Why Data Maturity Is More Puzzling Than You Think

By Alexander Igelsböck Co-Founder and CEO of Adverity

The pandemic has proved a major catalyst for boosting data capability, but one misconception is still holding back progress: the belief that data maturity follows a linear path.

Subscribers to this false narrative think once they hit standard benchmarks for each maturity level — such as implementing certain tools — they can move up to the next until they reach optimal data mastery. But real multidimensional data journeys are more like putting together the pieces of a puzzle; with a picture that is constantly evolving.

Investing in world-class tech is not a magic bullet for getting ‘ahead of the data maturity curve.’ It’s not that simple. Rather, data development begins with identifying which puzzle pieces are already in place and where there’s a piece missing. Only then can companies start edging closer to the holy grail of insight-driven decision-making.

Arrested development

So what’s getting in the way? Gartner research shows almost 90% of companies fall into the ‘basic’ category of data maturity. The reasons for this vary by business but it is possible to identify several common issues, and legacy data handling is high on the list. Many businesses still run intelligence processes using spreadsheets; an approach that breeds inefficiency as data is locked within isolated sheets owned by separate teams. As well as obstructing a single view of procedures and activities — such as customer journeys — this decentralization results in inaccuracies, duplications, and unusable insight caused by differing definitions.

A matter of perspective

COVID-19 was an economic shock to the system, compelling businesses to rethink how they operate on a day-to-day basis. Yet it’s also had a positive impact on the data maturity journey by forcing many to become more agile and dynamic. While different businesses are at various stages of data maturity, the pandemic has accelerated digital transformation across the board and is poking holes in organizational silos, driving companies to be more streamlined.

There’s also a prevailing mindset that tech holds all the answers. Companies believe implementing the latest tools and building data science teams will put them in a better position. This approach works well for data mature organizations like The New York Times and Vitality, which are already versed in using complex data and sophisticated, automated data processes. Vitality – a health and life insurance business – is integrating data feeds with its network gyms, grocery stores, and biometric screening partners to track customer behavior and monitor outcomes in real-time to encourage healthy living. Member engagement is rewarded with points that can be redeemed for incentives.

The New York Times brought a significant amount of analytics in-house to increase the expediency of insights, and establish the effectiveness of sales and paid media weight. Analytics are also applied to coordinate project roadmaps and editorial to understand engagement and drive content decisions – removing silos and driving the discovery of new strategies and business opportunities.

But businesses at the start of their data journey, as well as sectors like hospitality and CPG that are typically (though not always) less data mature, don’t have a basis of contoured data management and understanding. This means the value of hyper-charged platforms will be limited.

Three core pillars of data efficiency

Wherever they are on their journey, businesses have much to gain from ensuring the three fundamentals of data efficiency are covered: empowering people, data unification and predictive insight.

1. Unleashing human potential

Development should begin with a cohesive cross-company vision. This is an issue for many organizations where leadership teams remain fixated on controlling top-down communications, giving managers mixed messaging. In practical terms, this calls for a people-focused evaluation of skills, experience and data use. Gartner suggests ‘data blindness’ to identifying and assessing skill sets is a roadblock keeping organizations from being insight-driven. Recognizing skills gaps and offering tailored training is crucial to equip teams with the expertise to extract maximum value from data.

Businesses must also address inter-department disconnection. For example, poor alignment between marketing and analytics teams might stop valuable insights from guiding precise ad campaigns, while zero oversight of marketing KPIs may lead the finance department to underestimate bottom-line contribution. Across the organization, all teams should have everything they need to deliver the right answers at the right time. Through simple adjustments to workforce structure, businesses can enable better cooperation and use data for maximum collective benefit.

Historically, there’s been the tendency for organizations to present big marketing ‘reveal’, but this just highlights teams are not on the same page from the outset. Everyone should provide input from the start – there should be no surprises.

2. Consolidating data strength

Of course, tech has an important part to play in helping companies wield data. Take, for instance, the silo problem. Hooking together data from countless spreadsheets takes analysts weeks and delays availability on the marketing side. Implementing tools to automate data collection and integration cuts confusion and reduces errors, as well as allowing faster access to – and application of – holistic insight.

Even after companies move from spreadsheets to entry-level analytics platforms, there’s still room for instant data consolidation to drive improvement. The ability to instantly connect, harmonize and assess data about multi-channel advertising can offer marketers a complete picture of performance and ROI, bolstering planning precision and their case for an increased budget.

To assess their level of data maturity, businesses must ask: Are they basing business decisions on data from multiple departments and sources? Do they have a strategy in place to extract actionable insight from data? Is reporting consistent so data is an asset rather than a burden?

3. Optimizing real-time decisions

After upskilling the workforce and bringing order to data chaos, the next consideration is speed. Again, companies need to ask whether they have made the jump from retroactively measuring performance based on point-in-time information and quarterly reports, to engaging in proactive, forward-looking optimization using predictive analytics.

By embracing a new breed of artificially intelligent analytics that unearths emerging behavioral trends, patterns and anomalies, organizations can determine how activities should be adapted in real-time for optimal results — whether that’s changing supply chain routes for faster delivery or re-allocating marketing spend for higher returns. In short; they’ll have the puzzle pieces for all activities fuelled by up-to-date, granular insights.

There’s no one-size-fits-all formula for acing the data maturity puzzle; the gaps companies must fIll depend on their processes, systems and situation. But recognizing the pitfalls of linear misconceptions and aiming to address common problems offers a starting point. By ensuring teams have robust data skills, access to a single source of truth and the ability to harness forward-looking insight, businesses can create the foundation for consistent evolution and continually rising maturity.