The challenge to data analytics
- 07 April 2017
- 468 views
Big data, automation and analytics are revolutionising marketing, but what can business professionals expect from this exciting field in the next few years?
Big data refers to the huge swathes of information that have become available in the digital age, covering everything from credit card transactions to social media activity. Simply put, analytics involves combining and interpreting this data to make more informed decisions and shape commercial priorities.
The range and volume of data available to businesses is set to grow even further, both externally and from within organisations. A company’s internal data covers everything from e-commerce stats, website traffic numbers, to customer transaction records and addresses.
According to Jaime Merchán, director of analytics EMEA at IPG Mediabrands, the costs of failing to get to grips with your data are set to rise. “There’s a real risk of being left behind,” he says. “Data is becoming like a language that companies use to communicate. If you can’t speak this language, you’ll find it harder to participate in commercial transactions.”
The question might be, where to start?
Collating the data held by an organisation into a logical and interchangeable format is known as building a ‘data stack’. Until recently, this was mainly done by larger organisations. Now, however, smaller companies are beginning to understand the importance of harnessing their owned data.
Merchán believes this process demands new skills from marketers and business planners. “The skill set is moving more towards technology, statistics and data. Although it’s not necessary for a marketer to be a data scientist, they still need to be aware of the issues to help guide the business and colleagues working in these specialist disciplines.”
Although it’s relatively easy to amass data, the real challenge lies in drawing useful conclusions and acting on them. Recent years have seen many vendors offering automated platforms designed to collate disparate types of data and automate some marketing decisions.
This trend is likely to continue as companies seek to take advantage of broader data sources and increasingly reliable automation based on more sophisticated algorithms. However, according to Merchán, many larger businesses are now choosing to build their own automated platforms tailored to the specific needs of the organisation.
Businesses with smaller budgets can also develop bespoke tools at a fraction of the cost. For example, the free online software suite R theoretically allows firms to set up a one-person analytics department. This would likely involve hiring a programmer who understands the R language, but it is possible to teach yourself using online resources.
Google Analytics is another powerful free tool used by many companies already. But again, says Merchán, even this user-friendly platform requires some training to use. “Google provides this data, but how do you interpret it? How can you use it to structure your key performance indicators? You still need to convert this data into something your organisation is going to find useful and actionable.”
Data as an asset
By creating a stack, businesses are essentially turning their data into an asset – in other words, businesses can build a set of insights that have practical and monetary value for the organisation and its customers or partners. In future, we’re likely to see more companies monetising this asset by selling it to other organisations. Selling data is fraught with legal and privacy issues and there’s every reason to expect this to become problematic for marketers. However, the new General Data Protection Regulations, which come into force in 2018, will bring some clarity to this complex area.
The downside of data
Although the trend towards greater data use is certain to continue, marketers should always be aware of becoming over-reliant on metrics and missing the bigger picture.
For Merchán, it comes down to keeping an eye on the wider business environment. “There’s this assumption about big data that you push a button and the insight is going to be there – that it’s all automated and technology takes care of everything. In reality, there’s a very detailed process behind all this, and you will always need to understand the context. It’s really about nuance.”
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