Building a Data Strategy That Drives Real Value: Why Foundations Matter More Than Tools

By Brad Petry, Director Technology, Tenet Advisory & Investments

#1 IAPA Top 25 Analytics Leaders Awards, 2022

As data professionals we’ve been there. Building out what we think is a great dashboard or analytics tool and waiting for the user counts to go up. You deploy to the environment and inform your stakeholders that this great new tool is available and guess what: nobody ever looks at it. Worse? Another stakeholder will inevitably come to you asking to build the same thing one month later.

Data analytics initiatives that sit on a shelf don't help anyone. Despite this, in many organisations across Australia, sophisticated analytics capabilities remain underutilised because the strategic foundation to unlock their potential wasn't built first.

The reason is often straightforward: many organisations approach data strategy backwards. They invest in technology, hire talented analysts, and build impressive dashboards only to discover that insights go unused, buy-in stalls, and business problems remain unsolved. This can lead to the rise of offline spreadsheets - or worse, an organisation that blindly makes business decisions without using evidence and data.

The missing piece oftentimes is a deliberate, thoughtful approach to understanding the maturity of your business, knowing where you need to go to become data driven, and building the organisational capability to get there.

Understanding Your Starting Point

In my experience helping organisations on their data strategy, a common scenario is that data is scattered amongst siloed systems, there’s no source of truth, and people can’t trust the numbers due to real or perceived data quality issues.

Before charting a course forward, first understand where you’re currently standing. A data maturity assessment isn't bureaucratic busywork; it's a compass. It evaluates how effectively your organisation collects, manages, and uses data today. Are decisions primarily made on instinct and historical precedent? Is data scattered across disconnected systems? Are insights generated but rarely acted upon? These aren't deficiency questions; they're baseline questions. Organisations operate at different maturity stages, and that is normal. What matters is knowing which stage you're at so you can develop a credible path to your desired level of maturity.

The assessments that work best should look across multiple dimensions: governance structures, data quality, technical architecture, analytics capabilities, people and skills, and underlying processes. This holistic view reveals interconnected gaps that isolated projects often miss.

Building the Right Team: Diversity as a Strategic Asset

Analytics is fundamentally a human endeavour. However, many organisations approach team composition as a technical hiring exercise, looking to the market to find analysts, engineers and data scientists. If you build a data team of only technical staff, you’ll miss the true value you can create in your organisation with analytics.

Diverse analytics teams that span different backgrounds, experiences, academic disciplines, and ways of thinking solve problems better. There are so many benefits of diverse teams, but importantly they are more capable of recognising their own biases and solving issues when interpreting data and making decisions. They can also identify blind spots that more homogenous teams miss and are good at asking the right questions.

Equally important is breadth of expertise. You need a strong technical capability, yes. But you also need people who understand your business domain deeply, who can contextualise what the data is telling you and what you should do about it. You need communicators who can translate requirements and findings for non-technical stakeholders, who can be in the room with your technical teams and play the role of the user. You need people comfortable with change management, who understand that deploying analytics isn't primarily a technical challenge but an organisational one.

But hiring a diverse team isn't enough. Creating the conditions for that diversity to flourish is what is important. That requires building an inclusive culture where people with different perspectives feel safe expressing views that challenge the prevailing consensus. When a junior analyst from a non-traditional background holds a different interpretation of data, how do people react and respond? Are alternative explanations explored? Or are they quietly dismissed in favour of the loudest voice? Or worse, are they not even comfortable putting their perspective on the table?

To realise the value of analytics, you must set yourself up with a diverse team that can straddle all parts of the business and translate often vague requirements into actionable insights products.

Securing the Enabling Environment

Strategic alignment determines whether analytics creates value or becomes an expensive hobby. Too often, analytics teams operate in isolation, optimising for rigour rather than business impact.

Strategic alignment requires deliberate alignment between data strategy and business objectives. When executives understand not just what analytics can do, but why it matters to their specific challenges, buy-in follows naturally. Conversely, when executives sense that analytics teams are pursuing technical elegance disconnected from business priorities, support evaporates. And usually so does the budget.

Executive buy-in isn't secured through technical presentations. It's earned by demonstrating clear line-of-sight between data initiatives and business outcomes: faster decision-making, cost savings, revenue opportunity, or risk reduction. Real examples matter more than abstract possibilities, and success stories resonate more than capability statements.

This buy-in can come in so many different forms, and it’s important for analytics leaders to spot them early. Who’s the person around the table that is a natural advocate for data driven decision making? How do we get an MVP data product in the hands of the most influential executive leader in our business so they can spread the word on the value of analytics? This is often overlooked but one of the most important roles that analytics leaders can play for their team. Listen to what people need, translate these into requirements for the team, deliver something tangible, even if small, to demonstrate value.

This alignment also requires clear governance. Who owns the data strategy? Who decides on how to prioritise work? Who ensures that data-driven decision-making is embedded into how the organisation operates? How are we going to monitor our progress?

All of these questions need answers, ideally coming from the executive rather than the analytics team.

Execution Capability as Competitive Advantage

Finally, having the right strategy, team, and enabling environment means little without execution capability. This means concrete answers to unglamorous questions: How do we actually build data pipelines? Who maintains data quality? How do we manage the governance as systems evolve? How do we sustain momentum when the initial enthusiasm fades?

Organisations that excel at analytics have standardised their processes. They've automated what can be automated. They've built disciplines around data quality, documentation, and knowledge sharing. They've invested in tools and platforms that make it easier for people to find, understand, and use data. They also have a bias to action. Trying, failing and retrying is far more effective than debating the specifications of the product and scrutinising every field but not actually building anything.

The Path Forward

Building analytics capability isn't a sprint. It's structured progression through maturity stages, each building on the previous one. Organisations that attempt to skip stages and move straight to advanced analytics without foundational data quality and governance inevitably stumble.

The organisations that create genuine value from data are those that treat strategy seriously. They invest time understanding where they are. They build diverse teams with the right mix of skills and perspectives. They secure genuine executive commitment to data-driven decision-making. And they develop the operational discipline to sustain analytics as a competitive advantage rather than a temporary initiative.

My marker for progress is when you, as the analytics leader, start to notice that non-technical stakeholders around the meeting room table challenge assumptions and ask to see the evidence. This is a far greater signifier that you are maturing your data capability than the number of reports you produce.

About the Author

Brad is a data and technology advisor who works with organisations across a range of sectors to develop practical, execution-focused strategies that harness digital technologies, data and AI. He was named the #1 in the IAPA top 25 analytics leaders in 2022.