When most people think of the "big data" movement, they typically envision a development that's affected large corporations. The bigger the business, the more potential it has to invest in massive stockpiles of information about consumers, and the more resources it has to apply its data and improve operations.
Gradually, though, that perception is changing. Big data analytics is becoming a mainstream idea, not one that's only discussed in the boardrooms of huge multinational conglomerates. Everything about the movement is becoming more accessible - companies are finding easier channels for collecting information, technologies for data storage are becoming more affordable and tools for analyzing data are now available everywhere. Even higher education is playing a role, giving more people the backgrounds in data science that they need to succeed.
"Big data" and "big business" are no longer an exclusive couple. Small companies now have the same ability to turn data clusters into business results. This is becoming easier every day, according to Forbes. Gary Evans, chief technology officer of IT storage firm EMC Ireland, believes that improving technologies have played a significant role in bringing data analysis into the mainstream.
"Data scientists and business analysts, by way of secure access to EMC's global data sets, can now generate their own analytics reports on our infrastructure, and the results are back in minutes instead of hours or even days," Evans stated. "This service allows business units to crunch their own data specific to their needs, which is where the real value is. In my opinion, every organization should have access to such a system."
Small businesses who venture into data analysis have a good chance of success. These four objectives are the key to these companies maximizing their results.
Drawing on in-house expertise
Data analysis is a relatively new field, and not every established company has in-house talent that's up to the task. However, it can be expensive to hire new workers solely for taking over analytics endeavors. For a small business, this might not be viable. It's best if companies either have talented statisticians already in house, or train someone on the job to get up to speed. Otherwise, the venture might not be cost-effective.
Using data to cut costs
Ideally, you want your analytics objectives to help you make money, not lose it. Implementing big data is often a long-term process, with no guarantees of immediate payoffs, but you can look for ways to find quick financial gains. For example, data analysis can reveal ways for a small business to save money on health insurance or other employee benefits. This way, the venture pays for itself.
Using affordable tools
Some hardware and software tools for data analysis can be very expensive and time-consuming to deploy in a business environment. Companies should plan carefully and look to use tools that provide instant results without breaking the bank. PC World notes that using polling software, such as Qualtrics, is an easy way to gather people's opinions and quickly compile data sets.
Constantly measuring ROI
Because big data is a rapidly evolving field, you will have to constantly reevaluate the financial risks and rewards associated with the undertaking. You have to compile data about your data - meta-data, if you will - to determine whether the endeavor is financially viable. If you discover that you're not getting an adequate return on your investment, it may be time to reevaluate your strategies or abandon the effort entirely.
It might be difficult, but businesses of all sizes have an opportunity to do more with analytics. If they work doggedly to gather information, ensure data quality and perform sound analyses, they have boundless potential.