The hype around big data is certainly top of mind with executives at most companies today but what I am really seeing are companies finally making the connection between innovation and data. Data as a corporate asset is now getting the respect it deserves in terms of a business strategy to introduce new innovative products and services and improve business operations. The most advanced companies have C-level executives responsible for delivering top and bottom line results by managing their data assets to their maximum potential. The Chief Data Officer and Chief Analytics Officer own this responsibility and report directly to the CEO. (more…)
Has big data entered the “trough of disillusionment?” That’s what I’ve heard recently. Like many hyped up technology trends the trough can be deep and long as project failures accumulate, or for ‘hot’ trends that evolve and mature quickly the trough can be shallow and short, leading to broader and rapid adoption. Is the big data hype failing to deliver on its promise of increased revenue and competitive advantage for companies that leverage big data to introduce new products and services and improve business operations? Why is it that some big data projects fail to deliver on their promise? Svetlana Sicular, Research Director, Gartner points out in her blog Big Data is Falling into the Trough of Disillusionment that, “These [advanced client] organizations have fascinating ideas, but they are disappointed with a difficulty of figuring out reliable solutions.” There are several reasons why big data projects may fail to deliver on their promise: (more…)
In a recent webinar, Mark Smith, CEO at Ventana Research and David Lyle, vice president, Product Strategy at Informatica discussed: “Building the Business Case and Establishing the Fundamentals for Big Data Projects.” Mark pointed out that the second biggest barrier that impedes improving big data initiatives is that the “business case is not strong enough.” The first and third barriers respectively, were “lack of resources” and “no budget” which are also related to having a strong business case. In this context, Dave provided a simple formula from which to build the business case:
Return on Big Data = Value of Big Data / Cost of Big Data (more…)
Informatica supports Agile Data Integration for Agile BI with best practices that encourage good data governance, facilitate business-IT collaboration, promote reuse & flexibility through data virtualization, and enable rapid prototyping and test-driven development. Organizations that want to successfully adopt Agile Data Integration should standardize on the following best practices and leverage Informatica 9.1 to streamline the data integration process, improve data governance, and provide a flexible data virtualization architecture.
1. The business and IT work efficiently and effectively to translate requirements and specifications into data services (more…)
Adopting Agile may require a cultural shift and in the beginning can be disruptive to an organization. However, as I mentioned in Part 1 of this blog series, Agile Data Integration holds the promise to increase chances of success, deliver projects faster, and reduce defects. Applying Lean principles within your organization can help ease the transition to Agile Data Integration. Lean is a set of principles first explored in the context of data integration by John Schmidt and David Lyle in their book on Lean Integration. First and foremost Lean recommends an organization focus on eliminating waste and optimizing the data integration process from the customers’ perspective. Agile Data Integration maximizes the business value of projects (e.g. Agile BI, Data Warehousing, Big Data Analytics, Data Migration, etc.) because you can get it right the first time by delivering exactly what the business needs when they need it. Break big projects into smaller more manageable deliverables so that you can incrementally deliver value to the business. Agile Data Integration also recommends the following: (more…)
Most organizations will admit that it takes much too long for the business to get the data they need. IT projects take too long to deliver — from the time business requirements are defined until the project go-live. With traditional waterfall-style project management there is a tendency to throw requirements and deliverables over the fence to the next phase so that by the time the project is delivered the business has moved on and the requirements may have changed. It is a well-known fact that most IT projects fail from the customers’ perspective. That doesn’t mean the project was not delivered but instead means that the project failed to deliver the expected business value. (more…)