IT Productivity – A new primer

Let’s imagine a possible situation. You are a new CIO in a new company and you finally figured and implemented where all the pieces and players now go. Excellent. Good job. Now how do you go about measuring IT productivity?

Back in the ancient history days of 5 years ago you (CIO) might get away with a ‘No news is good news’ which meant you could apply a sort of laissez-faire approach to team management and focus your CIO endeavors and calendar in a more project (let’s call it strategic) oriented venue. In the current and modern era this approach might put a new CIO in dangerous and possibly cold waters. 

When we refer to CIO level IT team productivity measurement we imply two cardinal pieces: internal IT team productivity measures and enhancements and second but much more complex and just as critical is how the IT organization lifts overall business opportunity.

For today’s post we will cover the internal IT measurement only. Let’s first take the case of a Big Data team as large amounts of investment money are poured (and will continue to be) in this area. Big Data is about distributed data and knowledge such that processes and products can deliver improved value for customers. Hence efforts in this area are not just about data stream quality but velocity as well. Executives and companies will need to shift perspectives into data streams and not reports or data sets. As such I suggest three basic metric areas to help guide productivity measures:

• Business team data access velocity
• How long does it take for business teams to review data streams after they are produced by the data science teams
• This is a crucial measure especially when related to the diversity data axis. This means that CIO must clearly understand if there are any bottlenecks early on because potential time lags will increase exponentially as the Big Data initiatives grow in sophistication and data sources. This can ultimately lead to failure of the Big Data initiatives
• Data Modeling and preparation velocity
• How long does it take data science teams to correctly model and prepare the data for analysis?
• Does the data modeling process take days, weeks or longer? How long is the competition believed to take? This is another crucial measure that has a direct influence in data access velocity and has to be counterbalanced to the overall business team expectations
• CIOs should take a close look at the data analysis tool set and data stream origins to ensure the tools in use are adequate and not hindering the data model stage
• How long does it take for the business organization to produce changes or insights from the available data streams?
• How useful is the data once produced? Not all data insights will produce revolutionary products or services. Some data streams will fall into the ‘I knew it already’ category and this is fine for a CIO
• How long does it take for business teams to access and absorb data insights? These measures are highly relevant but need to be reviewed from a senior management or executive level perspective to avoid unproductive internal competitive behaviors.

CIOs should keep in mind that Big Data is about small but incremental shifts over time. Getting a good handle on the above metrics will help ensure success in your Big Data team endeavors.

More information and articles in my blog at: International CIO