There is a saying in management, “you cannot manage what you do not measure.” On first pass, this sounds straight-forward. The quote is most often attributed to two individuals W. Edwards Deming, the statistician and quality-control expert credited with having launched the Total Quality Management (TQM) movement, and Peter Drucker, a very well-known management consultant 1. Despite the simplicity, the statement is misleading. Gathering data for data’s sake provides zero value. Data analysis with no vision is like being on a boat set adrift. With IOT device proliferation, cheap computation and storage, there is no easier time than now for organizations to take measurements and collect data. This blog post addresses the importance of having a plan for the data you capture to obtain valuable results for your organization.
The human mind is amazing. We are incredibly good at using our intuition to make decisions with incomplete information. The brilliant minds working on machine intelligence are reminded daily how amazing our talents are. A mindless task like walking through a doorway is a ridiculously hard problem for a machine to solve. Now consider skills and more human elements like empathy, compassion, or friendship. Businesses and organizations are managed by people that must interact and coordinate with one another. We have limitations though, our memories are fallible, we are susceptible to biases, and we experience the world with limited bandwidth on many dimensions. Current data systems can track an incomprehensible quantity of information, spot unintuitive correlations, and operate with high levels of information throughput. It is logical to have technology track and build datasets that are beyond our abilities. There are definite areas where human and current machine competencies complement each other.
Today we have the ability to capture unimaginable amounts of data, IOT sensors costs have been falling exponentially and we are all familiar with Moore’s Law for computation. The value of data is not the information that it contains, but the story it provides informing the ultimate decision to take action.
At the end of the day (at least for now), businesses are managed by people. We all make decisions based on our intuition from the story generated by the data the organization has captured, processed, and analyzed. Like a physical supply chain, it is the system that provides value, not the parts. What does this all mean? Data must be linked to a future decision to act. What is the vision for the organization? What areas of the organization does the vision impact? What parts of operations or processes can be optimized? How is the data captured, processed, and analysed? Does my organization have the data science capabilities internally? What is an effective baseline for which future progress can be judged? What governance structures need to be established to manage information flow, approvals, and reporting?
For instance, a resource development firm in the energy industry might seek out environmental data to monitor impacts during construction and operation of a remote facility. The vision could be to ensure regulatory compliance to prevent fines and improve social acceptance. Data could be imagery, site samples, inspection/audit reports, construction event logs, weather, etc. The data could be prepared as reports and dashboards for operators, field managers, regulators, specialist contractors, and executives. Governance could be structured by executives by setting quarterly performance targets. Operators and field managers are then responsible for adapting processes to meet these targets using the established data supply chain for feedback and validation.
To stay in the game, firms have found success using data driven decision making. However, invisible to the outsider is the time and effort invested to not only to build this sea of data, but also to establish the supply chain linking a vision, data management processes, and governance to a decision-making process.