If you think you can cast a spell with large amounts of data and can easily boost your business, then take off your magic coat and throw your wand away because large amounts of data are not magic. But rolling up your sleeves and cleaning up your data can help you get great business results.
Big data looks really impressive, but it's not quite perfect. There are several challenges, and data quality is one of them. Many companies recognize these problems and turn to the Big Data Analytics specialists to solve them. But why do they actually deal with it when big data is never 100% accurate? And how good is the good quality of big data? You'll find out soon.
What happens if you use poor quality big data?
Relatively poor quality big data can either be extremely dangerous or not as serious. Here is an example. When your big data tools analyze customer activity on your website, you obviously want to know the actual state of affairs. And you can do that. However, that would not be necessary to save 100% accurate records of visitor activity to see the full picture. In fact, it wouldn't even be possible.
However, if your big data analytics monitor real-time data on the heart monitors in a hospital, for example, a 3% error rate may mean that you couldn't save someone's life.
That means it depends on what kind of company and sometimes what kind of task is available. And that means you have to stop for a moment before hurrying to make your data as accurate as possible. First, you should analyze big data quality requirements and then determine how good your big data quality should be.
What does good data quality mean?
In order to distinguish bad or dirty data from good or clean data, we need a number of criteria that we can rely on. However, you should make sure that this concerns data quality in general, not only in connection with big data.
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There are several data quality criteria to apply, but we have selected 5 main ones that should ensure that your data is clean.
• Consistency - logical relationships
There should be no inconsistencies such as duplications, contradictions, gaps in correlated data sets. For example: it must be impossible to have two identical IDs for two different employees or to refer to an entry that does not exist in another table.
• Accuracy - the true state of affairs
The data should be precise and continuous and reflect how things really are. All calculations based on such data show the true result.
• Completeness - all necessary elements
Your data is likely to consist of several elements. In this case, you must have all interdependent elements to ensure that the data can be interpreted in the correct way. Example: You have a lot of sensor data, but there is no information about the exact sensor positions. In this way, it is impossible for you to really understand how your operating equipment “behaves” and how this behavior is influenced.
• Auditability - maintenance and control
The data itself and the data management process as a whole should be organized so that you can perform data quality audits regularly or as needed. This will certainly help to bring the adequacy of the data to a higher level.
• Orderliness - structure and format
The data should be arranged in a specific order. All of your data format, structure, range of values, specific business rules, etc. must be met.
Influence of Advanced Analytics on the Success of your Business
The business world has had to adapt to the new times. Those businesses that have become static, using only information from the past to make decisions that affect their future, tend to depend on the reputation they already have. In fact, some fail to keep up with today's market.
Many companies use the collection to then perform the Big Data analysis in their favor. Thanks to this, they can keep up with how variable the market can be. Through predictive analysis, you will be able to make the most accurate decisions on how to offer your products or services, depending on what will be needed.