Now that the data has been identified and located, the next step is to go get it.
Step Three: Gathering the Data
There are actually three different sub-activities required to gather the data that was identified in step two. First, a location has to be identified for where the data will be stored—ideally a secure, durable environment where the data can be safely maintained and access can be carefully controlled. Next, processes have to be put in place to capture the data from the source system and place it in the identified location. Finally, the data has to be effectively organized so it can be accurately identified, and in many cases it will need to be “cleaned” or “transformed” so that it can be properly analyzed. This step in the data insight process, while not necessarily challenging from an analytical or cognitive standpoint, typically requires the greatest amount of effort if it is to be performed effectively.
Determining where the data is to be stored after it has been identified and accessed requires a great deal of forethought and planning. A district may already have a functioning data warehouse of some sort, which might make this process easy. However, traditional data warehouses can be expensive to build, complicated to maintain, and difficult to adapt to changing needs. Conversely, some districts end up with data being stored all over the place—on different desktop and laptop computers, or on storage attached to servers in the data center. Having data distributed across different platforms can introduce security risks, and it can also make it difficult to do the necessary analysis. But there are now emerging some solutions for data storage and management that can allow a district to safely, securely, and inexpensively create a data storage environment “in the cloud” that is much easier and quicker to implement and enhance. I will discuss some of these new models in a future posting—if you want a head-start, read this article.
While data can help users answer questions, its real power exists when advanced analytics are used to help make decisions that enable people to perform better and achieve more.
Next time, we’ll look at how to conduct the analysis that turns the data into actionable insight.
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