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Data Science and the Outsourcing Advantage

Knowledge is Power Data science is a term that has increased in popularity in the past several years. In 2012, the Harvard Business Review published...

Paul Azorin

By Paul Azorin

Chief Marketing Officer Paul Azorin defines BairesDev's vision while leading company modernization and fostering a customer-centric culture.

10 min read

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Knowledge is Power

Data science is a term that has increased in popularity in the past several years. In 2012, the Harvard Business Review published an article titled “Data Scientist: The Sexiest Job of the 21st Century.” According to the report, demand for skilled data scientists is racing far ahead of supply. In fact, this shortage of data scientists is creating market disruption in some sectors. But what exactly is data science and why is it so important?

Data science is a field of study that uses scientific approaches and methods, coding, applications, mathematics, statistics, and other systems to extract useful insights from data. In a business setting, a data scientist can extract those valuable insights for the company’s benefit. A business hires a data scientist to mine, analyze, and manage stored data and then report any beneficial or profitable findings. But why exactly are skilled data scientists in such high demand? The answer is, quite simply, the Internet.


Background on the rise of data science

In the early 1990s, the Internet was just starting to take off. But in just a few short years, millions of people were already using the Internet to send emails and transmit data across the web. Now, in 2019, the amount of data transmitted and received over the Internet is mind-boggling. According to Domo, 2.5 quintillion bytes of data are transmitted over the Internet every day. And this number is only getting higher.

Data is flowing in and out of almost every machine we use today, from cellphones and computers to devices such as cars, thermometers, phones, coffee pots. These devices are processing and sending data over the Internet and companies store them for analysis. But with so much data, how can companies possibly keep up? How can they possibly analyze the sea of data they find themselves in? This is where the data scientist comes in. However, skilled data scientists are few and far between. And without effective data scientists, valuable business data will remain untouched and unanalyzed.


Nearshore outsourcing for data science

This is not the case for many companies, however, who are finding other alternatives to the data scientist shortage problem. Data science outsourcing is quickly becoming a highly favorable solution to this problem. Businesses skip to the front of the line by working with a software outsourcing firm who can provide their services at a highly competitive rate.

With the right outsourced data science provider companies have quick access to teams with highly specific skill sets, industry expertise, and state-of-the-art data management and compliance tools. These remote teams can provide regular analysis, insights, and data health checks. And companies don’t have to worry about what leverage they may be missing from their data. By outsourcing these services, businesses will have that information at hand.

The data science service providers, however, have a great responsibility. To be successful, these providers cannot simply hire a few data scientists, ask for company data, and run some analytics processes to determine hidden patterns. The service provider should have expertise in business intelligence and analytics, enterprise data warehousing, big data, and data integration and processing.

Here is a brief overview of each of these areas of expertise, how they impact companies, and how they relate to data science in general.


  1. Business intelligence and analytics

Business intelligence and business analytics each have very significant roles to play in business environments. Business intelligence (BI) is descriptive in nature. It utilizes software and services to analyze data and provide reports, summaries, charts, and graphs to describe the state of the business as it is. BI describes a past and present state.

Business analysis (BA), on the other hand, is predictive. Analysts utilize software to help predict what will happen and what could happen for the business based on pre-existing data. Business analysis is often theoretical, whereas the basis of business intelligence is hard data.

Both can play a crucial role in helping businesses determine where they can cut costs and improve operations. These services can also help improve organizational strategies and enhance tactical decision making. Both BI and BA heavily rely on effective data science. The reports and predictions from these services also rely on proper data structuring and data storage.


  1. Enterprise data warehousing

With so much data collection occurring, it is crucial for businesses to secure and store their data properly. Enterprise data warehousing (EDW) is very important for effective data science and business continuity. EDW is the practice of controlling, managing, and consolidating data. Data could be coming in from a variety of different sources, but EDW ensures that the data is stored logically and is easily accessible. Without it, data scientists would not only be responsible for analyzing data and reporting on it, but they would also be responsible for knowing where it is coming from, collecting it, and consolidating it.

