Merging Long-Standing Tradition and Modern-Day Analytics with Qlik


Humans are storytellers, and have been for ages. Many stories that exist today are hundreds of years old but have been given a modern twist. I never thought I’d be a part of such a story.

I joined Australian law firm MinterEllison almost two years ago as the inaugural Head of Data and Analytics. I came to the position with about 16 years of business insights and analytics experience, so I clearly understood the world of analytics and the art of creating new value from data. This time, however, the circumstances of my role were different.

Typically, in my experience, Enterprise Data and Analytics formed part of Digital/IT, Finance, or Marketing functions. However, at MinterEllison, the Enterprise Data and Analytics Centre of Excellence (CoE) sat within the Change and Transformation team, reporting directly to the Chief Transformation Officer. The CoE was set up for success and aligned to the firm's strategic imperatives from day one.


One of the next important considerations for me was whether to have a centralised, decentralised, or hybrid CoE model. I landed on a hybrid model—with a core centralised team offering capabilities around data engineering, data architecture, data governance, data science, and data democratisation. The model would enable and support a highly-engaged decentralised community embedded across the firm and leverage their business acumen to provide more relevant analytical solutions.

MinterEllison is not your average law firm. We are almost 200 years old and have the client relationships and reputation that come along with that legacy. But the differentiator for me was the organisational context behind the transformation. 

Throughout its existence, the firm has acted as a trusted partner to our clients, using our legal knowledge and deep understanding about our clients' business to help them reach their goals. However, in both life and business, survival requires adaptation. MinterEllison wanted to evolve beyond legal services and expand its offering to truly make a lasting impact for clients, people, and communities. That required a multidisciplinary approach of providing advice to our clients and stakeholders.

Charting the Transformation

In large part, the drive towards digital transformation was the result of company growth and increasing complexity of our environment. As the company expanded, we naturally began to accumulate more data. This growing data provided a lot of opportunity, and I was tasked with realising these opportunities. 

As part of this, we needed a way to use our existing tools and internal data to create more value, and where we didn't have the right tools, establish what we needed. I was brought in to take this idea of being data-driven and make it a reality.

One of my first steps with MinterEllison was to establish a three-year plan to become more data-driven. Sometimes, the hardest part of such a transformation is finding the right technology. Fortunately for me, the firm had already begun to modernise and adopt more digital platforms before I started, so I didn’t have to start from scratch.

Many business intelligence (BI) experts walk into a situation lacking the appropriate tools, people, or data processes. Indeed, I was hired as a one-person department. But the company had previously purchased Qlik as part of the move toward digitisation, so the core tools were already in place. Unsurprisingly, buying the software platform alone did not lead to transformation. Like most transformations, it is all about the implementation. I quickly came up with a roadmap to take advantage of our existing tools and kick our data-driven transformation into high gear.

The three-year roadmap that I put in place had a few horizons to serve as journey markers. First, we began with a critical assessment of our capabilities, both comparing ourselves to other firms and other industries including our new competitors. We had to understand what we were up against, and where we needed to adapt.

No data analysis transformation can succeed if the organisation can't trust the data source.

Once we completed that self-assessment, the next stage was to re-evaluate our approach to analytics. We needed a more agile and human-centred design. It wasn't enough to create a powerful data repository and data access platform. We had to have the competencies necessary to benefit from this new source of information. The third horizon was related to data literacy, and creating a program to educate staff and create awareness on the importance of being data-driven and simple actions they can take to help drive this program.

There were also a number of smaller, more technical requirements involved in our journey. For example, no data transformation can succeed if the organisation can't trust the data source. We had to make sure that the firm's data repository was reliable, well organised, and easy to maintain. The last thing we needed was to begin churning out data and making decisions based on incorrect data.

We also needed to revitalise Qlik and make it part of our plan. We launched dual initiatives related to Qlik and the firm's data repository so we could simultaneously curate reliable data and understand the software platform. Insights and reporting cannot exist if your team don't have the data expertise or if the team doesn't understand why analysis is important. We’d previously lacked the capacity to expand on any data literacy program, but now, with support from the firms' leadership, we expanded our team. 

Almost 18 months into our journey, we found ourselves starting to turn our attention to horizon number four. Once we completed the assessment, enhanced our capabilities, and pushed data literacy, we were ready to begin the analytics and data science stage. At this point, we invested in an AI/machine learning platform to complement our Qlik offering. 

