As today’s trends bring more and more data to our fingertips, the next challenge is figuring out what to do with it all. The insights gained from proper data analysis have major implications for business. Proper data analysis takes careful consideration, appropriate infrastructure, and understanding of data science.
This is the process of using data to draw conclusions or predict outcomes. You probably use data science already. For example, a restaurant owner may keep records of which dishes are ordered. Over time, he may notice that chili is ordered more frequently on rainy days than on sunny days. This may encourage him to prepare larger batches of chili when the weather calls for rain. This is a very simple example of data science. You can see how understanding patterns and trends in data can help prepare your business. Data science can contribute to the following business improvements:
Machine Learning takes it one step further. If you have really large quantities of calculations to run, or large amounts of data collected to mine, machine learning can help you. This is the practice of encouraging machines to learn like humans do, with experiential data. Learning algorithms are designed to process large sets of test data that are used to draw conclusions about the world. Based on these conclusions, the machine can then process new information and make informed decisions. These programs can restructure themselves based on input learned from data sets. Machine learning applications are valuable to businesses for a few reasons.
Define your problem – Data science and machine learning work best when used as a targeted approach. Don’t get bogged down with too much data before you know what you’re looking for. Come to the table with a specific problem or question. Then you can gather the relevant data and ignore the noise.
Gather the data – Once you have identified your problem, it’s time to collect the data. You may already have this in your database. If so, great. If you find that you don’t have what you need, it’s time to look at ways to gather your relevant data. Send out customer surveys, add pertinent form questions on subscriptions, etc. The more specific, the better.
Process/analyze the data – Data scientists are trained to design algorithms and processes to extract what you need from your data sets. If you decide to hire one, it’s important to have the infrastructure in place to support them. You will get the most useful insight if you aren’t asking them to design your data warehouse from the ground up or take care of any IT needs. Let them do what they’ve been trained to do. If you aren’t ready to hire a full-time data scientist, consider using a consultant as needed. This way you can address specific problems as they arise without as much investment. At Dobler Consulting, we would be happy to connect you with a data scientist to help with your data analysis.
Make strategic changes – Use the insights you have learned from your analysis to effect data-driven changes. Relaunch your marketing campaign toward a more receptive audience. Restructure your operational processes. Redesign your business processes. Trust the data.