It is common for companies to neglect data when it is spread out across different servers, networks, or continents. When this occurs, valuable company information and insights sit idle in data storage containers. Implementing EDW and sending data to one central repository enhances business intelligence and business analytics. It also directly affects the success of data science in a company. If data is fragmented and stored in different areas, it is very challenging to ensure comprehensive data analysis. Business insights can change drastically with the introduction of a new data set.


  1. Big data

There is often a lot of confusion about what the term “big data” means, what it represents, and how people use it. Forbes describes it succinctly: “Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.” Big data can consist of digital data like website metrics, customer behavior, and social media traffic and statistics. It also includes traditional data sources such as financial records and inventory.

Big data consists of unstructured data and multi-structured data. Unstructured data is typically unorganized and is often hard to interpret. It is usually text-based data, which would include meta-data (data about data) and website traffic stats. Multi-structured data comes from a variety of different formats and sources. This could consist of text-based information and visual information, such as photos or videos. As companies begin creating new technologies that are more and more complicated, multi-structured data will continue to increase in volume.

Ultimately, data science would not exist if it were not for big data. Without the collection of digital and traditional data, the data scientist would not be able to review data, solve problems, and provide business insights.


  1. Data integration and processing

Data integration and processing are directly related to big data. As discussed earlier, multi-structured data comes in a variety of different formats and sources. Without data integration systems and processes in place, businesses would not be able to benefit from or find value in any of the data they obtain.

Data integration is simply the process of collecting data and combining it from different sources. Managers then consolidate, cleanse (or adjust for errors and corruptions), and store the data. Data processing occurs after data integration. A master server or console typically controls data processing and is responsible for retrieving and classifying data. This is a crucial step in making data actionable for data scientists. Without effective data integration and data processing, businesses miss out on highly valuable business insights.

These areas of expertise—business intelligence and analytics, enterprise data warehousing, big data, and data integration and processing—all play a crucial role in successful data science. Data science service providers should understand these areas and how they impact data science as a whole to increase marketability. But what does it take to become a successful data scientist? What are some of the skills they need?


The path to successful data science

As discussed, the need for skilled data scientists is in high demand. However, the supply is very low. This simple fact is disrupting the job market. In 2017, IBM reported that “there is growing concern that the supply of DSA [data science and analytics] workers is lagging dangerously behind demand,” which could eventually “bring the productivity gains from Big Data to a grinding halt.” On average, data science job postings remain open for 45 days, five days longer than the market average. Filling data science positions is difficult because of the pre-requisites and skills required.

According to the University of California, Riverside, only one-third of U.S. universities offer degrees in data science, and only one-sixth of those universities offer data science programs to undergraduates.

Beyond the educational requirements, data scientists must also have a variety of “hard” skills or technical abilities. They must be skilled programmers, software engineers, and machine learning engineers. They must also be skilled in statistical and mathematical analysis, SQL querying, and data munging (transforming raw data into a usable format).

Successful data scientists must also have “soft” skills or people skills, social skills, and communication skills. Being able to translate the interpretations of data into an easy-to-understand format is very important for businesses. They must be able to tell a story and help team members visualize what the data represents. This is not easy and requires years of practice and training.


Why outsource data science?

So why are companies considering outsourcing data science as a possible alternative to directly hiring employees? Because it is so difficult to find skilled data scientists that meet every one of these necessities and prerequisites mentioned above. The demand for data science can’t keep up with supply and companies need these skills now.

By outsourcing, businesses can work with a team or a variety of teams that are not only data scientists but also have expertise in business intelligence and analytics, enterprise data warehousing, big data, and data integration and processing. Outsourcing can be a one-stop shop for a company’s data science needs.

Outsourcing companies move fast, and if a business is resistant to change, this may not be a good option. Directly hiring employees may be a better option for those types of organizations. As discussed, data science is a multidisciplinary field that incorporates a wide range of other elements and expertise. Finding a data science team, or even a data scientist, that is capable of handling all areas discussed here is not going to be easy. For those companies willing to dive in and start taking advantage of their data right away, outsourcing is a great option that companies should consider.

Paul Azorin

By Paul Azorin

Chief Marketing Officer Paul Azorin outlines BairesDev's vision and mission while leading company-wide modernization initiatives and fostering a customer-centric culture globally. Paul spearheads core business strategies and the overall brand experience.

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