Metadata is “data about data.”1 There are two metadata types: structural metadata, about the design and specification of data structures or “data about the containers of data;” and descriptive metadata about individual instances of application data or the data content.

For example, a picture (data) is comprised of pixels, ink, and creation date. This would be considered a picture’s “metadata” — the data that composes the data.

Now let’s think of metadata in relation to people. Sara (the data) might previously have had metadata that looked like this: female, born 7/18/89, 5’8”, 120 lbs, brown hair, blue eyes, lives in Chicago, and graduated from Northwestern.

Today, due to multiple platforms of technology, Sara’s metadata might look like this: female, born 7/18/89, 5’8”, 120 lbs, brown hair, blue eyes, lives in Chicago, graduated from Northwestern, favorite color is green, loves chocolate, has three good friends (one whom she is upset with right now), mostly wears jeans instead of dresses, colors her hair frequently, is considering an MBA, recently purchased a Labrador Retriever puppy, commutes by train (except for last Thursday when she got a ride from a coworker), and has had four boyfriends within the last three years. Oh, and she currently has a tooth ache.

One form of Big Data is the accumulation of multiple levels of data from different sources that identify a person’s purchasing profile within the consumer industry. Predictable purchasing habits of a single individual allow us to develop direct advertising of consumer products to that said individual. This is an example of the power unleashed today by the CIO to drive profits.


Financial industry. The financial industry has probably been using Big Data techniques the longest of any other industry. Originally, metadata was analyzed to evaluate trends concerning fraud and high risk customers. From there, metadata was used to develop purchasing profiles and discretionary spending habits. Today, algorithms are designed to trigger trades based upon predesigned data points previously created by a programmer. The applications that the financial industry use are based upon customer driven benefits. While it previously took a week to recognize if a check went through, now it only takes seconds after a purchase to see the new balance.

Retail industry. The first week of March is when the retail industry’s annual reports come out identifying gains and losses. This year Target, Kohl’s, and Costco showed gains and JCPenny and Sears reported losses. While advertising and merchandising plays a big role in the retail industry’s success, technology is now a vital factor in driving profits. Here are two ways that technology is playing key roles concerning profits:

  • Electronic price tag (EPT). EPT adjusts pricing based upon product popularity and inventory. If a product is selling well the price may go up to further generate profit. If a product is not selling well and has been on the shelf for a season, the price is lowered to move the product. While many companies will create an outlet store to move stale product that hasn’t sold, EPT users only have to adjust their pricing in their existing store. This eliminates the need to open an outlet store.
  • Customer profiling. In many cases a retail company will buy databases or trade information based upon purchasing habits they receive off of credit cards. This is the concept that we previously mentioned concerning metadata.


While it is not the objective of a municipality to generate profits, it is their objective to generate revenue by attracting businesses and households to their city. Therefore, when we look at technology drivers that generate tax revenue, the investment in technology within cities is based upon several key elements:

  • Mobility applications. Including transportation systems, trains-bus systems, parking apps, traffic congestion, and property/local tax information.
  • Green energy technologies. Including construction materials, smart grids, alternative energy resources, and incentives for new building technology driven infrastructure.
  • Safety technologies. Streetlights that offer emergency call buttons and connect to water/electrical grid monitoring. Emergency response infrastructure including city-wide wifi and weather alerts.
  • City maintenance technologies. Including solar compactible waste trashcans, street light sensors (and cameras), GPS snow removal routing, rat disposal via data analytics based upon migration, etc.

While there are several technologies that are created to help the City CIO, the basis of a city’s IT strategy is created around six pillars: mobility, people, economy, environment, living, and governance.


As a majority of readers know, developing a data center facility program is the first step in building a new data center. Where the programming phase previously encompassed questions around tier infrastructure, space requirements, rack layouts, etc., the new approach to programming encompasses the CIO objectives in a much different manner. Today’s questions when developing a new data center include additional topics such as:

  • What applications are being considered for migrating to the cloud?
  • re there any new mobile applications and what affect do they have on racks and load?
  • What is the tiering or importance of critical to non-critical applications?
  • What is the migration strategy into the new data center and how does it affect step load functions?
  • What are the limitations of the existing network, and what is the program to update the existing network?

While these are only a few of the questions for the CIO, the importance of evaluating growth and infrastructure from the applications level is important. It is the applications and benefits that they create that drive profit, and thus become critical in building a new data center.

Works Cited