One of the requirements of building and operating an IT infrastructure in a highly regulated industry (such as the pharmaceutical industry, which is regulated by the FDA) is to qualify, or validate the servers, network, and storage when they are being built. Once built, any changes to the infrastructure should undergo a change control procedure.

Building the infrastructure and making changes to it should undergo verification. They should also be documented so that they can be easily managed and traced. These activities are really not that different from the best practices guide in operating an IT infrastructure, or even from the ITIL processes.

FDA does not dictate how to perform IT infrastructure qualification or validation, as long as you have documented reasonable procedures.

The problem is that some companies overdo validation and change control processes. The common problems I’ve seen are: (1) too many signatures required to make a change, (2) no automated procedure to perform the documentation – many still use papers to route documents (3) and finally, the people who perform the checks and balances sometimes do not understand the technology.

The result is that IT personnel get overwhelmed with paperwork and bureaucracy. This discourages them to make critical changes to the infrastructure such as performing security patches on time. This also leads to the relunctance of IT personnel to implement newer or leading edge technologies into their infrastructure.

Fortunately, the International Society for Pharmaceutical Engineering (ISPE) has published a Good Automated Manufacturing Practice (GAMP) guidance on IT Infrastructure Control and Compliance. Companies can create their own IT infrastructure qualification program and procedures based on the GAMP guidance document. They should be simple but comprehensive enough to cover all the bases. It is also important that these procedures be periodically reviewed and streamlined to achieve an optimized procedure.

There is too much hype on big data these days, promising the next big revolution in information technology which will change the way we do business. It purports to have a big impact on economy, science, and society at large. In fact, big data right now is at the “peak of inflated expectations” on the Gartner technology hype cycle.

Big data “refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it.” It answers questions that are sometimes not so obvious.

Big data definitely has tremendous potential. After all the hype has subsided, entities that do not take advantage of its power will be left out. In fact big data is already being used by technology companies such as Google, Amazon, Facebook, and many other companies. IT vendors such as Oracle, EMC, and IBM started offering big data solutions for companies and enterprises.

There are three drivers that is making big data possible:

First, a robust and cheap IT infrastructure – powerful server platforms that crunch data, advanced storage systems that store huge amount of data, and ubiquitous network – Wifi, 4G, fiber, etc.

Second, the explosion of data from mobile devices, social networks, web searches, sensors, and data from many different devices.

Lastly, the proliferation of powerful analytics and data mining tools suited for big data, such as Hadoop, MapReduce, NoSQL, and many other software yet to be created. These tools will only get better and better.

I recently read the book entitled “Big Data: A Revolution That Will Transform How We Live, Work, and Think” by Viktor Mayer-Schönberger and Kenneth Cukier.

The book is spot on its predictions. With big data, there will be yet another paradigm shift on how we understand the world. With big data, “what” is more important than “why”. Big data is also the processing of complete data, not just a sampling of data. It also means accepting less than perfect accurate result.

The book also talks about the dark side of big data – such as the loss of privacy. It also talks about how big data predictions can be used to police and punish individuals, and how organizations may blindly defer to what the data says without understanding its limitations.

I highly recommend the book to those who like to fully understand big data and its implications.

I am a fan of open source tools. The Internet, as we know it today, will not exist if not for the open source movement. We owe this to the countless architects and developers who dedicated their time and effort to write open source software.

Enterprise IT departments can also take advantage of open source software. Numerous companies have been using them for years. One particular area where they can be used is network and server monitoring.

There are a lot of open source network monitoring tools out there. Leading the pack are Nagios, Zabbix, and Cacti. My favorite tool though is OpenNMS. I particularly like it because it is very easy to setup and administer. It can automatically discover your nodes on the network. There were very few tweaks when I first set it up. It provides simple event and notification via email or pager. In addition, its web-based management interface interface is very easy to use.

I have been using OpenNMS for several years now and it has been running rock solid. I definitely recommend OpenNMS for IT departments who do not want to pay a hefty price to monitor their network and servers.