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5 Key Behavioural Elements that Build Successful Data Cultures

While many organisations are investing a lot into becoming more data-driven, there is a gap between ambition and reality. Organisations can bridge that gap by considering five key behavioural elements.

Analytics and artificial intelligence (AI) are the engine of modern business, but good data is the fuel that powers it. Feed your analytics engine with good data and it will reveal the insights you need to make the operational improvements that will produce financial benefits, improve operational efficiency, and increase customer satisfaction.

That’s why many organisations are investing a lot into becoming more data-driven, and using their new data centricity to drive analytics transformations. 

The benefits of doing so are clear. Increased close rates, faster customer response times, increased employee engagement, and improved customer retention leading to revenue growth are just a few examples of the pot of gold that lies at the end of the analytics rainbow.

However, there is a known gap between ambition and reality: While 66% of Australian respondents to a Snowflake study said their businesses have a data literacy program in place, only 41% said the majority of all decisions are currently informed by data.

Barriers to data-informed business decisions

Despite the availability of solid technology choices and solutions, it is behaviour and practises that are creating barriers to data-informed business decisions. This affects data quality, which negatively impacts an organisation’s ability to leverage reliable, insightful and trustworthy analytics, artificial intelligence (AI) and generative AI.

For example, organisations that create data via separate applications risk creating unresolved duplications, which may cloud analytics insights. And a lack of consideration of the applied use of data beyond a single team’s needs compounds the problem. 

This can create a situation where an organisation has plenty of data, but doesn’t know what to do with it. Undocumented data sources, inconsistencies across reports, no clear data ownership, and a lack of accountability are all indicators of an absent data culture.

This lack of accountability is leading to poor data quality for many Australian companies – according to research from Experian,

89% of Australian companies are struggling to manage their data, and 65% said it’s difficult to turn their customer data into useful insights.

These organisations risk losing skilled analysts to better jobs elsewhere, and, at the same time, can see the cost of data sourcing, movement and development increase due to poor economies-of-scale activities that could otherwise simplify or reduce data costs.

5 key behavioural elements all successful data cultures share

All digital transformation is data transformation, and this starts with people and their relationships with each other. These relationships – between employees, and with customers and partners –  are a critical ecosystem of information exchanges that are central to the success of all organisations. That’s why taking a human-centred approach that focuses on experiences your people have with data and technology is the vital first step on the road to effective data transformation. 

But changing human behaviour often requires a massive change management effort. So, what are these behaviours and practices that lead to good data cultures?

Becoming truly data-driven requires changing mindsets, attitudes, and habits that support embedding data quality into the identity of the organisation. Ultimately, people have to want to use data and encourage others to do the same.

As such, successful data cultures typically focus on these five key behavioural elements:

1. Trust

Effective leaders trust their people, and high-performing people are empowered with trusted data to make confident decisions.

However, this trust in data requires a solid data governance model that supports secure, widespread access to a single source of truth, and balances centralised data governance with decentralised self-service analytics. This approach breaks down silos across teams, builds trusting, collaborative relationships, and shares data across the organisation to identify impactful solutions.

Reflect on your organisation. Is there disagreement on whose data is correct? Is your data still siloed? Are metrics linked across your organisation to assess and promote data value, performance, and innovation?

2. Committment

Successful data cultures have full commitment to realising the value of their data assets. That means not just storing and collecting data, but helping people use data to make better decisions. 

This commitment should be evident in all aspects of your organisation – from your organisational structure to day-to-day processes. It also requires an assigned executive that is accountable for your organisation’s data use and ensuring that analytics projects tie back to critical business efforts.

This is what it means to treat data as a strategic asset, and demonstrates why business outcomes must inform data collection and processes, and why support – including funding for long term data maturity programs – is a must.

Still, according to an ADAPT survey of Australian heads of data and analytics, just 41% of respondents are confident in delivering on their data strategy, with 46% remaining neutral, and 13% not confident.

Ask yourself: Is there a commitment in your organisation to treating data as a strategic asset? Is there a transactional attitude towards data? Is your data strategy operationally solid, however still lacks business relevance? Are you leading by example?

There is an important role for an executive champion to lead good data practices that focus on the critical practice of continually improving your data strategy and execution.

3. Talent and skills

If people don’t understand how to work with data, they can’t be data-driven. Everyone in the organisation should feel confident finding the right data, applying analytical concepts to their work, and developing data curiosity.

