5 Problems data scientist
face in business
Fátima Ramos
In the last few years, data has become one of the most important tools for a company.
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 Data Scientists have become one of the most coveted assets a company could have.
6 uses for Human Resources departments to get the most out of data analysis.
There is no use for a business to retrieve data if they are not able to obtain useful insights from it. This is why data scientists are in such high demand. Moreover, this is why thanks to these data professionals 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. Therefore they find recurring problems within their community which makes work harder. Shapelets team of experts have recognized these problems, specially when creating data visualization apps.
Issues when creating Data Apps
We have made it our main objective to solve them. Have a look!
SILOS OF INDEPENDENT DATA
This data is managed by the companies but is not shared between their departments. Every part of the business has its own sphere of operation, which leads to communication problems within the business. One of the most prominent examples of a team using different tools is marketing and sales teams. Usually, they are using different programs such as Hubspot or Salesforce creating unnecessary problems that could be easily avoided by a program that can connect all the data unanimously, such as Shapelets platform. Thus, a software solution improves the way analytical teams collaborate, reduce latency, cleanse data sources and empower fast discovery.
LOW QUALITY DATA
Too many companies are collecting too much data. This creates a mountain of information that can become difficult to analyze. In fact, many businesses collect more data than they can process, or even need! For this reason, 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 use its resources in the most effective way. The faster you are able to process the data, the more accurate and beneficial it is for the company, as it could save many errors.
On the other hand, too much data could create issues when visualizing large time series too. This makes navigation difficult for data analysts and data scientists. This is why a solution might also be needed to guarantee fast time series navigation directly on that raw data.
DUPLICATE DATA
In Shapelets, we have noticed that many other businesses have the presence of repetitions in data requires storage space. This drastically reduces the company’s ability to efficiently analyze and digest its data. More repetitions, less new data, therefore the analysis is less efficient. Companies should aim to reduce the number of duplicates and redundant repetitions in their data sets
DUPLICATE DATA LACK OF DATA ONSISTENCY AND TRANSPARENCY
It is key for a company to have a consistent and transparent use of data to support their goals. It must be in line with the company’s objectives. They should choose data that will help move toward their objectives. Companies could do this by creating a dictionary of data which collects different types of data. Furthermore, problems may arise if the workers themselves lack access to the data, meaning they are not able to work with it. Interaction and insights sharing between business users is key for efficiency in the data management process
Explore accelerated data ingestion and optimized data visualization solutions! This is key in order to generate answers and save costs. Shapelets platform has the versatility and agility to access, digest and analyze the data you need. You need a smarter tool, and Shapelets is here to help!
Daniel Ramírez
Engineer
Daniel is a Data Engineer for Shapelets. He is an integral part of the back-end development team, where we develop a high-quality platform ensuring the best product design with valuable functionalities for data scientists. Daniel supports the team with his diverse background in Software Engineering.