The pros and cons of outsourcing data analytics

Today, companies utilize data analytics not only to understand past performance but also to predict future trends and make strategic decisions. However, as the volume and complexity of data continue to grow, many companies find it challenging to manage their data analysis needs in-house. Outsourcing data analytics has emerged as a potential solution to this challenge, and it is expected to become an increasingly popular option.

According to a report by Allied Market Research, the data analytics outsourcing market is projected to see an impressive compound annual growth rate of 29.4 percent between 2017 and 2023. In this article, we will explore the pros and cons of outsourcing data analytics, thereby providing insights to assist businesses in making informed decisions regarding this critical aspect of their operations.

Pros of Outsourcing Data Analytics

Outsourcing data analytics brings a plethora of benefits to businesses across various sectors. Some of the major benefits are as follows:

1.     Costs Savings

One of the main advantages is cost savings. Companies that outsource their data analytics don't need to invest in infrastructure such as hardware, software, and office space. Operational costs, including utilities and maintenance, are also minimized. The outsourcing partner absorbs these costs and spreads them across their client base, resulting in a lower cost per client.

In addition, hiring, training, and retaining a team of data analysts can also be expensive, especially in regions with high labor costs. By outsourcing to a BPO, businesses can leverage the skills of expert data analysts at a fraction of the cost. This is particularly beneficial if the BPO is located in a region with lower labor costs.

2.     Access to Excellent Talent

BPOs often have excellent teams of data scientists, analysts, and engineers who have a deep understanding of data analytics methodologies and best practices. They are skilled in using advanced analytics tools and can adapt to new ones more quickly due to their exposure to a variety of tools across different projects. BPOs work with clients across various industries, which gives them a broader perspective and allows them to bring best practices from one industry to another. They can provide insights that an in-house team, which only has exposure to one industry, might miss.

3.     Efficiency and Productivity

Furthermore, outsourcing can significantly improve efficiency and productivity. Specialized analytics teams can process large volumes of data rapidly and accurately, leading to better insights and faster decision-making. They can achieve economies of scale that individual companies can't, allowing for quicker, more efficient data processing. Furthermore, many BPOs operate on a 24/7 basis, providing continuous data analysis and faster turnaround times. Combined, these factors often result in superior productivity when compared to in-house teams.

4.     Scalability and Flexibility

BPOs offer superior scalability and flexibility to many outsourcing firms, primarily due to their extensive resources and dedicated focus on service delivery. BPOs can quickly adjust the level of service provision to match the changing needs of their clients, whether that involves scaling up during periods of high demand or scaling down when demand decreases. This level of scalability and flexibility is challenging to achieve with in-house teams.

5.     Risk Management

Risk management is another area where outsourcing can add value. By transferring some data management and analysis responsibilities to a trusted third party, companies can mitigate potential risks. This can result in improved data quality, better regulatory compliance, and a reduced likelihood of costly data breaches.

6.     Access to the Latest Tools and Technologies

Lastly, BPOs have access to the latest data analytics tools and technologies. They can afford to invest in these tools and talent because they distribute the cost across multiple clients. This allows even small and medium-sized businesses to benefit from advanced analytics tools that they might not have been able to afford on their own. These tools and technologies have revolutionized the field of data analytics, offering powerful capabilities for data management, predictive modeling, machine learning, and more.

Cons of Outsourcing Data Analytics

Despite the numerous advantages, there are potential pitfalls associated with outsourcing data analytics that businesses should be aware of.

1.     Quality of Wok

One such concern is the quality of work delivered by BPOs. If the BPO lacks experience in a specific industry or domain, there is a risk of data misinterpretation or inaccurate insights. These inaccuracies can significantly impact the effectiveness of business decisions. For instance, incorrect insights can lead to misguided strategies or missed opportunities. Consequently, businesses may experience financial losses or damage to their reputation.

2.     Security and Confidentiality

Security and confidentiality are fundamental elements to consider when outsourcing data analytics. When businesses share sensitive data with BPOs, there can be exposure to security risks and potential breaches of confidentiality, particularly if these tasks are outsourced to less competent or inexperienced BPOs.

These BPOs may lack robust security protocols or fail to adhere to best practices in data management, thereby heightening the risk of data breaches. This concern is particularly heightened in sectors where stringent data protection is an absolute necessity, such as healthcare and finance.

3.     Loss of Internal Knowledge

Outsourcing data analytics might also lead to a loss of internal knowledge and skills, making it harder to build in-house capabilities in the future. This loss could potentially limit a company's agility and capacity for innovation.

4.     Communication Challenges

Additionally, communication challenges can arise when working with external vendors, especially when dealing with different time zones, languages, or cultural norms. These communication barriers can slow down decision-making processes and create inefficiencies.

Mitigate Risks and Maximize Benefits

Conducting thorough due diligence is a critical first step. Businesses should carefully evaluate potential BPOs, taking into account factors such as industry expertise, reputation, and a proven track record of success.

Establishing clear communication channels and protocols can also mitigate potential challenges. Open and transparent communication with the outsourcing vendor can help set expectations and address concerns as they arise.

Further, it's important to define the scope and expectations of the outsourcing project clearly. Outlining the scope of work, timelines, and deliverables can ensure that both parties understand their roles and responsibilities, thus minimizing potential misunderstandings and conflicts.

Protecting data and intellectual property rights is another crucial aspect of outsourcing. Businesses must implement robust data security measures and establish clear agreements around intellectual property rights to safeguard sensitive information.

Finally, developing a transition plan can help minimize disruption and ensure a smooth transition when shifting data analytics responsibilities to the outsourcing vendor. This plan should include training sessions, documentation, and support for both in-house staff and the external team, ensuring seamless knowledge transfer.

Conclusion

Outsourcing data analytics can offer considerable benefits to businesses, including cost savings, increased efficiency, and access to specialized skills. However, it also comes with potential challenges, such as concerns over quality and security risks. By carefully weighing the pros and cons, conducting thorough due diligence, and implementing strategies to mitigate risks, businesses can make informed decisions about whether outsourcing data analytics is the right choice for their organization. Ultimately, the decision should align with the company's unique needs, resources, and strategic goals. In the rapidly evolving world of data analytics, striking the right balance between in-house and outsourced capabilities can often be the key to staying competitive and driving business growth.

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Mike Gunion, is VP for Sales & Marketing at Infinit-O. Passionate, high-energy senior executive business leader, entrepreneur, cross-functional team leader, motivator & innovator. Mike is focused on results, building winning processes, teams, and execution plans. Broad-based skills built and applied across Clean Tech, Medical Equipment, Telecommunications, Information Technology, IoT, Financial Services, Manufacturing, and HVAC industries. Successful in enterprises large and small, building and growing businesses from VC-backed start-up ventures to running P&Ls in Fortune 500 firms with hundreds of employees. Deep background and interest in developing and scaling technology-based product and service businesses -- from strategy development through operational and financial planning. Particular interest in AI and IoT.