"Adding more resources can have mixed results. If the right people are involved at the right time to perform tasks within their abilities, the benefits will outweigh the costs. However, adding more people will not always fix an issue and make up for lost time. There is no guarantee that these steps will put a project back on target." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"As a general rule, implementations do not just spontaneously combust. Failures tend to stem from the aggregation of many issues. Although some issues may have been known since the early stages of the project (for example, the sales cycle or system design), implementation teams discover the majority of problems during the middle of the implementation, typically during some form of testing." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Implementation issues are not confined to the data and system realms. On the contrary, many of the problems encountered during a typical implementation stem from people, the roles they are required to play, political issues, and comfort zones." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Implementing new systems is not like baking a cake. Organizations cannot follow a recipe with the following ingredients: three consultants, six weeks of testing, two training classes, and a healthy dose of project management. Nor do projects bake for six months until complete, after which time everyone enjoys a delicious piece of cake. For all sorts of reasons, a well-conceived and well-run project may fail, whereas a horribly managed project may come in under budget, ahead of schedule, and do everything that the vendor promised at the onset." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Implementing new systems provides organizations with unique opportunities not only to improve their technologies, but to redefine and improve key business processes. Ultimately, for organizations to consider these new systems successes, the post-legacy environment must ensure that business processes, client end users, and systems work together." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Organizations cannot expect new systems to concurrently deliver the benefits of integration and decentralization. By definition, more of one means less of another. Decide in advance the acceptable trade-offs, and live by those decisions." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Organizations considering customizations should look carefully at their available options, ideally with the help of experienced business and technical consultants. It is imperative that they carefully consider the short- and long-term implications of these customizations, lest they be stuck with an unsustainable status quo and paint themselves into a corner." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Organizations face challenges of all kinds after activating their new systems. To be sure, these challenges are typically not as significant as those associated with going live. Still, executives and end users should never assume that system activation means that everyone is home free. Systems are hardly self-sufficient, and issues always appear." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Organizations often fail to understand that business processes do not exist in a vacuum; they must be viewed against the backdrop of the technology used to enable those processes. Systems and business processes are related in a symbiotic - but not causal - manner." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Pre-implementation, post-implementation, and ongoing data audits are invaluable tools for organizations. Used judiciously by knowledgeable and impartial resources, audits can detect, avoid, and minimize issues that can derail an implementation or cause a live system to fail. Rather than view them as superfluous expenses, organizations would be wise to conduct them at key points throughout the system’s life cycle."
"Some end users are so accustomed to seeing data in a certain way that they insist that new reports present the data in a manner identical to legacy reports. This is a problem: no two systems represent data in the same manner." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"The best managed project may fail, whereas a horribly managed project may come in under budget, ahead of schedule, and do everything that the vendor promised at the onset. In reality, however, organizations are unlikely to find themselves in one of these extreme scenarios. On a fundamental level, successfully activating and utilizing a new system is about minimizing risk from day one until the end of the project and beyond. The organization that can do this stands the best chance of averting failure."
"Understanding the causes of system failures may help organizations avoid them, although there are no guarantees." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)
"Data science is an iterative process. It starts with a hypothesis (or several hypotheses) about the system we’re studying, and then we analyze the information. The results allow us to reject our initial hypotheses and refine our understanding of the data. When working with thousands of fields and millions of rows, it’s important to develop intuitive ways to reject bad hypotheses quickly." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"It’s a mistake to think of data and data visualizations as static terms. They are the very antitheses of stasis." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"Just because data is visualized doesn’t necessarily mean that it is accurate, complete, or indicative of the right course of action. Exhibiting a healthy skepticism is almost always a good thing." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"Metadata serves as a strong and increasingly important complement to both structured and unstructured data. Even if you can easily visualize and interpret primary source data, it behooves you to also collect, analyze, and visualize its metadata. Incorporating metadata may very well enhance your understanding of the source data." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"The term linked data describes the practice of exposing, sharing, and connecting pieces of data, information, and knowledge on the semantic Web. Both humans and machines benefit when previously unconnected data is connected." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"There are myriad questions that we can ask from data today. As such, it’s impossible to write enough reports or design a functioning dashboard that takes into account every conceivable contingency and answers every possible question." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"To be sure, data doesn’t always need to be visualized, and many data visualizations just plain suck. Look around you. It’s not hard to find truly awful representations of information. Some work in concept but fail because they are too busy; they confuse people more than they convey information [...]. Visualization for the sake of visualization is unlikely to produce desired results - and this goes double in an era of Big Data. Bad is still bad, even and especially at a larger scale." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"Visual Organizations benefit from routinely visualizing many different types and sources of data. Doing so allows them to garner a better understanding of what’s happening and why. Equipped with this knowledge, employees are able to ask better questions and make better business decisions." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"We acquire more information through our visual system than we do through all our other senses combined. We understand things better and quicker when we see them." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"We are all becoming more comfortable with data. Data visualization is no longer just something we have to do at work. Increasingly, we want to do it as consumers and as citizens. Put simply, visualizing helps us understand what’s going on in our lives - and how to solve problems." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
"While critical, the arrival of Big Data is far from the only data-related trend to take root over the past decade. The arrival of Big Data is one of the key factors explaining the rise of the Visual Organization." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)
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