Data is often referred to as the new oil, since the profits available to those able to extract and refine it are substantial. For businesses, that means a deeper understanding of consumers and greater operational efficiency. But as much as 73% of data goes unused within companies.
For many, this ‘data blindness’ stems from not knowing where to start – not from an inability to actually collect it in the first place. This guide is all about putting your data designs into practice – the steps you need to take to ensure you get the most out of your data analytics.
The benefits of data for companies
More companies are starting to understand the opportunity data analytics represents. A recent EY study found that 73% of surveyed businesses considered data analytics crucial for understanding consumers. It carries the promise of segmenting customers into smarter groupings based on purchase histories and demographics, to identify specific trends and tastes.
Supermarkets are highly effective at utilizing this type of analytics. Through using loyalty card schemes, supermarkets are able to identify individual purchase behaviour and create consumer profiles for targeted advertising. It also helps them upsell goods based on what other customers within the segment purchased. Through this type of data analytics, customers can be better understood and anticipated – improving both service offered profitability.
But it’s also hugely valuable for improving internal operations across the whole company. By understanding everything that goes into different tasks and identifying inefficiencies within those workflows, business waste can be dramatically reduced. It means fewer overheads, improved productivity and a more reliable operation.
Automation seems to be the answer here. Increasingly, everyday work tools are building automation into their offerings to help companies access a richer understanding of their own behavior. Take time tracking for example – automatic trackers like Timely can collect everything an employee works on without any manual input, so they get an accurate picture of what they’ve done without wasting productive effort. This insight can help identify blockers, broken processes and inefficiencies, helping everyone in a business work smarter.
How to overcome data blindness
In view of these benefits, it’s surprising to learn that the number of companies identifying themselves as “data driven” is actually declining. Though businesses consider data analytics important in a range of use cases, the difficulty and short-term costs of implementing data strategies often put companies off.
But this paints a worrying picture for future competition, since companies who ignore data do so at their own peril. Data doesn’t have to be daunting; it just takes a few practical steps to help your business embrace data and start reaping the rewards. Here’s how to go about it:
Forge a data mindset
If you’re serious about using data, you need to forge a company-wide data mindset. Effective change starts from the top and requires a whole shift in company culture – you can’t just hire big data analysts and hope to start seeing results. Being data driven requires a conscious commitment to identifying, capturing and storing data. People need to understand why you’re interested in it and what it means for your whole organization. Everyone should understand the importance of grounding all decision making and problem solving in data. Until your culture actually values and prioritizes data, you can’t move forward.
Use relevant data
Bigger data doesn’t always mean better data. Collecting the stuff in bulk doesn’t immediately mean you’ll start making more informed decisions, and it might actually stall your progress. By collecting irrelevant, you risk oversaturation and it quickly becomes difficult to manage. Instead of overcoming data blindness, you just amplify the problem – and put people off data in the process.
So to get the most out of data, you need to understand which types of data will be useful for which problem. It’s all about being data aware at the collection stage – knowing exactly which insights are useful. Uber provides a strong example of this: whilst it collects a ton of data, the company’s success is not down to total volume. Instead, it’s a result of using the right data which focuses on the very specific task of dispatching cars in the most efficient way. You need to be clear on what improvement you want to make and then recruit only relevant data to achieve it.
But keep it compliant
Data enthusiasm is great, but you need to apply it responsibly in accordance with regulation. You can only access the benefits of data by getting there legally and fairly. In view of the increasingly stringent data protection controls being introduced across the world, it is necessary for every company to talk about their data protection policies. Failing to do so can lead to substantial fines and undermine consumer trust – and then no one will want to share their data with you.
Taking the GDPR as an example, there are a number of limitations on how you can collect and process data. Clear and understandable consent is required for any data processing which may take place. Moreover, data has to be handled in a safe and secure way, with any potentially adverse effects weighed up against the benefits of processing. Finally, data analytics needs to be transparent, with data subjects able to request what data is being processed about them.
Don’t rely on data alone
Perhaps most importantly, to keep insights meaningful you need to ask the right questions. There is a common misconception that big data speaks for itself, that its insights provide the answers. But this idea, commonly termed technological determinism, is misguided – data always requires context.
Data blindness can occur if parts of a dataset are missing or you apply the wrong analytical lens. Take the case of Googles targeted advertizing in 2014 – it was shown to be far more likely to advertize high paying jobs to men than women and, from a purely technical standpoint, there was nothing wrong with the results analytics were providing. However – ethical concerns aside – when considering that the purpose of these adverts was to recruit the best candidates, the output was clearly sub-optimal.
You need to continue to look at data critically in order to ensure that insights are fair and accurate. That means understanding the flaws present in data analytics and asking critical questions of the results. Don’t take any result at face value – if something doesn’t look right, it probably isn’t.
Amazon, Google and Facebook have all utilized data analytics to become some of the world’s most profitable companies. When you break down what these companies are doing, there is nothing which can’t be recreated on a smaller scale. Through committing to using the right data and asking critical questions, every company can gain greater insight into its consumers and internal operations, and ultimately run a more useful and effective business.