Digital transformations are all around us – technology innovation is enabling new applications, new insights and new operating models that are being woven into our day-to-day processes, routines and habits. Digital transformation is an umbrella term that examines the end-to-end value chain of the organization – from product ideation to the customer’s purchase, and all of the associated departments, processes and operations.
Since digital transformation can be so all encompassing, organizations typically focus on processes that serve the customer and that directly impact the top line revenue and/or the bottom line through removal of inefficiencies and friction points along the value chain. The customer experience can be a good focal point for digital transformations, since achieving a seamless experience requires upstream efficiencies throughout the organization.
Every business needs to continually evolve and respond to market opportunities and competitive threats to its customer base. The key is agility – the organization needs to be able to innovate quickly and bring new products and services to market as market needs and the competitive landscape evolves. If legacy systems bog the organization down with incompatible data formats that cause friction or integrations are hard coded and brittle, the ability to innovate quickly will be impaired.
Innovation through digital transformation requires a consistent data and information architecture along with the business processes, governance and change management to keep it fresh, relevant and aligned with business goals. A proper information architecture is one that is not unnecessarily complex, supports specific business processes and objectives, enables consistency across departments and systems, and is flexible and extensible enough to accommodate changes in technology and business requirements.
Getting that balance right is a challenge. If the architecture is too narrow, it will reduce future options. If it is too broad, it may not help the customers find what they need. The best approach is to start with the big picture organizing principles or buckets, and then dive deeply into particular processes and applications to optimize the flow of information. The result is increased efficiency, the ability to make changes as customer needs change, and a competitive advantage in the marketplace. The information architecture should embody the elements that are important to your customers, as well as the customers’ own attributes.
Effective information architecture results in a simpler customer experience, because it allows for automated processes and incorporation of AI. But making things simple for the customer requires complexity behind the scenes. Information architecture can include content models, product data architecture, user experience flows and visualizations, customer journey models, and other design decisions that support friction free information flow.
Since digital transformations runs on data, the data must be properly governed. Orchestrating the interplay between customer data and content about products or services to support sales and service processes is the key to success. Different audiences may need different kinds of content to support their purchase or may be at different points in their buying journey – perhaps researching and discovering options. This is achieved by creating a customer attribute model that can help to differentiate ambiguous terms.
Governance consists of decision-making bodies and decision-making processes to manage this data. Governance is a critical part of innovation design and strategy. It consists of decision-making bodies and decision-making processes. This is the last thing that a UX design team with new and cool data and content technology wants to hear. “Governance and change management aren’t innovation!” they say. But in order to harvest the insights from data analytics tied to customer data, content and product information, governance has to be part of a well thought out design integrated across systems.
Any data or content technology strategy has to be tied to the experience of users and measured in some way. If something cannot be tied to an ROI, measured through data analytics from multiple customer experience systems and linked to an improved experience, it will be difficult to get the attention of stakeholders and even more difficult to fund in the long run. By installing a metrics framework to monitor performance and decisions, decisions can be more intentional, and changes can be linked to changes in various scores.
At the lowest level are measures of data and content quality and completeness. The data supports processes which should also be measurable (all critical processes should have scorecards of some sort). Processes in turn enable a business outcome, and outcomes support the enterprise strategy. By monitoring each level, data-driven decisions can become standard operating procedure.
An established metrics framework continually measures outcomes and performance, and ensures that decisions are based on data and not on opinion. An intentional plan for building on a foundation of data and processes also helps justify investment in the data and information architecture, because a linkage is established between data and business outcomes. Data becomes an input to a decisions which at the strategic level are made by people, but are supported by objective facts.
Optimization is a journey, not a destination. The ability of a company to optimize its data depends on the maturity of its underlying capabilities – for example, if the correct data standards are not enforced when products are added or brought to market, fixing that issue downstream will be very costly. The problems need to be solved upstream, and the benefits will be realized downstream throughout the enterprise.
Today’s data and technology systems can yield tremendous insights that will help the organization differentiate and compete in the marketplace. Design strategy of the customer experience has to be considered holistically and digital technologies are only as good as the quality of data inputs. When multiple systems and technologies are linked with inconsistent or siloed architecture, the digital transformation strategy will not realize its potential or fail outright.
Innovation requires that leadership understand and apply insights that data analytics can provide, but only if the right architecture underpins the systems and technology solutions. User experience and interaction design has to be considered at every level of a digital transformation, including the ways that insights from the data are communicated at the strategic and tactical levels. If designed correctly, an information architecture and data analytics plan can provide insights at every level of a digital transformation strategy.
To learn more about how we apply the science of organizing information to solve business problems, check out our white paper: Attribute-Driven Framework for Unified Commerce.