Metrics and Big Data: How to measure the value of big data for your business

With the herald of innovative, personal, smart and targeted marketing, ‘big data’ offers enticing and varied advantages for businesses.

The ability uncover and harness data, to tackle and tame the informational chaos presented, is increasingly expected of most organisations. But how do you know how useful and profitable data will be for your business uniquely? From improving customer retention to increasing revenue, opportunities are substantial but big data can be dangerous if gathered mindlessly. To gain value, focus must always remain on predefined data goals – proven profitable uses, which are organised using a monetisation strategy.

Making big data your ally and asset

To access and assess the value locked within your data, there are certain strategic steps which must be in place. This ensures the data you act on is relevant, ensures marketers can identify the specific monetisable data they need (usually about 5% of information), proves that your data programme is tested and stable (which secures financial certainty and backing), and above all, certifies that your data plan adheres consistently to your unique business goals.

To implement a profitable big data strategy and to assess it’s value for your business, there are five strategic steps which should be taken:

1.   Take a consumer-centric focus

Why do you want to introduce a data strategy? Of course to increase value and revenue, but think further than that. To be successful, a business must cater to its clientele, and consistently aim to deliver consumer value and resolve consumer issues. Your strategy should improve the consumer experience in some way most likely leveraging a multifaceted, deeply-informed view of your consumers.

  • Establish the consumer-related issues you wish to solve via big data, such as enhancing your consumer view to create more targeted marketing across all channels.
  • Once you have outlined your data objectives, ensure they are consistent with your overall business objectives.
  • Choose where to begin. Prioritise by identifying potential issues and opportunities, then sketch out approximate project costs.

2.   Audit the data you already have

An informed, profitable strategy makes the most of existing assets - it maximises the revenue of current consumer data. Part of any data audit will involve a degree of database cleansing - data has a short shelf life. ‘Dirty’ datasets, containing out of date information must be updated to be of value. A data audit will outline your project’s starting point, by establishing the information you have, and the information you need.

  • Begin by creating an information profile - of the data sources you have, and that you can readily (and legally) access. Then you can aim to fill the gaps.
  • ·     An audit will establish and ask: What data is currently generated? Is it captured anywhere or curated at all? Do you have ready access to it, and permission to use it? Does any documentation exist about the data’s structure and content? Is the data structured, semi-structured or unstructured? Can it be joined to any other customer data sources? Can you get a snapshot of this data picture to use in a proof of concept?

3.   Build from the ground up

Once you’ve outlined your foundation, you can build a structural plan – plan A. This will prepare your business by visualising an actionable data solution to test, and if valuable, invest in.

To be of value, data solutions must be able to: Quickly absorb and utilise new data sources, automatically accommodate the volume, velocity and variety of big data, be able to analyse raw data and decipher its uses, be able to locate and remove useless or junk data, and ensure data can be made actionable efficiently while complying with privacy legislations.

4.   Test your solution

Integral to establishing the value of your big data solution for your business, testing secures trust, and proof of ROI.

Tests should:

  • Search for predictive data patterns and processes, and aim to relate these to likely real life situations and scenarios.
  • Provide a set of deliverables: a collection of evidence, which will support your business case and outlined project objectives. A solid understanding of available resources must be shown alongside the procedures necessary to utilise them.
  • Deliverables must also showcase and demonstrate ROI to secure complete business trust and financial faith.
  • ·     Where needed, adapt your plan to create a final, practical data strategy.

 5.   Retain an adaptive perspective.

So, you have your proof of concept – your tested and proven approaches, and have a plan to build upon, and operationalise them. But simultaneously, you need to be able to identify and test new approaches as they occur – you must be adaptable.

To stay adaptive, a balanced perspective between your main focus, and new peripheral approaches must be achieved. Successful data strategies manage current signals while simultaneously evolving – unearthing and acting upon new signals to retain long term value. As well as technical and process implications, there are also talent and organisational implications, such as creating a structure that can manage ‘business as usual’ and have that working seamlessly with a team focused on finding the next ‘potential’ data and ideas. And, of course, structures need staffing with talent that’s increasingly in demand and hard to find. Ignore talent at your peril.

Remember, being able to locate and utilise the data that is of value to your business is just the first step. Partner this with a data monetistion strategy, and your data will be worth more to your organisation than you thought possible.


About The Author

This article was written by Jed Mole, European Marketing Director at Acxiom a data analytics and software-as-a-service company.


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