Data analytics has become the next golden ticket that transforms any mom-and-pop shop into the next billion dollar company; but how? Many businesses use data analytics to help their employees at all levels better their policies and protocols. Especially in the manufacturing industry, being able to predict how much product or what method of production is the most effective would save the business tons of money.
The bottom line is that data analytics is incredibly powerful if done correctly, but it’s easy to get lost in the myriad of jargon-filled articles and intimidating books. So, we will try to explain why some businesses are not using data analytics efficiently, and instead of obtaining nice predictions, are left with the dreaded disparate data.
Imagine collecting tons of data from various sources, having smart data management systems analyze it using complicated algorithms and instead of having the desired result (a clear prediction) you get a bunch of jumbled numbers. Worst nightmare, right? There are several causes of what is called “disparate data,” but the most common one is having more than one data management system, outdated operating systems and human error including mislabel information. The problem with having lots of different database systems is that each system has a different way of storing and integrating data with a data management system. That is to say, that MySQL will have a different way of integrating the data than Oracle. For example, if department x has MySQL and department y has Oracle, there might be a slight chance that it will store data in a different way. That said, data analytics has a lot of moving parts making it easier for data to get lost.
Multiple database systems essentially function as a single data warehouse, which is where data management systems are able to access groups of data and interpret them. Data management systems are designed to analyze a large group of data through different algorithms (but usually come to the same conclusion), but that’s where problems start to occur. Data management systems have different formulas, which means that at times if data management systems cannot access the data warehouse, it will output the wrong data (if anything at all). So if a company is using different data management systems in different departments connecting to different data warehouses the probability of having disparate data significantly increases.
Another cause of disparate data is you fail to ensure that your data management systems and operating systems have a clear understanding of common terms in the industry. For example, in the engineering department of your manufacturing company may call an item a “part” whereas in other departments they may refer to the same item as a “component.” If your data analytics system thinks part and component are two separate items, then it’s analysis will most likely result in disparate data.
The most obvious way you fix this would be to attempt to make sure that data management systems are congruent, that terms are well defined and operating systems are compatible, but that simply isn’t an option for all companies.
Okay, so what do you do if you’ve already established your data warehouses and data management systems? Well, the good thing is that there’s a solution for you too.
There’s this handy new methodology called linked data that was introduced by Sir Tim Berners-Lee. He explains the concept of linked data in a potato chip metaphor. Potato chips are referred to “crisps” in the UK but are also called “chips” in the US. Berners-Lee suggests looking at this items commonality, that crisps, and chips are the same item and are made from potatoes. That’s a link. By creating such links it is easier to interpret disparate data. Ta-Da!