Fox Data Systems, LLC

Overview

So, you have data. You use that data to make decisions. Congratulations! You are a data driven organization.

But take a moment to consider the following. How confident are you that your existing data resources are correct? Suppose a new system comes online, or a new feature is added to an existing one. Such changes come with new data, and are you prepared to integrate this new data quickly and accurately?

Truth is most organizations are not exactly on top of their data. The reasons for this are both straight forward and complex. Data is inherently complex. However, the solution is straight forward if you know the steps you need to take to lay the foundation for effective data management.

So where do you start? The best place to start is to learn about the principles of effective data management. Luckly for you, you are in the right place to learn what they are.

Here we introduce 18 principles of effective data management broken up into 3 categories:

• Architecture & Lineage (1.x)
• Quality & Governance (2.x)
• Operations & Personnel (3.x)

Not everything fits precisely into a category or principle, and we will discover there is lots of cross-over between them.

An obvious one is data governance. Despite having its own category, there is not an aspect of effective data management that governance does not touch in some way. From how data is created to how it is used, governance has something to say and we should listen.

Fox Data Systems will be publishing a series of articles that dives deep into all 3 categories and 18 principles on LinkedIn. Below you will find short summaries of each principle and links to the articles as they become available.

Articlies Published (3 of 18)
Next: Creation (1.1) on 2/26/2026

The Principles of Effective Data Management

Architecture & Lineage (1.x)

Creation (1.1)

You should know how your data was created, by which system(s) and why it was created that way. This is baseline knowledge that must be documented and well known.

Transmission (1.2)

Moving your data from point A to point B sounds simple enough, until you deal with file corruption and a Kafka service going down. Data transmission is a common fail point.

Data Transmission: What Could Go Wrong?

Ingestion (1.3)

Once data is moved off the source system, it must be placed into another. That may include keeping the data in its native format or placing it into a database table.

Transformation (1.4)

Most of the time, data needs to be modified. That could mean reorganizing the data to changing the values contained within to bring it up to company standards.

Dissemination (1.5)

People (and other systems) need to access data. It needs to be well organized and optimized for speed and efficient extraction. Only systems or individuals who need access should have it.

Structure (1.6)

Structure applies to both how the data is created and disseminated. The native structure is not always the best way to present the data when disseminated and both must be well understood

Quality & Governance (2.x)

Validity (2.1)

Data must be monitored to make sure it is correct. Errors can arise during processing, a source system can fail to populate an element correctly, or a new and unknown value shows up

Definitions (2.2)

We must know what the data means. For example, transaction and reasons codes are simple and short in source systems but must be defined clearly when we present them in reports.

Security (2.3)

Security applies to every aspect of the data journey, from keeping connections between systems secure, to tokenizing account numbers and other PII when data is at rest.

Ownership (2.4)

An ambiguous term that is open to interpretation. In practice ownership is often a side job. OINO (owner in name only) plagues many businesses and the data they need to manage.

Audits (2.5)

Any system of effective data management needs to be enforced. Audits are not to be feared as data owners should be confident and welcome the identification of any gaps in the process.

Training (2.6)

Even data that is processed and organized well can be complex to company veterans and new employees alike Preceding data that is used properly is the training on how to do so.

Operations & Personnel (3.x)

DEV (3.1)

When new systems are added, or new features to an existing system, new data comes with it. How projects are run in order to capture this new data are important.

Data Management, Agile & Tech Debt

PROD (3.2)

Once new data asset have been created (or an existing one modified), it is handed over to a run team for execution. Errors happen. How they are dealt with are critical to operations.

Environment (3.3)

Often overlooked are the systems and tools DEV and PROD have at their disposal to do their jobs. It is important that they can complete their jobs with no unnecessary headwinds.

Talent (3.4)

The success of any data management program comes down to the people who execute it. Attracting and retaining the right individuals is the glue that will keep it all together.

Recognition (3.5)

It is said good data management is a thankless job. It shouldn’t be. Managers should be on the lookout for people doing great work and publicly recognize them.

The Cost of Missed Recognition

Growth (3.6)

Today’s leaders were yesterday developers. While coding expertise is the most important aspect of a developer's role, they should be learning skills that will benefit both their future and the company’s.