These days, everything is all about big data. No matter what industry you work in, you are at a disadvantage if you aren't constantly collecting and analyzing data about your customers, target audience, production, marketing, etc. Everything can be monitored or measured in some way. However, working with a large amount of data can be overwhelming if you've never done it before. Here are some basic guidelines to get you started.
Your first order of business is to figure out where you will be keeping all of this data. It will need its own space and a lot of it. This usually means that you will need to turn to the cloud and either construct or lease a database. You can invest in your own hardware for this, like servers, or outsource to a company that will host the servers for you and sell you space on them. The latter is recommended for those less technically inclined, as it requires less expertise and often comes with perks like technical support. If privacy is a priority, and PLEASE assure that it is, it might be a good idea to go through the effort of setting up your own servers.
Upon collection, your raw data may look messy. On top of that, a lot of the data that you end up storing will probably be duplicated or synthesized data. To save on storage space and prepare your data for analysis, you'll want to clean it up. Data cleansing is your first step in the data preparation process. Cleansing can analyze, identify, and correct raw data, while getting rid of the extraneous stuff. Without doing this, any subsequent analysis could be inaccurate or altogether meaningless. Once this is done you can separate the large set into smaller, virtual data sets that are managed centrally.
Now that your data is stored, cleaned up, and organized, you'll want to determine who has access to it. Depending on what type of data it is, this can vary widely. For example, medical data is extremely private so only very specific users, like a limited set of medical practitioners, should be able to see it. On the other hand, things like sales data you may want to be accessible to your entire sales department. You can set up protections and access keys through your storage set-up or server.
You should also make sure that once your employees have access to the data, they can comprehend it. Graphic interfaces and dashboards are handy for this, allowing your workers to quickly search what they need to make decisions quickly. Often these interfaces can perform quick analyses, as well.
When it comes to analysis, you first need to figure out what questions you want to answer or what problems you want to solve. Are you interested in customer purchasing patterns, driving data, or demographics? Do you want to try to help predict issues before they arise? These questions will guide everything you do in the analytic process. There are basically four types of analysis possible with big data: basic analytics for insight, advanced analytics for insight, monetized analytics, and operational analytics. Basic analytics are essentially monitoring and reporting procedures that give simple visuals; monetized analytics directly contribute to increased revenue, like Amazon's recommended items based on your purchases.
This is an area that you will most likely want a specialist to step in. Many times, programmers will write applications to run analysis on data automatically, because the sheer amount of it is too much to sift through manually. There are usually entire teams dedicated to data management. Once you start collecting it, the scope of the data and its impact will only grow larger, so you should meet that challenge accordingly.
Once you have a reliable set-up for all of your data to keep it organized, searchable, and workable, you will probably start seeing your business become more successful. It is highly recommended that you either train current employees or hire data management specialists who are dedicated to this important aspect of your business. Don't get left behind by the competition or lost in the world of security breaches, and join the world of big data.