Celgene’s Perspective: How a Mixed Martial Arts Fighter Would Approach Data Analytics
By James Royster, Director, Commercial Analytics, Celgene
Mixed Martial Arts (MMA) combines striking, wrestling and other fighting techniques into a unified sport. Every martial art and fighting technique has its strengths and strategic advantages. Boxing is known for punching but also provides footwork, guard position and head movement. Wrestling relies upon takedowns. Karate features striking techniques such as kicking. MMA is a hybrid of all of these (and many more) drawing upon each mode of combat as needed for a given competitive situation. If an MMA athlete competed against a boxer or karate expert, the mixed martial artist would clearly have an unfair advantage. MMA’s real strength is its versatility and its ability to absorb new methods.
DataOps is the mixed martial arts of data analytics. It is a hybrid of Agile Development, DevOps and the statistical process drawn from lean manufacturing. Like MMA, the strength of DataOps is its readiness to evolve and incorporate new techniques that improve the quality, reliability, and flexibility of the data analytics pipeline. DataOps gives data analytics professionals an unfair advantage over those who are doing things the old way — using hope, heroism or just going slowly in order to cope with the rapidly changing requirements of the competitive marketplace.
Agile development has revolutionized the speed of software development over the past twenty years. Before Agile, development teams spent long periods of time developing specifications that would be obsolete long before deployment. Agile breaks down software development into small increments, which are defined and implemented quickly. This allows a development team to become much more responsive to customer requirements and ultimately accelerates time to market.
Data analytics shares much in common with software development. Conceptually, the data analytics pipeline takes raw data, passes it through a series of steps and turns it into actionable information. Files, such as scripts, code, algorithms, configuration files, and many others, drive each processing stage. These files, taken as a whole, are essentially just code. As a coding endeavor, data analytics has the opportunity to improve implementation speeds by an order of magnitude using techniques like Agile development. DevOps offers additional opportunity for improvement.
The difficulty of procuring and provisioning physical IT resources has often hampered data analytics. In the software development domain, leading-edge companies are turning to DevOps, which utilizes cloud resources instead of on-site servers and storage. This allows developers to procure and provision IT resources nearly instantly and with much greater control over the run-time environment. This improves flexibility and yields another order of magnitude improvement in the speed of deploying features to the user base.
DataOps also incorporates lean manufacturing techniques into data analytics through the use of statistical process controls. In manufacturing, tests are used to monitor and improve the quality of factory-floor processes. In DataOps, tests are used to verify the inputs, business logic, and outputs at each stage of the data analytics pipeline. The data analytics professional adds a test each time a change is made. The suite of tests grows over time until it eventually becomes quite substantial. The tests validate the quality and integrity of a new release when a feature set is released to the user base. Tests allow the data analytics professional to quickly verify a release, substantially reducing the amount of time spent on deploying updates.
Statistical process controls also monitor data, alerting the data team to an unexpected variance. This may require updates to the business logic built into the tests, or it might lead data scientists down new paths of inquiry or experimentation. The test alerts can be a starting point for creative discovery.
The combination of Agile development, DevOps, and statistical process controls gives DataOps the strategic tools to reduce time to insight, improve the quality of analytics, promote reuse and refactoring and lower the marginal cost of asking the next business question. Like mixed martial arts, DataOps draws its effectiveness from an eclectic mix of tools and techniques drawn from other fields and domains. Individually, each of these techniques is valuable, but together they form an effective new approach, which can take your data analytics to the next level.
