82% of decision-makers believe analytics can strongly improve their business activities and processes. This figure illustrates a trend that’s gaining ground with more and more companies: data democratisation. In theory, a data-driven approach seems almost simplistic: you open up access to data to all employees, and you expect each person, at his or her own level, to decide which information is useful for his or her own work, based on the numbers. The result? Reduced costs to access information, greater efficiency, and faster decision-making.
Sure, but with this kind of project, the traps can be numerous. Security, data responsibility, questions of governance, the human factor, training, change management… many things must be considered to avoid pitfalls and make your project really happen. Here are a few principles, some general and some very specific to the digital analytics field, which we think are crucial to apply.
Get project support from a member of upper management
Buy-in from general management is crucial. If you forget to get an influential person from the board on-board with your adventure, it can be penalising… This person should be there to support and promote your data democratisation initiative internally by:
- Guaranteeing the alignment of your analytics project with the company strategy and priorities
- Safeguarding the project’s very existence (which could be threatened by any internal conflicts or pressure)
- Providing significant pull when it comes to acquiring analytics budgets
So who should this super ambassador be? There’s no set position or role, but this person should be both convinced of the value of a data-driven approach, and sufficiently influential to protect your project and help it advance.
Consider the impact and disruptive effect on decision-makers
By all logic, if nobody from the executive management level is a stakeholder in your data democratisation project, no strategic decisions will be made based on analytics data. But beyond this observation – and even if data is only one influential element (amongst others) in decision-making – the idea of using data to manage business can be a major point of contention in certain companies. It’s not always easy for a decision-maker to rely on the numbers and give up some of his or her decision-making power. The biggest challenge in tempering this radical change in culture is proving the effectiveness of democratising analytics in boosting business growth.
Align analytics KPIs with the company strategy
A direct link should exist between the company strategy and your key performance indicators (KPIs). In other words, all your efforts should support the company’s overall strategy: product offering, business objectives, areas of development, key markets, internal organisation, etc. Each person who uses data can, at his or her own level, set objectives and a scope of measurement. This could revolve around geographic areas to address, specific business operations that are underway or upcoming (like sales periods, the holidays, etc.), channels to be optimised (mobile sites vs. apps, for example). After determining these areas of focus, KPIs and metrics can be determined which will enable each specific job role to better drive and manage its digital performance.
Address the question of data responsibility
The person who collects, processes and publishes data is not (necessarily) accountable for the results it reveals. Data can be a powerful catalyst, whether it serves to justify a decision, stop a campaign, renew a budget… or to fuel the flames of an internal conflict, or hamper the evolution of a given project. The political aspect of data is very real, and its impact can be felt throughout the company. The analyst who reports good or bad news is only the messenger. He or she shouldn’t have to suffer because of poor performance… nor should he or she be congratulated for good performance, either. Nonetheless, the analyst is the guarantor of strong data quality (data that’s accurate, consistent, timely, clean and complete). So be very clear about things. Help each person be conscientious and determine responsibilities for analysis, processes, usage, security and results. In short, lay the foundations for a solid governance strategy, and don’t leave any room for ambiguity. The analyst must procure and maintain this independence for his or her own survival.
Don’t bet everything on your analytics tool
You’ve got an awesome tool, super-advanced features, everything should just take care of itself, right? Alas, no! You mustn’t count on your solution alone. Even the best analytics tool in the world is useless without the experience of competent and trained individuals who can implement the tool and use it to its full potential. The higher the technical complexity, the more critical human analysis becomes for interpreting numbers, detecting weak signals, and putting data into context. Do not bypass or neglect the human factor. Train your teams on how to best use your analytics tool, and let an expert agency help you interpret your analytics data.
