It happens every few years and it’s happening again. A new technology comes along and a significant segment of the IT and business community want to toss out everything we’ve learned over the past 60 years and start fresh. We “discover” that we’ve been wasting time applying unnecessary rigor and bureaucracy to our projects. No longer should we have to wait three to six months or longer to deliver technical solutions to the business. We can turn these things around in three to six days or even less.
In the mid 1990’s, I was part of a team that developed a “pilot” object-oriented, client-server (remember when these were the hot buzzwords?) application to replenish raw materials for a manufacturing function. We were upending the traditional mainframe world by delivering a solution quickly and iteratively with a small team. When the end users started using the application in real life, it was clear they were going to rely on it to do their jobs every day. Wait, was this a pilot or…? I would come into work in the morning, walk into a special room that housed the application and database servers, check the logs, note any errors, make whatever fixes needed to be made, re-run jobs, and so on.
It wasn’t long before this work began to interfere with my next project, and the end users became frustrated when I wasn’t available to fix problems quickly. It took us a while and several conversations with operations to determine that “production” didn’t just mean “the mainframe”. “Production” meant that people were relying on the solution on a regular basis to do their jobs. So we backtracked and started talking about what kind of availability guarantees we could make, how backup and recovery should work, how we could transition monitoring and maintenance to operations, and so on. In other words, we realized what we needed was a traditional IT project that just happened to leverage newer technologies.
This same scenario is happening today with Hadoop and related tools. When I visit client organizations, a frightening number will have at least one serious person saying something like, “I really don’t think ‘data warehousing’ makes sense any more. It takes too long. We should put all our data in Hadoop and let our end users access whatever they want.” It is indeed a great idea to establish an environment that enables exploration and quick-turnaround analysis against raw data and production data. But to position this approach as a core data and analytics strategy is nothing short of professional malpractice.
The problem is that people are confusing experimentation with IT projects. There is a place for both, and there always has been. Experimentation (or discovery, research, ad-hoc analysis, or whatever term you wish to use) should have lightweight processes and data management practices – it requires prioritization of analysis activity, security and privacy policies and implementation, some understanding of available data, and so on, but it should not be overburdened with the typical rigor required of projects that are building solutions destined for production. Once a prototype is ready to be used on a regular basis for important business functions, that solution should be built through a rigorous IT project leveraging an appropriate – dare I say it – solution development life cycle (SDLC), along with a comprehensive enterprise architecture plan including, yes, a data warehouse that provides integrated, shared, and trusted production data.
An experimental prototype should never be “promoted” to a production environment. That’s what a project is for. Experimentation can be accomplished with Hadoop, relational technology, Microsoft Office, and many other technologies. These same technologies can also be used for production solutions. So, it’s not that “things are done differently and more quickly in Hadoop”. Instead, it’s more appropriate to say that experimentation is different than an IT project, regardless of technology.
Yes, we should do everything we can to reduce unnecessary paperwork and to speed up delivery using proper objective setting, scoping, and agile development techniques. But that is different than abandoning rigor altogether. In fact, using newer technologies in IT projects requires more attention to detail, not less, because we have to take the maturity of the technology into consideration. Can it meet the service level needs of a particular solution? This needs to be asked and examined formally within the project.
Attempting to build production solutions using ad-hoc, experimental data preparation and analysis techniques is like building a modern skyscraper with a grass hut mentality. It just doesn’t make any sense.
Guest Blogger Kevin Lewis is responsible for Teradata’s Strategy and Governance practice. Prior to joining Teradata in 2007, he was responsible for initiating and leading enterprise data management at Publix Super Markets. Since joining Teradata, he has advised dozens of clients in all major industries.
Latest posts by Guest Blogger (see all)
- Data Modeling Requires Detailed Mapping — Learn Why - November 30, 2015
- The Revenge of the Schema: Why Structure Is Crucial to Success with Big Data - November 16, 2015
- A Vision for an Analytic Infrastructure - October 12, 2015