Data Management Vs Data Science
“Hello, Chichi. Pleased to meet you. So what do you do?”
With my best smile “I’m a Data Manager.”
With a confused smile “Ermm…what does that mean?”
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Since Data became very popular, I can bet (even though I don’t gamble) that you must have heard about the role of Data Scientist. If you don’t quite understand what it is, watch out for my post on some key data professions. On the other hand, the Data Manager role is rare. So what really is it?
In a broad sense, management is the coordination of people and/or activities to achieve some goal(s). Similarly, data management is
“ the coordination of people, processes and data flows in order to achieve some set goals-which should include or result in deriving value from data.”
A cursory look at that definition may paint a picture of data management as just data governance. The truth is, data management is a lot of data governance, but much more. The Data Management Body of Knowledge defines data management as
“ the development, execution, and supervision of plans, policies, programs, and practices to deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”
Data management activities range from the technical such as data engineering to the non-technical such as data governance. The Data Management Body of Knowledge specifies 11 Knowledge areas that cover:
- Architecture & Modelling
- Storage & Operations
- Security
- Master data, Reference data, Document, Content & Metadata management
- Integration & Interoperability
- Warehousing & Business Intelligence
- Quality
- Governance
So, “where is Data Science?”, you may ask. (If you don’ mind some humour, it’s in chapter 14 of the 2nd edition of the Body of Knowledge.)
Data Science is the analysis and visualisation of Big Data. It’s a specific technical role that builds on the application of several data management knowledge areas.
Let’s get a bit more practical.
A Data Scientist is primarily concerned with seeing what’s possible with a particular big dataset. The Data Scientist needs to find insights and answers for questions that were not pre-determined (unlike the analyst who explores how to answer some known business questions with data). Meanwhile, the Data Manager is concerned with the entire enterprise/department/domain data, not only a specific dataset. The manager is concerned with maintaining the integrity of the data through its entire lifecycle and ensures that it can be efficiently accessed by those who need to harness it. This data role requires an acute awareness of the business goals, as well as what should be done on the technical side.
And now, let’s get a bit more realistic!
The dilemma of data professionals is that the lines between roles are blurring all the more, yet the need for depth in specific areas is simultaneously on demand. If you want to be a more valuable Data Manager, you should have more than a basic level of expertise in Data Science. Similarly, a forward-thinking Data Scientist should not pride in statistical and algorithmic prowess alone but should think of data as a living entity going through a cycle, and that needs to be managed.
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So what do I do?
I help organisations derive value by developing, executing and supervising strategies, policies, processes and projects that acquire, enhance and use data, and provide easy future access to it. If the data happens to be Big and there’s a need for Machine Learning, I don’t hesitate to train the models!
