AIQ: a novel approach to data and AI, centered on 11 key capabilities shown to facilitate successful workflow integration and return on investment (ROI).
This blog post will deep-dive into one of the 11 capabilities: Data Operations. We’ll discuss what it is, how it’s done (when it’s done right), and why it matters. Or, you can read an overview of AIQ, including all 11 capabilities, in our blog post, “AIQ: What We Mean & What You Stand to Gain.“
Many employees lack the technical know-how to work with unprocessed data. Specialists in Marketing, Sales, Human Resources, and similar departments have skills better spent on the tasks for which they’re specialized—not wrangling data in systems they don’t know or can’t manage on their own. Furthermore, customers value products that provide simplified, efficient access to data applications and processes with low knowledge- and skill- barriers. They don’t want to wade through oceans of data or take a crash course in data science in order to use a product. They want access to data and ready-made tools to work with it.
So, an organization must have (i) reliable infrastructure that provides access to data in a secure manner unobtrusive to the user and (ii) personnel to support it. We call this infrastructure Data Operations. Personnel dedicated to managing the data infrastructure and access comprise the Data Operations team.
Data Operations ensures that internal and external access to an organization's data is facilitated, documented, and reversible. Why? Two reasons. First, data consumers—including employees, clients, and others—need data access to do their jobs. Data Operations facilitates it by providing tools to increase efficiency and prevent misuse. Second, organizations need protection against data abuses such as cyberattacks and confidentiality violations. Data Operations provides controls and documentation to guard against these abuses and reversibility as a “safety net” when problems arise.
Data Operations practices will vary between organizations, but all should include the following:
Infrastructure as Code: automation to facilitate configuration management and deployment of data infrastructure
Observability: diverse means of logging, monitoring, and flagging data pipeline processing and access.
Backup and Disaster Recovery: recovery systems to ensure high availability of data infrastructure
Additionally, every organization should consider its research and production needs. How will various roles access data to fulfill their responsibilities and meet business objectives? How will the organization support data applications? Since no two organizations will have the exact same answer, Data Operations practices must be personalized to each.
If you’re interested in YOUR organization’s Data Operations, consider these questions:
Does my organization use an identity management solution to guard access to its data?
Does my organization have and regularly test recovery and backup systems?
Does my organization have dedicated roles and standards for Data Operations?
If you can’t answer “YES” to every question, your organization might be vulnerable to Data Operations issues. Learn more about your organization’s Data Operations maturity (and how to improve it) by taking our in-depth AIQ Assessment or scheduling an AIQ introduction call.
Data Operations facilitates productive data access. It ensures when data infrastructure problems arise there are protocols and tools in place to remedy the situation quickly. Without Data Operations, customers will have a poor experience and internal staff will not be productive leading to revenue and profitability challenges.
You can learn about Data Operations and how it fits into AIQ by reading our blog. Or, take our AIQ assessment to determine where your organization stands for each of the 11 capabilities.