The volume and complexity of big data that organisations gather across all channels makes it hard for brands to know where to start when trying to implement plans to make the most of this consumer information.
However, if you follow this five-step process, you can reap the rewards...
The phrases 'structured data' and 'unstructured data' have become buzzwords in marketing circles. As the amount of information consumers produce grows exponentially, brand guardians need to ensure they're doing more than pay lip service to big data.
But getting to grips with the deluge of details at their fingertips is going to be easier said than done.
Part of the problem, of course, is the rapid expansion of data created by social media. Unstructured data is here and it's only going to increase.
For example, earlier this year YouTube blogged that its users were uploading an hour's worth of video to the site every second. Meanwhile, by the end of 2011, Twitter had amassed 200m active users generating 60m tweets per day.
So brands need to find a way of coping, bringing structure to the collection and analysis of this information. Take the estimated 4.5 petabytes of video uploaded last year; how much of this was either relevant to marketers or copies of previously uploaded files?
Marketers are recognising they have a chance to make the most of the opportunities for growth that big data represents – if only they can understand it.
The volume and complexity of the job in hand necessitates a cast-iron plan for dealing with it. Consider this analogy: human vision features both focused and peripheral abilities.
We walk down the forest track looking at the path ahead, not seeing every leaf around us, but when we sense movement our eyes immediately swivel 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 for doing so isn't always clear. However, I believe a five-step plan can generate quick wins:
1) Put the consumer first
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.
One of the challenges is deciding where to begin.The first step of a journey into the fast-moving world of big data should be 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. And in most cases, that includes the unstructured territory of social media.
2) Define the 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.
Outside marketing, IT should definitely play a part as the department is likely to be most at home with data. Data analysts should also be involved as they are already familiar with combining and using disparate structured and unstructured 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 – including whether it's structured or unstructured - and identification of the gaps where the data is unavailable.
3) Create 'Plan A' – a proportionate response
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 decision making to accommodate the variety and velocity of Big Data.
- Carry out all work in a way that is compliant with current data laws.
4) Run a big data proof of concept test
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.
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.
5) Create a roadmap
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, 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.
For example, compare the investment in the capability to take unstructured data and turn it into large volumes of structured insight against the expenditure needed to capture a more modest volume of structured data. The yield of each option must be identified and the answer may be to do both.
Consumers are creating big data so marketers are best-placed to turn it into new revenue streams. Thanks to digital media, and in particular social networks, big data has been evolving for some time. It's time for marketers to take it on.
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