Equipping Data Scientists with the Tool to Perform Productively

By Geoff Soon, Managing Director of South Asia, Snowflake

A long sought-after resource, data scientists are often relegated to spending a significant portion of their time on tedious, mundane but fundamental tasks such as data cleaning and preparation. An IDC-Alteryx infographic1 also reports that up to 45 percent of time is wasted every week because data workers are unsuccessful in their activities. There is little disagreement, however, among data workers including data scientists, that collecting, organising, and cleaning data are the least enjoyable tasks they undertake.

In contrast, data analysts are in abundant supply in many companies. Although they may be well-equipped to address business problems directly, they lack the requisite technical background in data science to build their own machine learning (ML) models. It is paramount that organisations find an alternative method through which they can deploy their data analysts to unlock and monetise valuable insights with their data.

Data Engineering Tools Improve Data Scientists’ Efficiency

Remarkably, advancements made in 2020 point to growing trends for greater adoption and implementation of data science and ML in 2021. Emerging tools and technologies accelerate the work of data scientists while also empowering data analysts to move beyond descriptive analytics.

As more organisations acquire the ability to collect and store data inexpensively, they begin to see a need to build ML-powered data models. Organisations that store their data on-premises or rely on data centres may subsequently find their servers to be overloaded. By moving their data onto the cloud, organisations can easily consolidate their data in a single location, enabling fast and secure data sharing and analysis.

Built natively for the cloud, a data platform like Snowflake addresses efficiently hardware inspired challenges of data warehouses. These issues include the inability or lack of speed in scaling to meet business needs, data transformation, delays or even failures in process or data queries due to high volumes and myriad of other data operation activities.

Speed and Standard of Performance

Cloud native or cloud operations require a certain level of elasticity that pushes the infrastructure to scale up to keep up with extra computing needs and resources. Through the use of virtual environments including virtual warehouses, organisations are able to bring a degree of elastic computing to data science and analytics functions of the organisation. This can be critical in key business decision modelling.  

In addition, a nimble, agile cloud data environment enables support for structured and semi-structured data for analytics by optimising the data storage and queries.

When provisioning data analysts and workers in an organisation for example, the load on any traditional warehouse can be significant with queries and compute activities competing for resources. As a result, the delays or lack of concurrency become a key issue in delivering the outcomes for the organisation’s data analysts or scientists.  

Through a data platform, concurrency issues are addressed through a multicluster architecture where unique queries from one virtual warehouse are segregated and do not impact other virtual data warehouse environments. Data specialists including scientists can function productively without suffering delays compounding the mundane, tedious functions.

Seamless Data Sharing

It is critical that data scientists and analysts be able to extract, load and share data, especially on findings and reports with other key stakeholders. The Snowflake Data Cloud enables sharing not only within the organisation but with external users in a safe and secure manner. Today, as data analytics requires data inputs from a variety of sources, it is becoming a greater imperative that data sharing is seamless and effective.

Built to be of high availability, the Data Cloud ensures high performance operations across platforms including hyper scaler platform AWS or Azure. This enables data workers including scientists to perform their mission critical functions to deliver the reports that make a difference.

The Data Cloud Delivers Value to Analytics

In summary, it is obvious that a modern platform is crucial to analysing and sharing data quickly and with security and in-built governance. Organisations require architecture that enables data consolidation, efficient data preparation, and an extensive partner ecosystem. In doing so, they can benefit greatly from new trends in data science and ML when they mobilise their data.

A data platform that provides companies with the ability to consolidate their data into a single source of truth delivers unparalleled value and benefits. When data analysis and insights can be shared internally and externally without needing to be shifted around, teams will be able to collaborate smoothly, increase their productivity, and engage better with their work. This unified approach also enables data scientists to resubmit findings from their ML activities for general-purpose analytics, as well as embed them within the decision-making process.

In short, data is fast transforming into an actionable asset. Data scientists and data analysts alike benefit from cloud technologies that provide virtually unlimited amounts of compute resources. When organisations manage their data effectively, they will then be able to harness the full power of data to improve their business effectively.

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