The role of Agile Analytics in instilling a data-driven culture

In the digital economy of today, everyone strives to be agile and instil a data-driven culture in the enterprise. Business leaders are requested to back up their business strategy with data-driven decisions. Long gone are the days when operations were driven by a hunch or precedents. Departments today are expected to present their strategies supported by numbers and metrics. 

But there’s still a challenge with the communication of insights between the analytics team and stakeholders. Data analytics team are hired and they do their job by providing insights, but the problem is that they rarely come across acceptance on the other side.

Robert Alexander Johnson, formerly from YouSee, currently Head of Data and Marketing Performance at Biites branded video content distribution, found himself in the exact situation as Head of the Data Analytics and Performance team. During his presentation at the Data Innovation Summit this year, he told the story of how a company went through a complete organisational transformation towards building a data-driven culture with agile analytics.

Turning the pyramid upside down

“As part of the analytics team, we had a lot of insight to sell, but nobody was buying”, says Robert. His team followed the traditional method of pushing reports vie emails with hopes people would read them, but stakeholders weren’t heeding them. Their goal was to introduce a pull scenario with data analytics where employees would ask for insights themselves and know where to search for them. But in order to achieve that, they needed to change the structure of the company and the mindset of the people. Traditional organisational models don’t fulfil the needs of an agile, data-driven culture.

Considering the complexity of a modern organisation, the different levels of hierarchy, interpersonal relationships between employees and impervious structures, completely changing how a company functions is a Sisyphean task. But YouSee is an example that with the right agenda and strategy, it is possible to achieve a data-driven culture. The company went through a massive organisational change from a bureaucratic, top-down hierarchy to a holacratic organisation.

As part of the analytics team, we had a lot of insight to sell, but nobody was buying.

Roberts describes the transformation as a radical change which meant that leaders and managers had to give up their power and authority. This also meant that they would no longer be the ones making the decisions. In a holacratic organisation, the people on the ground, the experts (UX, journey experts, editorial managers, developers), have better ideas about how a product could better perform for the consumers.

Tribes, squads and agile organisation

In the past, the concept of agile emerged as people realised that the employees are the one who should have the deciding power because they are the ones that are committed. Having this in mind, YouSee developed a structural concept of a tribe representing a holistic group of people devoted to a particular service.

The tribe is divided into vertical squads related to services, such as sales, marketing, service login and horizontal roles, such as product owner, editorial manager, journey expert, user experience, front-end developer and back-end developer. Each of the verticals and the horizontal chapter had a devoted analytics team offering support to them.

On the journey to convert pushing data into pulling insights with agile analytics, Robert’s analytics teams at YouSee adhered to three driving principles:

• The squad has end-to-end responsibility and people are accountable for their dedicated squad.
• The squad has multi-functionality with diverse sets of roles and competencies.
• Whenever possible, all work is carried out at the level of the squad. Squad members don’t need to borrow resources from other departments.

Apart from transforming the different departments into an agile mode, the analytics team also needed to a change of mindset into agile transformation. And in order to do that, they needed a sense of purpose. The analytics team had to ask themselves the question – Why do we exist. “In our case, the answer was – to instil a data-driven culture”, points out Robert. 

Following that purpose, the analytics team set up a set of success criteria:

1. All Aware of data and KPI’s available – all the members of the tribe would be aware of what data exists and the KPIs they can track to get insights.
2. Great performance monitoring tools in place – they would develop tools to enable stakeholders to make decisions, and not reports.
3. I know Who to Go to When I need What – every analyst made themselves known as a resource so employees across the tribe know whom to go to when they needed something.
4. Less rocket ships, more MVP, KISS Interactive – they would stop making rocket ships and focus on developing minimum viable products and keeping it simple and interactive.
5. Effective value in relation to time – what value can be made in the shortest amount of time.

