Episode 1:5 - Information Management Maturity Model

Developing a Data Strategy can be difficult, especially if you don’t know where your current organization is and where it wants to go. The Information Management Maturity Model helps CDOs and CIOs find out where they currently are in their Information Management journey and their trajectory. This map helps guide organizations as they continuously improve and progress to the ultimate data organization that allows them to derive maximum business value from their data.

The model can be seen as a series of phases, starting from least mature to most mature: Standardized, Managed, Governed, Optimized, and Innovation. Many times an organization can exist in multiple phases at the same time. Look for where the majority of your organization operates, and then identify your trail-blazers that should be further along in maturity. Use your Trail-blazers to pilot or prototype new processes, technologies or organizational structures.

Standardized Phase

The standardized phase has three sub-phases. Basic, Centralized, and Simplified. Most organizations find them self somewhere in this phase of maturity. Look at the behaviors, technology, and processes that you see in your organization to find where you fit.

Basic

Almost every organization fits into this phase, at least partially. Here data is only used reactively and in an ad hoc manner. Additionally, almost all the data collected is stored based on predetermined time frames (often “forever”). Companies in BASIC do not erase data for fear of missing out on some critical information in the future. Attributes that best describe this phase are:

• Management by Reaction
• Uncatalogued Data
• Store Everything Everywhere

Centralized (Data Collection Centralized)

As organizations begin to evaluate data strategy they first look at centralizing their storage into large Big Data Storage solutions. This approach takes the form of Cloud storage or on-prem big data appliances. Once the data is collected in a centralized location Data Warehouse technology can be used to enable basic business analytics for the derivation of actionable information. Most of the time this data is used to fix problems with customers, supply chain, product development, or any other area in your organization where data is generated and collected. The attributes that best describe this phase are:

• Management by Reaction
• Basic Data Collection
• Data Warehouses
• Big Data Storage

Simplified

As the number of data sources increase in an organization, companies begin to form organizations that focus on data strategy, organization and governance. This shift begins with a Chief Data Officer’s (CDO) office. There are several debates on where the CDO fits in the company, under the CEO or CIO. Don’t get hung up on where they sit in the organization. The important thing is to establish a data organization focus and implement a plan for data normalization. Normalization gives the ability to correlate different data sources to gather new insight into what is going on across your entire company. Note without normalization, the data remains siloed and only partially accessible. Another key attribute of this phase is the need to develop a plan to handle sheer, the massive volume of data being collected. Because of the increase in volume and cost of storing this data, tiered storage becomes important. Note that in the early stages it is almost impossible to know the optimal way to manage data storage. We recommend using the best information available to develop rational data storage plans, but with the cavett that this will need to be reviewed and improved once the data is being used. The attributes that best describe this phase are:

• Predictive Data Management (Beginning of a Data-Centric Organization)
• Data Normalization
• Centralized Tiered Storage

Managed (Standard Data Profiles)

At this phase organizations have formalized their data organization and have segmented the different roles in the data organization: Data Scientist, Data Stewards, Data Engineers are now on the team and have defined roles and responsibilities. Meta-Data Management becomes a key factor in success at this phase, and multiple applications can now take advantage of the data in the company. Movement from a Data Warehouse to a Data Lake has taken place to allow for more agility in development of data-centric applications. Data Storage has been virtualized to allow for a more efficient and dynamic Storage solution. Data Analytics can now run on data sets from multiple sources and departments in the company. These attributes best describe this phase:

• Organized Data Management (Data Org in place with key roles identified)
• Meta-Data Management
• Data Lineage
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