Data is everywhere. Data is used to back up assumptions presented as facts. We look to data to create information that we base business, personal and community decisions on. Not only are the absolutes of the numbers important, but how they were obtained, how they are analyzed, and what assumptions are made are critical to assessing the value of the data. This creates uncertainty and even applying the scientific method is no basis for comfort.
One such example is “p-hacking,” in which researchers test their data against many hypotheses and only report those that have statistically significant results. Why?
“The current system has done too much to reward results,” says Joseph Hilgard, a postdoctoral research fellow at the Annenberg Public Policy Center. “This causes a conflict of interest: The scientist is in charge of evaluating the hypothesis, but the scientist also desperately wants the hypothesis to be true.”1
Also consider the case where data collection algorithms are just not properly designed or applied. Just as in every facet of our world, truly groundbreaking findings just don’t happen as often as researchers would like. The vast majority of what we see as innovation is really improving on already well-understood or established processes and technologies. What that means is an opportunity to “over” interpret results. Consider:
“We know that as much as 30 percent of the most influential original medical research papers later turn out to be wrong or exaggerated.”2
But this is science. That stuff means something right? As I said at the beginning, data is everywhere. In business, multiple roles from all areas of a company are concerned with data. They include IT, data management, manufacturing, R&D, marketing, customer service, sales, operations and more.
Let’s take the example of supply chain management. Data quality impacts the entire journey. On the freight management side, if a container is only ¼” different in height than the bill of lading says it is, 1,000 fewer cases per truckload will get shipped consisting of 20 fewer pallets per truck resulting in needing 6 more trucks!3
Taking it down a level, if quality and trust in data is a significant concern in scientific research and in moving goods through a supply chain, what about in our little world of ITAM and ITSM? All we are concerned with is simply how many assets there are, where they are, and who is using them.
IT data quality underpins every element of running a business. In discussions around CMDB strategies with Gartner, a key concern of IT leaders was data quality in over 60 percent of those conversations.4 Other research has shown 48 percent of respondents spent 15 hours or more per week reconciling data accuracy issues, largely because they are using multiple data collection systems. Couple this with the situation that the number one ITAM tool remains Excel and this compounds the problem. You are now faced with static data where service delivery is dynamic and expected to reflect the current state of a user’s profile or a data center configuration.
It is apparent that data quality issues have business, social and personal repercussions and can lead to mistrust of reports, results and predictions. From an ITAM and ITSM perspective, they impact cost, efficiency, governance and security. Improving this data to the point it can be considered information requires people, process and technology to work together. It is important to attempt to eliminate the siloed approach that has been so common for decades. If you would like to learn more about how this can be accomplished, please join the upcoming webinar, “Why Can’t I Trust You? IT Asset Data Quality Pitfalls and Opportunities” on the 9th of November at 2:00 p.m. Eastern/1:00 p.m. Central/11:00 a.m. Pacific.