There’s a well-known fact in data science teams, the fact that most of the data science role is spent cleaning and preparing data. Data sets are often not immediately ready for analysis, more so when doing data analysis on multiple data sets or unstructured data, it can cause a serious issue for a data scientist or even the results themselves.
It is vital you, and other team members understand your role within the data management eco-system. That’s right, data management already exists in your organisation to some degree. If you have relational data stores, Data products or have any systems where data processing is part of it, you have some form of data management. Data Scientist Jobs will always include an element of data preparation and wrangling; it’s crucial to data analysis and advanced analytics. What, however, you should not be doing is the core data quality tasks like de-dupe, cleansing, fighting the business for accessibility etc. – its simply not your role.
Your organisation should have Data Analysts dedicated to data quality task like this; your management team should navigate the stakeholder politics so you it’s easy to get hold of data while you focus on the value add for the predictive modelling, advanced analytics or even artificial intelligence where relevant. Your Data Governance team, who look after the standards, controls, and how the business define data are so important to you as a data scientist. Well governed data is a true ally for you as the data scientist, it allows to provide more accurate ‘real world’ insight and conclusions, it means your results are relevant already to the business and therefore allows you to communicate your findings in a clear way that the business will already understand.
What’s more, recent research by Dataversity suggests it provides a level of validity throughout data science programs, as well as providing security around the misuse of data and corruption of your scientific methods. Data Governance is not accountability of the Data Science role, but you are responsible for working within its parameters and the way you handle data and the data analysis you provide.
Big Data analytics has boomed in recent years, the growing number of data sources and large amounts of data from within your organisation and externally means there is a huge amount of data processing that takes place in your data products and IT systems. As a Data Scientist, you must have the technical skills, programming languages and relationship with the Data Engineers to really succeed, but all of this is nothing without your knowledge and skills in data management. Data Pipelines need to be robust and scalable, secure and well-governed with data quality a top priority so as data moves around your company it remains accurate and fit for purpose and when you come to analyse it, it’s ready.
The diversity of data you analyse, the multiple data sets and multi-structured data means data management should always be high on your agenda, especially if you are informing and driving business decisions. The use of a predictive model or Machine Learning or any form of Big Data Analytics is becoming more mainstream every day and as it does the importance of the confidence in the data is driven by the data management practices you employ. Furthermore, you can utilise machine learning techniques to bolster your data management armoury, implementing machine learning in validation or in data quality checks etc. could be a formidable force to proving more value out of data science.
In summary, your role as a data scientist must always have an element of good data management, collaborating with Data Quality and Data Governance resource, the business and moreover the technical teams to ensure the data you analyse is correct, relevant and you provide valuable data visualisation that is meaningful already to your target audience.
If you are looking for a data science job, then please talk to the specialist team at Agile Recruit. We have offices in both Manchester and Milton Keynes.