Data is the new Wood

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Data is a necessity within any organization.

How data gets created, processed, and used is a complex story. In a larger organization there are multiple people working on or with data. It helps to look at these roles across the gamut of data – from who creates and stores the data, to who works on and refines it throughout the organization. One analogy for data within an organization is looking at how wood is used in a society – it can be used to build houses, tables, and even turn into works of art.

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Wood, or lumber, does not simply arrive at a town or factory for people to start using – Lumberjacks go out into the forest and cut down trees, which are eventually shipped to a sawmill or other processing center – the first step in woods' eventual use by society. This wood will be shaped to the right size, cut and used to create many different objects.

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Similarly, data can be said to start with Software Engineers – they do not necessarily create the data, but they create tools and websites, that when properly tagged, start tracking and storing unprocessed click and event data. This saved and unprocessed data will eventually be turned into processed data tables, reports and business insights!

However – this is a simplification, and many companies have external data sources as well – sometimes purchasing market or survey data sets from specialized companies. This can be thought of as a society importing a special type of tree that is not local – think Christmas trees in a tropical climate!

The people downstream from the Lumberjacks are the Carpenters or Millworkers who turn the large, unwieldy tree trunks into something usable by society - such as a house or building. People in the rest of society will live in this house and use it as a home or workplace for their daily lives.

Data Engineers are comparable to the Carpenters – they work directly with the raw parquet, JSON, or CSV files to turn them into data tables – widely available and accessible in a data warehouse such as Redshift, Bigquery, SnowFlake and more. The people downstream from them will work with the data warehouse “house” or working environment that they have created. Overall, data engineers are essential for bringing many datasets from many sources into one central, easily accessible place.

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Downstream from the Carpenters, or Millworkers, is where roles start getting a bit ambiguous – much as in the data world. Many people in society can come into a building material store and buy processed wood – many types of Craftsmen, Repairmen, Artists and even non-professionals who do not daily work with wood.

In our simplified view – we consider the people downstream as Craftsmen. They work on turning the house into a home. These people build furniture which is indispensable to making a house more comfortable and usable, in short – making a home livable. Their skills are widely applicable - Craftsmen can build many pieces of furniture to improve any room of a house – a bed for the bedroom, table for the dining room or shelves for the kitchen, and much more!

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Data Scientists are comparable to Craftsmen. Their work improves the company’s website, product, customer service and many other departments.

They use advanced supervised or unsupervised machine learning techniques to:

  • Predict future data based on historical data
  • Categorize the behavior of millions of website visitors into something meaningful, such as “Rare visitor” or “Active Shopper”
  • Detect credit card fraud based on geo-data, usage patterns

They work inside the data warehouse that the Data Engineers have created, with processed and cleaned data, and also frequently produce their own data, such as the categories of website visitor or credit card transactions (“Fraud” or “Not Fraud”). Other datasets they produce come from surveys or experiments (e.g. AB tests). This new data that they create often ends up back in the data warehouse for others to use. Overall - data scientists are vital to an organization for improving fundamental aspects of the company.

Downstream from the Craftsmen are the Artists – but that doesn’t mean they are any less important. Once a society has a home, the artist’s work is often what takes pride of place – such as an art piece on a table, or carving hanging in the living room for all to see. An Artist also has the ability to take blocks of wood and experiment until they produce a beautiful sculpture. Img

Data Analysts are similar to Artists in being at the end of the stream. They rarely create their own data, but instead work on using data for practical business applications – such as creating dashboards, or reports that will drive discussions during management meetings. They also often have to dig into the data to find insights for the business which aren’t captured in the high-level metrics.

An Artist carving multiple blocks of wood until creating the perfect sculpture is comparable to an Analyst working on a dataset until finding the right insights for the business, this often takes multiple SQL queries and pivot tables, checking metrics across different dimensions.

Within the world of Analysts – there can be a wide range of roles, and in some cases overlap with Data Scientist tasks. For example – some Analysts can focus on Sales Operations, working more closely with certain tools, while others can focus more on supporting sales teams, requiring a different skill set, and still others can have very specialized functions – such as Finance Analysts or Marketing Analysts. Certain Analysts also focus on experimentation – which brings their work closer to that of a Data Scientist sometimes.

Data is a complex topic within an organization – hopefully the analogy with wood and how it travels through society helps when it comes to thinking about your own company and how to look for new people on your data teams. Being clear on the new hire’s function will allow you to look for the right people and test for the right skills among the seeming chaos. Img

To help you hire people with the right skill set, Skillfill developed a software to assess candidates’ skills.