This editorial was originally published on Sept 25, 2014. It is being republished as Steve is out of the office.

How valuable is your data? It's a good question, and certainly the type of data your organization has along with the business in which you are engaged will make your data more or less valuable. More and more we find the differentiation between companies is in the way they collect, manage, and use the data available to them. So much in business is based on guesses, but more and more the guesses have some basis in data. We are starting to see those who make decisions in business feel some need to justify or support their choices with data.

Is data the new oil? Oil was arguably the most important commodity of the twentieth century (and perhaps still is). The SQLRockstar wrote a piece with that same title, with the idea that knowing more about how valuable data can be will make you more successful in business. The post is based on the review of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, and talks about some of the challenges of using data to make decisions.

I certainly believe in the power of data, and that more data often gives us more insight into how the world works, as well as allowing us to draw some inferences about the future. Not necessarily better insight, but certainly more. I do think that computer extrapolation of patterns to the future is vastly overrated as most of our algorithms are far too simple, using too little data and discounting the increasing effects of small variables as scale increases. In short, I don't think we're anywhere close to a Foundation-like computer that can help us predict the success of new products, much less the future of a country.

However I think that using analytics to make small decisions, and help guide our directions is important. We will still need humans that apply their internal supercomputers to interpret data, and continue to evolve the algorithms, and I hope that more and more of you are gaining deeper industry insight in your particular field. After all, many of us data professionals will be needed to help guide analysts in gathering, transforming, interpreting, and displaying data in ways that allows us to make decisions with more confidence.