Commercial success shouldn’t be defined by the size of data, regardless of a company’s ability to crunch it. The hunger for data within the industry is creating digital landfills of information that will never surface. The problem is that, although buying and storing data is cheap and easy, creating a strategy for improving businesses with new data is more of a challenge. Companies need to ask themselves, what is more beneficial for their bottom line - big data or better data?
The following tips can help brands get smarter about their data projects.
Focus on business strategy
The first step when approaching a big data project is to consider the strategy of the business: the aim of the project, the obstacles it faces, how and when the success of it can be measured, and the key stakeholders and their objectives. Project managers should also take into account how quickly the project can be executed and then work to a strict deadline. Effective planning and analysis will help brands to eliminate a large chunk of inefficiency right off the bat.
Break any remaining organisation silos
It’s important to remember that a big data project is not an IT project. That’s not to say that IT should be excluded from the process, but the valuable resources available from across the business can’t be ignored. Multi-channel marketing has broken down several of the organisational silos and, as a result, stakeholders from each organisational unit need to be aligned to deliver big data to ensure an integrated, robust solution.
Focus on data diversity
Analyse a small section of data that has never been looked at before. Split the data into three key segments: what is available, what can be bought in and what would add to the data. For each of these data sources it’s important to keep velocity (the speed that data arrives and needs to be processed), volume (the amount of data received) and variety (the different types of data received) in mind. Businesses don’t need every piece of data from every feed to add value. Seek to ask the most pertinent and relevant questions to extract high-quality insight.
Beware the big bang
A big data project should be incremental in nature. Think small bursts of insight, funding, testing and learning. Delivering both value and profitable projects will present the work and the project sponsors in a positive light. If everything is attempted all at once, the business will probably have moved on by the time it has come up with the data requirements, let alone begun tackling them.
Remember data governance and ethics
With big data comes big responsibility. The data repository that has just been created contains a wealth of information about consumers. That information is useful to the business, but also equally useful to unscrupulous third parties. Companies hold the key to their customer intelligence and it’s vital that it does not get lost.
Apply science and data scientists to the problem
When approaching big data, it’s important to ask a lot of questions in order to identify the problems that need to be addressed. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organisation. Implementing a big data solution without keeping business objectives front of mind will result in an alphabet soup of new technology, spiralling costs and, ultimately, doomed projects.
Companies shouldn’t be fooled into thinking that storing 100 times the volume of data will result in 100 times better insight for their businesses. By thinking in terms of smart data rather than big data, brands will be on their way to creating a successful business strategy.