Turn to your analytics team
In other words, it’s important to sufficiently and meaningfully invest in your analytics human talent. The team (or the individual) in charge of your analytics will be the driving force behind your data democratisation project. Obviously, your analytics team will be involved in implementation, analysis, reporting, and performance management on a daily basis, but will also play a key role in change management and evangelisation to your other colleagues. Make sufficient investments, and create an organisational structure that reflects the complexity of your measurement needs, your business activity, the number of data users in your company, their data needs, and their digital analytics maturity.
Don’t forget data quality control
After the analytics solution implementation phase, it’s verifying your data quality which will determine the success of your project. If data is inaccurate, inconsistent, outdated or unclear, it can be a source of judgment errors, threaten your credibility, and sink your entire data democratisation initiative. And unfortunately, the analytics sector has long suffered from an association with data produced by free tools, which is often sampled and unreliable. To earn the trust of your decision-makers, and to have a true impact on your company’s strategic decisions, your data must be robust. Use tools like Tag Crawler and Tag Inspector for data quality control before working with your data and sharing it. Questions about this vast topic? Have a look at our comprehensive free guide on data quality in digital analytics.
Remember, your colleagues are not all analysts…
… and they’ll be able to sort everything out with their data by themselves, easily. No, unfortunately, this is not the case. They probably don’t understand all your jargon and are sometimes total newbies when it comes to interpreting an indicator. As an analytics expert, your role is to lay the groundwork for them. This means first defining a real strategy for using analytics data within your company. Ask yourself the right questions: Can your colleagues work independently enough to consult their performance by themselves? What are the 3 essential KPIs they should focus on? How should requests be managed and prioritised? These considerations – and many others – will help feed your organisation’s analytics best practices.
Encourage simplified access to and visualisation of data
Data visualisation technology has become increasingly popular. There’s no doubt about it, dataviz tools encourage the trend of democratising data for audiences who are not familiar with analytics. The formula is simple: intuitive tools, simplified interfaces, streamlined UX and immediate access to relevant information. As the provider of an analytics solution, our challenge is to give users the ability to work independently so that they can easily decode their data and extract insights.
Consider privacy and security issues
Placing analytics data in the hands of all your colleagues doesn’t come without some risk. With the GDPR coming into effect in May 2018, questions about privacy are, more than ever, at the heart of the debate. It’s therefore absolutely necessary to think about an analytics governance system that addresses these new legislative aspects. It’s also a way to protect your employees (and avoid any blunders!). Here’s an example related to your analytics solution’s access rights system: to save money, certain companies will have shared logins via standard email addresses. This can create huge security breaches, as these shared logins often remain active even when a person leaves the company. Take great care to make access rights secure and personalised in your analytics solution (which should most definitely give you this option!).
Invest in training (for the long-term)
Your analytics tools are constantly evolving, as are your objectives. Democratising your digital analytics is a fundamental effort. You must therefore consider this project more as a marathon, rather than a sprint. Whether you need to learn to interpret results, or learn how to use new features, training should be a part of your long-term plan. Lack of skill is a major roadblock to many analytics initiatives. Certain metrics can create confusion for non-experts (like “visitor” vs. “visit”, for example). So without any training on the basics of web analytics, your colleagues may not understand their data, and might even interpret it wrongly. What’s worse, if it finds itself in the wrong hands, data can be stripped of its meaning and even manipulated to be harmful for the company. Training ensures that shared vocabulary is used among colleagues, and also guarantees the trust so necessary for analytics within your organisation.
Go beyond the context of analytics
To be used on a company-wide level, analytics data must be enriched. Too often, we restrict ourselves to a few standard analytics metrics, without looking to go any further with our analysis. When used to its full potential, a (good) tool can give you so much more information than number of visits, visitors, page views and bounce rate. Take this example: marketers generally use several tools to measure and optimise marketing actions (emailing, dataviz, BI, DMP, and testing tools…). If your analytics solution interconnects with other systems, it unlocks maximum value for you: importing and extracting data, enriching your CRM base, and visualising 3rd-party data. In short, this aspect is fundamental to providing relevant performance indicators to your business users, and to helping your data democratisation strategy flourish.
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