Data Science Outsourcing: The Missing Piece of a Winning Data-Driven Culture

The past two years’ events have re-wired business stakeholders across industries to become data-driven. In 2022, nobody questions the value of data in nurturing business growth and speed. Instead, companies are striving to secure the best data science resources that will allow them to establish a consistent, repeatable framework for data-driven decisions. Data science outsourcing might be just what they need to accomplish that goal.

What you'll learn

Let’s not talk about how data science helps businesses thrive. By this time, savvy stakeholders will already know that data is the lifeblood of their prosperity and efficiency. Data tools like interactive dashboards and reporting have become a staple in modern enterprises (78 percent of them use dashboards or interactive analytical apps, according to MicroStrategy’s 2022 report). Moreover, 60 percent of enterprises use data analytics to drive process and cost efficiency, according to the 2020 edition of the report.

An upgrade from that—deploying software that collects, aggregates, and processes data to offer insights—is proactively participating in the data revolution by defining use cases that custom data science solutions can serve, solve, and augment. And that revolution is already happening in enterprises globally. Think about data science teams building AI-powered interfaces that support onboarding customer journeys across the web and mobile apps. Or logistics data departments using data analytics to prevent various risk scenarios (tariff hikes, port strikes, inclement weather) from impacting deliveries.

The data-driven trend is so widespread that Gartner predicts that by 2024, three-quarters of enterprise decision-makers will have established a data and analytics center of excellence to insulate their companies from disruption and strengthen competitiveness. But you don’t need to be an industry behemoth (regardless of vertical) to embed data in every process, team, and decision. The data-driven culture is for organizations of all sizes to embrace. So if your company cannot invest in building expert capabilities internally, data science outsourcing is the answer.

Data scientist: a jack of all trades (and a master of theirs)

If we were to describe an ideal data science expert in one word, it would be a multidisciplinarian. According to the scientists quoted in Harvard Business Review, data analysis takes only about 20% of a data scientist’s time. The rest is spent on preliminary work, such as research, verification, analysis, and coding—tasks involving various skills. 

The multifaceted nature of the data scientist profession is one of the reasons why the demand for that role exceeds the supply. And that trend will continue, as it is expected to grow by 268% within the next ten years. So, let’s see what it takes to be a qualified data scientist:

  • Programming skills. Data scientists work with complex software, so they must possess at least basic programming skills. Python is the most popular programming language for data science now, but knowing C++ or Java is also useful.
  • Understanding of mathematics and statistical analysis. Data science stems from statistics and involves complex math operations. It transforms endless strings of digits into statistical models that translate into business insights. While a machine does computations, a person who programs it and feeds the data has to understand what processes to launch to get the desired outcomes.
  • Strong grasp of machine intelligence algorithms. AI and machine learning are essential in data-driven software solutions that make business predictions, provide analyses, or suggest possible scenarios. For example, IoT and machine intelligence together power transport grids in smart cities or expedite auto insurance claims processing by providing damage estimates within a few seconds, eliminating the need to make appointments with appraisers. 
  • Inquisitiveness. Since data science is constantly evolving, specialists within this domain must demonstrate an unquenchable enthusiasm for learning and acquiring new skills. Additionally, questioning everything and never taking anything for granted helps them remain objective and avoid cognitive bias.
  • Creativity. The essence of data science consists of finding creative ways of applying a selected data set to deliver new, insightful solutions. Creativity also helps build intelligible, rich visualizations for business users to act on insights faster and get better outcomes.
  • Domain knowledge. This aspect is often ignored when discussing key traits of excellent data scientists because it is not considered crucial. We beg to differ. In data science, understanding the subject domain translates into shorter project phases, more efficient execution, and enhanced problem-solving.

The issue with in-house data science teams

Let’s pretend for a moment that the skills shortage in the data science job market does not exist. Even then, the costs remain a burning issue when building in-house data science teams. In the US, the estimated total pay for a data scientist is $120,508 per year. Varied reports reveal that data scientists in Germany earn between €55,700 and €63,000 per year. In the UK, their salaries start at €60,000. And this is just a fraction of the total cost of sustaining a single position in-house for a company. 

Apart from the lack of available skills and high pay expectations, another argument against hiring in-house data scientists is the dynamic nature of data-driven projects. Demands are constantly shifting. For example, now, you may want to build a powerful data engine that links fuel use with mobility patterns of individual drivers or optimizes routes to preserve the residual value of your fleets. But once that’s done, your company may not have the data needs sufficient to sustain a team of highly-paid specialists full time.

For these reasons and more (which we are happy to discuss with you on a quick consultation call), data science outsourcing is a popular option among future-driven companies that want to protect their growth. In this arrangement, an organization delegates all tasks related to data science to an external, specialized company that works exclusively on data science projects. These tasks include:

  1. collecting,
  2. cleansing,
  3. interpreting,
  4. modeling,
  5. aggregating,
  6. analyzing,
  7. optimizing,
  8. reporting,
  9. and visualization of data.

