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The Data Science Industry

The Data Science Industry: Who Does What

Nowadays, the data science field is hot, and it is unlikely that this will change in the near future. While a data driven approach is finding its way into all facets of business, companies are fiercely fighting for the best data analytic skills that are available in the market, and salaries for data science roles are going in overdrive. Companies’ increased focus on acquiring data science talent goes hand in hand with the creation of a whole new set of data science roles and titles. Sometimes new roles and titles are added to reflect changing needs; other times they are probably created as a creative way to differentiate from fellow recruiters. Either way, it’s hard to get a general understanding of the different job roles, and it gets even harder when you’re looking to figure out what roles will fit your company and its needs.

 

The Roles

A business analyst is one who understands the specific domain of the project (ex. retail, merchandising to be specific, supply chain etc.). His/her role is to understand the business problem, analyze the current state and capture requirements using various tools like surveys, interviews, group discussions and then provide recommendations and create a Requirements document for sign off.

A business systems analyst is kind of extension to the business analyst role. A person will need to have some technical knowledge of the area he/she is in working in order to perform detailed analysis of the current state and test the proposed solution. Ex. you will need to know SQL while working on implementing a new product to check if the data setup is correct etc.

Desired skills are: SQL, data visualization and their tools (e.g. Tableau), conscious listening and storytelling, BI understanding, data modeling

        Mindset: Resilient project juggler

Data analysts collect, process and performs statistical data analyses – one that works with lots of data to derive meaningful insights to either address business problems or discover hidden trends and patterns that can be leveraged to meet the business objectives.

Desired skills are: R, Python, HTML, Javascript, C/C++, SQL, Spreadsheets (e.g. Excel), Database systems (SQL and NO SQL based), communication & visualization, Math, Stats, Machine Learning

Mindset: the data detective – intuitive data junkie with high ”figure-it-out” quotient

statistician collects, analyzes and interprets – qualitative as well as quantitive data with statistical theories and methods.

Desired skills are: R, SAS, SPSS, MatLab, Stata, Python, Perl, Hove, Pig, Spark, SQL, data mining & possibly machine learning, statistical theories & methodology

Mindset: historic leader of data – logical and enthusiastic stats genius

Data scientists are as rare as unicorns. Their role will involve analyzing data but more importantly to come up with proprietary algorithms and mathematical equations to develop business tools and products that address the business need. A data scientist will also be involved in creating new tools/products. Cleans, massages and organizes (big) data.

Desired skills are: R, SAS, Python, MatLab, SQL, Hive, Pig, Spark, distributed computing, predictive modeling, story-telling and visualizing, Math, Stats and Machine Learning

        Mindset: curious data wizard

Business Intelligence(BI) Analyst works specifically in the area of BI and it is important for him/her to understand data related concepts. Few years back it would have been enough to know SQL, data transformation tools like Informatica, Reporting tools like MicroStrategy. However, with the advent of Big Data, one will need to understand those concepts as well to succeed in the role.

Desired skills are: SQL, data visualization and their tools (e.g. Tableau), conscious listening and storytelling, BI understanding, data modeling, Alteryx etc.

        Mindset: BI Analysts Focus More on the ‘What’ than the ‘Why’

Additional roles existing: Data Architect, Data Engineers, Database Administrator, Data and Analytics manager (cheerleading the team ;))

BUT..

Both data scientists and BI analysts do visualizations.

 

Tools are just tools.

BI analysts may be part of deploying a visualization (or a few of them in a dashboard) in a ‘production’ environment more than data scientists. But they certainly both ‘do’ (conceptualize, design and build) visualizations.

 

Both data scientists and BI analysts work with data sources.

 

Managing data is just managing data.

Some may work with traditional data warehousing technologies, star schemas, normalized tables and BI cubes. Some may work with more modern distributed or cloud based storage technologies. But they both work with data sources.

 

Both data scientists and BI analysts do programming.

 

Coding is just coding.

Loading a modeling library in python, writing up scripts or SQL queries to perform and check ETL, or building a D3 app may typically exist in very different parts of an analytics project pipeline. But they all require programming skills.

 

But in addition to that, data scientists are expected to be statistically literate, run experiments, interpret data with a mind on causation and – most often – do modeling.

 

That is the main distinction in my mind, and the high level answer to this question. I am sure BI analysts can do these things if they want to, and I do not doubt that many of them have the capability to. But they would not be responsible for it.

 

Experimentation, statistical interpretation, plus building, testing and using models – whether they are statistical, optimization, time series or machine learning – sit primarily with data scientists.

 

BI vs Data Science

A BI analyst’s main task is to find patterns and trends in your business’s historical data.

That makes BI largely an exploration of past trends, while data science finds the predictors and significance behind those trends.3 Both views are ultimately valuable and complementary. The data aggregation and transformation BI analysts conduct puts data into a format that data scientists can easily repurpose when building models.

The typical analyst’s toolkit consists of software like BI dashboards — which make visualizing business performance easy (although dashboards lack the flexibility and capacity of code) — and1 programming languages like SQL to manipulate data and query databases. Using these tools, BI analysts can evaluate the impact of certain events on a business’s bottom line or compare a company’s performance to that of other companies in the same marketplace.

However, they are rarely required to forecast future business metrics with a high degree of accuracy, as that requires a more technical skill set.

Data Scientists Apply an Algorithmic Approach

Data scientists, on the other hand, have a toolkit of algorithms that they use to understand and predict a business’s performance.

Understanding that performance requires a more technical skill set based in statistics, machine learning and programming. In addition to languages like SQL, a data scientist is expected to know how to code in languages designed for mathematical analysis like R, Python or Scala.

Using those programming languages, a data scientist can create a framework that leverages historical data — as well as the data currently being created — to predict how much money a business’s customers will spend.