5 Levels of Data Infrastructure Management
At this level, an organization may function, but without any formal processes or systematic management. Data infrastructure management is left to the whims of individuals, who carry relevant knowledge and know-how in their heads. Documentation of individual duties does not exist, and professionals simply go about duties such as backing up data, monitoring networks and administering patches to servers. Informal networks seen at this level lead to inconsistent performances across various systems.
This stage shows some semblance of governance, which includes writing down standard operating procedures and putting controls in place to better manage workflow within its data environment. This may include deployment of a repository, in which data professionals are required to document changes or processes they have completed. At the enforced stage, stronger management takes hold, and data professionals gain a greater focus on performing tasks in a more systemized fashion, doing what is needed versus what they want to do at that moment. A greater sense of teamwork begins to evolve at this stage.
Here, processes are established to handle various aspects of data management and automation is introduced. Data management becomes less dependent on individual discretion and is more likely to take advantage of automated or systematic approaches. In addition, standards begin to get established across the various data environments, providing a more cohesive approach for assuring the viability of enterprise data sources. The organization may even establish a data warehouse or metadata repository at this stage to more effectively collect data from across the enterprise in a more consistent fashion.
In the actualized stage, organizations are able to start getting more creative with their data. Data can be extracted from across the enterprise, as well as from outside sources, combined to create useful information, and applied to create new knowledge and new value to the business. No longer is data confined to silos, and all data management issues are addressed on a systematic basis. As an organization moves into the actualized stage, it is able to explore, using data assets as leverage in new and higher-value ways, as its underlying data infrastructure management is managed in a systematic and highly automated way.
At last, the organization reaches the pinnacle of development, where it can devote all its resources to high-level strategic initiatives, rather than administrative issues. At peak performance, it no longer needs to spend inordinate resources on data management issues. Data management is self-managing, automatic and embedded into every system and process of the organization. In essence, the data infrastructure manages itself. At this stage, IT leaders are actively looking for ways to capitalize on their data. They are actively engaged in efforts to drive collaboration with partners, suppliers and customers, and are thinking far beyond the boundaries of the organization to continuously drive entry into new markets.
For a full article outlining these tips and methods from author John Bostick, click here.
All images excluding the pyramid (pictured) were used with permission from ThinkStock.
What can psychology and Maslows hierarchy of needs teach us about data infrastructure management? For starters, some of the basic principals of information management problem solving.
7 Key Considerations When Choosing a Data Pipeline Service
8 Steps to Success With Self-Service Analytics
What the Top 8 Hadoop-Related Data Skills Pay
What the Top 14 Business Intelligence / Reporting Skills Pay
What the Top 14 Relational Database Skills Pay
What the Top 9 Data Visualization Skills Pay
What the Top 15 Machine Learning / Statistics Skills Pay
What the Top 15 Data Management / Big Data Skills Pay