Hiring specialized support to fulfill one’s data science needs means you retain your intellectual property rights and ownership over the data and the insights, but hand off all work requiring technical data science expertise to an external team.

Data science outsourcing: the key to unlocking business advantage  

Today, companies are moving towards a truly data-driven mentality, where they don’t react to data insights but define business goals and use data as the means to achieve them. This approach, where data is treated as a continuously evolving product that matches the current demands of an organization, requires dedicated specialists who keep abreast of the latest technologies and can apply them hands-on in an agile manner. Outsourcing provides a golden opportunity for companies that can’t afford to hire an in-house data team to source such specialists. And there are numerous benefits of this solution:

    1. Cost-efficiency: Outsourcing third-party data science specialists that cater to specific projects and demands can be more affordable than setting up an internal team. That particularly applies to small and medium organizations. 
    2. Time savings: Unlike in-house teams, external data science specialists take almost zero time to launch—they already have the required tools and skills. Besides, you can skip the lengthy cycle of job interviews, pay negotiations, and notice periods and instantly get your data science project off the ground. Still, a briefing is essential to start a project on the right track. 
    3. Access to expert knowledge. Working with expert data science outsourcers means two things: first, you work with the best, vetted, and proficient experts; secondly, you take advantage of the most advanced solutions in data science, as external companies must be up-to-date with the industry to stay relevant. Both aspects translate to optimized business outcomes. 
    4. Broader choice of tools. Your organization can only deploy, use, and handle a given amount of tools. Professional data science outsourcing providers have access to a much wider variety, resulting in greater efficiency (you get the tool that matches your needs and potential) while also helping you save on license costs. 
    5. Faster time to market. Working with experts means working faster. They don’t need to research anything from scratch. Instead, they apply success scenarios that make the most sense for each customer based on their experience. For you, this means shorter product or service delivery cycles and possibly a greater profit. 
    6. Scalability. With an outsourced team, you decide when to add more bandwidth and speed up work or scale down the scope. Project changes cannot be made on a whim, but working in sprints gives you a great deal of flexibility when it comes to fluctuating project demands.  
    7. Competitive advantage. Data science evolved from academia, and many professionals are past or current researchers seeking a greater impact by seeing practical applications of their work. This means companies outsourcing their skills can often access the latest research and development, which gets them ahead of the competition.

Things to consider when hiring external data scientists

Despite the numerous benefits of outsourced data science teams, partnering with an external vendor always carries some risks, especially as huge volumes of sensitive data worth thousands of dollars are concerned. To mitigate them, it is essential to discuss the following key topics when engaging with a data science services provider: 

  1. Data security and confidentiality: This category embraces all aspects that warrant due protection of all analyzed data. It covers cybersecurity (malware and spyware protection, anti-phishing measures, etc.), data anonymization (erasing or encrypting information that could compromise the identity of the people whom the data concerns), and compliance with the local regulations on collecting, storing, processing, and sharing of data (like GDPR in Europe or the proposed AIDA regulations in Canada). Because this is such a broad and complex field, there is no one-size-fits-all policy that would protect you on all fronts. Each contract needs to be reviewed and adjusted accordingly. Still, establishing security protocols and measures and agreeing on rules related to access, vulnerability management, incident reporting, accountability, etc., is a non-negotiable step of any data science outsourcing arrangement. 
  2. Control over information: Data ownership is also essential to discuss with your outsourcing partner. Who owns the data collected by software custom-built by an external vendor? What about the information gathered from third-party IoT sensors, such as telematics devices in vehicles? Can the outsourcer retain control over data they cleansed, processed, and analyzed? There are no right or wrong answers, but these aspects need discussion.
  3. Clear lines of communication: Establishing and executing communication rules helps streamline project delivery and facilitate the resolution of issues. These rules should include communication times and channels, rules of engagement and escalation, and platforms for project management and file sharing.

Conclusion: The data-driven future is outsourced

According to the report created by Mordor Intelligence, the value of the data science outsourcing market was nearing $3.04 billion two years ago. Experts expect this number to increase more than three times by 2027 (to around $9.45 billion). The dynamic growth of the data science industry, in combination with the significant skills gap, means that the demand for data scientists will continue to increase. Seeking a partnership with accomplished, trusted experts will allow forward-looking organizations to access data science capabilities essential to identifying future values, building customer-oriented products, and providing excellent services to enhance profitability.

Key takeaways

  • Data science outsourcing is bound to propel business growth globally.
  • A reliable, efficient data scientist should be competent on multiple fronts.
  • The many benefits of data science outsourcing overshadow the potential risks.

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