six steps to CREATE A DATA CULTURE – and why you need one
A data culture is one in which data is not only recognised as an important business asset but is used, wherever possible, at every level of the business to make improvements.
New and growth organisations were often born into a data culture, and from day one they used data as a strategic asset to make better business decisions, to enable a drive deeper understanding of the customers, execute more targeted marketing efforts, unlock a more efficient supply chain, and identify new revenue opportunities.
Many organsiations have made strides towards a data culture, but recognise the continued activity required to shift from gut-based to fact-based decision-making.
The results are often outstanding, Harvard Business School research identifies a clear % margin advantage for “data leaders” vs “data laggards”: Unlocked because they are more likely to optimize production runs based on demand forecast, predict equipment downtime using advanced analytics, and prescribe business actions to limit customer churn (etc.)
Data culture is created by delivering on the data strategy. The data strategy encompasses the strategic business needs that are unlocked through data; the concept of data as an asset; the data services and workflows model; the data people, competencies and culture model; the technology choices and architecture; the data governance framework; and the data change journey. Each of these elements plays an important role in enabling and creating a data culture.
Taking these in turn, the steps are:
#1. CLEAR BUSINESS OBJECTIVES THAT EVERYONE IS BOUGHT INTO
A data culture is created when data clearly supports business objectives that both leadership and the whole organisation believe in and are bought into. These objectives include better, faster, simpler decision-making; personalization of the consumer proposition; more efficient and effective operations; and so on.
"If you simply rely on having huge quantities of data in a data lake, you’re kidding yourself. Volume is not a viable data strategy."
Rob Casper, Chief Data Officer of JPMorgan
#2. TRANSPARENT AND EASY ACCESS TO DATA
Data must be thought about and managed as an asset or product: that is owned, clearly defined, well designed, validated for data quality vs. expectations, consistently delivered to very high service levels, and readily accessible for broad-based consumption. This in turn means that it can be understood, applied, and trusted. – Increasing the rate at which data will be used to improve the business.
#3. SLICK SERVICES AND WORKFLOWS
In a labour market where the average employee spends less than five years at a job, it’s important to have strong structures in place to institutionalize data knowledge. This happens via. our services and workflows: enabling staff to rapidly access and understand the data, to raise and resolve data quality issues, and to seek improvements in our data assets. Data Governance plays a key role in defining and improving these processes and workflows.
#4. LEADERSHIP FROM THE TOP, WITH DEDICATED ROLES AND RESPONSIBILITIES, AS WELL AS CULTURAL CHANGE AND EMBEDDING
Data culture depends on the collective buy-in from staff at all levels to measure outcomes, act based on available data, and build on existing knowledge over time. This demands leadership from the top: the shift to a data culture must be driven by those at the top and cascade down through every layer of the organisation. Senior leadership teams must lead by example and use data as the basis of what they do. If the leadership makes a commitment to basing decisions and the way the business is run on data, then those below will follow.
Success further requires dedicated roles and responsibilities – e.g., business data owners, data stewards, data management and governance, and so on. Finally, training, coaching, and embedding must be well-conceived – for example, Marks & Spencer have launched a Data Academy; whilst Diageo undertook a hands-on, market-by-market activation of their “Marketing Catalyst” fact-based marketing investment decision-making capability; and The Guardian have heavily promoted internally their Ophan editorial data analytics capability, including via. daily all-hands email updates.
#5. TECHNOLOGY THAT DEMOCRATISES DATA TO OUR PEOPLE
Tooling must be easy and intuitive to use, yet powerful enough to unlock insight and so inform the business decision-making. Technology should be integrated, all the way through from data sourcing and ingestion, to warehousing, integration, access, analytics, business intelligence/dashboards, and governance (e.g., data catalogue)
#6. RAPID IMPROVEMENT INITIATIVES, SEQUENCED WITHIN THE CHANGE JOURNEY
The data change journey should prioritise a sequence of “quick wins” that rapidly deliver business value, and so generate the momentum for change. At the same time, it is important to get right the data foundations as identified above – so that the impact is sustainable, and so that data converges into a joined-up asset, as opposed to fragments into complexity. This is achieved via. a top-down structured data change journey, with well-coordinated leadership, business case, funding, resourcing, and activity plan; and with explicit benefits measurement to validate that the expected outcomes are being achieved.
Data culture is no mean feat and has the power to impact every layer of an organisation. If you would like to learn more, contact Henry Crawford and discover how he created a lasting data culture at Deliveroo and other enterprise and hypergrowth companies.