In the era of startups where technological advances have never been faster and barriers to entry are at their lowest, often a successful and fast-growing startup’s biggest issue is how to scale efficiently without losing control of quality of work and its people costs ballooning.
As startups grow, so do workflows. Whether it’s the need for more data, the need for more review and filtering, lead generation or simply administrative and other workflows, the workload can skyrocket quickly. All of a sudden founders and their team find themselves preoccupied with all these time-consuming and manual workflows while not finding time to focus on what really matters — their product.
Below we outline 4 ways how startups can solve this problem and scale such workflows efficiently:
1. In-house expansion
Many founders consider expanding their in-house team as a first option, especially when the work requires little skill and therefore labour costs could be kept to a relatively low level. This brings some advantages. Having your team in-house allows you to be on top of their work all the time, gives you real-time visibility over the process and hence allows you to change/improve it very quickly. It also enables you to train the team directly.
However, in-house hiring for such workflows can be troublesome. Firstly, when scaling, how far can you push it in-house? There are concerns around how expensive such expansion can get, not to mention spatial constraints. It can also distract from the core focus of the business as hiring may be constantly needed. Churn is also an issue. Unless you want to switch priorities to workforce management, it might be better to try and find an external solution to this problem.
Crowdsourcing work through platforms like Amazon’s Mechanical Turk can be a cost-effective solution. It’s great for teams that are willing to set up guidelines, estimate rewards and workforce requirements and the workflows/projects are relatively (entirely perhaps) straightforward. Companies simply register, enter the rules and guidance, set the price per task/unit and let the crowdsourced workers do their job, assuming their reward is high enough.
However, in considering such platforms it is important to note that the workers performing the task are anonymous. Hence, there is no possibility of training those workers prior to the project or of obtaining any accuracy/quality guarantees. The workers are also unable to ask questions, which may be a risk if they start working on tasks that are not 100% clear to them. This creates two risks: given larger volumes of tasks and data, companies are faced with a larger amount of crowdsourced workers and hence greater uncertainty when it comes to their quality of work. Secondly, inaccurate tasks would have to be re-done, allowing for the possibility of wasting resources and project costs increasing.
3. Classical Outsourcing
There is always the option to go down the traditional BPO route, which can be an attractive option depending on the workflow/project that companies need to scale. It is usually less expensive than hiring in-house and workers/teams can usually be trained to follow certain guidelines and business rules to ensure good quality of work. This makes it a better choice than crowdsourcing for tasks that are not super-straightforward or require a bit of nuance.
However, traditional outsourcing partners lack the quality and flexibility that companies often require from their workers and expect for workflows. Often workers are not versatile enough to switch from one workflow to another and need a lot of training and feedback to effectively take over tasks. Communication is often also an issue as workers are often not directly exposed to the client, which results in delayed responses to questions and inaccuracies. Lastly, companies cannot trust such workers with important workflows and often when they cannot fully rely on their BPO partner, in-house teams tend to keep working on the tasks themselves.
There is a hybrid option that is less widespread and can be the most effective one when it comes to workflows that have nuances and require critical thinking. This option is perfect when it comes to scaling workflows that might be more important or more complex than what you could leave to crowdsourced workers. It allows the company to hand over tasks and workflows to external well-trained, high-quality versatile analysts that are dedicated to that company, while being in constant communication with them. We at DataBee believe this is often the best option and here’s how it works:
Worksharing enables high quality analysts to join your team on a full-time or part-time basis. They get integrated into your communication and work channels as much as you want them to. They can join your Slack and even your team meetings when needed! Instead of only one, you can delegate multiple tasks and workflows to them. Through time trust is built directly with the analysts, enabling you to hand over more and more complex workflows, freeing your team’s time and capacity in a cost-efficient way. This is the perfect model when it comes to scaling efficiently, while retaining the high quality and versatility of people you would normally hire in-house.
Worksharing is extremely well-suited to complex data tasks and important workflows and can boost your team’s efficiency like no other solution out there.
Have a workflow that you‘d like to take off your hands? Get in touch!