Typically, large enterprises try to ride the Big Data wave by gathering as much as data as they can and investing heavily on storage technologies to accommodate all the data they have gathered. They wait while they figure out how to use all this data. SMEs can’t afford this approach and therefore tend to stay away from Big Data. This doesn’t have to be so. You can certainly use big data, as an SME, if you are practical in your approach. Here are 3 ways SMEs can tap into big data:
1. Focus On The Critical Points Of Your Business
The most important trick to drive business performance using data is to optimise your business model. What are the key leverage points of your business? Can you grow your sales if you knew more about your customers’ preferences? Which aspects of customer behaviour can help you target them better? If you are able to monitor a process, can you improve it? If you can analyse product usage better, can you deliver more relevant features to my customers? If so, which data points are you looking for?
Answering these questions will help you figure out how to use big data to gain useful insights and add value to your product or service. Once you’ve identified which critical areas of your business you can grow based on more data, you can go about gathering additional information for them. If you can’t think of a question that can’t be answered with your existing data, then most likely you don’t need big data.
2. Problem Solving On A Shoestring Budget
Once you’ve found an interesting question and gathered additional data, try to answer it with the resources that are currently available with you. Find the person in your business who knows most about the problem you’re working on and the most data-savvy person you can find in your organisation.
Begin working on the problem with your existing analytics tools & techniques. See if you can solve the problem for a smaller scale using a sample of your data. See how far you can go with your first line of analysis before you look for exotic solutions. SMEs don’t have the privilege to spend on large scale storage solutions unless they’re sure it will provide benefits. Therefore, if you are able to solve the problem at a smaller scale, you just need to test the solution at a larger scale. This can save a lot of time and resources up-front for SMEs.
You may realise that you can find an answer without big data, or you had the wrong question and need to try again, or you don’t have the required data and need to build a business process to obtain it. The initial analysis helps you weed out all the tactical problems you will most likely face during data analysis, whether you use big data or not. It enables you to streamline analytics and make it more effective, at lower costs.
3. Rent It Instead Of Owning It
If you had the right question but couldn’t find the answer with your existing tools and techniques, then it’s time apply the magic of big data. But you don’t have to build or own storage solutions for it. Cloud services like Amazon’s EC2 allow you to create your own analytic sandboxes and pay as you use it. You can easily fire up an environment to conduct the initial analysis and turn it off when you’re done, paying only for the time you’ve used it.
If your initial experiment is successful, you can scale up your environment to handle the full load of Big Data. When your project is completed, you can simply turn it off. This way, the total cost for your project goes down drastically. Also, you can use a fail-fast model to explore many possibilities for your analysis before deciding to fund an initiative.
Even if you need external help, you can always reach out to a data consulting firm, a graduate or doctoral student working in your area of interest. Since you have already defined your question and performed the initial analysis, you’ll know exactly whom to hire. Not a data scientist or big data generalist, but someone with relevant experience about the specific types of problems you are trying to solve.
Focus on problems that are key to business growth, define measurable methods to understand those problems, use analytics solve those problems and create value.