Complete Guide to Data Science Outsourcing and Why Your Business Needs It

Every day, the world produces at least 2.5 quintillion bytes of data. With this rate, we can have a collective sum of 175 Zettabytes of data by 2025. This trend paved the way for the rapid adoption of data science outsourcing across all industries.

Still, data science remains new and overwhelming for many business leaders. We’ll try to take the shroud off the concept in this article. 

We’ll make data analytics easily digestible using an interesting analogy: flying an airplane. First, let’s deconstruct the essential elements that make up a successful flight: the fuel, the plane, and the pilot.

Every time customers use the internet, they leave behind digital footprints that offer insights into their personal and behavioral data. This information gets stored and compounded throughout time, and hence, becomes big. 

Use Cases of Big Data 

All businesses, regardless of size and industry, can benefit immensely from processing big data. Here are some common industries that rely heavily on big data and the ways they benefit from incorporating the process into their system:

• Telecommunication. 87% of telco companies use big data to acquire and retain customers and enhance network solutions. 
• Financial. About 76% of financial institutions rely on big data analytics to assess risks, detect and prevent fraudulent activities, and provide personalized customer service. 
• Healthcare. About 60% of healthcare organizations mainly use big data to provide personalized treatment, predict patient admission, and manage operations efficiently.

To get fuel, one must mine oil first. Same with big data. To extract meaning and benefits from it, one must dig through a bottomless pit of data. However, with the volume of data we generate daily, turning them into benefits is impossible using traditional methods. And just like fuel, when data is left unprocessed, it becomes stale and useless. 

Enter artificial intelligence and algorithms.

Similar to how a plane converts fuel into mileage, data science turns big data into meaningful insights. It is a multidisciplinary field that involves the systematic use of methods and tools to collect, observe, test, and resolve a specific problem. 

The primary tools used to perform data science and analysis are artificial intelligence, machine learning, and programming languages. These tools automate many of the time-consuming processes involved in data analytics, such as data collection and sorting.

On the other hand, the methods used in data science are a combination of principles from statistics, math, science, computer science, psychology, and more. 

So that these elements work together cohesively, there should be an expert to pilot the operation. Those experts are the data scientists.

Data scientists and analysts perform data science. To become a successful data scientist, one must possess solid math, statistics, and computer science skills. They also need to have strong business acumen, critical thinking, and communication visualization skills.

Broadly, the job of a data scientist is to analyze large amounts of data to extract actionable insights that businesses use to create the most sensible roadmap for success. But to achieve this, data scientists perform a myriad of tasks that include: 

• Identifying and collecting data relevant to the goals of the organization.
• Organizing disparate data from multiple sources.
• Ensuring that data are free from bias, inaccuracies, and inconsistencies.
• Building advanced computerized models and algorithms to simplify data collection. 
• Performing data analysis and interpretation based on trends and other findings.
• Relaying findings and insights to stakeholders.

Benefits from analyzing big data vary and fall under two categories: profitability and competitive advantage. 

Let’s take an in-depth look at some specific ways data science helps businesses remain competitive and profitable. We’ll also explore why outsourcing data science tasks is essential for businesses this 2021.

Even with data analytics being a relatively new field, it has undergone significant transformation over the past years. In 1962, a mathematician named John W. Turkey forecasted that modern-day computers would substantially impact the way people use data analytics. At that time, the field was not widely used for empirical studies.

When modern computers became widely accessible in the 90s, many businesses started employing data science to improve various aspects of their operations. And as modern computers evolved, so did the application of data science.

Today, with everything going digital, there seems no be no hitting the break to how data analytics is shaping the business landscape:

• As of 2013, about 64% of the global financial sector had already incorporated big data in their infrastructure.
• In 2019, the big data banking analytics market reached $29.87 billion.
• Around 97.2% of organizations are making big data and AI investments.

Companies all over the globe continue to ramp up extensive data processing through strategies such as data science outsourcing. Many are also pushing for the mainstream use of data management and machine learning algorithms. If you haven’t leveraged data science today, it’s high time you should.

The uncertainties brought by the COVID-19 pandemic have tested the decision-making skills of business leaders. Risks and uncertainties always exist in business, but they can be mitigated when you decide based on reliable data. 

