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In this post, we’ll explore data culture through the lens of Schein’s framework and explore what data culture looks like in practice.
Culture is a social framework of unwritten rules in a company. Exploring it is like exploring an iceberg – much is implicit and lies beneath the surface. This makes it hard to define a company’s culture, especially from the inside. Often, the most insightful cultural observers are outsiders.
Schein offers an interesting take on culture. He suggests that the aspects of culture that are least visible on the surface – the deeper underlying assumptions people hold - have the biggest influence in driving change. We can break down culture into three layers in order from most observable to least observable:
1. Artefacts
These are the tip of the iceberg. They are the visible, tangible structures and processes in a business. Artefacts can be identified through observed behaviours and rituals.
2. Beliefs and Values
These are ideals, goals, and aspirations that are consciously and explicitly articulated.
3. Underlying Assumptions
These are unconscious, un-questioned habits of perception, thought, and feeling. They are taken-for-granted assumptions about the way things are done in a business.
You can’t have a good or bad culture. It’s just strong or weak, depending on how similar employees’ beliefs are. With this in mind, how can you drive change towards a strong data culture?
Culture is a big barrier to becoming data-informed. To be truly data-informed, employees must share genuine beliefs about the benefits of being data-informed, which I outlined in “Why startups should be data-driven”. The aim for companies is to share common values around data use that ultimately become underlying assumptions about how you operate. Ted Colbert, CIO at Boeing, said, “When people begin to believe in the data, it’s a game changer: They begin to change their behaviors, based on a new understanding of all the richness trapped beneath the surface of our systems and processes.” These ‘shared beliefs’ are just the benefits of being data-informed. In short, they are:
Once employees genuinely share these beliefs, the following artefacts or observed behaviours of a data-informed business may appear. There is no one-size-fits-all, but generally, the observable patterns in data-informed businesses are…
1. Data is easily accessible
Data-informed companies have figured out a way to democratise access to their data. This means that company data is accessible such that an employee doesn’t have to request the data from others. Employees not only have data access, but they have a self-serve way to analyse and visualise data on their own. This empowers employees with the ability to act on their ideas, experiment, and innovate. But, a key hurdle is the data literacy skill gap. Realistically, not everyone in your organisation is data-literate. For data to permeate your business at all levels, your ‘self-serve way’ should require little to no data literacy.
2. Data is used for small decisions and big decisions
In a typical business, data is often reserved for high-stakes, strategic decisions made by leadership rather than regular day-to-day decisions. But, in reality, a company is built through many small decisions made by employees, alongside large leadership decisions. The impact of these smaller resolutions compounded over time is huge. Data-informed companies embed data into everyday workflows. Their employees have data at their fingertips, so they can optimise micro-decisions by making them more data-informed.
“Being a data-driven organisation means … building capabilities to put that asset to use not just for big decisions but also for everyday action on the frontline.” - Ishit Vachhrajani, AWS
3. Data capabilities are more decentralised
Many companies often view data as a departmental asset, not a shared resource. Having a centralised data team can more be efficient, but overall it can act as a gatekeeper and slow experiment speed. Data-informed businesses treat a data team as a distributed capability, rather than an isolated ‘cost centre’. They use a collaborative data platform that allows them to access, share, and converse around data. Businesses that struggle to create value through business intelligence or analytics tend to develop their capabilities in isolation or sporadic pockets of poorly coordinated silos.
4. Data is proactively shared and consumed async
Data isn’t only shared at regular meetings; it’s shared proactively and consumed asynchronously. The value of data insights generally decays with time, as new data becomes available and circumstances evolve. So, proactively sharing insights maximises their value and ensures they reflect the current situation when consumed. Ideally, companies have an automated method of discovering and receiving data insights asynchronously. This increases the relevance of information and allows teams to move quicker.
5. Decisions are high-velocity
Data-informed companies often make decisions fast. Because data is accessible, democratised, and proactively shared, the right people can have the right information at the right time. This ties into the accepted wisdom that business success is not just about quality to market but also time to market. In data-informed businesses, tools enable employees to easily monitor data required for decision-making. The more friction there is to check data, the slower data-informed decisions will be made.
6. Cross-functional data sharing is standard practice
Data isn’t siloed across departments; it’s the connective tissue of the company. The idea here is that a business is a tangled mess of causation. And sharing information regularly between departments helps to untangle it. Once you figure out this chain of causation, you can understand growth drivers and leverage them. Cross-functional sharing also helps to reduce the Common Information Effect. This says that common information: is more likely to be raised in discussions; will be reinforced by agreement; is more likely to be remembered; and is more likely to be perceived as credible. Everyone wants to make a safe contribution, right? This means that less highly valuable, unique information is shared.
7. Employees aren’t afraid to fail
Successful experimentation typically stems from using data to iterate quickly and tighten feedback loops. But, in reality, experiments are experiments – most of them fail. To experiment successfully, employees must feel able to fail fast. If they feel able to fail fast, they may experiment more and increase their use of data. Perhaps they’ll feel more empowered to leverage data that dispute as well as support their hypotheses. Quality leadership often enforce this fail-fast mentality by talking openly about their failures and quantifying them. They celebrate failure and adopt this mindset: lack of failure ⇒ lack of experimentation ⇒ lack of innovation.
Education around data culture benefits and levelling up your data capabilities are paramount in driving data culture. At Calliper, we tackle the latter. We believe everyone should be able to leverage data in their decision-making, no matter their technical skills. They should be able to reap the benefits of being data-informed. Simply put, the product is a plug-and-play business intelligence solution for SaaS operators. It connects to your SaaS data sources like Stripe, Hubspot, and Mixpanel out of the box with no coding. Setup takes minutes. Calliper democratises your business data, increases data velocity, and empowers everyone to be more data-informed. Unlike other BI tools, it provides a feed of automated insights based on your data. If you’re interested, check it out here.
The Calliper platform has been acquired by Accoil, and we’re thrilled to announce that the incredible technology behind Calliper will soon have a new home.
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