My first taste of the internet was when I had an Intel 386 based PC. I discovered that I can use my super-fast phone modem to access content that did not live on my hard drive. With the help of MS Windows 386, and a dial up connection to “CompuServe”, I was reading text base feeds and downloading GIF files to proudly display on my new EGA monitor. I was no longer constrained by content coming from floppy drives and my world started to move past playing “Leisure Suite Larry”. Back then, my view of the internet was completely focused on what content this medium could deliver to me. Content is the blood that flows through the internet’s veins.
Content is a general name we give to the pieces of information that we are trying to communicate. As the internet grew, the type of content that was available to be communicated evolved. From plain text to hypertext (HTML) to pictures, music and video, use of the internet as a medium for content has grown organically.
All internet content can be considered pieces of data. Conversely, data itself is content. Data elements as well as complete data sets can be sent from sender to receiver to enable our businesses to work more efficiently.
Now we have what is being called “The Internet of Things”. Computers and machines consume and provide data through the internet enabled to work more intelligently. This offers businesses advantage. To maximize the potential of this advantage, businesses must re-think how they look at data. Data becomes a commodity requiring a higher level of management.
- What can data do for us that it is not doing today?
- What data do we expose and to whom?
- What are the potentials for misuse?
- How do we expose the data and protect our intellectual property?
- How should it be organized?
- How can we store it and retrieve it optimally?
- What gains can be made through its use?
All of these questions call for data leadership and data governance. Both are needed to ensure optimal effective data use, and have responsibilities significant and specialized enough to warrant dedicated resources. Many believe it substantial enough to warrant a “C” level position. It is very common now to see businesses with “Chief Data Officers” who are well versed technically, but lean more towards the functional optimization of the data content of their organization.
How does data evolve? Understanding how data can become ready to be consumed by the “Internet of Things” is important for anyone developing a data value maximization strategy.
All data starts as a raw entity that is collected or created for singular business processes. As strategic use matures, data is presented in different ways that allow additional use cases to consume them.
- Shared Data – Data that is made accessible to applications other than its origin via APIs or Services.
- Open Data and Enabled Data – Data accessible as a service. Catalogued and exposed through an interface open to users to fetch and consume as needed.
- Machine Readable Data – Once data has been opened, it is formatted in a manner that allows it to be consumed programmatically.
- Open Standards Data – Standards are applied to the machine readable formats which facilitate consumption by entities or machines that may be agnostic of the data source.
- Linkable Data – Data accessible based on an “electronically accessible” catalog of metadata describing the data’s attributes.
- Linked Data – Data sets from disparate sources that together present a desired result
Data that is linkable is empowered by its ability to join with other linkable data to create much richer, more robust and more directed results for programs that may be consuming them. This translates to better decision making and a greater competitive edge. Here is a link to a video that explains it nicely: https://www.youtube.com/watch?v=uju4wT9uBIA
Lack of data leadership can result in disparate methods of data access. Organizations may assume that data and I.T. fall under the same umbrella. This model can focus on the mechanics of storing and moving data, but is too distant from the business content of the data. So, each element is met with individual solutions to meet the specific requirements presented by a discreet requestor. This results in a lack of standardization necessary to orchestrate an overall strategy. Additionally, it contributes to the problem of I.T. becoming a cost center rather than a value center…too many solutions with different administrative resources required to keep them going.
Sound Data Leadership is foundational to establishing a position that is prepared to evolve…both for “The Internet of Things” and whatever may come next, while at the same time governing an overall view of the return that data can and will provide.