The V’s of Data and How They Tie to Process
There are five attributes typically associated with data – Volume, Velocity, Variety, Value and Veracity. Each relates to important considerations for the collection and use of data, and as data scientists, the use of data is paramount! These same five attributes can be used to direct process evaluations, and in combination with data, help inform innovative opportunities outside the typical data and process paradigms.
Volume. Data asks us to evaluate quantities: how many transactions or records; how many tables or files; how much space is needed to store the data. Broken down to this level, it becomes quite obvious how volume equates to the evaluation of processes – transactions and records are the result of a process. When volume is high and processes are inefficient, the result is costly operations. Using volume as a guide for process evaluation helps us gain valuable insight into where we should start with process evaluation for efficiency and effectiveness gains.
Velocity. The rate at which data is generated and our ability to capture, analyze and use that data is a key component to competitive advantage. With processes, the velocity of data translates to leading and lagging indicators and our ability to capitalize on those indicators in a timely manner. Velocity from a process perspective also contributes to efficiency and effectiveness evaluations – if a process moves with great speed but is broken, we simply increase how quickly the process fails and how costly that failure may be for an organization!
Variety. The range of types and sources of data, or data variability, can create major challenges as we strive to contextualize data and create useful information – but it doesn’t suggest there is a problem. Process variability, on the other hand, suggests instability and opportunity for improvement. When working to improve a process, it’s the outliers that present the low-hanging fruit, and once removed, promote an ability to overhaul processes for exponential returns.
Value. By adding context to data, organizations can begin to develop information that illuminates insights and promotes added business value…think Amazon, who has figured out how to monetize their data and use it to drive growth! We benefit from the same monetization and growth opportunities when contextualizing data for process purposes. When analyzing processes, we evaluate three dimensions related to value: cost, quality and timeliness. These dimensions standardize the types of metrics we consider, and help us focus our statistical and correlative analysis for the greatest return.
Veracity. With data, veracity equates to the correctness and accuracy of information. Data Quality, Information Governance, Metadata Management – all are foundational components that support the veracity of data and enable its usefulness in organizations. In process improvement, veracity ensures our process metrics are repeatable and reproducible. Process measures that are repeatable and reproducible help ensure statistically valid tests of change and promote control mechanisms that support sustained improvements.
By considering the five Vs of data, organizations are able to build and support business intelligence ecosystems that optimize the use of information for their benefit. By using the same five Vs in process evaluation, we are able to parallel that optimization as we strive to build effective and efficient processes.