Working with data

Stéphanie Krus
Nov 3 · 8 min read

Data terminology, resources, how to manage, interpret and visualise data.

photo of a desk with someone working with paper showing graph, pie charts and a phone
photo of a desk with someone working with paper showing graph, pie charts and a phone
Photo by William Iven on Unsplash

Data is everywhere

So whether you like it or not, it’s probably best to understand the various aspects of it. This is what I’m trying to do here.

“The world’s most valuable resource is no longer oil, but data” — The Economist

buildings looking like oil rigs with logos of the main data companies on them Amazon, Uber, Microsoft, Google Facebook, Tesla
buildings looking like oil rigs with logos of the main data companies on them Amazon, Uber, Microsoft, Google Facebook, Tesla
Cartoon from David Parkins use by The Economist

Data terminology

Some terms are probably familiar already.

Big Data: is a field that analyse, systematically extract information, or deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. You need parallel computers to work with it.

Data lake: a massive, easily accessible, centralised repository of large volumes of structured and unstructured data from various sources. Its architecture is a store-everything approach to big data. A data lake is less structured compared to a conventional data warehouse. When the data are accessed, only then are they classified, organised or analysed.

Data swamp is a deteriorated and unmanaged data lake that is either inaccessible to its intended users or is providing little value.

Data mining: a process used by companies to turn raw data into useful information. It implies analysing data patterns in large batches of data using one or more software.

Edge analytics: analysis of data from some non-central point in a system, such as a network switch, peripheral node or connected device or sensor. You usually don’t keep the data, and just process it at the point of capture.

AI: collection of different technologies. When they come together, they enable a machine to sense, comprehend, act, and learn with what seems like human levels of intelligence. (I’ve written a Medium story if you want to learn more about this.)

Machine learning: find patterns in data.

Venn diagram between IT, Maths and Business /Domain expertise to define Data Science at the intersect of the 3
Venn diagram between IT, Maths and Business /Domain expertise to define Data Science at the intersect of the 3
Data Science definition — from ICAEW

Data literacy: desire and ability to constructively engage in society through and about data — Bhargava et Al 2015

This is just one definition, I think the way you present it also helps people to engage with your data:

“Most of us need to listen to the music to understand how beautiful it is. But often that’s how we present statistics: we just show the notes, we don’t play the music.” — Hans Rosling

graph going from Truth (tables, dashboards, reports, to Beauty (interactive maps, scrollable stories, Data art)
graph going from Truth (tables, dashboards, reports, to Beauty (interactive maps, scrollable stories, Data art)
From Benjamin Cooley — on Medium

More about data and visual literacy in this article:

The 7 V’s of Big Data

(some claim 3 Vs, some 5, some 7 or 10 …. )

Volume : how much data we have — what used to be measured in Gigabytes is now measured in Zettabytes (ZB) or even Yottabytes (YB).

Velocity: the speed in which data is accessible. It’s often real-time now.

Veracity: making sure the data is accurate, which requires processes to keep the bad data from accumulating in your systems.

Variety: it can be unstructured (voice or images as opposed to text for example) and it can include many different types of data from XML to video to SMS.

Variability: constantly changing data can have a huge impact on it’s homogenisation.

Visualisation : for your data to be useful you need to provide an efficient and meaningful visualisation of it. (more below about this)

Value: data can improve decision making and operations. As a product, data has to be cleaned and treated so it can be used.

Different types of analytics: Descriptive, Diagnostic, Predictive and (the holy Grail) prescriptive

Graph: what happen, why did it happen, what will happen and how can we make it happen, hindsights vs insights vs foresights
Graph: what happen, why did it happen, what will happen and how can we make it happen, hindsights vs insights vs foresights
Adapted from Gartner Analytics Maturity Model

Maybe a better way to explain these with this diagram:

Venn Diagram where Data, Machine Learning (Forecasting), Business Intelligence (Insights) intersect yo show various analytics
Venn Diagram where Data, Machine Learning (Forecasting), Business Intelligence (Insights) intersect yo show various analytics
From: 4 Stages Of Data Analytics Maturity: Challenging Gartner’s Model — Taras Kaduk

Interpreting the data

The human brains is wired for visual processing: When we see something, our brain tries to make a sense of whole instead of focusing on individual parts. So we need to make it easy to see patterns in data.

Also, our brains are pretty bad at angles: no pie charts (unless you are showing things like time as clocks, or pizza slices).

a pie chart showing preferred toppings for pizza with some instructions about how to do a pie chart
a pie chart showing preferred toppings for pizza with some instructions about how to do a pie chart
From pinterest.com

Pre-attentive processing

This takes place in our sensory memory; it requires no conscious effort from the viewer to do this processing. It’s automated and takes less than half a second to complete.

There are 4 pre-attentive attributes: form, colour, position, and motion.

