The perfect 360 Data Platform
What are the key features?
To be able to harness the power of a data scientist or data professional, you need to provide them with adequate tools for analysis and machine learning training. By providing a data platform that is powerful, flexible, and easy to use, each company has the possibility to optimize its business processes.
Nowadays, data has become one of the most important company assets. It allows it to improve its efficiency, control its environment, make forecasting, etc… Therefore, it is important to harness its potential by giving the right tools to your data wizards. As the volume of data collected by companies is increasing, businesses are forced to develop systems that allow them to find the most relevant insights.
Not all companies manage to get the most out of their data science teams, as professionals often work with different platforms that complicate the agile extraction of conclusions from the data, as well as its communication. That is why, centralizing the management of data, helps professionals to have the same tool and not overlap work.
There are 5 key reasons why using a data platform will help data scientist to maximize their efforts and redirect them in the right direction. An efficient data science platform needs to be: Scalable, Collaborative, Integrative, Easy to use, and Autonomous.
Scalable
To be useful, a platform must adapt to the evolution of the business needs, exponential increases of data, and the different actors working on projects. Therefore, a perfect platform must be able to support batch, and streaming of data but also able to increase the ingestion through time. It should also be compatible with the data science tool that is required for the ingestion (Hadoop, Databricks, Sparks, etc…). It should also be flexible enough to accommodate each type of data scientist.
In a perfect world, this platform would also integrate data scientists’ own algorithms into the platform in a distributed manner in less than a minute. A new algorithm could be added and used by any major language through the DSL front end, regardless of its language. Each worker could use his native libraries (NumPy, pandas …) allowing full composition with existing solutions; use any of his favourite environments. And have a simple deployment of the algorithms.
Collaboration
Analyzing a complex dataset could be tidy for a single worker, but it is possible to make a mistake that could compromise the whole project. Using collaboration on a project could reduce the number of mistakes, moreover, it could also be a great opportunity to improve the model with more information or insights. It is important that all professionals working on a platform have appropriate access to all the data, resources, and the platform itself.
Integration
It is also essential that the platform can be adapted to new resources that facilitate the integration. In this way they will be able to access new tools that appear on the market or that come from new academic developments, avoiding the use of obsolete tools. The perfect platform would be able to read data wherever it may be; retrieve streams of data from sensors, network logs, web clickstreams, social media, etc. It could also act as a primary data source for additional data surrounding the sequences. It would also adapt to any type of new platform, and the preference of data scientists.
Easy usability
Another key element to look for when starting to use a platform is that it can be installed and learned to be used quickly and easily. When selecting a service, it is important to assess that it can be used immediately, without compatibility problems between systems and that its adoption will not involve major difficulties. Offering a Data app that natively offers publications and analytics to tier parties such as Tableau, Qlik, SAS, and Excel, would help Business and data analysts to create more visual presentations of the study case.
Autonomy
In order to extract value from data and communicate it within the organization with no barriers, data scientists must be able to individually create automatization on the platform that would help detect anomalies. The perfect data platform would be able to use pattern recognition and anomaly detection to improve the analysis. It would also try to use the correlation between data to intuitively discover any cause and effect on the output.
To conclude, the best Data Science platform would be composed of five key elements that would increase the productivity of data wizards. It would be scalable enough to integrate an increasing amount of data in a small amount of time while being flexible enough to adapt to every library type and have a simple deployment of the algorithm. It would also be a collaborative platform where all workers would have access to the data and analysis. It would be integrative, in the sense that it would be able to support any type of data wherever it came from and be easily adaptable to any type of platform. It should be easy to use for workers (intuitive) in the data ingestion process to the analysis and to the exportation of the report. And finally, it would have enough autonomy to assist the worker in detecting anomalies and have a global vision of the data.
We hope this article helps you find the right intelligent technology for you to level up your work. If you ever need any assistance or have questions, you can contact me here or our team of experts at Shapelets. We will be happy to share our experience with you.
