Article

Emily Pick
Emily Pick 3 May 2022

Why Advancing Data Maturity Will Unleash Your Digital Potential

There’s no denying that brands which leverage and invest in capabilities with data will be the ones to maximise their full revenue potential. But whilst today’s marketers have a wealth of data at their fingertips, knowing what data to use and how to use it are two of the biggest challenges of all.

In this article, we share a strategic framework to help businesses identify where they are on their data maturity journey, as well as how to enhance their data capabilities and literacy to drive innovation and growth.

The digitisation of the economy has led to an explosion in the creation of data. Over the past ten years, the world has gone from creating 6.2 zettabytes in 2012 to approximately 97 zettabytes in 2022.

The opportunity of such a dramatic increase in data is not lost on business executives across industries, many of whom have scrambled to use the data to analyse as much as they can about their customers. Big data analytics solutions spending is set to reach $215.7 bn this year worldwide.

However, the use of data across businesses is an inconsistent experience. Some companies have invested wisely in developing their data maturity.

With little notice, they can flexibly pivot to maximise revenue opportunities in an unpredictable economic environment. Others experience a cycle of diminishing returns as they attempt to replicate prior successes with mixed results. 

Data Maturity in Marketing

Marketers who are proficient in leveraging advanced data and analytical techniques to achieve critical objectives are understandably in high demand.

As opposed to marketers in businesses with low data maturity, marketers with advanced data maturity and literacy understand how to transform data into critical business assets. They govern its application and impact on business goals, using data to gain significant competitive advantages.

But what exactly do we mean by data maturity? In this article, we discuss our definition of data maturity, share some of the learnings we've experienced and outline a simple framework that marketers can use to benchmark their own level of data maturity.

What is Data Maturity?

Data maturity refers to the evolution of a firm’s data capabilities. Although at first glance it can seem contentious, perhaps implying less developed companies are somehow ‘immature’, that’s not the case in practice.

The term data maturity has a very specific contextual meaning. The emphasis is on development, with an appreciation that every firm starts from a different place and requires specific actions to progress.

Why is Data Maturity Important?

Understanding your organisation's data maturity is important for five significant reasons. An appreciation of data maturity can help marketers to:

  • Align: Understand which problems and challenges the wider business is trying to solve and adapt strategies to support objectives.
  • Appreciate: Dispassionately analyse what the business is good at doing today and where improvements need to be made to drive better data decision-making.
  • Evaluate: Benchmark data literacy levels, applying training and upskilling resources to ensure the introduction of an open learning environment to support innovative thinking.
  • Anticipate: Look forward to exciting advanced analytics capabilities as the business’s data capabilities mature.
  • Calibrate: Optimise tech and infrastructure to not only extract maximum value right now, but accurately plan for resources they might need in the future.

As we mentioned earlier, some organisations have managed to nail the balance and accelerate their capabilities way ahead of the competition. Let’s take a look at some of the best examples of how advanced data maturity has positioned a few companies head and shoulders over the rest.

Monzo

Founded in 2015, Monzo has prioritised internal collaboration to an impressive degree. The challenger bank has opted for a structure where there are no traditional BI (Business Intelligence) teams internally. Instead, data scientists can autonomously conduct the end-to-end analytical workflows. 

Monzo encourages teams to develop data models that benefit the business as a whole. Despite a fiercely competitive fintech market, Monzo continues to cement its reputation as a market force in retail banking. In 2020, Monzo was valued at $4.5 bn (USD) and is Europe’s third most valuable neobank.

Asos

The retail sector has struggled in the past two decades, since the primacy of Amazon upended traditional high-street retailing.

ASOS is one of the fashion brands that has successfully bucked the trend though. The fast-fashion giant credits a great deal of its success to its data strategy, specifically testing hypotheses in miniature to prove their value before scaling up.

The company has run brand lift studies alongside geographical experiments across markets. It used the intelligence gathered to determine its strategy in 2020, as the pandemic began to bite. In 2021, the brand was worth $1.3 bn (USD), growing its value at an unprecedented time for the global economy.

Netflix

Netflix’s recommendation algorithms generate suggested content, recommending titles to viewers in a dynamic way. Each has a different category (usually a genre or theme), with the title that’s most likely to match a user’s interest placed first.

Recommendations are automatically generated and ordered based on a number of factors including watch history, length of viewing titles, the device currently in use and more. The company captures data on which suggestions were skipped by the user, and which titles the viewer passed over.

The state-of-the-art recommendation algorithms help to increase the amount of time that users spend in their applications. In the attention economy, Netflix continues to dominate where its competitors regularly struggle, enjoying 34% of all US streaming minutes, compared to 8% for Amazon Prime and just 4% for Disney Plus.

Babylon

Babylon has embraced transparency to great effect. Rather than keeping its findings totally confidential behind locked doors at the company, Babylon offers to share what the platform knows about its users.

The company states that its aim is to remain upfront, providing data resources for customers in a bid to drive enduring loyalty amongst its user base.

The examples above represent some of the best-in-class uses of advanced data maturity. But it is important to bear in mind that organisations do not just appear out of nowhere with advanced data maturity. There are steps you can take to progress.

At twentysix, our work with senior marketers and CMOs has led us to develop the LEAP framework: a simple data maturity curve with three steps and stages.

To understand the LEAP framework, it’s important to first appreciate the pivotal role of internal structures in defining data maturity.

