The capacity to innovate, make better decisions, and improve profitability are just a few examples of the data science advantage. These are perhaps some of the compelling reasons why organizations across the globe are looking towards data science to extract information from the vast sea of data at their disposal.

Businesses today are looking to operationalize the data that they have faster. For that, they not only need ace data scientists but along with this, they need data science platforms that can help them design and test out the algorithms that matter.

A data science platform should democratize data science as data becomes the lifeblood of every business department. With an extremely easy interface, it should help business users become more data-driven in their decision-making process.

However, it is also true that not all data science platforms are created equal. It can be easy to get distracted by fancy features but make sure that the data science platform that you choose helps you achieve the following:

Model development and workflow designs

Selecting and creating the right data model is an important factor that contributes to the success of any data science initiative. This involves identifying the right data, curating the right data sets, and selecting the right and appropriate algorithms to create a model that suits the business purpose.

The data science platform has to assist this activity and help with data extraction, cleaning, and then apply the right algorithms to help them reach their business objectives.

The platform also has to assist in creating workflow designs to help business users understand how they can go from a business problem to business value. The platform should also be intelligent to allow business users to apply the right data models to the right data and gain predictive insights – quickly and easily.

Work the data

We will all drown in the sea of data unless we can work it. Automating the data model and data refresh are actions that any data science platform should enable. These tasks can be time-consuming, error-prone, and just add to the cognitive load of the data scientist when done manually. Given the surging volumes of data, data scientists also need the data science platform to capably automate these models and enable data refresh.

Additionally, the data science platform also has to have robust data integration capabilities and should be able to integrate big data, legacy data, and textual data with ease. The platform thus should enable scheduling of Model Runs, EDGE, APIs, and ETL.

Predictive intelligence

The speed of change is accelerating and thereby increasing the need for business users to become proficient in fine-tuning their predictive capabilities. A data science platform has to be democratic enough to enable business users to use predictive intelligence by allowing them to create, deploy, and maintain predictive models easily. Using these models, business users can then capably anticipate business trends, take pre-emptive steps to minimize risks, and improve decision quality.

Segmentation & Recommendation Engine

Organizations today need the capability to categorize and segment their customer demographic better to drive business results. Clear segmentation capabilities are perhaps one of the greatest advantages that data science brings to the table.

A data science platform should enable both data scientists and business users to develop their segmentation and recommendation engine needs without the effort of stringing together voluminous and effort-intensive lines of code. This can be achieved using Regression, Classification & Clustering, and makes it faster for business users to target the correct market segment.

Forecasting and optimization

Data science has become as huge as it has because it helps organizations to take a peek into the future. It helps greatly to improve demand and price forecasting capabilities and removes the guesswork from this crucial exercise.

Price movements, demand forecasting, price, and revenue optimization, and insights are capabilities that a good data science platform must-have. It should also be simple enough for business users. By employing forecasting and time series techniques, a data science platform can make forecasting and optimization easier and can help business users also leverage the data advantage.

Sentiment analysis and social listening

The volumes of data are growing exponentially owing to the proliferation of the internet, smartphones, and subsequently social channels. This data is a treasure trove of information that organizations want to use for several purposes…whether it is to conduct sentiment analysis or to listen actively to what their customers want.

For this, data science platforms should have robust text analytics capabilities. It should also have a pre-built set of Linguistic, Statistical, NLP, and Machine Learning techniques to Model & Structure textual data for analysis, visualization, and collaboration and help the business user employ these to drive business decisions.

Playing with the data – Data Storyboards, Data Exploration, Impact Analysis, and Dashboarding

Data Storyboards, Data Exploration, Impact Analysis, and Dashboarding are key responsibilities of data science. Just having data is not enough. Having the capability to work the data, explore it for insights, create data storyboards, and dashboarding for better understanding and generating impact analysis are essential capabilities of a data science platform.

The business users should be able to easily conduct ‘What-if’ Analysis and create rich Data Visualizations by integrating complex datasets across various business and analytical areas.

The ultimate aim of a data science platform should be to democratize data science and help business users become citizen data scientists. It should give organizations the capability to glean intelligent insights from data to optimize their operations and maximize business value across the employee chain.

A robust data science platform with the above-mentioned capabilities will not only come to the aid of the data scientists but will also enable organizations to convert their regular employees into citizen data scientists – that is when we will be able to unlock the real value of data.