1-844-696-6465 (US)        +91 77600 44484        help@dezyre.com

What is a data science platform and why does your business need one?

A new market research report by Markets and Markets estimates the data science platform market to grow at a compound annual growth rate of 38.9% reaching USD 101.37 billion by end of 2021, with North America dominating the data science platform market. Forrester named data science platforms among one of the top emerging technology trends in 2016 with 88% of insight leaders following a platform approach for their data science technology stack.

The phrase “Data Science Platforms” is the most talked about topic in data science conferences, meet-ups and top publications these days. However, the concept of data science platforms is not new in the big data space  but still many do not know what is a data science platform, why a company needs a data science platform, what are the best data science platforms out there in the market. Data science platforms are the buzzword of 2017. This blog walks you through answers to the following questions –what is a data science platform, what are the features of a good data science platform, why a company needs a data science platform and list of some of the best data science platforms available today in the market.

Data Science Training

If you would like more information about Online Data ScienceTraining, please click the orange "Request Info" button on top of this page.

What is a Data Science Platform?

Best Data Science Platforms

The easiest way of defining a data science platform – “A data science platform is a framework of the entire life cycle of a data science project. “

Data science platform contains all the tools required for executing the lifecycle of the data science project spanning across different phases –

  • Data ideation, integration and exploration
  • Model Development
  • Model Deployment

A data science platform helps data scientists enhance their analysis by helping them run, track, reproduce, share and deploy analytical models faster. Usually, all these tasks require lot of engineering effort and hassle to build and maintain analytical models but a data science platform gives you the extra “power tools” to speed up analysis. Data science platforms give the data science teams a leg up in the competitive race to leverage analytics effectively.

“The only way to get more work done next week than last week, short of hiring, is to invest in efficiency … a data science platform is an investment in technological efficiency.”- said Kevin Novak, Head of Data Science Platform at Uber.

Types of Data Science Platforms

Data science platforms are categorized as –

i) Open Data Science Platform

Open data science platforms are the one that provide data scientists with the flexibility to choose the programming languages and packages they want to use as per their requirements. An open data science platform allows data scientists to use the right tools for the right job based on the situation and also lets them experiment with different languages and tools.

ii) Closed Data Science Platform

Closed data science platforms are the one wherein data scientists have to use the vendor’s platform specific programming language, GUI tools and modelling packages. This restricts data scientists on the tools that can be used on top of the platform.

CLICK HERE to get the Data Scientist Salary Report delivered to your inbox!

Why your company needs a data science platform?

Every team in an organization uses some kind of a software platform to support their workflow – just like the engineering team of a company uses source code control systems, the sales team of a company uses CRM systems and the customer support team uses ticketing system. Similarly, to perform data science at scale, organizations need to rely on data science platforms. It’s time for companies to bid adieu to data science processes that depend on disjointed tools and widespread engineering effort to perform data science. Data science platforms bring everything that a data science team needs at a centralized place so that data scientist can pool resources and team up effectively speeding up the process of deploying models instantly.

Challenges Data Scientists Encounter in the Lifecycle of a Data Science Project

  • Data science process begins with exploring the data to understand what is on the plate for analysis. Ideation and exploration can be a time consuming process if you do not know what other team members have already accomplished as you might be redoing the same thing.
  • Data scientists run experiments to test different ideas, review the output and make changes. This phase of the data science workflow is likely to slow down in the absence of a data science platform if the experiments performed are computationally intensive.
  • It is necessary to operationalize data science work to gain value from the outcomes of analysis. This requires engineer resources incurring additional costs and increases the time to market.

Need for a Data Science Platform

  1. To facilitate better collaboration among Data Scientists

 Just imagine if the data scientists in a team, are solving the same problem in various ways, it would not deliver effective value to organization as this decreases the productivity. The best solution to ensure effective collaboration among data scientists is to provide a centralized flexible platform with the set of tools required for data scientists to work upon. Using a data science platform ensures that all the contributions of the data scientists i.e. data visualizations, data models and code libraries reside at a single shared accessible location.  This helps data scientists facilitate better discussion around research projects, reuse the code, and share best practices to make data science less resource exhaustive, repeatable and scalable easily.

  1. Help Minimize Engineering Effort

Data science platforms help data scientists move analytical models into production without requiring additional DevOps or engineering effort. For instance, if a data scientist at Walmart builds a product recommendation engine then it will require the engineering efforts of a software engineer to test, refine and integrate the data model before the users can start seeing the product recommendations based on their behaviour on the front end. A data science platform ensures that the data models are available behind an API so that the data scientists do not have to rely much on engineering efforts.

  1. Help Offload Many Low Value Tasks

Data science platforms help data scientist offload many low value tasks like scheduling jobs, reproducing past results, running reports and configuring environments for non-technical users.

  1. Facilitate Faster Experimentation and Research

Data science platforms allows people see what others are working on and how they work so that data scientists do not have to deal with excess of data management tasks. Moreover, whenever there is a new hire in the data science team, he/she can get up and start working quickly  as it is easier to preserve the work of the people who leave through a centralized platform over multiple isolated tools.

What makes a data science platform valuable?

A good data science platform is the one that overcomes all the above challenges faced by data scientists. Data science platforms should -

  • Explore the data present on large machines without the intervention of the devops, or engineering setup.
  • It should easily help the data scientist understand the past work of his colleagues so that he/she does not have to begin from scratch.
  • A data scientist should be able to use any desired tool or package without disturbing the work of other team members.
  • Ease the process of tracking the work so that it can reproduced easily.
  • Allows data scientists to publish models easily as API’s so that systems in other programming languages can easily use them without any additional re-implementation effort from the engineering team. For example, a good data science platform should easily allow integration of models developed in Python and R programming languages with business applications that are written in Java programming language.
  • Stakeholders should be able to view the results of the work in the form of static reports and dashboards.
  • If the data science project requires data scientist to run compute intensive experiments then the platform should easily scale out compute resources.
  • Last but not the least, a good data science platform is the one that is secure enough and provides access only to the right people.

Best Data Science Platforms

i. Domino Data Lab Data Science Platform

A visionary in the Gartner magic quadrant for data science platforms having a central hub for research, rapid model of deployment and scalable compute platform. Check it out here

ii. Wolfram Data Science Platform

 A data science platform making it easy to apply the most complex machine learning algorithms and visualizations to data. Using Wolfram, data scientists can create formatted reports to be deployed on the mobile, through API’s or directly on the cloud.

iii. Sense Data Science Platform 

A modern data science platform part of Cloudera that speeds up data science process right from exploration to production using Python, R and Apache Spark.

iv. Rapid Miner Data Science Platform

A leader in the 2017 Gartner Magic Quadrants for data science platforms. An open source lightning fast data science platform with over 1500+ in-built functions.

v. Google Cloud Platform

With managed Apache Spark clusters and fast SQL analysis to complex machine learning algorithms, Google’s Cloud platform for data science helps data scientists analyse and strategize more intelligently without having to worry much about the underlying infrastructure.

Companies that continue the data science process with standalone tools are likely to fall behind their competitors as data science platforms become an industry standard in 2017.


Certified Data Science Training

comments powered by Disqus