The pros and cons of outsourcing big data engineering

Big data is everywhere. It touches every industry and business. And as businesses only continue to amass more data, there is an increasing demand for skilled big data engineers who can manage and analyse this information.

But not every business is able to build an in-house big data team. Between the process of hiring for an in-house engineering team and onboarding your new employees, there are dozens of factors to consider. Plus, building a new team can be expensive, not to mention incredibly time-consuming. That’s why many businesses turn to outsourcing to scale their big data engineering capabilities.

To help you decide whether outsourcing your big data capabilities is right for your business, let’s explore the realities of big data outsourcing in more depth. In this article, we’ll look at the pros and cons of outsourcing data science and how you can kick off the team-building process for your company.

So, what kind of work falls under the category of ‘big data projects’? Big data typically refers to data sets that are too complicated for traditional processing tools. Instead, businesses need to hire big data engineers who specialise in creating, testing and maintaining the complex processing systems that allow companies to manage large data sets.

To put this into context, let’s quickly look at six common examples of big data projects companies like yours may work on:

• Developing predictive analytics models to forecast customer behaviour and market trends through data processing
• Building data visualisation tools that allow employees to interpret complex data sets more easily
• Creating custom machine learning algorithms to automate business processes
• Implementing data governance policies to ensure data accuracy and compliance
• Building data warehouses to store and manage large data sets, including structured and unstructured data
• Solving business problems through data mining

On the surface, it seems ideal to keep all your business functions in-house. However, this isn’t always an option, especially for companies that are scaling quickly, have a limited budget for team building, or lack the expertise to manage engineering projects. These are just some reasons a business may outsource its big data projects.

By outsourcing its big data projects, a company can gain access to the specialised skills and expertise they need without having to spend large amounts of time and money building an in-house team. Building an in-house team from scratch is a lengthy and complex process which requires businesses to consider how this new business function will integrate with its infrastructure, processes and culture. Outsourced teams, on the other hand, offer businesses the flexibility to scale their data projects up or down as needed.

Not sure whether outsourcing is right for your business? Let’s explore the advantages and disadvantages of outsourcing your big data projects.

5 Benefits of Big Data Outsourcing

• Cost savings: There are significant cost savings associated with outsourcing. Building an in-house team can be expensive as you need to pay competitive wages, benefits and large overhead costs.
• Access to specialised skills: Outsourcing agencies typically have access to a much wider roster of talent — many of whom they’ve independently vetted — located around the world. This provides you with direct access to talented individuals that you may not have been able to hire in-house. This is especially true with offshoring partners; you can access talented specialists located overseas at a competitive rate.
• Flexibility: Outsourcing allows businesses to scale their data projects up or down as needed without committing to hiring or firing employees.
• Faster time-to-market: Outsourced teams are designed to be agile and flexible, helping you get work done faster. That means bringing your data projects to market in months rather than years.
• Reduced risk: By choosing an outsourcing partner well-versed in data management and security, you can reduce the risks associated with big data projects.

5 Challenges of Outsourcing Big Data Projects (and How to Overcome Them)

• Communication barriers: Communication can be a challenge when outsourcing, particularly if there are language or cultural barriers. To overcome this, it’s important to establish clear communication channels and set expectations upfront.
• Quality concerns: Quality control can be more challenging when outsourcing, as there may be different quality standards or expectations between the business and the provider. To address this, it’s important to establish clear quality control processes and standards.
• Data security risks: Whenever information is shared outside your business, the risk of data breaches or other security risks increases. Luckily, you can mitigate these risks by choosing a trusted outsourcing partner and making sure you establish clear data security policies and procedures that your team will follow.
• Limited control: Outsourcing can limit the amount of control a business has over the project, particularly if the provider is in a different location or time zone. To address this, it’s important to establish clear project goals and expectations upfront.
• Dependency on third-party providers: When you outsource any aspect of your work, whether that’s your HR capabilities or big data engineering, you make your business more dependent on third-party providers. If the provider experiences downtime or another issue, this can impact your business’s operations. Make sure to establish backup plans and contingency measures in case the worst-case scenario happens.

Have you weighed up the pros and cons and found that outsourcing data management projects is the right option for your business? Let’s look at the key steps to take when approaching outsourcing:

• Step 1: Define your project goals and requirements. Setting out what you want to achieve and any specific requirements before researching outsourcing options will help you find the perfect partner faster.
• Step 2: Choose the right outsourcing partner. Research potential outsourcing partners thoroughly and choose one with experience in your industry, expertise in big data engineering, and a proven track record of success. At WeAssemble, we’re experts in offshoring and can build you a dedicated team of experts in data engineering.
• Step 3: Develop a detailed project plan. Work with your outsourcing partner to develop a detailed project plan, including timelines, milestones, deliverables, and expectations for communication and reporting. If you choose an offshoring partner, you’ll also review the job roles you need to recruit for and what technologies you’ll need your team to work with.

• Step 4: Build your team. This is the exciting bit! Based on the requirements and job roles you set out in the last few steps, your offshoring partner will find the best talent to work on your team.
• Step 5: Establish clear communication channels. Set up regular communication channels with your outsourcing partner and new team of big data engineers to ensure you stay informed about project progress and any issues.
• Step 6: Monitor progress and quality. Regularly monitor project progress and quality to ensure your new team delivers the work to the required standard.
• Step 7: Grow and scale. Continue to nurture your professional relationship with your outsourcing partner and get ready to achieve project success. Your team will scale over time, allowing you to take on more complex projects and expand the department’s capabilities.

Outsourcing big data projects can offer significant benefits, including access to specialised expertise, cost savings, and increased efficiency. With WeAssemble’s support, you can maximise the value of your big data projects by building a dedicated offshore team and leverage the possibilities of outsourcing to achieve your business goals.