Jim Harris says learn the lineage of the data that fed the analysis before you get dazzled by visualizations or algorithms.
Get faster value out of your data by empowering business users to work with data on their own.
As the application stack supporting big data has matured, it has demonstrated the feasibility of ingesting, persisting and analyzing potentially massive data sets that originate both within and outside of conventional enterprise boundaries. But what does this mean from a data governance perspective?
In the previous three blogs in this series, we talked about what metadata can be available from source systems, transformation and movement, and operational usage. For this final blog in the series, I want to discuss the analytical usage of metadata. Let’s set up the scenario. Let's imagine I'm a
As I discussed in the first two blogs of this series, metadata is useful in a variety of ways. Its importance starts at the source system, and continues through the data movement and transformation processes and into operations. Operational metadata, in particular, gives us information about the execution and completion
In the first blog of this four-part series, we discussed traditional data management and how we can apply these principles to our big data platforms. We also discussed how metadata can help bridge the gap of understanding the data as we move to newer technologies. Part 2 will focus on
Traditional data management includes all the disciplines required to manage data resources. More specifically, data management usually includes: Architectures that encompass data, process and infrastructure. Policies and governance surrounding data privacy, data quality and data usage. Procedures that manage a data life cycle from creation of the data to sunset