How applying a multi-skilled team to a data analytics project delivers results

We work with businesses of all sizes…and they have a wide range of data-focused employees. The big businesses have large data teams…the SMEs have one analyst…and the start-ups have multi-skilled individuals who do everything in the business…literally everything. But, when it comes to data, we see a common mistake — asking one data person to do everything on a data project.

A trio of data skills

We believe you need a trio of data skills, on a data project, as a minimum:

If you’re missing one of these skills on a project, it won’t work…or not as well, at least. If you miss out the data consultant, you risk the project not delivering any commercial value. If you miss out the data analyst, you risk not being able to access, combine and analyse the more complex data sources. If you miss out the data visualiser, you risk not sharing the insight from your work in an intuitive way. You need all three to be assured of a successful data project.


The data consultant will create the use case…

This person needs to properly understand the business, the product/s, the market, the customer/s, the distributors and the business levers that can be pulled to influence costs and revenue. This knowledge needs to be applied to creating a commercial use case for the data project, for instance:

  • How can we use data to make money?
  • How can we use data to save money?
  • How can we use data to create a competitive advantage?
  • How can we use data to innovate?

Without this perspective, the project risks being a waste of time where the end result could offer no demonstrable value to the business performance or offer no actionable insight. The data consultant brings the ‘so what?’.

The challenge here is looking beyond the obvious. Just because past business performance shows a certain trend, that doesn’t mean it will continue in future as there might be market, regulatory or other external influence that will impact supply and demand of a business’ product or service in future. Or, just because the business has had certain revenue streams in the past, that doesn’t mean there can’t be a new one that could be data-driven.

Data consultants are often described as interpreters — because they sit between the commercial part of the business and the technology part of the business. They need to be able to speak the language of business and interpret it into tech speak.

The data consultant needs to be the innovator — the challenger, the thought-provoker, the commercial idea generator.


The data analyst will prepare the data…

This person needs to be a coder, a programmer and confident in dealing with data in all different formats, sizes and complexity. The data analyst will need to:

  • Extract the data from a myriad of data sources
  • Cleanse the data and remove or process anomalies or false data
  • Combine data sources by finding or creating matching criteria
  • Analyse the data using a range of statistical and analytical models
  • Prepare the data for analysis
  • Connect up live data feeds.

Without this perspective, the project risks being limited to only the simplest data sources and the simplest analytical methods which could result in far less valuable insight. The data analyst brings the tech.

The challenge here is juggling large volumes of data, sometimes from a range of data sources and often in a wide variety of data formats. Matching and combining data can be an additional challenge where there are not unique IDs for each record, so this can often be a complex process. Then there are the analytical methods which can include creating models for value analysis, segmentation, trend analysis and predictive models.

The data analyst needs to be the data wrangler — the data connector, the problem solver, the model builder.


The data visualiser will tell the story…

This person needs to be both customer and user orientated. They need to be focused on who will use the data tool they’re building — who are they? How do they work? What do they care about? What actions can they take using the tool? This understanding needs to be interpreted into building the data visualisation tool so the data visualiser will:

  • Visualise the data to understand patterns and trends
  • Create a data story for the user
  • Design the interactivity of the data tool so that it matches what the user will want to see/do
  • Select the appropriate visualisations and graphs to tell the story in the best way
  • Act on feedback from the users to refine and iterate the data tool
  • Train the users to use the data tool.

Without this perspective, the project risks having a poor end result, confusing data visualisations or a lack of understandable insight. The data analyst brings the story.

Visualisation can often be seen as the pretty part, the easy bit or the quick task. It’s the complete opposite. Whilst it’s really easy to make something look complicated, it’s really hard to make something look simple.Getting the story right, choosing the right graphs, allowing the appropriate level of interactivity — these all require deep user understanding, masses of experience and a dedication to data storytelling.

The data visualiser needs to be the storyteller — the user investigator, the completer-finisher, the designer.


Is one data analyst ever enough?

We don’t think so. You cannot expect one person to be the data consultant, data analyst and data visualiser combined. No one person would have all these skills developed to the required level for a data project. One person cannot be all things to everyone.

Our multi-skilled data team powers our clients’ businesses on a daily basis. We always work as a trio. Sometimes in larger teams when the projects require more. But never alone. One data analyst is NOT enough.

We’re Data — we use data analytics to power business performance for our clients. Can we help yours?