We need to use them to show patterns and key information we want our audience to focus on.

More on this:


Visualisation: communicating the data

People like stories. So structure the communication of your data like you’re telling a story.

a data visualisation needs a Beginning, a Middle, and a New Beginning
a data visualisation needs a Beginning, a Middle, and a New Beginning
From Duncan Geree on Medium

Process to follow to communicate your findings

Think of:

  1. the problem / question to be answered?
  2. the wider context for the question being asked
  3. analysis done
  4. the key points of the output: what action do you want to happen (‘so what’) your title should reflects this output (give the answer in it)
  5. the format of the story: how is it presented, how long have you got?
  6. creating the journey that leads to the key points (only include information that supports the key points)
  7. creating the output: where is attention focussed?

A classic example of data visualisation — bring the data back to numbers easier to process

An example of using your data to tell a different story

Bar diagram in blue showing the number of deaths on decline during the Iraq war
Bar diagram in blue showing the number of deaths on decline during the Iraq war
different title: Iraq’s bloody toll, same diagram is upside down an in red
different title: Iraq’s bloody toll, same diagram is upside down an in red
From Simon Scarr for the South China Morning Post

“Three simple choices create the strong emotive message: title, colour and orientation of the bars.

The title tells you how to read the chart. Just three words — “Iraq’s Bloody Toll” — establish the author’s agenda. Second, the bars point downward. Finally, they are red. What do you see? A smear of blood, dripping down the screen or page.” — Andy Cotgreave

How GOV.UK shows performance data of various data

Nice and simple. You can select ‘Service data’ in the top menu to see other service s— not just Voter registration.

How to chose the most appropriate graph for your data?

Two really good links from this article:

http://experception.net/ to access a PDF

poster on different decision trees about what graph to chose depending on the type of your data
poster on different decision trees about what graph to chose depending on the type of your data
This poster is available here — this is from the article above.

Data management

I’ve left this part to the end as it is clearly not my favourite part …

Data quality: garbage in / out (GIGO). You’ll never get perfect data. A lot can happen: it gets out of date, or missing, in the wrong format, duplicate, or even plain “wrong”.

Man at his desk, in front of a computer with garbage items coming out of the screen and spreading to the floor on him
Man at his desk, in front of a computer with garbage items coming out of the screen and spreading to the floor on him
Garbage in, garbage out — Your analysis is only as good as your data — from QQWallpapers

You need to improve your data at the point of capture ideally, but it can go bad all along the way when processed.

Data quality needs:

  • completeness
  • consistency: can we match the data set across the data store?
  • validity
  • accuracy
  • uniqueness: single view of the data
  • timeliness (have a timestamp about when it was captured)

And needs to be monitored on a regular basis. At the minimum, monitor your key data items.

Data quality rules:

Business statement: this is the business context of a rule. It may also state the business process it is related to or why the rule is important to the business.

Rule specification: written in pseudo code, this explains at a database level the definition of quality data.

Coded rule: the actual programmatic rule, written in the system language of choice.

Data remediation: activity to remediate: every time someone call or every 2 years for example.

Data definition: There is often no common understanding of the data across the business. To write a good definition, it needs to be concise, precise, non circular, distinct and unencumbered.

Areas of data management: data governance

  • modelling and design (what you need)
  • storage and operations
  • security
  • integration & interoperability (how to access it)
  • document en content management
  • master data and references (key to access a set)
  • warehousing and business intelligence
  • metadata (the data about data)
  • quality
  • architecture

Data life cycle

If you google this, you will find lots of colourful diagrams with more or less steps, with arrows between two steps

Obtains / store / share / use / archive / need / check / destroy / create / analyse / monitor

Like this one from Defra’s Target Operating Model:

Round diagram going from on step to the next: need > check > obtain > store > share > use > archive
Round diagram going from on step to the next: need > check > obtain > store > share > use > archive
From this Medium story

But you don’t just go from on step to the next like the arrows seem to imply. It’s more complex and messy in most cases.

Data management diagram: ‘plan’ then a circle that say ‘store’ with inside acquire, share and use, you go out via ‘dispose’
Data management diagram: ‘plan’ then a circle that say ‘store’ with inside acquire, share and use, you go out via ‘dispose’
From the same Medium story

“Good data management is not the harness that forces data to jump through the hoops of the data lifecycle. It’s not a step in the lifecycle, or a box to be ticked. It’s the entire context for the conversation, the canvas on which we draw the data lifecycle. Even if what we draw isn’t always tidy.” — Beck Strickland


This post is based on a training day (paid by my employer Scottish Enterprise) I had with This is Milk by Jo Watts and various articles I read this year, most from Nightingale (The journal of Data Visualization Society)

Stéphanie Krus

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Service Designer | Web Developer | Permaculture & Accessibility advocate | French in Scotland