According to a study, organizations that rely heavily on data are three times more likely to show significant improvements in decision-making. Regardless of the size of your business, you can be more competitive by leveraging data analytics. 

Through data democratization, data analytics outsourcing companies help business leaders make confident data-driven decisions even if they have no technical knowledge or background. In fact, with the right data science outsourcing partner, you can make all members of your organization work comfortably with data.

Basing strategies and decisions from a single, unchallenged data source is not enough to gain an upper edge. Comparative analysis is a tool used in data science wherein data, documents, processes, and other qualitative and quantitative factors are compared. This process helps give business leaders insights into how they are performing against their competitors. 

Internally, comparative analysis helps organizations revise or polish existing processes to achieve the most efficient solutions. It ensures that the track you are walking in is the most effective and suitable option available. 

Why Businesses Outsource This Data Science Process?

Over the past years, different sectors have seen the benefits of data analytics to solve pressing business problems. For instance, FMCG manufacturers turn to data science outsourcing after experiencing unstable sales performance. 

The step came after realizing that they lacked the capability to analyze the root cause of the problems and that growing an in-house team would take a considerable amount of time and financial resources. Data analytics outsourcing enables businesses to address a pressing issue at the soonest possible time.

Social listening is a crucial audience research method that helps brands gain organic consumer insights. The process involves tracking every mention of a brand, product, or service on various social media platforms. Performing social media listening enables you to: 

• Gauge the pulse of customers about your products, services, ads, marketing campaigns, and more.
• Personalize marketing campaigns according to your clients’ needs and wants.
• Capture trends in real-time to adjust strategies accordingly.
• Gain a better following.
• Engage and capture different types of consumers.

Why Businesses Outsource This Data Analytics Process?

Social media is the Big Apple of the digital world. It’s busy. It’s noisy, and it never sleeps. It just keeps growing, making social media listening an insurmountable task. Take a look at these statistics:

• In 12 months, 520 million people became global social media users. That’s an average of 16.5 new users per second.
• As of July 2021, there are 4.8 billion social media users worldwide. This number still doesn’t take into account non-eligible users (users below 13 years old).
• The global population of social media users spends 10 billion hours on social media platforms daily. 

Extracting insights from such a tremendous amount of data requires sophisticated algorithms and technology that cost a fortune. This is why outsourcing this data science process is a must for businesses that want to gain from the valuable advantages of social media listening.

According to the U.S. Small Business Administration, “As a general rule, small businesses with revenues less than $5 million should allocate 7-8 percent of their revenues to marketing.”

Of course, the budget allotted for marketing may vary depending on the size of the business, the type of campaign, and many other factors. But regardless of how much funds are set aside for marketing, it is vital to ensure that investments yield tangible or intangible returns. This part is where marketing analytics comes in. 

Marketing analytics seeks to find patterns and insights from data collected through marketing campaigns to measure effectiveness and optimize future campaigns. Here are some specific ways marketing analytics helps marketers:

• Find out which campaign elements drive the most revenue for a specific period.
• Compare the performances of different campaigns against each other.
• Discover how customers interact with your brands.
• Align marketing budgets to campaigns and platforms that perform.
• Understand the preferences of your customers. 
• Predict future outcomes based on historical data. 

Why Businesses Outsource This Data Science Process?

Expertise is among the indispensable benefits that data science outsourcing brings to the table. Poor data management and analytics impact not only your marketing efforts. It can also taint existing data, rendering it useless. When you have experts sorting out and managing data, you can maximize the benefits of marketing analysis.

Do you know what your customers really want? They wanted to feel heard and valued. Every time a customer mentions or interacts with your brand online through reviews or feedback, they leave clues to their underlying emotions behind. Sentiment analysis is a tool used in marketing to understand the psychology of consumers and how they view or feel about your brand, campaign, product, customer service, and more.

This process uses automation and analytics to evaluate the keywords’ tone, intent, and emotions in the post that mentions your brand. Then, it classifies the emotion as either positive, negative, or neutral. Having this information at hand can help you steer your business in directions that lead to:

• Increased brand recognition.
• Enhanced customer satisfaction. 
• Reduced customer churn. 
• Expanded market reach.
• Improved processes, service, campaigns, and more.

Why Businesses Outsource This Data Analytics Process?

