3 Key Types Of Data Projects You Will Need To Take On As A Head Of Data Analytics

Data and analytics teams are often responsible for several key pillars in a company.

This can pose a challenge when you’re the head of data and analytics and you need to pick your next data project.

Where do you even start when you’re constantly bombarded with requests from your management team.

In this article I want to discuss how you can start selecting your key projects to work on as a head of data analytics.

First, I started by breaking down your projects into 3 key types of projects.

Analytics, automation and data products.

Below we will discuss what each of these projects are and some considerations you should have when taking these projects on.

Source:Portable

Analytics Projects

Analytics projects on their own can often be a great quick win. Starting with a few key core questions that you’ve gathered from the business and ending with some form of insights.

But how do you pick a solid data analytics project?

And what do analytics projects look like?

What Do Analytics Projects Look Like?

Let’s answer that last question first. Data analytics projects can look like an ad-hoc data report, a cleaned-up Jupyter Notebook, a dashboard or (shudders) a Powerpoint.

Each of these mediums can be utilized to tell a narrative to your stakeholders about what is happening in their business.

Generally speaking you will likely always have some form of ad-hoc analysis phase as you work to figure out if there is value in digging into the data further. Once you have finished your initial analysis of the data, then you will need to package your analysis.

But how you package your analysis will be up to you as well as your stakeholders in the end. The key point here is creating a narrative with data. Meaning, based off your ad-hoc analysis, you should have 1-2 key take aways you want your management to glean from your analysis and build around them.

Have a concise message and make sure your data, charts, graphs, and other pictograms support it.

How To Pick Your Analytics Projects

How you present your project is important.

But you will also need to make sure you’re working on projects that drive clear business value.

To identify the highest-value projects for your company, you will need to meet with your stakeholders to figure out what questions are most pressing at the moment as well as what would answering them impact in the business.

In order to figure this out you will need to talk with directors and business owners that manage the key departments in your company.

What questions should you ask?

Well for that, I will take a few questions provided by Ethan Aaron, CEO of Portable and Ex-Head Of BI from his article The 10 Steps To Building A Great Data Team.

These were:

1. What Key Performance Indicators (KPIs) do you use to run your business?

2. Where do you find them?

3. What metrics do you wish you had at your fingertips every morning?

4. What is the action you take based on each?

5. How do you measure the impact on the business? Is it critical?

Another important point Ethan had when it came to figuring out which analytics project to take on was that:

The goal of analytics is not to present data for the sake of data. The goal is to inform actions that have a material impact on the business. - Ethan Aaron

Once you have a strong understanding of what questions your business leaders have you can start to create a list of high-value projects. From there it will be about making sure you align your projects with your managements goals.

Automation Projects

Automation has several key outcomes.

• Increase the scalability of a a process,

• Reduce its costs

• Reduce the amount of human caused error

The problem with automation is much of the work happens behind the scenes so to some degree, management doesn't really care.

Unless the specific work is directly impacting their bottom line in a huge way, the C-suite probably won’t be as enamored with the output.

However, in order for all data driven businesses to run, they will need solid and robust automated systems.

Automated data pipelines.

Automated data QA.

Automated model deployment.

and so on.

Finding the right projects to automate can be difficult. The truth is, yes, automation can save time. Of course, creating automated systems also creates tech debt. Even when using low-code solutions.

So what should be automated.