Data Management Sins: The Worst Mistakes That You Should Avoid at All Costs
The modern world thrives on data. Computers, smartphones, and even a simple switch can carry data. And the ability to process data effectively and accurately is a skill that is highly sought after because of the sheer amount of data collected on a daily basis.
This applies more particularly to the realm of business, where data is the buttress of all major business decisions. Without reliable data, there can be no reliable information. And with no reliable information, you are essentially blind to the probable outcomes of your decisions.
It’s for this reason that data management experts are paid well and held in high regard. Now, whether you’re someone who’s aspiring to delve into the realm of data management, or you’re a business owner who relies heavily on the skills of his data management specialists, you’re going to want to be aware of these data management sins so that you can either avoid them or avoid the people who commit them.
So, what are these sins of which I speak? Well, when we’re talking about big data, there are actually only two major errors:
Indiscriminate Data Collecting
When companies collect a huge amount of data, that is, in fact, a good thing. However, when it’s near impossible to make sense out of the data collected (and thus turn it into workable information), it’s not really going to have much bearing because of the fact that raw data is exactly that — raw and unprocessed. Inedible, if you want to use a culinary standpoint.
Raw data does not have any meaning to the average business user. The mere copy-pasting of data does not merit a business any more than it does the reader. An ideal data output would be one where the data has, to a certain degree, already been processed. One good example is when the data has already been organized enough to show percentages. https://spreadsheeto.com/percent/ is a prime example of how you can achieve this.
Prioritizing the Wrong Things
When you handle data, there should always be specific questions that you have the objective of answering. It’s one thing to collect data indiscriminately and another to carelessly categorize data without a clear purpose in mind. While it’s indeed a task worth appreciating, and there are certainly benefits to categorizing data, the folly happens when after all the organizing you are still left with no answers. That is time and effort wasted. This is further compounded by the fact that the main point of data management is to be able to organize data into meaningful and workable information.
So, regardless of the nature of the project you’re dealing with, there will always be one constant objective of managing data. To make it truly useful, you should be able to interpret data into information that is relevant to the user and is able to help them make well-informed decisions. After all, the best leader is the one who is well-informed.
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