BIG data – a five step plan can generate quick wins by Matt Hollingsworth, Acxiom

Marketingblog exclusive .. By Matt Hollingsworth, Managing Account Director, Acxiom

Even though the advent of social media has fuelled a boom in data volumes over the last few years, the term ‘big data’ has only become a conversation piece in marketing circles relatively recently.

Once the sole preserve of the IT department, big data is driven by constantly changing consumer behaviour. The rise of Smartphones, Apps and social networking means real-time data volumes have taken off. Marketers need to take note, or risk falling behind the curve.

The volume and complexity of big data means marketing departments need a clear plan for dealing with it. Consider this analogy: human vision can be split into focused and peripheral abilities. We walk down the forest track looking at the path ahead, not seeing every leaf and branch around us, but when we sense movement our eyes immediately turn to the source and respond to the threat or opportunity. Marketers need to create the ability to manage signals but continuously be aware of new ones within the noise and react appropriately.

The starting point isn’t always clear. But I believe a five-step plan can generate quick wins:

1) The customer is king

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. Big data does not change this, but creates new opportunities. Data can be used to enhance the product, improve the price and make the promotion far more relevant to the place.

But where should you begin? The first step is to ensure that you are aligning your efforts to your business objectives. For marketing and insight teams this typically means focusing on initiatives that benefit your consumers.

2) Paint a clear starting line

It’s necessary to have a good overview of what big data your organisation has. The way to approach this task is by conducting a consumer-centric data audit.

Who should be involved in the process? Outside marketing, IT should definitely play a part as the department is likely to be most at home with data, and may be able to help uncover silos of data unknown to the rest of the team. Data analysts should also be involved as they are already familiar with combining and using disparate data sources and will ask the right questions of the data owners when compiling the data audit. The privacy or legal team should also be engaged to ensure the boundaries of data usage are properly respected.

The specific deliverable from this step is production of 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) Create ‘Plan A’

Having understood the objectives and your organisation’s landscape in steps one and two you must now build a strategy for managing and making big data actionable – a ‘Plan A’. The challenge here is the volumes and variety of data involved. Furthermore, unless the data gets processed and actioned rapidly then it quickly becomes stale. Your organisation must:

• 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
• carry out all work in a way that is compliant with current data laws.

4) Test big data at a micro level

This step is a reality check. There may be a significant investment required to establish a big data environment, not just in terms of hardware but also skills. Demonstrating return on investment is therefore crucial to securing sponsorship from the business.

A paradox here is that many of the use cases do not actually require big data. The volumes are so large that the decisioning necessarily becomes simplified. There is nothing fundamentally new about big data, it just requires a new mindset. It should be perfectly possible to execute big data use cases without building a new, full-blown environment for doing so.

Typically, the activities at this stage will include statistical analysis (mining), searching for predictive patterns within the data and then attempting to turn these into processes which can be tested in real-world scenarios.

Moving forward from this step you should be in possession of a solid body of evidence to support the business case, and a clear understanding of the resources and processes required to underpin these, possibly including outsourcing to third-party experts.

5) Build a roadmap to drive your big data

Finally, you are ready to build on the results of the proof of concept. This is where you can begin to achieve both focused and peripheral vision: the ability to focus on the data that matters most whilst being aware of other data, and be constantly on the lookout for new or previously unavailable data.

Your roadmap should outline how any proof of concept tests can be operationalised and how they will help the organisation move forward, with future tests already defined. It will also implement your peripheral vision. Priority consideration should be given to what big data can most quickly be captured and translated into these existing environments. You need to conduct a value analysis and prioritise.

The roadmap should be a strategic and tactical plan that puts big data trials into practice, generates results, enables learnings and identifies potential opportunities so that marketing, your brand and its customers continuously benefit.

Consumers are creating big data and brands must capitalise on this by engaging, serving and delighting them to be successful. Because of this emphasis on the end user, marketers are therefore in the best place to dig for golden nuggets in the big data mine. Big data has been evolving in the background for some time; now it’s time for marketers to meet its challenges head-on.

By Matt Hollingsworth, Managing Account Director, Acxiom