27937

Guest post by Oliver Halter and Ritesh Ramesh

Capturing all available, relevant information and transitioning it into insights to drive business value continues to be a major challenge—even for companies that consider data and analytics as the lifeblood of their growth agendas. Most of the time, we can attribute this challenge directly to the old fashioned and rigid information architecture foundation that companies have built over the years to enable their data and analytics capabilities. Companies that developed their information architecture foundations based on standardization on few technology vendors, homogeneous tools, and poor guiding principles suffer more in handling new age information problems.

We were talking to a senior technology executive at a large financial services company recently who put it aptly – ‘Availability and access to high performance technologies at lower cost is no longer a challenge, but if you don’t use even the most powerful technology available in the right way for the right analytics problem, you will end up destroying enterprise value’. We couldn’t agree more.

Break neck innovation is fueling low cost, high performance, scalable and commoditized technologies, but the byproduct of this innovation is technology complexity and uncertainty of historic proportions. Businesses of all shapes and sizes are reeling from pervasive market forces such as digitization, smart machines, and consumerization. They are grappling with a largely federated internal stakeholder base across multiple business units with diverse data and analytics needs.

Businesses need what we define as a “Next Generation Information Architecture,” a comprehensive, results-driven and forward-thinking strategy to enable their data and analytics capabilities.

A Next Generation Information Architecture has four key characteristics. It is 1) capability driven—based on current and future information and analytics needs of internal and external stakeholders, 2) a hybrid set of both traditional and emerging technologies and platforms, 3) a heterogeneous mix of ‘right fit’ open source and commercial solution components, either hosted in the cloud or on premise, 4) driven by the right operating model that recognizes the sophistication and analytics maturity at a functional level, and enables the required capabilities with the right blend of processes, tools and support.

When developing a ‘Next Generation Information Architecture’, five principles can guide the way:

1. Establish the baseline: Identify current and emerging information and analytics needs from stakeholder groups across the enterprise while keeping in mind your core capabilities—areas where your company excels that competitors can’t match. Categorize stakeholder needs based on how they digest information (e.g. batch vs. real-time vs. near real-time), analytics requirements (e.g. ad hoc analysis, data scientists/business analysis), and business function. This step is critical in defining your future state architecture.
2. Build not just for today but for the future: Assess which architectural components your business needs to enable core capabilities to achieve the vision. Will a data lake environment help you execute your business strategy? How will you handle master data management and data integration? What business intelligence tools will be best for reporting, data visualization, and ad hoc analysis? Will you host each component on premise or in the cloud?
3. Take an ‘outside-in’ view to technology investments: Before investing in new technologies, consider if you can upgrade any of your existing technologies. Take an ‘outside in’ view of emerging open source data and analytics technologies, e.g. Hadoop, NoSQL etc. that is transforming your industry but look at investments through the lens of business strategy. Map all tools and technologies to your future state architecture.
4. Develop a transition plan: Assess your current information architecture and develop transition plans to achieve the future state. This effort will expose complexities and intricacies inherent in the physical architecture, including any technology re-platforming, code development, data migration, and conversions required for the transition.
5. Develop a data and analytics roadmap: According to PwC’s Global Digital IQ Survey, top performing companies effectively utilize all of the data they capture to drive value. Define a short-term, mid-term and long-term roadmap depicting initiatives required to realize the vision. Including pilots to prove business value incrementally is extremely critical.

Many successful businesses are already executing on the next generation agenda to drive innovation and growth. One of our clients, a large big box retailer, was grappling with information and analytics challenges due to the deluge of data from their emerging digital channels (web, mobile, and external apps). The business developed a Next Generation Information Architecture, which resulted in a robust foundational data platform that improved customer targeting, drove higher conversion rates, and lowered costs.

Another client, a large health services business, transformed their Analytics Center of Excellence function by automating and liberating the function of data integration. This was part of a multi-year analytics strategy to streamline the information architecture, emerging technologies, and agile processes. The effort enabled on-demand availability of data sets, infrastructure, and tools to solve business problems.

Regardless of the size or shape of your business, pondering how a next generation information architecture will transform your organization should be a key consideration for all business and technology executives.

Image shared by Justin Grimes

27937