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In one Dutch studynd were told they would be watching a new high-definition program. Afterward, the subjects said they found the sharper, more colorful television to be a superior experience to standard programming.”No surprise there, right? After all, a high-definition television is expected to produce a high-quality image.“What they didn’t know,” McRaney continued, “was they were actually watching a standard-definition image. The expectation of seeing a better quality image led them to believe they had. Recent research shows about 18 percent of people who own high-definition televisions are still watching standard-definition programming on the set, but think they are getting a better picture.”I couldn’t help but wonder if establishing an expectation of delivering high-quality data could lead business users to believe that, for example, the data quality of the data warehouse met or exceeded their expectations. Could business users actually be fooled by altering their expectations about data quality? Wouldn’t their experience of using the data eventually reveal the truth?Retailers expertly manipulate us with presentation, price, good marketing, and great service in order to create an expectation of quality in the things we buy. “The actual experience is less important,” McRaney explained. senses. In psychology, true objectivity is pretty much considered to be impossible. Memories, emotions, conditioning, and all sorts of other mental flotsam taint every new experience you gain. In addition to all this, your expectations powerfully influence the final vote in your head over what you believe to be reality. Your expectations are the horse,ut when your expectations determine your direction, you shouldn’t be surprised by the journey you experience.If you find it difficult to imagine a positive expectation causing people to overlook poor quality in their experience with data, how about the opposite? I have seen the first impression of a data warehouse initially affected by poor data quality create a negative expectation causing people to overlook the improved data quality in their subsequent experiences with the data warehouse. Once people expect to experience poor data quality when using it, people stop trusting, and stop using, the data warehouse.Data warehousing is only one example of howparaphrasing of an old idea: Science without philosophy is blind; Philosophy without science is empty; Data needs both science and philosophy.“A philosopher’s job is to find out things about the world by thinking rather than observing,”the philosopher Bertrand Russellonce said. One could say a scientist’s job is to find out things about the world by observing and experimenting. In fact, Russell observed that “the most essential characteristic of scientific technique is that it proceeds from experiment, not from tradition.”Russell also said that “science is what we know, and philosophy is what we don’t know.” However, Stuart Firestein, in his bookIgnorance: How It Drives Science, explained “there is no surer way to screw up an experiment than to be certain of its outcome.”Although it seems it would make more sense for science to be driven by what we know, by facts, “working scientists,” according to Firestein, “don’t get bogged down in the factual swamp because they don’t care that much for facts. It’s not that they discount or ignore them, but rather that they don’t see them as an end in themselves. They don’t stop at the facts; they begin there, right beyond the facts, where the facts run out. Facts are selected for the questions they create, for the ignorance they point to.”In this sense, philosophy and science work together to help us think about and experiment with what we do and don’t know.Some might argue that while anyone can be a philosopher, being a scientist requires more rigorous training. A commonly stated requirement in the era of big data is to hire data scientists, but this begs the question: Is data science only for data scientists?“Clearly what we need,” Firestein explained, “is a crash course in citizen science—a way to humanize science so that it can be both appreciated and judged by an informed citizenry. Aggregating facts is useless if you don’t have a context to interpret them.”I would argue that clearly what organizations need is a crash course in data science—a way to humanize data science so that it can be both appreciated and judged by an informed business community. Big data is useless if you don’t have a business context to interpret it. Firestein also made great points about science not being exclusionary (i.e., not just for scientists). Just as you can enjoy watching sports without being a professional athlete and you can appreciate music without being a professional musician, you can—and should—learn the basics of data science (especially statistics) without being a professional data scientist.In order to truly deliver business value to organizations, data science can not be exclusionary. This doesn’t mean you shouldn’t hire data scientists. In many cases, you will need the expertise of professional data scientists. However, you will not be able to direct them or interpret their findings without understanding the basics, what could be called the philosophy of data science.Some might argue that philosophy only reigns in the absence of data, while science reigns in the analysis of data. Although in the era of big data there seems to be fewer areas truly absent of data, a conceptual bridge still remains between analysis and insight, the crossing of which is itself a philosophical exercise. So, an endless oscillation persists between science and philosophy, which is why science without philosophy is blind, and philosophy without science is empty. Data needs both science and philosophy., I discuss the practical aspects of doing data governance with John Ladley, the author of the excellent bookData Governance: How to Design, Deploy and Sustain an Effective Data Governance Program. Our discussion includes understanding the difference and relationship between data governance and factors for data governance.John Ladley is a business technology thought leader with 30 years of experience in improving organizations through the successful implementation of information systems. He is a recognized authority in the use and implementation of business intelligence and enterprise information management (EIM).John Ladley is the author of Making EIM Work for Business, and frequently writes and speaks on a variety of technology and enterprise information management topics. His information management experience is balanced between strategic technology planning, project management, and, most important, the practical application of technology to business problems.As development economist William Easterly explained, “A Planner thinks he already knows the answer; A Searcher admits he doesn’t know the answers in advance. A Planner believes outsiders know enough to impose solutions; A Searcher believes only insiders have enough knowledge to find solutions, and that most solutions must be homegrown.”I made a similar point in my post Data Governance and the Adjacent Possible. Change management efforts are resisted when they impose new methods by emphasizing bad business and technical processes, as well as bad data-related employee behaviors, while ignoring unheralded processes and employees whose existing methods are preventing other problems from happening.Demonstrating that some data governance policies reflect existing best practices reduces resistance to change by showing that the search for improvement was not limited to only searching for what is currently going wrong.This is why data governance needs Searchers, not Planners. A Planner thinks a framework provides all the answers; A Searcher knows a data governance framework is like a jigsaw puzzle. A Planner believes outsiders (authorized by executive management) know enough to impose data governance solutions; A Searcher believes only insiders (united by collaboration) have enough knowledge to find the ingredients for data governance solutions, and a true commitment to change always comes from within.