It doesn’t matter how big or small your company is: analytics will grow your business. But small business doesn’t necessarily equate to small-scale analytics—in fact, it’s quite the opposite, according to an article in a recent SAS Insights Report. Check out this excerpt from the article Small business, big data with IT considerations for businesses of all shapes and sizes.
Whether you’re in health care or the service industry, you need to start thinking about the requirements and design for your analytics projects. As your data grows, so do your IT requirements and, oftentimes, the gap between the business need and the IT infrastructure. To overcome these challenges, consider these points:
- It’s not size that matters. While it’s interesting for the technical discussion to focus on size, the focus should be on business value first. Identify your business challenge or goal. Do you have a need to leverage blog and social media data to analyze customer churn? Do you need to strengthen your fraud analysis approach by mining clickstream and other forms of content? Do you need to analyze many data points at the customer transaction level? Focus on the business value so you can align your goals with your technical and solution approach.
- Think about a different kind of big. From a design perspective, think about the big picture. You certainly don’t need to take a big bang approach in terms of implementation, but leverage standard architecture principles to ensure that you don’t box yourself in.
- Look beyond the hype. If you do much research on big data, you’re bound to run across a lot of articles on Hadoop. This new software framework for big data is getting a lot of attention, and it’s a great technology, but it is not a realistic solution for SMBs and midsize companies. However, just because Hadoop isn’t for you doesn’t mean big data is irrelevant altogether. Consider what is best for your organizational growth before you invest purely based on price or hype.
- Analytics is the key. In most cases, we think about leveraging information management technologies like data integration and data quality to prepare data for analytics. Although this is certainly an important step, the biggest differentiator will be how you can apply analytics to determine what to do with your organizational data, determine which data is relevant, and how or whether data should be stored.
- Resources are scarce. Lack of resources, especially the right resources to analyze big data, is critical. In some recent Economist Intelligence Unit research, lack of the right skills to manage data effectively is among the top two challenges cited by survey respondents (30 percent), followed closely by “We can’t get the data to the right people in the organization” (23 percent) and “We don’t have the analytic skills to know how to use the data effectively” (22 percent).
But using data doesn’t require hiring a team. In fact, many successful companies start by looking internally for the people who are always asking questions that everyone wishes they had an answer to and pairing them with a statistician who can help them learn to dig into the data. In “Four steps to analytic success” we offer suggestions on how to deploy your company’s internal assets to take advantage of big data.
If you were intrigued by this article, you’re in luck — there are many like it in the SAS Insights Report, Big data, bigger opportunities. Dig into the report to find out what analytics can do for companies of all sizes.