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 end customer 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 lifecycle and customer experience can be a good focal point for digital transformations since achieving a seamless experience requires upstream efficiencies throughout the organization.
How does artificial intelligence affect digital transformation?
Disruptive digital technologies such as artificial intelligence and machine learning are leading to innovation throughout the customer experience. With these technologies, decisions about interaction design can be backed up by insights from data analytics rather than the opinions of a few people.
Frequently, a new platform requires a change to the operating model. New analytics systems must be designed that can inform the design with insights gained while monitoring customer experience interactions. However, many leaders are not clear about how to align business objectives with the supporting data that enables a seamless customer experience which becomes part of the overall digital transformation strategy.
Artificial intelligence can be applied in different ways:
- Use tools that contain AI “under the covers,”
- Build applications on top of AI platforms from the big technology players that do the heaving lifting of advanced data science and machine learning,
- Build an internal AI capability by hiring data scientists and using AI as a competitive differentiator.
The role of data in digital transformation
When embarking on a digital transformation initiative, the foundation has to be built on data with a supporting information architecture design. Since “digital” anything can only be achieved through the application of data, having the right data can be more important than the applications or design.
Digital transformations will fail if the underlying digital machinery (which includes data architecture and the data itself) is not in place. What part does data play in digital transformation? A fundamental, critical and indispensable role.
Customer, content, and product information
Since digital transformations runs on data, orchestrating the interplay between customer data and content to support sales and service processes is the key to success. When a customer is looking for your products, it helps to know something about them so your site can serve up what they need. 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.
The proliferation of content and technology solutions in the customer experience space increases the complexity of a digital transformation strategy and brings less exciting topics like governance to the forefront technology innovation. This means that boring governance is a critical part of innovation design and strategy. 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.
Speaking of insights, another critical piece is collaboration and expert knowledge. Consider tier-two support people, product merchandisers or sales engineers. Those experts walk around with knowledge about various topics critical to value creation. That expertise needs to be designed into systems and captured in a way that is reusable. That is where knowledge management and a knowledge architecture come in.
How do you optimize data for digital transformation?
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. Optimization is a journey, not a destination. (One could say the same thing for digital transformation however large, intensive programs have to achieve targets as well as contain the mechanism to maintain and improve over time.) Optimization will depend on underlying maturity of 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. If those accountabilities are not in place, it will take longer to improve data. 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.
Enterprise architecture, information architecture, AI technology and digital transformation
You might be familiar with terms like information architecture or enterprise architecture. What is the relationship of information architecture to enterprise architecture strategy and emerging technologies like AI? An architecture is a structure or framework. One of the notorious challenges in the technology industry is the use of inconsistent terminology or use of different interpretations of terminology. Digital transformation will mean different things to different stakeholders. One definition of “enterprise architecture” is the structure of, and relationships between, applications, processes and data flows that enable day-to-day operations and the customer experience. Sometimes this definition is narrowed to concern the data side when people refer to enterprise information architecture. Sometimes that term is used in the context of master data management. While enterprise architecture is the big picture, information architecture defines data, customer and content in a way that produces a digital experience with the lowest possible cognitive load on the user. That means people don’t need to think – the information they need is served up to them by structuring the information in a way that aligns with their mental model – the way they think about their problem and needs. Of course, making things simple is very complex behind the scenes and 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. Many of these elements can be defined as part of an ontology – which describes the knowledge and data domain of the organization. These structures are needed so that AI tools deployed during a digital transformation know something about the company’s products, services, solutions, processes, customer types and so on. Without product names and details, an AI will not make the experience better. The technology cannot make up data. We have to tell it what is important.
What will a proper information architecture will mean for the business?
Every business needs to continually evolve and respond to market opportunities and competitive threats. 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 too narrow, it will reduce future options. If too broad, it may lose relevance. We have to zoom in and out – 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 embodies the things that are important to customers that you can deliver. And the faster you can respond to customers, the greater the competitive advantage.
Digital transformation strategy can become an abstract and ambiguous concept unless it is tied to the customer experience and measured by linking the various technology systems that comprise that experience through concurrent data analytics programs and initiatives.
What processes and rules do you need to enforce and preserve the effectiveness of an information architecture?
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. Use 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, a data analytics program can provide insights at every level of a digital transformation strategy and ensure that the information architecture is functioning effectively and providing value. 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, there are measures of the data and content quality, completeness and other indicators of the nature of the data and content. Data supports a process which should be measurable (all critical processes should have scorecards of some sort). Processes enable a business outcome and outcomes are in support of the enterprise strategy. By monitoring at each level, data driven decisions can become standard operating procedure. This process continually measures outcomes and performance, ensures that decisions are based on data and not opinion and helps to justify investment in the data and architecture because a linkage is established between data and business outcomes. Of course the data is an input to a decision making process and decisions are made by people. So in addition to the analytics, a structure and governing body has to be established. Governance consists of decision-making bodies and decision-making processes. When supported by metrics, the information architecture will remain effective while being continually improved.
- A good information architecture speeds information flows which increases agility
- Information architecture and digital transformation strategy have to be aligned to achieve the benefits promised
- Artificial intelligence can be applied at multiple levels of sophistication – from using a pre-configured application to building on AI cloud-based platforms to developing in house data science expertise and proprietary machine learning algorithms. Every digital transformation will use AI in some way, shape or form
- An AI will not make up for a poor information architecture and bad data
- Having a big picture “enterprise architecture” ensures longer term flexibility and the appropriate information architecture solves specific problems and comprises the user experience
- Information architecture is important because it tells the systems and applications how to communicate and exchange data and identifies what is important to the customer and the organization
Nothing about this is easy (or sexy) but it needs to be done if your initiatives are going to make headway. Our team of information science experts can help.
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.