4 Common Data Challenges – and How to Overcome Them

It’s high time we stopped talking about the need for better data execution. Most marketers know the current rate of data production is running at 2.5 quintillion bytes a day.

They are also aware of growing consumer demand for uniquely relevant and anticipatory experiences. And, according to recent research from the Chief Marketing Officer (CMO)Council, they are conscious that harnessing huge data stores to deliver these experiences isn’t easy; just 7% of marketers feel they are exceeding expectations in delivering real-time, data-driven engagements across touchpoints.

What’s less certain is how common challenges can be overcome to achieve data utopia: one-to-one experiences and smart business decisions, powered by accurate intelligence.

So to move the conversation on, let’s explore today’s top four concerns — and solutions.

Ensuring data integrity

With more data generated over the last 24 months than in the previous 5,000 years, it’s safe to say that the potential for insight error has considerably increased. Not only is it difficult to define which data aligns best with each campaign and target audience, but it’s also hard to ensure quality remains high.

And when data value is low, relevance and performance often are too; indeed studies show as much as 6% of annual business revenue is lost to bad data.

To address this problem, marketers must adopt data management tools that provide a clear view of data assessment, and take robust cleansing measures. When selecting platforms, they should look for technologies that continuously monitor and filter data input to make sure any poor quality or expired insight is quickly removed.

For example, platforms may use techniques such as cartographic data to validate location-specific intelligence, or deploy multiple source linkage during broader analysis to identify discrepancies and duplications.

Achieving a 360-degree view

Another key issue with constantly multiplying data sources is the tendency to collect data in fragmented silos. For instance, insight about digital consumer activity such as website visits is frequently stored separately to real-world data, like retail store purchases; especially if it’s spread across isolated platforms.

As a result, marketers can’t connect the pieces of omni-channel consumer journeys to generate a complete picture of individuals, which in turn means they can’t tailor touchpoints to maximise engagement or guard against repetitive messaging that could cause irritation, and cost sales.

There is, however, good news. Along with recent developments in Artificial Intelligence (AI) has come a simple and efficient solution to the silo issue: data intelligence. In short, this is a term that describes the procedure of using AI-powered tools, or software as a service (SaaS) products, to combine disparate datasets and make them usable.

It entails merging an array of sources — be that smart phone, car and home data or information about location and the weather — to create a 360-degree view of individual consumer journeys, both on and offline. So, with such cohesive insight at their disposal, marketers can finally keep pace with consumers and precisely determine when, where and how to serve messages for optimal resonance.

Safeguarding user privacy

The majority of marketers are already aware that in May 2018, the General Data Protection Regulation (GDPR) will transform global data management; and the proposed UK equivalent will also mean a significant transition on home ground.

Soon, marketers will need to request permission to access any UK or EU-citizen data classed as personal (information that renders individuals personally identifiable) and plainly explain what data is used for, as well as where it is held. Yet, at present, this is a transition half of businesses feel unprepared to meet.

Of course, there is no single template for GDPR compliance, methods will vary depending on how much data a business processes, and the way they do so. But the central factor they must bear in mind is that the GDPR and UK Data Protection Bill are about transparency.

Intended to give consumers more clarity and control, the new laws are an opportunity for businesses to build trust by being open with consumers. The best option marketers can take is auditing data to establish what their current assets are and streamlining them for better adherence, and efficiency.

Taking the right business action with real-time resonance

As hyper-connectivity becomes the norm — with 45 billion devices due to be wired to the web by 2023 — consumers are beginning to demand experiences that don’t just deliver what they want now, but anticipate and meet needs they will have. In terms of data this means two things: analysis must be done in real-time and there is no room for any insight to be out dated. But can this be done?

Once more, AI has the answer, yet this time it involves a subset of AI: machine learning. In the last few years, an evolution in marketing technology has spawned platforms capable of immediately analysing data and accumulating it to spot patterns over time.

For instance, with current messaging, intelligent platforms can use real-time data to adapt marketing messages on multiple channels for in-the-moment impact. Yet they can also look ahead to give marketers a competitive advantage: amassing insight about past purchases and serving offers for products individuals are most likely to want next.

Though it’s beyond a doubt that data is the fuel of the future, it must also be acknowledged that a carefully constructed strategy is the engine for success.

If marketers want to move past the data implementation discussion, they must focus their efforts on practical and achievable goals; adopting smart technology and processes that will enable them to master data now.

Also published in MarketingTech.