Ingredients of an Agile Analytics Team

As part of their major competency transformation, the analytics team needed some fresh blood in their team. They asked themselves whether they should go for the digital natives or try to teach old dogs new tricks. The end result was a combination of both. Roberts shares the four ingredients they found helpful during the recruitment process:

1) T-Shaped competencies – The horizontal T part refers to having a high amount of commercial understanding, BI systems knowledge and data awareness. While the vertical side of the T should include sales & service, web analytics, a deep understanding of SEO, tracking/IT, architecture, tag management and data layer.

2) Integration with stakeholders – which entails getting the analysts out of their element and in the field to understand how the stakeholders work. In order to achieve this, they implemented several changes such as agile seating, immersion in problem-solving, sharing in the KPIs success of the stakeholders, and sharing and comparing solutions among other analysts.

3) Transforming static reporting to interactive learning – abandoning standard reports and encouraging stakeholders to engage, find answers via interactive reports.

4) 24/7 access to relevant insights across multiple devices – the insights were available on interactive reports on PC, mobile, tablet, on screens, reminders on posters. They made sure they spread the word about the structure of analytics, their mission, purpose and insights.

Give a man a fish and he’ll eat for a day. Teach a man to fish, and he’ll eat for a lifetime.

Components of a Digital Analytics Team

Roberts describes the three main components of their digital analytics team and shares what they’ve learnt with this re-organisation. Their digital analytics team was divided into to-site analytics, onsite analytics and tracking.

Tracking – which is enabling the ability to capture data. Some of the other golden rules Robert’s analytics team have learnt along the way are:

• Always remember to track. If it’s not tracked, you won’t know – and if you don’t know you can’t manage, advises Robert.
• Plan a tracking consultation with every project.
• Follow best brief practice and the order should be as follows stakeholder – analyst – tracking architect. The analyst is equipped to translate the stakeholder requests and KPIs into a more technical question about what needs to be tracked.
• The best way for stakeholders to present what they want t know is to put it in the context of a user story to show what they want to do with the information, what experience they want to have and what decisions they want to be able to take.

To-Site Analytics –  mostly relevant to the marketing department and traffic optimisation. What Robert transfers as knowledge from his experience with to-site marketing is:

• Marketing needs structure in order to be measured. And as a creative environment, marketing lacks structure and it’s a bit chaotic as a department. However, marketing employees need to understand that each channel/formats need to be properly tagged in advance to be able to determine which of them brings the most traffic to the website.
• People need to understand the roles of different marketing performance systems. Organisations tend to use multiple performance systems such as Google Analytics, Adform, Adobe and hence a confusion arises as to what numbers can be trusted. So it’s crucial to establish what is the purpose of each system and what decisions are made based on which system.
• Strive to make your external partners redundant.
• Take down the silos between IT and marketing. 

Onsite Analytics – the important key takeaways for this digital analytics teams are:

• Having a diverse team complementary in different skills. 
• Teaching old dogs new tricks. For example, teach your BI specialist web analytics, or challenge your web analyst to learn advance analytics.
• Collaborating with BI  for improving the value and the time to deliver insights.
• Building stakeholder self-competency and fewer reports. Enable your stakeholder to make better decisions by teaching them analytics. This will remove the bottlenecks in analytics and make stakeholders self-reliant.

At the end of the day, the most important criteria in a data-driven culture are whether your analysts and stakeholders are happy.

“Give a man a fish and he’ll eat for a day. Teach a man to fish, and he’ll eat for a lifetime.” rounds up Robert, adding that that was their philosophy in dealing with their stakeholders.

. . .

How to measure success 

As a true analyst, Robert knows the importance of measurements in everything you do. He states that it’s impossible to say whether the transformation towards an agile data-driven organisation was successful without feedback. Two years after operating with the agile model, he conducted a survey among the stakeholders, the analysts and employees. The feedback he received helped him understand how successful the transformation was. Robert looked at three criteria 

• Are analyst happy and satisfied?
• Are stakeholders getting what they need to make better decisions and how fast?

At the end of the day, Robert affirms, the most important criteria are whether your analysts and stakeholders are happy. That’s what you are striving towards and that’s what matters in a data-driven culture.