How data analysts help brands break down silos

In most companies, there are people whose job is to combine data from different sources to answer business questions.

These people aren’t usually considered technical by software engineers, but they still exercise creative, business, and logical judgment. The people who do this work are usually called “data analysts.”

Data analysts are expected to figure out how to make new data (specifically new records) out of a company’s raw data.

Data analysts don’t “analyse” or “break down data” so much as they remix source data into something new – something that makes sense to stakeholders. 

Organisations hire data analysts to break their data out of artificial silos. Your ERP, CRM, e-commerce, PLM, marketing, web analytics, inventory and fulfillment, sales, and privacy systems have their own picture of the relationship with the customer – but they don’t all talk to each other.

Data analysts exist because:

Data analysts are critical to building an organisation’s semantic layer. We expect them to develop repeatable, consistent, and precise logical formulas for combining records from different systems, as quickly as possible. 

Oftentimes, the automation of those formulas is then handed off to engineers. Engineers are tasked with building tools and platforms to make that semantic layer construction process easy and transparent. Data analysts are then free to build the semantic layer as fast as they can, with as little friction as possible.

This division of labour lowers the daily stress on data analysts, and they get the best use of their skills.

An experienced data analyst has more than one of those skills, and has in-depth experience and perhaps a preference for at least one. Very few people end up with decent skills across the board, but the more experienced have at least tried and failed.

That willingness to try and fail is crucial for data analysts.

None of us are born with the ability to merge datasets just the right way or explain results so someone can use them. Good data analysts are confident they can make seemingly disparate datasets come together sensibly. They may eventually be wrong, but a data analyst is willing to try to find a join.

The ability to optimise business impact, reusability, and feasibility is important. Semantic layers are made up of lots of individual decisions about business logic, reconciled into a consistent whole. An ability to keep an eye on the big picture while also meeting short-term needs is invaluable. Success inevitably requires a combination of both good luck and insight, and not always experience.

Data analysts play a foundational role in creating the semantic layer in every business. They provide an invisible but critical service – gluing together data sources for business users. They do all sorts of creative, logical, technical, and business analysis work to meet their user’s expectations.

A data analyst’s integrations hold companies together and are critical to an organisation’s success.