Why Enterprise Architects Need to Think About Data First
Enterprise Architects (EAs) are increasingly being asked to think 3-5 years out. This means that they need to take an even more active part in the strategy process, and to help drive business transformation. A CIO that we talked to recently said;
“Enterprise Architecture needs to be the forward, business facing component of IT. Architects need to create a regular structure for IT based on the service and product line functions/capabilities. They need to be connected to their business counterparts. They need to be so tied to the product and service road map that they can tie changes directly to the IT roadmap. Often times, I like to pair a Chief Business Strategist with a Chief Enterprise Architect”.
To get there, Enterprise Architects are going to have to think differently about enterprise architecture. Specifically, they need think “data first” to break through the productivity barrier and deliver business value in the time frame that business requires it.
IT is Not Meeting the Needs of the Business
A study by McKinsey and Company has found that IT is not delivering in the time frame that business requires. Even worse, the performance ratings have been dropping over the past three years. And even worse than that, 20% of the survey respondents are calling for a change in IT leadership.
Our talks with CIOs and Enterprise Architects tell us that the ability to access, manage and deliver data on a timely basis is the biggest bottleneck in the process of delivering business initiatives. Gartner predicts that by 2018, more than half the cost of implementing new large systems will be spent on integration.
The Causes: It’s Only Going to Get Worse
Data needs to be easily discoverable and sharable across multiple uses. Today’s application-centric architectures do not provide that flexibility. This means any new business initiative is going to be slowed by issues relating to finding, accessing, and managing data. Some of the causes of problems will include:
- Data Silos: Decades of applications-focused architecture have left us with unconnected “silos of data.”
- Lack of Data Management Standards: The fact is that most organizations do not manage data as a single system. This means that they are dealing with a classic “spaghetti diagram” of data integration and data management technologies that are difficult to manage and change.
- Growth of Data Complexity: There is a coming explosion of data complexity: partner data, social data, mobile data, big data, Internet of Things data.
- Growth of Data Users: There is also a coming explosion of new data users, who will be looking to self-service.
- Increasing Technology Disruption: Gartner predicts that we are entering a period of increased technology disruption.
Looking forward, organizations are increasingly running on the same few enterprise applications and those applications are rapidly commoditizing. The point is that there is little competitive differentiation to be had from applications. The only meaningful and sustainable competitive differentiation will come from your data and how you use it.
Recommendations for Enterprise Architects
- Think “data first” to accelerate business value delivery and to drive data as your competitive advantage. Designing data as a sharable resource will dramatically accelerate your organization’s ability to produce useful insights and deliver business initiatives.
- Think about enterprise data management as a single system. It should not be a series of one-off, custom, “works of art.” You will reduce complexity, save money, and most importantly speed the delivery of business initiatives.
- Design your data architecture for speed first. Do not buy into the belief that you must accept trade-offs between speed, cost, or quality. It can be done, but you have to design your enterprise data architecture to accomplish that goal from the start.
- Design to know everything about your data. Specifically, gather and carefully manage all relevant metadata. It will speed up data discovery, reduce errors, and provide critical business context. A full compliment of business and technical metadata will enable recommendation #5.
- Design for machine-learning and automation. Your data platform should be able to automate routine tasks and intelligently accelerate more complex tasks with intelligent recommendations. This is the only way you are going to be able to meet the demands of the business and deal with the growing data complexity and technology disruptions.
Technology disruption will bring challenges and opportunities. For more on this subject, see the Informatica eBook, Think ‘Data First’ to Drive Business Value.