Top Characteristics of the Best Data Science Platform

Organizations are becoming more and more data-centric and a majority of their business operations are pivoted on the Cloud-native platforms that ingest and process tons of data to deliver actionable insights with accuracy and transparency in a secured environment.

Despite having access to the best data science platforms, a majority of the organizations are falling short of their expectations. There are many reasons for missing out on the goals, and poor choice of data science tools is the primary reason for this. Data scientists and analysts waste more than 60 percent of their time gathering data that would never deliver the results they are aiming for. In order to build efficient IT systems that can handle faster deployments, we need a data science platform.

Let’s identify the best characteristics for data science platforms that you can vouch for before taking the ultimate decision to invest in these solutions.

Enterprise DMPs

When we speak of enterprise data management platforms  (DMPs) available for data science projects, we are making a proximate reference at metadata management, which includes the following types of data sources;

  • Descriptive
  • Structural
  • Administrative

The number one choice for any data science engineering team while choosing the data science platform is the ability to lay hands on unified solutions that bring together various aspects of data mining, segmentation and classification, modeling, analytics, and visualization. The space is loaded with powerful tools and solutions that can ingest very large volumes of structured and unstructured data, but only a handful of platforms can actually deliver on the promise of truly becoming an enterprise data management for the entire organization.

Pre-built workflows and templates

Do you wish to know another reason why the data analytics teams pick data science platforms for their business intelligence operations? The analysts and engineers prefer working with solutions that enable them to work in a high-speed, hyper-personalized platform to build, deploy, process, monitor, and upgrade their existing data integration pipelines. Pre-built templates and workflow automation features more or less make DevOps on these platforms extremely user-friendly and agile.

The advent of newer technologies for data science has led to the inclusion of advanced capabilities such as Artificial Intelligence (AI), Machine Learning (ML), Automation, and Predictive Analytics.

All these come together in the form of a solid data science platform to provide a viable solution for the creation of business value. Today, data science tools and solutions are enabling organizations to scale their business processes effectively with greater agility, speed, and governance – something that was unimaginable to gather all at one place only a few years ago. Big Data, AI, and ML have changed that for data science teams around the world.

Embedded AI / Edge AI/ Virtualization

It is impossible to miss out on a key feature that is as highly revered in the data science industry as embedded AI or Edge AI. The development and maturity of all data science and machine learning algorithms occur simultaneously. Embedded AI occurs at the software level, while edge AI is operational at the device level, which means any data engineering team can build simplistic ML workflows from remote devices using ‘drag and drop ’ and ‘plug and play ’ features. That’s exactly what’s happening with data science platforms that are involved in the DevOps environment for complex AI ML applications such as Computer Vision, NLP, Voice / Conversational AI, Recommendation Systems, and Image recognition, and so on.

That leads us to the efficacy of these data science tools in a virtualized environment where applications, servers, and networks (essentially the entire IT infrastructure) are created or simulated in a “virtualized” ecosystem using virtual machines (VMs). To excel in a virtualized environment, a data science platform has to live up to the expectations of delivering a hypervisor that is independent of the hardware involved at various stages of the data management cycle.

Automation in Data Integration/ Cloud Data Ingestion

Data science teams are looking for a higher degree of automation in their data processing operations, and yet remain reluctant at giving away their control to machines. This results in the ambiguity of command and authority at many levels. If a team is handling too much data from many different databases/ data lakes/ data warehouses, then it can give rise to grave confusion. This can be solved by utilizing data integration tools that are custom built to handle Big Data projects for distinct industries such as healthcare, manufacturing, marketing and sales, e-commerce retail, government data/ surveillance, and so on.