We channeled our expertise into AIQ: an innovative framework for AI-ready data strategy and AI implementation, focusing on 11 critical capabilities proven to effectively enhance workflow integration and return on investment.
In this blog post, we'll explore in detail one of these 11 capabilities: Data Architecture & Governance. For a broader understanding of AIQ, refer to our blog post titled "AIQ: What We Mean & What You Stand to Gain."
Imagine a vast chasm. On one side, there is data: electronic information, waiting to be given a purpose. On the other side, there are data uses: value-generating processes and products, including personalized marketing, predictive analytics, intelligent document processing, automated security, and many more.
As a competitive organization, your objective is to bridge the gap between simply having data and actually generating value from that data. Several AIQ capabilities play a key role in this “bridging”; you can read more about them in our blogs on Data Engineering, Data Operations, and Data Product Management. However, Data Architecture & Governance has the most fundamental role to play:
Data Governance is the framework of roles, standards, metrics, policies, etcetera that governs an organization’s use of data for value generation.
Data Architecture outlines how data flows through the organization to inform the strategy for managing and governing data
Data Architecture and Data Governance are a packaged deal, one cannot work effectively without the other: hence their pairing into one capability.
Data Governance & Architecture determines how an organization transforms its data into value—whether that value is revenue, improved workflow, or some other desired outcome.
Consider a construction firm that has acquired data from a client for the purpose of improving an internal process. Data Governance includes data quality: “a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it's up to date.” It’s important to avoid using low-quality data for applications that require precision. Therefore, the construction firm ought to create standards for its client’s data to determine whether it has data quality issues that require remediation prior to internal use.
The construction firm should also account for data compliance: “the practice of following regulations set forth by corporate governance, industry organizations, and governments.” This will ensure that the construction firm doesn’t violate data privacy standards set by the client, the law, or other regulators.
Finally, the construction firm must develop a Data Architecture to ensure that the abstract ideals it envisions from Data Governance (e.g., data quality and compliance) and Data Product Management are actually put into action. This includes understanding the underlying data entities (e.g., customers, products), which systems own specific entities (aka “Systems of Record”), and how that data should be transformed for use.
(How does Data Architecture become reality? Data Engineering).
Data Architecture & Governance defines and executes upon an organization’s practices and processes for its data (Governance) and the data relationships and transformations required to use it (Architecture). Without Data Architecture & Governance, organizations suffer from redundant, costly initiatives, islands of messy data, and frustrated internal staff and customers.
You can learn how Data Architecture & Governance fits into AIQ by reading our blog. Or, take our AIQ assessment to determine where your organization stands for each of the 11 capabilities.
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
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