Topic 3 – Data / information architecture layer

Data Stewards as Change Agents

June 21, 2020 by ars5522 Leave a Comment

Information Governance needs to be a core tenant of any Enterprise Information Architecture. Without a governance program, strategic decisions are based on unreliable data. Data needs to be assessed from multiple dimensions:

Accuracy — whether the data values being held reflect the properties of the real-world object or an event that the data is intended to model.
Consistency — whether the values of attributes managed or presented in multiple locations are the same.
Existence — whether a value is being held for a particular attribute.
Integrity — whether all expected relationships between data in multiple data stores, tables and files are intact.
Validity — whether the values held fall within the allowable domain of values established for an attribute.
Believability — the degree to which users of the data believe and trust it.
Interpretability — the degree of ease with which data is consumed and understood.
Timeliness — the degree to which the latency of data delivery matches the needs of the consuming individuals or processes

One strategy for maintaining data integrity across enterprise systems is to appoint Data Stewards; stakeholders who are responsible for defining, driving and enforcing data quality efforts. However even with a well positioned steward team with a framework well aligned to their enterprises capabilities, there needs to be buy-in. Not only from the resources responsible for a majority of the data quality work but from leadership with supportive strategic initiatives.

For instance, an organization focused on data economy solutions may have robust data architectures that promote consistency, integrity, and timeliness but due to the focus on dimensions that support application usage there is a lack of development priority to promote accuracy and integrity. Dimensions that derive from tasks in different parts of the development pipeline. This is where stewards need to be able to influence enterprise priorities.

References:

Friedman, T. (2007). Best Practices for Data Stewardship. Gartner.

Data Drives Nexus App Architecture

June 20, 2020 by ars5522 Leave a Comment

Having an inclusive data economy is a must-have for modern organizations. Effective information sharing is vital to reaching customers, opening markets, improving transparency, sparking innovation and improving trust among stakeholders (Newman, 2011). From an implementation perspective this means joining silos of information to meet emerging demands (Thoo, Friedman, & Beyer, 2013). However, developing a data economy positive information architecture to support the organization requires a data layer that is nimble and adaptable. Volume, velocity, and variety of data that is relevant has grown to a point that it has pushed many applications to the limits of their capacity (Yefim, Douglas, Ross, 2012).

New technologies like gRPC and graphQL can help organization overcome some of these hurdles. These services greatly decrease (halved in some cases) data transfer loads and allow custom request contracts to be created, essentially enabling a single service call to return data from multiple sources. These are new principles for application architecture but are required for modern information architectures that constantly have new and varying data types and sources all with their own management needs. Innovations like these are the result of data becoming the nexus of enterprise decisions, bringing the application architecture and information architecture domains closer together, providing even greater justification for a deeper investment in proper enterprise information architecture.

References:

Newman, D. (2011) Overcoming Silos: Evolving From Stand-Alone Information Architectures to Shared Information Architectures for the Emerging Data Economy. Gartner.
Thoo, E. Friedman, Ted. & Beyer, M. (2013). Data Integration Enables Information Capabilities for the 21st Century. Gartner.
Yefim, N. Douglas, L. & Ross, A. (2012) The Nexus Effect: How Big Data Alters Established Architecture Models. Gartner.

Sharing is Caring with Enterprise Information Architecture

June 20, 2020 by ars5522 1 Comment

In my experiences, Enterprise Information Architecture (EIA) tends to be a massively overlooked discipline. Most organizations I work with have different tiers of data architects, all aligned differently and developing differently. I often find organizations with silo’d standards and information models, regardless if there is a mature enterprise architecture in place. This fragmented EIA costs the organization money. Creating inefficiencies, redundancies, and inhibiting business strategies.

Often times this is a product of organizational growth that requires significant effort to right the ship, especially with large enterprises, but there are still opportunities for positive change. Focusing on creating a sharing environment, increases the value that can derived from information. With a collaborative data culture, information is shared among those who have it and those who need it (Newman & Gall, 2009). Allowing lines of business to integrate with one another; reminiscent of the benefits of InnerSourcing. Of course scope still needs to be maintained with principles of governance and structure, but advanced data sharing capabilities as part of a unified EIA will allow organizations to exploit information as a strategic asset and not just a by-product.

Here is a helpful guide for architecting information shareability:

(Gartner for IT Leaders Toolkit: How to Develop an Enterprise Information Architecture)

References:

Newman, D. (2011) Overcoming Silos: Evolving From Stand-Alone Information Architectures to Shared Information Architectures for the Emerging Data Economy. Gartner
Newman, D. & Gall, N. (2009) Architecting for Participation: How Information Sharing Environments Overcome Information Silos. Gartner.
Newman, D. Gall, N. & Lapkin, A. (2008)Gartner Defines Enterprise Information Architecture. Gartner.