In the last few years, data has become one of the most important things a company can have. So much so, that according to a study of Cap Gemini, 83% of the businesses that have data are able to monetize it.
That is why it shouldn’t surprise you that the Data scientists have become one of the most coveted assets a company could have.
There is no use for businesses to retrieve data if they are not able to draw conclusions from it. This is why data scientists are in such high demand and because of them businesses are able to harness, process and analyze the information they retrieve efficiently. They are able to help draw conclusions from it and allow businesses to progress.
Nevertheless, data scientists are part of a relatively new area. That is why there are recurring problems within their community which makes their work harder. Shapelets has recognized these problems and has made it their main objective to solve them.
Silos of independent Data
This data is managed by businesses but is not shared between their departments. Every part of the business has its own silo, which creates communication problems inside the business. One of the most prominent examples being marketing and sales teams. Usually, they are using different programs such as Hubspot or Salesforce creating unnecessary problems that could easily be avoided by a program that can connect all the data unanimously, such as Shapelets platform.
Low value data
The quality of data depends on many things: punctuality, precision, consistency, conformity, singularity, and integrity (just to list a few). If these qualities are lacking, the data will lose quality, which in turn would affect the precision, visualization and usefulness of the analysis drawn from it. That is why it is fundamental that the data must be collected with these qualities in mind, because the better the quality of the data, the more accurate conclusions can be drawn from it.
Too much data
Too many businesses collect too much data. This creates a mountain of information that can become impossible to analyze. In fact, many businesses collect more data than they can process, or even need! Therefore, it is important to keep in mind what data is needed to reach the objective. Only then will the business be able to collect relevant data and be able to utilize its resources in the most effective way. The faster you are able to process the data, the more beneficial it is for the company as it could save many errors.
Duplicates of the same data
Having repeats costs storage. This drastically reduces the ability of the company to efficiently analyze their data. More duplicates, less new data, therefore the analysis is not as efficient. Companies should aim to unduplicate their data – expand on how companies should be able to do this.
Lack of data consistency and transparency
The choice of data must be in line with the company’s objectives. They should choose data that will help move towards their objectives. Companies could do this by creating a dictionary of data which collects the different types of data they collect. Furthermore, problems may arise if the workers themselves lack access to the data meaning they are not able to work with them.