Decoding the myth behind the ‘Data Scientist’

Ivy Aug 20, 2014 No Comments

If you are anyway connected with analytics, database management, econometrics or BI, you will have heard of this new-fashioned title of the 21^st century, the data scientist.

Much like the conventional scientist who researches, experiments and innovates; a data scientist is a hands-on person who puts into practice well established principles and norms of data science into business practices. Just as a scientist can belong to any discipline ranging from the environment and epidemiology to ceramics production; a data scientist can work in the realm of business, healthcare, governance or social science.

Data science as in other sciences, involves learning a set of theories and principles. These are the mainstay of practices and skills implemented for redressal of various problems related to data.

So what does a data scientist do?

He or she applies skills to on-premise or cloud technology for clarity and direction in working with data.

As mentioned in the portal for data scientists,

 “Data Scientists don’t just present data, data scientists present data with an intelligent awareness of the consequences of presenting that data.”

The data scientist looks for hidden patterns in data, applying user-friendly or advanced technologies, self-served search tools, interactive data exploration tools, and other approaches using his knowledge of statistical modelling, domain knowhow, mathematics, finance, social science, business management, engineering and more.

Moving beyond the realms of pure science …..

The Data Scientist

# Brings science to the business process

As the title suggests, the data scientist has fine-tuned the skill of sifting, sorting, categorising, analysing, and presenting data in a usable form. He researches and experiments in the pursuit of knowledge – delving in social behavioral patterns, statistical controls and conducting real-world experiments with data. He brings science to the process of business operations and a data-driven DSS.

# Is an agent of change

The role of the data scientist has evolved as all-pervasive to the business process. He is now being tagged ‘an agent of change’, as he convinces decision makers of the organisation to take data-driven decisions for competitive advantage or pressing business problems .According to Anjul Bhambri, leader of the Big Data development initiative at IBM, “the data scientist can (and should) play a key role in advocating for a dynamic, information-focused view on business growth”.

# Makes analytics simple and user-friendly

Making use of statistical algorithms and machine learning techniques in historical or real-time data , the data scientist makes possible patterns to be visible from huge amounts of data. This makes analytics usable for end users not familiar with the system infrastructure. A good data scientist has a strong business acumen, who can “communicate his  findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge” (IBM).

# Prevents data silos

The data scientist makes use of existing platform and tools to ensure that queries and data access run smoothly and seamlessly. His role is to increase the analytic capabilities of the business by ensuring that data is available to everyanalyst or decision maker.

# Is part data analyst, part system analyst

The data scientist examines data from disparate and streaming sources to identify hidden trends, looks at it from various perspectives, establishes its significance and puts forward his recommendations on how to apply the data. As one who ensures that data does not get siloed, he liaisons internally with system administrators, colleagues and developers, assuming the role of a system analyst by default.

As this IBM blog says,

“Business-oriented data scientists may never receive a Nobel Prize for their work, but that doesn’t make them any less scientific. The prize for their hard work is obvious: business success”.