Whether due to inertia or fear of disruption, too many companies have opted to stay with the same web tracking analytics platform, despite concerns regarding privacy and complaints regarding functionality. But no longer. From the European data privacy rulings that deem companies using Google Analytics are in breach of the General Data Protection Regulation (GDPR) to Google’s decision to sunset Universal Analytics (UA) in 2023, the excuses have run out. There is no choice.
The new version of Google Analytics (GA4) is not backwards compatible with UA. Therefore, every business making the move will have to redesign its data model, change the technical implementation, build up historical data from scratch and train teams to use the new tools. All of the challenges associated with any analytics migration are now inevitable – leading many companies to ask: Is it time to change analytics platforms?
There is a plethora of tools available which offer greater functionality, a demonstrable commitment to data privacy and ethics, and a far more sustainable, privacy-first deployment. Now is the perfect opportunity to choose an analytics solution that truly reflects your business needs and outlook.
Enhanced analytics capabilities
Across Europe, recent data privacy rulings have thrown business reliance on Google Analytics into conflict. By systematically transfering European user data into the United States, Google Analytics—and by association, every business that uses it—is breaking the GDPR. While the ripples of these European rulings have yet to be felt in the UK, the additional announcement from Google that it is pulling the plug on UA in 2023 should be raising serious concerns.
This is not some minor upgrade or version change: GA4 is a completely different product, based on a completely new data model. Every business has to make a change – either to the new version or a different platform. The risk of losing data is real. Moreover, given the need to build up at least 13 months’ data in the new analytics platform to ensure consistency of year-on-year reporting, the pressure is on to make the migration decision right now.
Whatever analytics platform a business adopts, a shift to the new, event-driven data model is a given. This is the way analytics platforms now approach data capture, which means every deployment will require the migration of data streams into an event-based schema. But not all schemas are created equal – and data quality is a vital consideration. The problem with tools that rely on sampled data is that, inevitably, at some point the business will end up making decisions based on assumed knowledge – something that, when incorrect, can rapidly undermine user confidence in data accuracy and relevance. Opting instead for an analytics platform that doesn’t sample data and seamlessly connects data in all the tools, APIs and reporting interfaces is key to ensuring a complete view of a customer’s interaction with the brand and the comprehensive understanding to build trust throughout the business.
With consistent, reliable, real-time, complete data, every individual and every part of the business will use the same, consistent information – from the CEO looking at one Key Performance Indicator to the data scientist running Python with raw data sets. This is the foundation that then enables a business to explore the additional functional innovation on offer. Features such as enriching data, ad hoc data mining and the use of interfaces that improve data accessibility and understanding.
For publishers, for example, proactive alerting is a hugely valuable tool that supports a far more nuanced approach to content accessibility and value. Real-time tracking of anomalies on the website will rapidly highlight if a piece of content is trending, as a result of an Instagram or TikTok influencer. Rather than discovering the spike in interest the next day, by which time the opportunity has been missed, with a real-time approach, the publisher can immediately act and maximize the value of new traffic. A tailored response – such as subscription offer or registration option – is a far more flexible and dynamic approach to the value exchange that will be increasingly key to capturing readers and revenue.
This ability to be far more nuanced in approach also supports changing attitudes to data privacy. Ideological stances on customer data and customer privacy are changing – and vary across the world. For organizations evolving their data compliance and privacy stance, it is vital to understand not only how to ensure compliance, but also to communicate the strategy to customers through a clearly demonstrated consent policy.
It is also essential to recognise that the concept of data privacy will continue to evolve, and legislation will change. Businesses need to distinguish between a vendor that puts data privacy first, that acts on new legislation, proactively makes changes to ensure compliance and notifies customers – versus one that has limited, if any, interest in country-specific data regulations.
Businesses can take steps within the initial data design process to ease compliance. For example, with a data model that can be flexibly managed, the company can add a flag to any data that is considered to be personal data. In addition, a number of countries have tracking exemptions, where specific data can be tracked without obtaining prior consent. The ability to flexibly manage this data, alongside user sensitive information and user agnostic information, supports a business’ data privacy stance both today and as it evolves in the future.
The issues raised by a migration of analytics platforms, whether driven by privacy or functional objectives, underline the enormous changes that have occurred over the past few years. Analytics implementations even three years ago are now out of date. From the technology shift towards an event-driven model outlined above to the sweeping change to data privacy laws, analytics deployments require a new approach, a new way of thinking.
But this is a significant overhaul, and certainly not one to be repeated every few years. The data analytics process is increasingly business critical – and no data-driven business can afford the disruption associated with repeated changes to the analytics platform. Vendors that are committed to privacy and to supporting businesses in maximising the value of analytics data within the bounds of what is both possible and ethical will provide a far more sustainable, long-term solution. Relying on a vendor that is failing to pay even lip service to data privacy rulings adds significantly to business risk and could lead to another time-consuming migration within a couple of years.
Staying with the same analytics platform has been the easy choice – until now. But change is inevitable. With so many tools available that offer more features, a stronger data model, a commitment to data privacy and the foundation for a much longer, sustainable deployment, the onus is on companies to take this opportunity to implement an analytics solution that truly reflects both business needs and business values.