The massive amounts of data available to companies is both a boon and a curse. Within that data lies information that can help increase sales, decrease cost, improve time to market, increase quality, make wiser investments, satisfy customers better, etc. However, finding the gems of information within the mass of data takes effort, time, resources and investment with no guarantees that the results will be worth the costs.
Analytics The second thing to do is to assess your data. How can it be useful in making better decision? Is it useable in its current form? What other data should we look at that can provide useful insights? How can other data be linked thus increasing the value of your data? This requires planning and insight into your data and business needs. Data is an extremely valuable asset that can be used to improve your processes, product quality, customer satisfaction, reduce rework, reduce downtime, improve speed to market, lower costs, etc.
The third step is to clean your data. This means you must fix errors and inconsistencies in your data and often there are missing data that you can sometimes extrapolate for. The more correct and complete your data is, the higher the quality of the information you can glean from it.
The fourth step is to analyze and prepare your data for usage by whatever analytic tools and algorithms you choose to use. This can involve aggregating data, sometimes you need to smooth the data, other times normalization techniques can improve its usability. The quality of your data is critical to the quality of the resulting analysis work that you do. We are talking about the things that you must do before you start the actual higher level analytical work.