Stop the press! The first quantitative research is in, and while the results might not come as a surprise, they should definitely put a nail in the coffin for the 90 percent (Teradata) of companies that keep resisting the adoption of a data-driven approach.
Now tell us, how does a 5 percent increase in output and productivity sound to you? A number that according to Erik Brynjolfsson, economist at Massachusetts Institute of Technology, is ‘significant enough to separate winners from losers in most industries’. It’s time to stop making excuses and start crunching numbers. You do want to win, right?
Below, we’ve listed some of the most common excuses for not becoming data-driven along with how to turn those excuses into actions.
1. We don’t know where the data is
Ask. When the numbers are sitting on the answers you need, it’s not the time to be shy. If you have customers, you have data. Whether that’s demographic data, customer service data or digital interaction data - you have it. We all have access to an impressive range of analytics and optimisation tools (most of which are free), leaving us with no argument as to why we shouldn’t monetise on that data. The information you’re looking for may be buried in services such as Google Analytics, your CRM or in your own proprietary systems, and the time to use it is now.
Next step: Identify the systems and services in which your data is being captured and stored and determine the usability of each. Maybe there’s a better tool that will provide you with easier access and cleaner data that’s less time-consuming? Go get it.
2. We don’t have the right infrastructure
Build it. There’s no excuse for not implementing an information model that allows for better access to higher quality information. A data-centric and information liberating architecture is a long term win, and to implement a shift towards it will pave the way for future business gains.
Next step: Evaluate current architecture; advantages and disadvantages, gains and losses. If you’re not currently in a position to implement a new model, find alternative ways of getting what you need. Google Analytics is a good start. Start where you can, with what you can.
3. We don’t have any processes around data
Create them. A process doesn’t need to be a flowchart, a framework or some sort of rigid formula. It can be an email, a meeting or even a brainstorming session. Every company has different processes and it takes time to develop a process that works for your product, your company, the stage your company is at and your stakeholders. (Spoiler alert: You don’t need to wear a suit.)
Next step: Have an open and honest discussion with your team about what’s the best way for your company to tackle data. Don’t lose sight of the purpose: It’s about making data available and easy to use for every employee. Push the envelope, but be respectful of limitations.
4. We don’t know how to get cultural buy-in
Lead by example. Resistance to change is a natural thing, but it all starts with laying the groundwork. Showcase the benefits of using the right data in the right way. Constructively start building the confidence in data and the expertise around managing it.
Next step: Provide everyone with the information they need to understand the benefits of embracing a data-driven culture. Start from the beginning: Provide examples, use cases and statistics. Be prepared to answer questions.
Find out what Riley Newman, Head of Analytics at Airbnb, has to say about the components of building a data-driven culture here.
5. We don’t have the right skills
Look around. Chances are that you do. A common (and wrongful) assumption is that in order to understand and manage data, one must come from an analytical background. Does it help? Yes. But more important than anything else is to present the data in a clear and concise manner that allows people to ask the right questions. In order to ask the right questions, people need to know what they’re looking for.
Next step: Stop sending spreadsheets. Find the tools that will help you communicate the data in the most direct and seamless way possible that’s easily understood by everyone in the company. Explain the numbers and give people a chance to interpret and react to the data.
Eager to learn? Browse udemy’s data courses.
Got a bigger budget? Improve your data skills at General Assembly with this 11-week Data Science course.
6. We don’t know how to choose our metrics
Experiment. Choosing your metrics is a learning curve, a trial and error exercise. The key is to choose metrics that directly correlate to your business goals. Remember, a good metric is a number that drives change. Choose a handful to focus on to start with. If you can’t see any noticeable changes in behaviour or reactions to these numbers going up or down, chances are that they have little to do with achieving your end goals.
Next step: Before you choose your metrics, it’s imperative to have clearly defined business goals. Only once those have been agreed on can you move on to identifying the metrics that will help you increase performance and achieve the goals you’ve set.
Learn how to use data to build a better business faster in this free online Lean Analytics Workshop.
7. We don’t know how to act on data
Find correlations. First thing to look at is if there’s a relationship between your hypotheses and the collected data. However, even if there is one, you must still identify how confident you are in this relationship. What you’re looking for is a frequent correlation where the benefits of acting on your data outweigh the risks. Instead of acting on biased assumptions and personal preferences, it’s time to act on facts. First come the numbers, then comes judgement.
Next step: Get comfortable with your data by running tests, trying, learning and iterating.
8. We don’t understand how data will drive our ROI
Define ROI. Besides hard cash, the impact and ROI of a data-driven approach isn’t always black and white, nor is it immediately visible. It normally takes some time to develop a full understanding of the benefits of being data-driven. Bear in mind that every business will develop its own data culture along with various levels of ROI. Biggest payoff? You’ll be confident in your decisions as they’ll be based on customer data and less tainted by personal bias.
Next step: From a business perspective: Identify how much time, money and resources have been dedicated to strengthen your data-driven approach and compare these to what you have achieved over that same time period in terms of milestones and targets. From a company culture perspective: Evaluate workflows, processes and even individual contribution to see if these have improved.