Data Maturity in Internal Structures: An Introduction to the Leap Data Maturity Curve

Internal structures are entwined with the level of data maturity, as they can erect invisible barriers or enable cross-business collaboration to varying degrees. Based on our work across business sectors, we’ve split internal structures into three categories in relation to data maturity. 

1. Siloed

Businesses at the start of their data maturity journey will likely have internal structures that operate independently. Marketing, in particular, will probably exist as a separate function from the rest of the business. A lack of integration leaves departments working in silos. Insights are not as widely shared or accessible across teams. 

As a consequence, KPIs and objectives are quite basic and generic in nature as teams can only work with what they have access to or control over. Marketing performance will primarily be based on KPIs like onsite conversion, perhaps on a channel by channel basis. 

It isn’t possible for marketing to attribute conversions to offline customer sales, as that data is not readily available to them and sits with sales teams. Equally, the sales team may not have visibility over which marketing campaigns drove quality leads.

Objectives are typically focused on filling up the funnel, activities are reactive and there’s a high focus on cost-efficiency above all other considerations.

Status: Limited, isolated and reactive, focused on cost-efficiency and conversions.

2. Synced

Teams that are synced work together across departments and functions to achieve shared, common business goals. Platforms in use have been integrated for the rapid dissemination of shared learnings. Data governance is established and regularly reviewed, with ethical and compliance standards embedded in data usage across the business. 

Teams that are synched are more proactive, more organised across departments and value productivity and collaboration. Data is beginning to be applied and appreciated as enterprise assets.

Status: Organised and synchronised, with productivity as the main priority. Business-wide accessibility for data insights is prized and respected.

3. Smart

In this most advanced stage of data maturity, teams are highly integrated and the use of quality, consistent data is embedded deeply across all departments. Rapid experimentation, continuous improvements and co-creation are swift and intelligence is shared between teams regularly at pace. 

Data teams, in particular, are highly skilled, capable of building and deploying useful machine learning models that are used to generate more accurate insights, solving pertinent business problems.

Marketing campaigns are ethically and intelligently deployed. Platforms are unified and performance is understood from across multiple touchpoints in the consumer journey.

At the most advanced stage of data maturity, speed and innovation are prized.

Status: Optimised for maximum competitive advantage and customer intelligence.

3 Examples of Capabilities with Different Levels of Data Maturity

The LEAP framework is a useful way to understand the capabilities of businesses at each structural stage of data maturity. Each step is centred around progressing to the next level of data maturity and what marketers need to do to support their teams to advance.

Stage 1: Learning & Enablement

At the earliest stage of data maturity, marketers should expect to drive:

  • Fundamental analytics and data literacy training
  • Widespread use of fundamental analytics tools such as Google Analytics
  • Accurate tracking of basic KPIs, such as conversions
  • Data standards of accuracy and compliance.

Stage 2: Apply

At the next stage of data maturity, marketers should expect to have implemented:

  • Data-driven attribution models to take on increased importance
  • Server-side analytics, with internal self-service dashboards for accessible insights
  • Marketing activities incorporating conversion optimisation, with performance boosting and personalisation in the mix
  • Intent scoring, RFM modelling and LTV analysis for a deeper understanding of actual customer cohorts.

Stage 3: Predict

Finally, at the most advanced stage of data maturity, companies should expect to be able to apply predictive analytics from data assets confidently to power:

  • Unified offline and online integration
  • Single customer view and fuzzy matching
  • Cloud enabled, streaming event data and real time readiness
  • Big data ready infrastructure
  • Automation enablement
  • Pattern mining and sentiment analysis.

By this stage, a business should be ML-ready with always-on test and learn programmes. Marketing teams can rapidly leverage clarity from continuously optimised data driven attribution models.

They anticipate customer needs and pain-points through use of methods such as propensity modelling, eliciting higher engagement from active users with recommendation systems in place.

Breaking out of a siloed structure depends on a coordinated effort between stakeholders. So how can marketers assemble the right teams to drive data maturity?

How to Assemble the Right Team to Drive Data Maturity

Data maturity applies to the whole business. It’s an organisation-wide effort that stretches beyond the priorities of one sales or marketing team.

As such, it’s important to draw together various internal influencers to identify how changes to your data strategy can help everyone achieve the same goals. The composition of stakeholders is unique to every business, so it will depend on the priorities of your individual business.

It can be difficult for internal teams to break out of established siloed thinking, so consider working with an external partner to establish neutral ground. A qualified third party can help uncover the mutual roadblocks that can unify teams behind a project of advancing data maturity.

If everyone agrees on an approach, with a mediating influence to act as a counterweight, it’s more likely that the project will be readily adopted.

Upgrading Data Maturity to Predict the Future

Improving your data maturity is an essential step in transforming marketing functions. With a clear framework in place, precise goals that are aligned to wider business objectives and stakeholder buy-in across functions, marketers can deliver more consistent results.

In time, marketers can help their organisations capitalise on their enterprise data assets and advanced data maturity to rival trailblazing corporations.

However, success will depend on developing coordination between departments in challenging ways. External parties can help facilitate cross-departmental discussions, but marketers need to lead the charge to advance their data maturity and unleash the full potential of digital marketing.

Improve your Data Maturity to Future-Proof your Business

twentysix is an expert consultancy with extensive stakeholder management experience to help empower marketers to maximise their potential. We work with marketers to develop their data maturity and advance their capabilities with analytics.

For more on how CMOs and senior marketers can use data to increase their influence, download our white paper now.

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