The Complications of Raw Data

Complications of Raw Data big data for small business

Negosentro.comRaw data is both good for a group and bad. Handled properly, the data could help a company know what will take them to the next step. Handled incorrectly, data will cause a company to waste time and resources. The right algorithms and the right search parameters are what is needed. Raw data is usually a massive undertaking, but it can be well worth it. It is literally seeing the trees instead of the forest. Raw data is something that can go either way and a company has to make sure of their goals. The cleansing and sorting of the data is an undertaking that must be understood.

Data classification

One of the first parts to deal with raw data comes in classifying the different parts to it. This step can tax an algorithm that is not ready for all it should be. The variety of necessary data types need to be decided on, with a net for that which may not first be thought of as useful. There needs to be teamwork here, especially with those will be using the data for the purpose it was gathered. If the people who are part of this do not fully know what they are doing, there will be a lot of problems. Data can be lost, or not seen in time, if anything is done wrong.

Data culling

After some of the classification, there will need to be data cleaning. There will be copies of the same data. There will be data that will be of no use without any question of that classification. This data needs to be cleaned from the raw. That will cull the data down to a more manageable size. The cleaning will also allow for further classification, now that everything can be seen a little better. The culling process may also help find new data that had been previously missed in the classification process.

Making algorithms

The person who creates the algorithm has to have an understanding of data science and what the bits of information that they are looking for is. That means that they need to have people that know the industry to ask questions of. This is teamwork. The people have to communicate. This means that there has to be knowledge discussed. The programmer is going to have to know how to shape their program into something useful. The variables and statements will be made better and in a way that will be clear to others. That means they need to know the language and understand data science.

No reports

The first thing to realize is that the raw data does not need to be made into reports. Make a definite set of steps and follow them. Reports from raw data will confuse people and be too large to digest. Reports need to be refined before they are shown. Incorrect information can also be in the raw data. That would call to question the validity of everything that had been collected. The people compiling the report has to have the data finessed before it goes before anyone else.

Raw data has to be refined before there is any viewing by people outside of the data team. There can be mistakes if that does not take place. Company plans, research directions, and other types of group activities can go on the wrong path if the raw data is not used correctly. That is why cleaning, classification, and other refining techniques are necessary. The programmer, or programmers, need to be able to craft algorithms that can do all of this. Raw data needs to be refined before it can be used correctly.