It’s also important to note that Identifying data champions within your business requires an organisation-wide approach. They often are not data analysts, but people with a deep understanding of the company processes and challenges across departments. Equipped with the right tools and empowered by their leadership, they can become role models for others and eventually train other people.

The results show that an overwhelmingly concerning majority of businesses (Global: 88%, APJ: 88%) have yet to progress either their data technology and processes and/or their data culture and skills. 

This is a problem for the 34% of Australian respondents to the Experian survey that said their people lack skills to manipulate and gain insights around data. Successful organisations prioritise data literacy skills when recruiting, developing, and retaining talent, and executives must prioritise data skills as part of their talent strategy.

Job descriptions should clearly outline the data literacy skills required for all roles across the organisation, along with tailored training and incentives offered for data literacy improvement.

How is your organisation prioritising data literacy skills? Is analysis ad-hoc and done by specific experts? Are there efforts to formalise information requirements? Do your executives model data literacy? Is data literacy understood across various roles, supported through internal activities, and informing career development plans?

4. Sharing 

Your people must have a shared purpose – to use data to better the organisation and amplify the impact they can have. To achieve this, your organisation needs to foster a sense of community. This can be displayed through meetups, messaging groups, and portals, and formalised into active internal communities around data and analytics. 

That’s critically important because most problems worth solving with data depend on a range data inputs from multiple systems and collaboration across many teams. This requires a level of organisation-wide data access and control that is not present in many Australian organisations – 69% of Australian respondents to the Experian survey believe not having direct control over data impacts their ability to meet strategic objectives. 

People need to be supported to actively share best practices across the organisation through incentives and time availability for learning, coaching, and documenting. This includes collaboration between data analysts and departmental heads to share the nuts and bolts of good data processes, and what they are delivering for the organisation.

Consider how people are supporting each other and building a sense of belonging within your organisation. Is your strategy understood by everyone in your organisation, and is your data strategy clearly aligned to achieving specific business outcomes? Or is organisational misalignment and siloed data creating barriers? 

5. Mindset 

When people are curious and willing to challenge their own assumptions with data, experimentation and innovation comes to the fore. Teams refocus and measure business outcomes, not only operational metrics. Data-led decision making is viewed as a source of personal growth and career development.

This mindset is just as important as developing data skills, and it is shared across your organisation, open discussions will generate ideas that lead to exploration and innovation. 

Do you use data as a catalyst for holistic improvement and organisational evolution? If not, you may find yourself reactively firefighting spreadsheets, and progress could be dampened by inconsistent incentives and short-term investment.

The DNA of successful data cultures

Being ‘data driven’ means having access to timely and trusted insights that help your people deliver on business and service goals, and establishing a virtuous loop of testing and learning from your organisational data. 

This kind of data framework can transform the DNA of your organisation. Ashish Braganza, Director of Global Business Intelligence at Lenovo experienced this power after embracing an organisation-wide data transformation driven by Tableau. 

“When we first started with Tableau, we were just thinking about dashboarding and reporting. We never thought Tableau would fundamentally change the DNA of the organisation.”

Ashish Braganza
Director of Global Business Intelligence, Lenovo

But ultimate success comes down to the strength of your data culture. Successful data cultures have foundational trust and accountability; are committed to realising organisational value from data; set high expectations on talent for a wide range of data activities; share through incentivised collaboration and reduced silos; and have a mindset shift that encourages data exploration and curiosity.

Transform your business by fostering a thriving data-centric culture.

 

Hilary Cinis

Hilary is a human centred digital design leader dedicated to helping people connect to each other and with information. She has brought her cross functional design and leadership expertise to a wide range of organisations working on frontier technologies including Yahoo!, The ABC, NICTA, CSIRO | Data61 and Salesforce. She is a member of the Tableau Strategic Customer Engagement Team, where she leads engagements that combine a human-centered design-led approach and data experience specialisation with Salesforce’s world-class technology to help businesses shape strategic approaches to their data transformation and maximise their technology investments. Hilary’s strengths are in design thinking and systems thinking, collaboration design and facilitation, executable design strategy and rapid validation to ensure holistic considerations, feasibility and business value. She has extensive experience in data product design and is passionate about establishing holistic data cultures, social good and ethical digital transformation.

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