You can’t buy your way to data literacy
Learn how to embed data literacy in your organization’s DNA using three simple yet powerful truths.
Published by Rupert Morrison
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If recent years have taught us anything, it’s that uncertainty is now par for the course. Yet unlocking the power of data literacy and its potential for your organization isn’t something you can buy – it’s something you have to work on. Business leaders should pause and take stock of three truths that can set them free from their data-related woes.
First, tighten up your terminology
Data literacy is a language and, unless you treat it as such, it’ll be impossible for the whole organization to be clear on the definition of specific terms. This is a simple fact that I’m frequently reminded of.
Only recently, I was lucky enough to attend a conference and speak with many world-beating organizations and their leaders about their transformation and change programs. And it instantly struck me how many of those same leaders still struggle to understand a simple yet fundamental concept: their headcount.
Let’s consider this for a moment. Within a large, global organization, asking for an analysis of ‘headcount’ can quickly become a pointless exercise if you haven’t properly defined the term. Are we counting positions or people – because the two are very different. HR might be looking at the workforce, counting employees as part of its people cost analysis. Meanwhile, Finance will mostly likely be thinking in terms of open positions and vacancies, as well as total payroll costs.
The result is two wildly different interpretations of the data, neither of which is necessarily wrong, but both of which are likely to be useless without having clarity on definition from the outset. If you’re not clear with your language and throw words and terms around without agreement and understanding, it makes those words and terms weak. And if your terms are weak, your data is weak. And you guessed it, if your data is weak, your thinking is weak.
Be clear on your definitions
There’s a simple way to fix this. It starts with a common organizational glossary and definitions that everyone’s briefed on and committed to, from onboarding to delivery. You can’t say ‘people’ when you mean ‘positions’ or ‘vacancies’, and you can’t say ‘headcount’ when you mean ‘base salary cost’. Doing so risks allowing your data to fall into disrepair and no amount of tech will save you from the ambiguity that produces.
This is where the concept of master data management (MDM) is crucial. If you treat data literacy as a language, you’ll be able to grasp the potential that MDM offers as a discipline, helping the business and its many stakeholders ensure uniformity, accuracy, consistency, and accountability for the organization’s shared datasets. This will establish a single source of truth that runs through the DNA of the organization.
Second, tell stories through data to inspire action
Most businesses still have pockets of patchy data dotted all over the organization. They gather the intel they need but apply an archaic, short-term approach to work through it. More often than not, this results in monthly reporting that brings a wealth of data together but with no meaningful interpretation, leaving the organization’s leaders with cognitive overload trying to work out what, if any, action needs to be taken.
It’s messy and, frankly, pointless. And it leads to a lack of understanding across the business that’s compounded by poor communication and terminology silos, in turn leading to slower, less effective decision making.
When you consider this in the context of who’s interacting with that data to make decisions, the case for consistency and clarity is even greater. For instance, take the people in the finance function. Right from the beginning, there’s a clear process for these individuals. They’re given defined data, terminology, and a single language from which to operate from Day One. The same isn’t true for HR, on the other hand. So, when these two functions come together, odds are there’ll be misalignment and confusion.
Build a storytelling discipline
Again, the answer to solving this riddle lies in clear definitions and the discipline of storytelling through data. First off, if your data is hypothesis driven, what are the questions you need to ask to test the hypothesis? If you’re not sure, you’ve probably missed a crucial step.
The point is, you need to define your key organization dimensions (KODs) first to be able to ask the right questions and visualize the distribution of people and positions across functional, geographic, business unit, and grade properties within the organization.
Only then is it possible to start with the right data on each property, enabling you to calculate the measures from that data, analyze dimensions and finally generate insights for action. You can bring this process to life through storytelling to inspire action, which simply can’t happen with a bland monthly report that lacks any narrative on the changes you’re hoping to achieve.
Third, demand discipline
These practical steps all come down to a single source of truth for your data, both in terms of the reason why you’re analyzing it and what you hope to achieve as a next step. To do that requires discipline and a deliberate, structured approach to embedding it in the organization’s DNA.
Mark Twain famously wrote: “I didn’t have time to write you a short letter, so I wrote you a long one.” In other words, it’s easy to write a lot of words quickly but to write something concisely that packs real meaning takes more time and effort. The same holds true for the use of data within an organization. It’s easy to throw a bunch of figures and terms at people, hoping they’ll draw meaning from it, but addressing data literacy and getting everyone aligned on terminology and definitions is a discipline.
In the end, discipline isn’t something you can buy and nor is data literacy. But doing this type of work well make the difference that means, no matter whether it’s HR, Finance, Operations or any other function, your organization can move at pace – because you all speak each other’s language and understand the business context.
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One of the reasons many businesses struggle with organizational design in a transformation is a misdirected approach to workforce data. ‘Data harmonization’ is the process of bringing together workforce data from different systems or organizations, when going through an organization restructure or post-merger integration.
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