Data Science for the Modern Data Architecture

MMS Founder

Article originally posted on Data Science Central. Visit Data Science Central

Basing your business decisions by accurately predicting the future seems a very exciting and interesting prospect as it possess the capability to give business organizations the potential to gain a competitive advantage in the marketplace. However, truly leveraging data science for predictive analytics remains within the knowledge, ability, and skills of a select few. To expand the reach of data science, the modern data architecture (MDA) needs to address the below mentioned four requirements in the correct way.

  • It should equip apps with the smartness to consume predictions
  • Bring predictive analytics within the realm of IoT
  • Should be easier, accurate, secure and faster to deploy and manage
  • It should possess the ability to support efficiently the entire data cycle

Data Smart Applications

The end-users consume data, analytics, and the results of data science analytics via data-centric applications (or apps). However, the majority of the apps available today lack the smartness to competently leverage the power of machine learning and predictive analytics to deliver a better user experience. This problem though is being solved by building a new generation of enterprise and consumer-facing apps that can leverage the power of predictive analytics to facilitate consumer actions. Developing, however, these data-smart apps is not easy by any stretch of the imagination. This is because the developer apart from writing the code for traditional apps also requires coding to invoke predictive analytics. Common issues that developers face while developing these smart applications include anomaly detection, data quality analysis, entity disambiguation, etc.

IoT Edge

The IoT is growing at a tremendous pace. IDC estimates global IT spending on IoT-related items will reach $1.29 trillion by 2020. Edge Intelligence can deliver insights and intelligence at the requisite place at a much faster speed and that too without a network connection.

Faster, secure and accurate management

Modern businesses generate data at a rapid rate. The mammoth amount of data requires highly sophisticated pieces of hardware to leverage the power of big data analytics and data-smart apps. Hardware advances such a GPU, FPGA, RDMA etc, can facilitate deep learning and machine learning for predictive analytics.

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