How Has Data Science Evolved Over the Years?

Data Science was born from the notion of combining applied statistics and computer science, with the application of statistical methods to the management of business, operational, marketing, and social networking data. In recent years — as organizations have been flooded with massive amounts of data and turned to complex tools designed to make sense of it — the data scientist function has come to occupy a critical place at the crossroads of business, computer science, engineering, and statistics.

The Workflow of a Data Scientist

Data scientists add value to organizations in the following ways:

• They identify trends in data, test hypotheses, and recommend direct actions, for themselves and other teams, that help their organizations define business goals and navigate the competitive landscape.
• They reframe business requirements into algorithmic solutions and other analytical approaches.
• They integrate data with proven hypotheses and heuristics from domain experts to capture a more complete and accurate view of behaviors and probabilities.
• They develop and manage data models, and validate results in support of unbiased decision-making.
• Through the socialization of their findings, data scientists steer the business toward focusing on the most urgent needs.
• They establish best practices for the team through the vetted adoption of new tools and workflow changes.
• They equip the marketing and sales teams with tools that help them understand the audience at a very granular level, contributing to the best possible customer experience.
• By applying analytics such as outlier detection, missing value imputation or duplicate removal, they constantly improve the company’s data quality.

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Top 4 Common Challenges Data Scientists Face

1. Availability of Data from Multiple Data Sources

As organizations pull increasing amounts of data from multiple applications and technologies, data scientists are there to make meaningful judgments about the data. Without tools to ingest and aggregate data in an automated manner, data scientists have to turn to manually locating and entering data from potentially disparate sources, which, in addition to being time consuming, tends to result in errors, repetitions, and, ultimately, incorrect conclusions.

2. Reproducibility

Reproducibility is the ability to produce the same results each time a process is run using the same tools and the same input data; it is particularly important in environments where the volume of data is large. Certain elements of a data science project can be developed with an eye to reproducibility — generally called “idempotency” in this context — which can help not just with the current project’s productivity but also with future models and analyses.

According to aNature survey (2016), more than 70% of researchers have failed to reproduce another scientist’s experiments, and over half of respondents couldn’t manage to reproduce their own work. When it comes to data science, common contributors to a reproducibility crisis include limited data or model availability, varying infrastructure, and time pressure. It’s both technically difficult and time-consuming to manually run a repeatable and reliable data pipeline process.

3. Defining KPIs and Metrics

A good KPI provides an answer to “What does success look like for this project?” in a measurable way. But identifying the right KPIs is always difficult. Metrics and indicators need to both speak to a company’s long-term strategy and objectives as well as provide a clear map for action in the short term. And the rapid rise in data availability and organizational data literacy means there are more potential indicators to consider as KPIs, and more people with opinions about those options, than ever before.

4. Coordinating across teams

And then there’s the challenge of coordination across teams. One reason that only 20% of data science models are successfully implemented (according to a 2019report) is almost certainly the fact that data teams, IT, and operations teams all tend to use different tools. When it’s difficult or impossible to swiftly test model assumptions and push them from experimentation to production, or to iterate modifications of models in production, data scientists can find themselves held back from testing hypotheses and defining the best KPIs for a business in a productionized and sustainable manner.

How Does Kaspian Help Data Scientists

Kaspian is a managed data pipelining solution that provides a stable pipeline platform designed to interact with various interfaces and systems. Its workflows are defined, scheduled, and executed as Python scripts, with all of the complexities of the infrastructure taken care of.

Kaspian can make life easier for the data science team at every stage of a project, from exploration and development through to deployment, production, and maintenance. It can reliably and repeatably run complex processes that include monitoring, alerts, data quality checks, restarts, etc., and it enables be mapped quickly and efficiently.