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Unified Data Management (UDM) is the practice of unifying disparate data sources under one common umbrella, typically a singular data storage solution. Additionally, unified data management promotes good data management practices, such as data standardization.
Suppose a company has a large sales, marketing, and customer success organization. Typically, data may be siloed across different functions. For example, customer engagement may not be openly sharing churn data with sales or marketing, sales may not be sharing lead information, and marketing may not be sharing impressions or cost per click of campaigns. Unified Data Management seeks to combine these sources under one data storage solution to best enable the team to gain insights. For example, sales could look at customer churn information and realize that the lead quality of a certain industry isn’t as high as they once believed. Marketing could learn from sales that a lower CPC for a certain industry isn’t converting at the rate the sales organization would like it to.
At a macro level, having access to a full cycle of data could be beneficial to each of these distinct parts of the organization, enabling pattern and trend recognition and corrective action to be taken.
As stated above, UDM enables insights across a larger amount of data. Sometimes, it isn’t enough to have access to just a department’s data to get a full range of insights. UDM combines disparate data into a cohesive umbrella.
Unified Data Management encourages cost savings, avoiding both duplicative work across the organization and reducing hard costs, in terms of being able to unify across one data tool as opposed to a couple or dozens of data silos.
With more direct access to data, teams can iterate much more quickly, as they don’t need to sanitize data, request data, or otherwise gather their own data.
Sometimes, compliance needs such as HIPAA or GDPR require data to be in a particular form or handled in a certain way. UDM enables a team to have data in one place, which is a great starting point for creating universal standards for that data to meet compliance requirements.
Unified Data Management enables a team to create a standard way to sanitize and organize data across the organization, enabling a more consistent and potentially accurate data experience for all those running analytics queries against the data.
A semantic layer is an abstraction layer on top of a data source, meaning that a model of the data is added to simplify access. Semantic layers can do everything from providing data access metadata (such as allowed SQL operations) to altering the underlying data returned to the end user for reasons such as Personal Identifiable Information (PII) redaction or data aggregation needs.
Semantic Layers are distinctly different than UDM, but UDM can be a great first step before establishing a semantic layer for end users to access the data.
Absolutely. Oftentimes a UDM plan is a good first step in the data governance process, as unifying data enables consistency and organization, as opposed to data siloing which can lead to inconsistency, non-compliance, and technical debt.
Everyone involved in the data access process is responsible for UDM. Typically, software engineers, developer operations (DevOps) engineers, or data scientists will do the majority of the Unified Data Management migration process. However, anyone with access to important data (including sales, customer success, and marketing team members) must provide input on where data can be accessed and how to guarantee the consistently, accuracy, standardization, and integrity of a given set of data.
Absolutely, AI and LLMs will change UDM forever. Some of the biggest challenges surrounding UDM are the data migration processes and data sanitization methods. LLMs have shown themselves to be very good at quickly creating code-based connectors between tools and, additionally, sanitizing and correcting data.
There are a lot of tools out there for unified data management. The two most common buckets are data warehouses and Extract Transform Load (ETL) tools.
In order to centrally manage data, there needs to be a data store that backs up the data. Typically for an organization, this will be anything from Rockset to Snowflake, or any other data warehouse capable of scale. In its simplest form, a company may use Postgres to throw data into a table format.
While this term may sound complicated, ETL tools basically enable taking data from source A and putting it into source B. For example, one may need to get data everyday from Hubspot and put it into Snowflake, transforming the data into native Snowflake data types. ETL tools enable this to happen with a simple interface for setting up these cron jobs, or one-off jobs.
Unified data management has become increasingly common in organizations today, especially with the rise of big data and the need to make accurate, data-driven decisions. As LLMs become more popular, UDM will become even more important and easier for organizations of all sizes to execute on in a first-class way.
