Big data has helped large organisations from governments and airlines to global logistics firms solve their most pressing issues, but the same cannot be said for small and medium-sized enterprises (SMEs).
In fact, many SMEs have similar big data needs and are increasingly aware of the benefits of using big data to drive faster and more accurate decisions.
So, what’s stopping SMEs from taking advantage of big data? The lack of expertise and seemingly complex interfaces of analytics software was one factor.
That’s on top of the time and effort required to extract, transform and load large amounts of data from multiple sources into computer systems, before presenting the data visually.
Today, with the advent of self-service cloud-based analytics tools, these tasks can be completed by anyone without IT knowledge, making them good entry points into the world of big data.
That said, while data analytics technology is now within reach of SMEs, it takes more than just rolling out tools for any big data initiative to be successful. Here’s what SMEs need to know:
Align with business objectives
Many big data analytics projects fail because they are not aligned with the goals of the organisation. These projects tend to take a technology-centric approach, without considering how and if the outcomes matter to the business. So, in any big data project, it’s critical to identify business goals from the onset and define the right metrics. This could be improving customer satisfaction by specific percentage points or identifying new customer segments, just to name a few.
Develop a data strategy
Once you have outlined your business objectives, the next step would be to develop a data strategy. One of the key components of a data strategy is to identify datasets within your organisation that can be mined to reveal business insights. These may include structured data stored in spreadsheets and databases, and unstructured information in the form of emails, text documents and even photos or videos. This data also needs to be stored and accessed in a secure way – and anonymised in the case of sensitive customer data. To that, organisations can turn to solutions such as DocuShare to capture, index, and store unstructured and structured content to facilitate downstream use and automation in applications such as big data analytics.
As with any new IT initiative, it’s important to start small with a proof-of-concept (PoC) to demonstrate the business case of big data projects to stakeholders. This will go a long way to ensuring management buy-in and make it easier to scale up any big data project. When planning for a PoC, be sure to limit its scope so that it can be completed within a maximum time frame of no more than six weeks, with as few data sources as possible. For example, if the business goal is to increase the number of sales leads through a company’s website, a PoC that involves the analysis of website visitor traffic using clickstream data will do the job.