The data available today are not only massive. About 90% of the data are also unstructured or disorganized. Equipped with technology, process, and expertise, data science outsourcing firms help simplify the process of sentiment analysis by: 

• Sorting Ultra-Large Scale of Data at Speed. While it’s true that humans can better understand and gauge emotions, the speed and efficiency that machines offer are way superior. 

Through data science outsourcing, your business can instantly plug into the best available sentiment analysis software to perform what no human can do at speed. 

• Performing Sentiment Analysis Real-Time. When it comes to business, making a timely decision is as important as making the right decision. Every second that goes by without addressing a customer pain point puts your business at risk of loss. 

Data analytics outsourcing allows you to perform customer sentiment analysis in real-time, with access to enough workforce and other solutions that defy timezone limitations.

Cost reduction is still the top reason why businesses outsource data science. Consider this: the ballpark figure for a single machine learning project is around $130,000. This amount involves costs of data collection, labeling, research, and production. It does not include expenses for recruitment, overhead, and employee benefits. It may also vary depending on the complexity of the data.

With data analytics outsourcing, you get to pay a fixed cost for the expenses mentioned. Additionally, a data science outsourcing firm can reduce overhead expenses by 40-60% by leveraging offshore economics.

Over the past few decades, data analytics has birthed a lot of new jobs related to data analytics. Despite the increasing opportunities for professionals in data science, there is still a scarcity of experts in this field. The shortage is mainly because of the high demand for data scientists across industries. Studies show that:

• 78% of businesses have experienced difficulty filling data analytics positions.
• 59% of organizations expect more jobs related to data analytics to increase significantly over the next five years.
• 53% of companies are planning to expand their data-driven initiatives.

Organizations all over the world have recognized the invaluable benefits of making data-empowered decisions. This trend further tightens the competition for top data science talents. If you do not employ a robust strategy to fill the talent gap, you lose prime talents to your competitors. 

Data science outsourcing is the fastest and most cost-effective way to recruit the best data scientists and other professionals in the field. When looking for a partner for data analytics, consider a provider that has access to a deep pool of global talent. Usually, it’s the one with a strategic location offshore. Also, find one that is passionate about recruiting specialists.

The world has seen many notable trends happen in two years. Since the pandemic, businesses have moved from “The Great Remote Experiment” to “The Great Hybrid Shift” and from “The Great Attrition” to “The Great Rehire.”

Trends are changing rapidly, and the shifts come in great waves, bringing tidal impact to many organizations. What you need today may not be what you need tomorrow. This trend is among the reasons why businesses outsource data analytics.

Data science outsourcing helps businesses respond to market shifts and demands as needed—something that is not easy to accomplish when you manage data operations in-house. Outsourcing data analytics simplifies the otherwise complicated and time-consuming planning, hiring, training, and management processes.    

The important thing is to find an outsourcing partner that can help you remain agile. Here’s a checklist of the essential qualities of an ideal data analytics outsourcing firm:

• Enables you to add or downsize workforce and services accordingly
• Offers flexible recruitment arrangements
• Displays agility in their own operations
• Provides a full suite of scalable solutions
• Offers solutions tailored to your organization

Time is another reason why you should outsource data analytics. The processes involved in collecting, cleaning, analyzing, and interpreting data alone take a tremendous amount of time. But those are not all the steps involved in a data science project. 

You have to grind for months to recruit the best people and train and integrate them into your culture before they can spend time building models and design. Then, you have to allot more time and resources to manage the daily operations of the team. 

With the right data science outsourcing partner, you free up your core team’s calendar to focus on high-level level tasks. Studies show that multitasking reduces productivity by up to 40%. It also increases the risk of delivering output with lesser quality.

Improve focus and productivity by delegating non-core tasks to a data analytics outsourcing partner that offers a complete solution. Look for one that provides a full suite of support, including: 

• IT 
• Management 
• Customer Service
• Human Resources 
• Recruitment and Training

If data is the fuel, AI is the plane, and data scientist is the pilot, what is the role of outsourcing in data analytics? The answer: the airline. You don’t need to mine oil, build a plane, and hire and train pilots with data science outsourcing. All you have to do is contact the best service provider to enjoy the benefits of data analytics outsourcing. 

SuperStaff is here to give you a first-class seat as soon as you’re ready to let data science outsourcing take your business to new heights. Book a consultation with us today!