The world is literally drowning in data. Every day 2.5 quintillion bytes of data is produced, and 90% of the data in the world today has been created in just the last two years. Amid this explosion of data, the more answer-focused data analytics methods are limited in the insight they can provide. That’s why many enterprises are turning to data science, an emerging, highly specialized practice that exposes all the valuable insight that’s hiding in the growing mountains of data.
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The evolution and growth of data and how we use it has led to today’s demand for data science. It began in earnest when the world was first introduced to the term Big Data by Roger Mougalas in 2005. Big data, combined with the emergence of the cloud, prompted people to really start considering the power of data. The big data approach gave us a way to discover insight through data exploration and introduced a 360 view of the business. With big data, rather than merely reviewing the data at face value to get answers, enterprises started taking a deeper dive in it, exploring their data lakes and finding correlations that ultimately lead to actionable insight. As that curiosity and exploration has further developed, data science is becoming a highly sought-after way to mine the data to uncover intelligence that will power data-driven businesses today.
The growing demand for data science.
IT is often not able to manage both the increasing number of data sources and the exponential number of requests to explore data. Unfortunately, IT typically becomes a bottleneck where people within the organization can be left waiting days or even weeks to access the data they need for analytics, which is not acceptable in a world where faster time-to-insight separates an industry’s leaders from its stragglers. Enter the hottest emerging role today: the data scientist.
The need for data science experts is skyrocketing, and there is a shortage of qualified people to meet the demand. Data Scientist has ranked #1 on Glassdoor’s list of the best jobs in America every year since 2016. The Harvard Business Review called the data scientist the sexiest job of the 21st century. Although they are in high demand, the melting pot of complex skills required of a qualified data scientist have made them the unicorns in today’s data-driven landscape.
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The 80/20 rule of data science.
Data Science is a challenging discipline, because very often the data scientist doesn’t have access to the right data. And even if the data is accessible, the quality is often very poor. Time is required to transform and prepare the data to make it usable. This is how the 80/20 rule of data science came to pass: 80% of the job of the data scientist is to prepare the data, the other 20% is performing models on it and deriving value from the data. When applied to workdays each week, a data scientist typically gets only one day to develop algorithms and build models — the most critical part of the practice.
Building blocks of data science.
From simple to more complex, here are the building blocks of cognitive insight that play a role in data science:
- Classic descriptive analytics: An example is business intelligence. The anticipated product of BI is to get historical reports or dashboards that brilliantly illustrate trends or performance.
- Ad Hoc reporting: This involves performing interactive queries on a data set. For example: how many, how often, and where did a certain kind of item sell? And then when we answer those questions, we dig deeper and drill down, drill up, slice, dice, cube, to really understand what exactly is causing a dip in sales.
- Predictive analytics: Instead of waiting for things to happen and then acting (like the two building blocks described above), data can be analyzed proactively. Here’s a B2B example: you create finished goods that are based on all material that you purchased for a supplier. If the quantity of a particular material falls below a certain stock, an alert will trigger to notify the purchasing team to order more, so the production line working on this good won’t have to stop and wait for the material.
Data scientists combine the three analytics to get cognitive insight to tell them what the next best action is. Besides the analytical knowledge, data science requires in-depth computer science and mathematics skills, and a deep understanding of the business.
Data science as a team sport.
A data scientist uses a strict process to analyze data and build machine learning models and algorithms. To be successful, data science requires contributions from others in the enterprise, and thus should be treated as a team sport:
Fundamentals of Machine Learning now.
- Data engineers and architects can prepare and organize the data
- Business analysts can apply business strategy to the data
- Developers can write code to leverage the data science model and applying it into an application
In this era of data sprawl, enterprises need big data pipelines that can process and analyze massive amounts of data in real time. To aid in this department, most data scientists rely on machine learning to increase agility and response times. Talend helps reduce the complexities of machine learning by providing a comprehensive ecosystem of user-friendly, self-service tools and technologies that seamlessly integrate it into your big data platform. With Talend Cloud, you can build a systematic path that is repeatable and scalable to ease the data science process, as it is up against an unending flow of data.