Some thoughts on why orgs find data capabilities and leveraging insights so hard, despite years of investment and initiatives.
There probably isn’t a single organisation that has not had a clear strategic imperative to do more with its data in recent years. Whether data was the new oil, gold, or other high value asset, the focus on gathering, organising and cleansing data has been almost universal. But what did it lead to? Are we able to have more insightful conversations about the business, its products, services or performance with our employees?
Legacy Data Environments, old broken processes, outdated skill sets and a culture that doesn’t fully embrace and trust its data … the list of challenges is long. But given the levels of investment, why aren’t companies seeing more bang for their buck?
Early focus on tech over people
So much of the early effort in data initiatives focused on purely technical aspects. Where should data be stored? How should we manage it? What governance should we put around it? All valid questions, but not conversations that are accessible to many employees in the average large organisation. Efforts to increase data fluency and literacy have not been on the radar at this point; in fact, until technical decisions are taken and implemented, the end user often isn’t part of the picture. But being a part of a data-driven organisation requires more than technical know-how. Making people comfortable around data requires focus long before technology enters the picture.
A significant portion of the working population tends to think they are bad at maths, and carry bad memories from school; experiences of being made to feel stupid for not knowing the answer can make people reluctant to engage with data. But you don’t have to be good at maths to be good with data – this myth needs challenging early and often in any data-driven efforts.
This is a classic case of where focusing on developing a growth mindset can pay dividends. A can-do, exploratory approach to learning, and a realisation that logical thinking rather than high school maths is what matters can help become more comfortable around data, and for some people that is all that is needed right now. Focus, patience and practice take care of the rest.From: @gapingvoid
Teach data-adjacent skills too
We don’t need everyone to become a data scientist, but rather to give individuals confidence and clear techniques to incorporate data-driven insights into their work. For some, this means learning to ask better questions; for others, it might mean learning to check a dataset’s veracity; for others, it might mean learning practical skills to bring data to life through story-telling or reporting (a great introduction to this topic from Cole Nussbaumer Knaflic can be found here).
The journey required to learn how to turn data into insights, and then to leverage those insights in business decisions, does not have to be linear – it does not require that you understand the ins and outs of how data is gathered, cleansed, stored or governed.
A lack of data-adjacent skills is also a barrier to faster transformation, and take significant time to learn, embed and create new mindsets.
In the 2021 survey, 92.2% of mainstream companies report that they continue to struggle with cultural challenges relating to organizational alignment, business processes, change management, communication, people skill sets, and resistance or lack of understanding to enable change.
To reverse this trend of focusing on tech first in your own initiatives, engage people in data-adjacent skills from the moment an initiative is put on the table. Encouraging a growth mindset is an excellent way to flip the narrative on data fluency and help everyone become more comfortable around data & insights, whilst acknowledging and addressing any worries or fears about working with data.
Where is the actual demand? Where is the burning platform?
Often data technologies are chosen and built without specific, high-value use cases in mind. There is so much core system and environment work to set up first, that end-users cannot always engage with the why. To change this dynamic, for example, engage end-users in understanding missed opportunities that result from not having the right data, or from having too much of the wrong data to wade through before finding what is needed. This can help provide a clear reason and motivation for non-techies to grasp their role in solving problems using data.
Maybe they need to be more careful when filling out leavers information in an HR system, because not having that data in the right format costs the organisation time and money. Maybe they need to make consistent use of fields in the CRM, because failing to update entries in a timely manner is costing customers. Everyone contributes to the data environment, they just don’t know it!
Taking an agile approach to designing data initiatives can help – early sight of prototypes or an MVP can help people engage with the actual impact being created. But what if an MVP doesn’t apply to you? How can you measure the shift in ways of thinking and valuing data?
Embracing a measure such as ‘Essential Value Achieved (EVA)’ rather than just an MVP allows a dual focus on data technologies and end-user insights side-by-side. It focuses on the mindset shift required, not just on features. It can embrace any iterative approach from a series of experiments to a set of features, which helps data initiatives to acknowledge the many moving parts that are not tech-centric, such as developing new competencies and skills.
How to bring more focus to insights
Many organisations have opted for an initial insights team – a centralised skilled group of individuals who can support the business as they begin to consume data. Whilst this makes sense early on, a single centre of excellence with no real world business exposure often limits the effectiveness of insights and causes mis-communication and frustration.
Without embedded insights resources, teams are often made to feel more of an un-educated user of a service, rather than an equal partner in creating and leveraging insights for better decision-making. We end up with the classic and cyclical conversation of ‘they don’t understand the technical issues’ versus ‘they don’t understand what the business does’ – neither side is fully in the right here! Only by these employees coming together and co-creating an approach in which each side is acknowledged for bringing value can they begin to create the culture change that is so desperately needed.