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 play a foundational role in creating the semantic layer in every business.
What data analysts do for organisations
Data analysts glue your organisation’s data into a coherent whole, building more-or-less a “semantic layer” out of the data collected by your company’s applications.
They may need to do this by merging two spreadsheets, ten databases, five dashboards, or a mix. A data analyst won’t know until they take a look.
The people who do this job thus end up with a mix of skills, from data quality analysis and pipeline prototyping to data modeling, visualisation, and business analytics. Hiring good data analysts requires not losing sight of the forest for the trees.
“Data work” for most companies is in the vast space between narrow snapshots collected by software applications and all the business processes that depend on that data for insight. Very few data analysts learn all the skill-sets required to navigate the entire space. Most learn a couple of components well and consistently.
It’s also important to note that this kind of work is something your software engineers shouldn’t be doing.
Because of this, data analysts are critical to the way organisations work.
What doesn’t a data analyst do?
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.
What should brands look for in data analysts?
At DEPT®, we believe anyone who has one or more of these skills is a “data analyst”:
- Visualisation and report development
- Business, statistical, or other quantitative analytics and storytelling
- Data Science
- Data modeling
- SQL integration script development
- Data quality analysis
- Database discovery
- Data Architecture
Experience & opportunities
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.
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