You can’t get rid of it. You can’t avoid it. You better understand it. Dark data.
The latest buzzword to emerge from the shadow of big data in the truest sense of the word is dark data, and I have repeated the name three times. But before we go into more detail about what it is and the different degrees of darkness it can have, we must first examine why the term came into being.
Like most things with “data” in the title, Dark Data is an offshoot of Big Data. This means that it is a collection of information obtained by collecting data on various issues that may or may not be related to each other.
In other words, dark data is unstructured data that is never analyzed, meaning that you don’t even know whether the data will be redundant, outdated, or trivial if it isn’t located and analyzed. In this sense, the non-use also makes it clear that the collection of big data is a major exercise in partial failure. Companies must decide whether to use this large, juicy data or start reducing the amount of “dark data” they store unnecessarily.
The most important step is to explain exactly how dark data can generate business value. This is because most companies lack the technological tools to analyze their dark data, and lack the understanding of the data that can make analysis worthwhile. More than 75% of business leaders surveyed said that introducing tools that allow fewer technical staff to analyze large data sets would help to alleviate the problem of “dark data.”
Dark data is big data that does not fully exploit its potential and can be used to gain knowledge that helps your business improve and grow. The finding that dark data is not useful, expensive and possibly liable is a problem in itself. But asking the right questions will help you uncover insights that could be locked away if you keep expensive “dark data” and don’t use it.
Read on to learn more about how dark data can be used to gain insights, and what approaches organizations can take to more consciously capture and analyze it. One of the goals of a product called Business Intelligence (BI) is to increase the visibility of information within an organization and to merge it into actionable advice and insights.
Although it is virtually impossible to eliminate dark data, especially in big data initiatives, most companies can benefit from unnecessarily obscured data. Encoding dark data into something understandable gives organizations the ability to say, “We don’t know everything about this data, but we are actively using it. Moreover, thinking about what to do with the data beyond cost – benefit analysis – can remove some of the complexity surrounding the previously mysterious “dark data.”
Of course, some dark data that seem to have minimal value can become essential information at a later date. Some organizations believe that “dark data” is only useful as a means of gathering and processing information. This integration may also require additional data management tools that willingly integrate dark data into common data for everyday use, integrate it, and leave a gap that will soon be filled by additional “dark data.”
Dark data can also be used to describe data that cannot be accessed because it is stored on an outdated device. Dark data is created when different parts of an organization do not work together in data collection and analysis. For example, dark data can be stored in a data storage facility or a data shed, so the organization may not know that data has been collected. In some cases, “dark data” may have been produced by different parts of the organisations if they were not involved in data collection and / or analysis.
The problem is that dark data costs money to store, carries compliance and privacy risks, and masks its value because companies cannot analyze the files to extract business intelligence from them. When a company cannot clear up its data, it is referred to as “dark data,” which can pose a risk to the company, its employees or even its customers.
Although storing dark data is cheaper than analyzing it, some organizations store it for years, resulting in a loss of valuable knowledge and time. To keep up with competitors, measures are needed to find out exactly what “dark data” is, where it is, what it is about, where it can be found, and how it is useful. What should a data manager know about darkData and where is he located?
Although most available data shows that dark data can offer enormous benefits to an organization, the question is how to use it to achieve these benefits. What should companies do when they discover dark data, and what should they do after they discover it?
If, like most organisations, you collect dark data, that is, data you do not process. Dark data is only stored in your company in case it is needed in the future. Although DarkData contains the most information about your business, it is the least analyzed and edited. The unstructured, not yet indexed data, which still needs to be processed or analysed, is stored in a data repository.