Data-driven businesses have access to unprecedented amounts of customer data. And as technology evolves to create new channels, the volumes continue to increase.
Limitless Cloud storage, unstructured data tools and maturing data science disciplines have led many organisations to assume that Big Data is the ultimate end goal of their digital developments. Indeed, many proudly discuss how many millions of records and data assets they control during tradeshow presentations and the like.
But as organisations wrestle with digital transformation programmes, they are being forced to re-evaluate how Big Data fits into these plans. Do they really need to collect anything and everything?
Discerning data collection
In the very early days of computing, data analysts coined the phrase “GIGO – Garbage In, Garbage Out”. They had realised that if the data being processed was rubbish, any observations and outcomes using that data would also be of little value.
50 years since GIGO was first used, technology has changed beyond recognition. But the rule still holds true for analytical purposes.
It makes sense that businesses define the data they need to collect in advance to avoid wasted costs and resources. Data that is collected and stored without being used (or from which no value is ever extracted), decreases the return on your Big Data investments.
This realisation explains why defining metrics is so important – without them you can never accurately assess the success of your customer-focused efforts. An IBM study once found that 30% of businesses didn’t actually know how to apply the insights gained from Big Data analytics.
There are even suggestions that some Big Data advocates are setting content expiry limits on their data. Once information has passed a certain threshold, the value of any further insights is deemed to be minimal, so it is archived or deleted.
The reality is that digital transformation is not completely dependent on Big Data. Instead businesses must focus on using the information they do hold more effectively.
So far, the majority of the Big Data issues we have covered are problems for the CIO and CTO to solve. They are ultimately responsible for storing an ever-increasing amount of information.
But there is a new Big Data challenge on the horizon for compliance managers – the European General Data Protection Regulation (GDPR).
A new compliance headache in the near future
The GDPR is Europe’s latest attempt to strengthen the individual’s right to privacy, demanding a number of new protections and safeguards for the personal data you hold. And the scope is incredibly wide-ranging – the framework defines personal data as:
“Any information relating to an individual, whether it relates to his or her private, professional or public life. It can be anything from a name, a photo, an email address, bank details, posts on social networking websites, medical information, or a computer’s IP address.”
The new law is similarly clear in its expectations for businesses holding personal data – it must be removed as soon as it becomes “non-essential”. This “non-essential” demand is clearly at odds with Big Data programs that intend to hold information indefinitely.
The Compliance Manager faces a new challenge as they drive the digital transformation agenda. They must help to define the relevance of the data being collected, and help to set rules defining how ageing information is handled.
No avoiding the issue
Britain may have voted to leave the European Union, but until divorce proceedings complete, the UK is still bound by EU law. And for every other member state, it remains business as usual. Which means that businesses must continue their preparations to ensure the GDPR is applied by May 2018. Or face fines of up to 4% of their total global income.
The GDPR could be seen as a stumbling block to data-driven digital transformation. Approached from the right viewpoint however, and it could actually be the catalyst your business needs to refine its Big Data strategy.
Rather than simply collecting everything and dealing with the details later, your team can acquire smaller sub-sets of information to simplify and speed up the analytics process. Which will then allow you to deliver an improved service to your clients, faster.