The Issue or Opportunity Approach to Becoming Data-Driven

Where do you start? This is a question I’m increasingly being asked. For example, having a introductory chat with a new head of department, they asked, how would data help us?

It’s an interesting question as you (the question poser) are the expert in your department. I don’t have all the answers as a data leader, I have ideas to help you resolve some of your issues or how data can enable you to answer the questions tomorrow that you can’t answer today.

It’s like asking a chef what food do I want to eat at their restaurant?

How would you possibly answer that for someone else. You can start with, do you like; sweet or savoury, fish, meat or vegan?

That’s where you need to start with data – the high level. What’s your data strategy, direction of travel sponsored from the top. What are the issues, problems, opportunities that your data strategy will answer or enable?

This is where you need a choice, a data restaurant, maybe less tasty! Do you choose the Issue or Opportunity approach?

1. Issue Approach – What’s your burning platform, biggest pain point or issue causing most problems?

or

2. Opportunity Approach – What questions do you want to answer tomorrow that you can’t answer today?

For point 1. Issue Approach

Either choose one item or if you have a list of items prioritise these by taking the approach of start small, with the item that has the biggest impact, at low cost, with medium effort.

That way you start with something that matters, can be demonstrated that has a benefit, generate advocacy and support the business case for funding time and effort for the next item on your list.

For example 

We are hearing a lot of noise from customers and potentially risk losing them as they are unhappy that products are being delivered that are a different size to the information provided earlier in the order process.

Recommended actions:

Process map – draw the journey an item of data goes on through the business. Where does it start/ get created? What are the other departmental touch points? How does the data evolve, where and why? Where does the data get stored and how is it used?

Profiling – examine the data with free profiling technology to understand its trends, anomalies, outliers, patterns, standards and rules

Auditing – test the data against these rules that have been surfaced during profiling, do they work, do they improve the accuracy of the data?

Care – correct the data, clean it up and monitor it to ensure it remains clean and fit for purpose.

Assess – now you know what correct data looks like and what’s needed to get it there, does it solve your problem, if not what have you missed, what other data needs to be understood and cared for?

For point 2. Opportunity Approach

What is the first question you would like to explore. It could be:

How do we increase positive consent levels under GDPR for new and existing customers?

or

How do we understand absence levels better?

VIA – Value, Innovation, Analytics is a useful framework here.

Value – Determine the value you will get by answering the question. All data and analytics activity costs money. Organisations are reluctant to invest in data activities and technology that improves data to enable it to become reusable and trusted. So determining the cost benefit of answering the question should be paramount.

What value will this idea bring, who will benefit from this idea (internal/external customers), why is it needed? – all helping specify the value of the investment.

Innovation – What is the idea, how would it work / be monetised?

You have the why from the above value question, now identify what it is you are creating, how it will work and how it can be used. Again think of your customers, what is there interaction with your development?

Analytics – How will this idea be generated? What data is needed now, in the future? What will it look like?

What tools do you need? Don’t just think database and visualisation tools, as you need that middle system for data wrangling/ BI tool, to turn your data into breakthrough insights to display the story with your visualisation tool.

If you don’t have access to all the data needed today, what can you do with the data you do have and how will you access more data in the future?

How will you convey the insight you have generated, what’s the story? Do you need a dashboard, how will the business will resonate with the story with the visual you have created?

Add alt text

This is all looking backwards at what has already happened, great for providing a broad understanding of what is going on today. Wouldn’t it be helpful if we could predict absence levels for different scenarios or the impact a change in process or training could have on consent levels?

Can we take this further into predictive and prescriptive analytics. Identify future trends and model results of different actions and scenarios; in absence, what to look out for or if an employee is likely to resign? Demographics for customers likely to provide consent vs those unlikely to and store trends on receiving consent from customers depending on approach method.

All very exciting stuff. However, I leave you with the thought that; Achieving data-driven leadership remains an aspiration for most organizations — just 26.5% of organizations report having established a data-driven organization. Becoming data-driven takes time, focus, commitment, and persistence. Too many organizations minimize the effort or fail to correctly estimate the time and effort needed which these kinds of wholesale business transformations require. source: hbr.org 2022

How will you ensure you invest the appropriate levels to be successful and achieve results with a data-driven organisation transformation?

Image credits:

https://www.adverity.com/blog/crunching-data-need-tell-stories

How Genes Influence Food Choices, Obesity

https://www.entrepreneur.com/article/318584