Category: Big Data

5 Facts About Why we need Data Scientist On-Demand

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All you need to know about Data Science as a Service (DSaaS)

What is DSaaS?

Complex as it may sound, Data Science as a Service (DSaaS) is a simple yet powerful concept that overcomes lots of challenges in the deployment of analytics within an organization. In simple words, this is how it works. Business users upload operational data in predefined formats onto cloud-based platforms. 

This data is converted into actionable insights using automated statistical algorithms, and these insights are displayed back to the business users in the form of easy to interpret visualizations. To take an example, consider the regional sales manager of an apparel brand. Assume that he receives weekly sales data in a prescribed format from all the channel partners within his region. All he needs to do is upload this data using the cloud-based interface provided by his data sciences service provider. And he can get instant access to key business insights such as the top performing channel partners, fastest selling products, markdown performance and much more. As simple as that!

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4 Key Questions On Structuring Your Data Sciences Team

Data driven business strategy is not a ‘nice-to-have’ but a ‘need-to-have’ capability today.

Just the right skill sets and accurate data sources are not enough to guarantee an effective implementation of data sciences. One of the key success factors is the way project ownership is structured. It hugely impacts the overall cost, project timelines, the level of expertise achieved and the return on investment.

As your organization evolves and scales up, some vital decisions need to be taken in a timely manner to ensure that sustainable long term analytics capabilities are in place to support your business strategy. Following is a primer that will help you answer the ‘How’ part of analytics deployment at a broad level.

Following is a primer that will help you answer the ‘How’ part of analytics deployment at a broad level.

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How to build a successful Data Science Team

If you are looking to build long-term analytical capabilities within your organization, it is essential to understand the key building blocks of a successful data science team. You need to ensure that your data science team not only has the right skill sets but also that it is structured in a manner that allows users across the enterprise to leverage its analytical capabilities to drive performance.

If you are planning to build your data science team from scratch, the following is a primer on the types of resources you need to hire and the way they can seamlessly operate within your organization.

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Why you need a data scientist in your team

Having an in-depth understanding of customer and employee preferences can prove to be the largest competitive advantages in today’s dynamic markets. Any organization wanting to outperform competition consistently needs to leverage its operational data to the fullest for achieving these. A data scientist holds the key to generating specific practical insights from operational data which can drive business strategy and validate business decisions retrospectively.

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