Using a machine learning platform allowed us to learn, experiment, and fail much faster. It also gave us the ability to reduce some of the risks by taking a more incremental approach rather than starting with a large-scale project. Starting small and learning to fail has been key to this. We started with some key performance metrics and used the AI to make some elementary predictions. This was a great way to help our firm begin to understand some practical uses for data science.

What We’ve Learned About Data-Driven Decision-Making

We’re about halfway through our transformation, and have already learned some valuable lessons. 

It is absolutely necessary to include citizen analysts on the journey. This doesn’t mean building a small number of experts. Becoming truly data-driven means decision-makers at all levels of the organisation must have access to the same data. Creating that culture means starting from the beginning for employees without data backgrounds, and growing their awareness. We are in the early stages of our Data Governance Council, but that will be a key enabler of developing this culture as we go forward.

Becoming truly data-driven means decision-makers at all levels of the organisation must have access to the same data.

It is also important to choose the right data literacy partner. Qlik transforms companies into data workhorses every day, so we knew we could tap into their knowledge for education and training. We also made use of the Qlik Community. There is no reason to struggle through this journey alone when you have access to a partner that’s willing to offer guidance, and others who have done the same within their own organisations.

During the beginning stages of training, it is useful to make a plan to ensure that learning branches out into a shared community. It is not enough to simply give people instructions and then send them back to their respective departments. They need a way to grow together by sharing ideas and use cases. That way, staff can return to their own units and use Qlik to solve challenges in their own work. 

Everything you do has to be focused on broadly developing staff rather than choosing one person to mandate how data will be used. Start small. Keep your initial projects tied to specific use cases identified by your teams and closely aligned with your overall business strategy. 

Another thing to keep in mind when beginning your data transformation journey is that you cannot transform your organisation unless everyone trusts the data source. That means making sure your trusted data is ready before you begin making insight queries. Make sure the data is complete and correct, and that everyone agrees on the metrics and definitions. The last thing you need is to ruin your company's reputation by making ill-informed decisions by providing or using incorrect data.

Along those lines, getting buy-in from users is just as important as getting buy-in from management. While I was laying the groundwork for increasing our data capabilities, my manager and I agreed that I would get more support if I could put something tangible in front of people. That is critical in this process for two reasons. 

First, people understand it right away if you begin every stage and communication with a focus on business-centric problems. If you want to convince internal stakeholders that data is important, you have to present the information in terms that are meaningful to them. If leadership is focused on finding ways to manage costs and increase profitability, now is probably not the time for a general example of using data to build better widgets. Show examples of ways data can be used to solve existing problems.

Second, if you have something tangible to show right away, people can see that it works. To do this, you have to begin the data transformation with a focus on developing incremental value and growth instead of looking for that one huge project. If you start small and focus on routinely using your data analytic tools to address critical business outcomes, you can help staff to understand the everyday power of the platform. Let that everyday approach develop into the larger-scale transformation.

Predicting the Road Ahead

Early on in our work to adopt Qlik, we had about 15 users actually creating content. Today, that number has more than doubled. In less than two years we also went from zero users to 1,200 users in the firm active within Qlik—just under half our potential population.

Perhaps the best evidence for the impact of Qlik, however, is the emerging culture of curiosity.

Inquisitiveness and interaction with data leads to more informed decision-making.

As everyone knows, COVID-19 has completely changed the way we all live and work. Fortunately, because we already had these data systems in place, our staff faced the pandemic by asking more questions of the data. Everyone wanted new dashboards and reports that could help them make informed decisions during the crisis. This inquisitiveness and interaction is exactly what we had wanted all along.

As a result of this experience, we have begun asking additional questions about how we can use this data to create value and make data-driven decisions faster. We already know that in addition to internal data, our external clients all use their own sets of data to create insights. We hope to expand on our learning and provide more of these transformational experiences for our clients.

Qlik has been much more than just another analytics platform for MinterEllison. They played a key role in helping us to mature into our data-driven future. Bringing a centuries-old company into today's business intelligence world required a commitment to data and to excellence, and Qlik rose to the task. Now, our storied legacy has a modern approach, and we can share that commitment with our clients for another 200 years.