaned to create consistency and then integrated into a system of choice.

Think again of the healthcare patient. That data may come from many diverse sources — an email generated by the provider’s online portal, physical data entry from a form field completed in the office, and readings from a glucose monitor or a Bluetooth blood pressure cuff — and then must be combined and synthesized to provide the physician with an easy, understandable way to draw an actionable conclusion as to the patient’s condition.

When designing a data strategy, Chariot recommends that all clients plot out the types of data that will be collected and stored, the channels through which it will be collected, the security requirements for the data, and how the data will be surfaced or analyzed. With this in hand, next steps are to agree on policies or providers for:

1. Centralized database — Rather than warehousing data in multiple locations, a central data repository facilitates consistency in the data across channels and provides easier access to and management of the data.
2. Cloud services provider — Cloud-based data architecture allows for a scalable way to store and manage data and is often a more cost-effective option for businesses.
3. Data integration tools — Responsible for integrating data from multiple, different sources or channels into a central database
4. Data governance framework — These policies and procedures govern the use of data within an organization and are a critical part of security and compliance considerations.
5. Data encryption standards — Alignment on encryption standards involves industry-specific requirements as well as individual business and customer expectations.

These five basic elements can help form the foundation for a customized data architecture plan supporting a company’s unique omnichannel strategy and needs. Companies should also consider a data dictionary to ensure that data is applied consistently across channels, implement regular data backups to protect against loss or corruption and schedule regular reviews to maintain alignment with changes in the business.

Ultimately, a scalable and secure data solution aligned with your business goals will help ensure a robust, engaging omnichannel experience best able to grow revenue and streamline costs, boost cross-sell and up-sell opportunities, improve customer satisfaction and loyalty, and differentiate your brand from competitors.

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