Data Management Principles for Data Science

Back to Basics: Understanding key data management principles that data scientists should know.

Data Management Principles for Data Science
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Through your journey as a data scientist, you will come across hiccups, and overcome them. You will learn how one process is better than another, and how to use different processes depending on your task at hand. 

These processes will work hand-in-hand, to ensure that your data science project goes as effectively as possible and plays a key component in your decision-making process. 


What is Data Management?


One process is data management. Living in a data-driven world, data management is an important element for organizations to leverage their data assets and ensure they are effective. 

It is the process of collecting, storing, organizing and maintaining data to ensure that it is accurate, accessible to those who need it and reliable throughout your data science project lifecycle. Just like any management process, it requires procedures that are backed and supported by policies and technologies. 

The key components of data management in data science projects are:

  • Data Collection and Acquisition
  • Data Cleaning and Preprocessing
  • Data Storage
  • Data Security and Privacy
  • Data Governance and Documentation
  • Collaboration and Sharing

As you can see, there are a few key components. It may look daunting right now, but I will go through each one to give you an overview of what to expect as a data scientist. 


Data Collection and Acquisition


Although there is a lot of data out there today, data collection will still be a part of your role as a data scientist. Data collection and acquisition is the process of gathering raw data from a variety of sources such as websites, surveys, databases and more. This phase is very important as the quality of your data has a direct impact on your outcome. 

You will need to identify different data sources and find ones that fit your requirements. Ensure that you have the right permissions to access these data sources, the reliability of the data sources, and the format is aligned with your scope. You can collect the data through different methods such as manual data entry, data extraction, and more. 

Throughout these steps, you want to ensure data integrity and accuracy. 


Data Cleaning and Preprocessing


Once you have your data, the next step is cleaning it - which can take up a lot of your time. You will need to comb through the dataset, find any issues and correct them. Your end goal during this phase will be to standardize and transform your data so that it’s ready for analysis.

Data cleaning can help with handling missing values, duplicate data, incorrect data types, outliers, data format, transformation, and more. 


Data Storage


Once you have cleaned through your data and it’s of good quality and ready for analysis - store it! You don’t want to lose all those hours you just put in to clean it and get it to the gold standard. 

You will need to choose the best data storage solution for your project and organization, for example, databases or cloud storage. Again, this will all be based on data volume and complexity. You can also design architecture that can allow for efficient data retrieval and scalability.

Another tool you can implement is data versioning and archiving which allows you to maintain all historical data and any changes to help preserve the data assets and long-term access. 


Data Security and Privacy


We all know how important data is in this day and age, so protect it at all costs! Data breaches and privacy violations can have severe consequences, and you don’t want to have to deal with this problem. 

There are some steps that you can take to ensure data security and privacy, such as access control, encryption, regular audits, data lifecycle management, and more. You want to ensure whatever route you take to protect your data that it is compliant with data privacy regulations, such as GDPR. 


Data Governance and Documentation


If you want to ensure data quality and accountability throughout the data lifecycle, data governance and documentation are essential to your data management process. This process involves having policies, processes and best practices in place to ensure that your data is well-managed and all your assets are protected. The main aim of this is to provide transparency and compliance. 

All these policies and processes should be documented comprehensively to provide insight into how the data is structured, stored, and used. This builds trust within an organization, and how they use data to drive the decision-making process to steer away from risks and find new opportunities.

Examples of processes include creating comprehensive documentation, metadata, maintaining an audit trail and providing data lineage. 


Collaboration and Sharing


Data science projects consist of collaborative workflows, and with this, you can imagine how messy it can get. You have one data scientist working on the same dataset that another data scientist is doing further cleaning on. 

To ensure data management within the team, it is always good to communicate your tasks so that you do not overlap with one another, or one person has a better version of a dataset than someone else. 

Collaboration within a data science team ensures that the data is accessible and valuable to different stakeholders. To improve collaboration and sharing within a data science team, you can have data-sharing platforms, use collaborative tools such as Tableau, put access controls in place, and allow feedback. 


Data Management Tools and Technologies


Okay now that we’ve gone through the key components of data management, I will now create a list of data management tools and technologies that can help you in your data science project lifecycle. 

Relational Database Management Systems (RDBMS):

  • MySQL
  • PostgreSQL
  • Microsoft SQL Server

NoSQL Databases:

  • MongoDB
  • Cassandra

Data Warehouse

  • Amazon Redshift
  • Google BigQuery
  • Snowflake

ETL (Extract, Transform, Load) Tools:

  • Apache NiFi
  • Talend
  • Apache Spark

Data Visualization and Business Intelligence:

  • Tableau
  • Power BI

Version Control and Collaboration:

  • Git
  • GitHub

Data Security and Privacy:

  • Varonis
  • Privitar


Wrapping it up


Data management is an important element of your data science project. See it as the foundation that is holding your castle up. The better and more effective the data management process is, the better your outcome. I have provided a list of articles that you can read to learn more about data management.


Resources and Further Learning


Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.