Five Steps To Handling Big Data

No longer ring-fenced by the IT department, big data has well and truly become part of marketing’s remit. A deluge of data flowing from the ever-increasing number of offline and online media, coupled with rapid changes in consumer behaviour – the way people shop, work and relax – is making marketers’ job more difficult. But despite the complexity of big data, it offers huge opportunities for brands to drive value from their customer information.

The sheer volume and number of different sources producing big data means marketing departments need a cast-iron plan for making the most of it. Traditional offline sources are being constantly added too by new online streams of unstructured data, from social media networks to Apps. The starting point on the big data journey isn’t always clear, but I believe the following five-step process can create quick wins:

1) The customer is king: Big data creates new opportunities. Every marketer knows that for a brand to be successful, it has to have a compelling offer delivered to consumers via the right channel at the right time. Ensure you’re aligning your efforts to your business objectives. For marketing and insight teams this typically means focusing on initiatives that benefit your consumers.

2) Take a bird’s eye view: You need a good overview of what big data your organisation has – so conduct a consumer-centric data audit. IT should definitely play a part as the department is likely to be most at home with data, while data analysts should also be involved as they are familiar with combining and using disparate data sources. The privacy or legal team should also be engaged to ensure regulations are adhered to. Produce a comprehensive list of the raw materials you have available for any big data initiatives and identification of the gaps where the data is unavailable.

3) Get on your marks: You must now build a strategy for managing and making big data actionable. Unless the data gets processed and actioned rapidly it quickly becomes stale. You should analyse raw data to determine when and how it can be used; make data operational quickly and efficiently; identify, and if necessary discard, junk data which will clog the system; automate decisioning to accommodate the variety and velocity of Big Data; and carry out all work in a way that is compliant with current data laws.

4) Set off on a test run: A significant investment may be required to establish a big data environment, not just in terms of hardware but also human resource. Demonstrating ROI is therefore crucial to securing sponsorship from the business. It should be perfectly possible to execute big data use cases without building a new, full-blown environment for doing so. Activities at this stage include statistical analysis (mining), searching for predictive patterns and attempting to turn these into processes which can be tested in real-world scenarios.

5) Roll out a roadmap: You’re now ready to build on the results of the test. Your roadmap should outline how any proof of concept tests can be operationalised and define future tests. You should always be on the lookout for new, significant data. Priority consideration should be given to what big data can most quickly be captured and translated into these existing environments.

Brands need to engage, serve and delight consumers to capitalise on the big data they are producing. As marketers are the brand guardians in any organisation and are therefore closest to the consumers who are creating big data, they need to gear up to meet its challenges head-on.