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What is Data Processing?

Good Data Science starts with good data and this needs a deep understanding of how data should be manipulated and stored. Traditional data and big data need different approaches, but the underlying discipline is the same. The way we store data in legacy systems is not always appropriate for data science and we can advise on how to adopt your platforms for better insight generation.

Processing for Data Science

Companies are very experienced in implementing data processing for business transactions.

In the majority of cases, this will involve a classical database solution. These systems have evolved to be very useful tools in processing transactions. Unfortunately, databases are not well tuned to perform analysis and visualization. Many companies add on technologies such as online analytical processing after the fact to address this issue.

Modern data science needs a data processing environment that can scale both the space available and the processing power provided. Scaling a database is possible but is a costly and time-consuming exercise.

What makes a good data science processing environment?

A good data science processing environment will allow for many different types of data. From large data sets to small, structured data to unstructured. It should also accommodate both internal and external data; the environment must be able to do it all.

Very often the cloud is the best solutions for data science processing needs. Cloud based solutions take away all the worry around reliability, availability and managing backups from the data scientist. They are also designed to scale in size with extreme ease. The large cloud providers also offer mechanisms for analyzing and visualizing data.

Having set up your environment you will have to complete a few steps. Firstly, you should build the processes to gather data and store data. These processes streamline later work by preprocessing the data as it enters the data analysis environment. For example the processes can cleaned the data, change the data to use terminology that the business understands, and write it in a format that is highly efficient. It would also be a good idea to monitor and log these processes.

The Secret Recipe For Success

  • Think like a machine and not like a human. A common mistake is to store files which resemble spreadsheets with rows, columns and nicely formatted numbers. Machines do the Data science for us and they don’t need the files to look good. Machines need the file to be compact and fast to read.
  • When designing a data science processing environment think holistically. Don’t just consider the size but think about how the analysis and visualization tools that you want to use can work in symbiosis with the platform.
  • Data scientists need to have access to large datasets to be able to spot the underlying patterns and insights. They don’t want to always have to work around access permissions and security. Security of data is very important, however, especially when the data used is from a business process. This means finding a balance between control and flexibility.
  • Manage metadata carefully. Metadata is the term we give to the fixed attributes that we attach to describe pieces of data. For example, a transaction record can mention customers, location, products etcetera. Defining these attributes according to the business is must. We should standardize all metadata before data analysis. Furthermore, we should alter any external data to conform with the business standards.

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