Does Your Early Stage Startup Need a Data Scientist?
Data science is applicable in every industry. It helps to monitor, manage and collect performance measures to improve decision-making. The application of data science can help businesses to gain customer insights, increase security, analyze finances, streamline manufacturing and predict future market trends.Data scientists often write algorithms that can help to make sense of big data. They need to have technical data science skills as well as strong business acumen to discern problems that need to be solved. A data scientist can help your business to source, manage, and analyze large amounts of unstructured data.
Does My Early Stage Startup Need a Data Scientist?
An early stage startup may need someone to make sense of data, build proper data systems, and focus on cultivating a data-driven approach. However, data scientists don't come cheap so you need to know what's best for your startup right now. To determine whether your startup is ready to hire a data scientist, ask the following questions.
What type of startup is it?
Data scientists may find it easier to work with startups that have massive amounts of data. They often use a variety of techniques to collect, clean, and convert data into usable forms. If your startup is data-driven, then it may be worthwhile getting a data scientist on board from the beginning.Regardless of the industry, every startup needs to analyze consumer data and trends. Instead of hiring a full-time data scientist, you can choose to employ another professional with similar skills. For example, a marketing startup can get a savvy digital marketing manager to analyze key metrics.
Can you afford a data scientist?
It often takes about two to three years for a startup business to become profitable. So you need to consider the cost of hiring a data scientist. If you have enough funding from angel investors or venture capitalists, then it may make sense to use data science to scale and grow your business.Data scientists have a unique set of skills that are in high demand and they are expected to come with a hefty price tag. Glassdoor pegs the average data scientist's salary at $121,147 but it can go up to $160,000 for a senior data scientist with a master of science in data science degree.
Do you have the skills within your startup to manage data?
Data science can help your early-stage startup to make decisions on product and operating metrics. If you're not ready to hire a data scientist yet, you can choose an alternative. There are people who have data science skills but do not have a data science degree. They may have a different degree like computer science or information technology.Some may have business intelligence or analytical skills. If you have internal resources to manage data in the beginning, you may not need a data scientist yet. As your business grows, you could support that person(s) to grow into the role of data scientist by sponsoring further training, like an online Master of Science in data science degree.
Can you outsource your data management?
Many startups are not equipped enough to handle large amounts of data. They may lack human capabilities and have inefficient technologies. A good option in the early stages of business is to outsource data management. It's a cost-effective way to keep your data infrastructure organized.Outsourcing data management helps to reduce the workload and it can be very beneficial. You'll be able to gain more insight from your company's data, respond quickly to changes in data, receive accurate information and create a comprehensive overview of business performance.
When would be the right time to hire a data scientist?
Data scientists make it possible for startups to adapt and evolve effectively. They are a great asset to any team but it's best to hire them at the right time. If you hire too early, there may not be enough data for them to work with. If it's too late, you might have missed out on some opportunities. Here are signs you need to hire a data scientist.
- You’re not adequately managing data.
- You need more data insights.
- You’ve got a data infrastructure in place.
- Your leadership understands the value of data science.
Data management is as crucial as data collection and data analysis. It helps to minimize potential errors and gain reliable insights. A good data management system would help your company grow and serve the customers better.Most new startups don't need a data scientist till their business has grown to a certain extent. If your current system is failing, there can be serious consequences. So you need to look into setting up a new structure and hiring relevant skills.
Many startups have data but they are unable to analyze and pull meaningful insights from it. As consumer trends and systems continue to evolve, you need to use data insights to keep up and make better strategic decisions.Data scientists can accurately analyze data and help you make better decisions rather than rely on mere instinct. They can help you discover new locations to branch out to, how you should expand your product line, and what customers like or dislike about your business.
Data infrastructure is the physical retention and storage of data through various equipment and software. This is the backend computing system that makes it possible for companies to acquire, transport, store, query, and secure data.Before data can be collected, analyzed, and used, you need the bones in place first. So, you may start out hiring a data architect or data engineer to help set up the data infrastructure. Once the system is built, you can hire a data scientist.
Data science offers many advantages but many startups don't understand why it's important. It makes it easier to understand your audience so you can create personalized customer experiences. It also helps to determine when and where your products sell best.The insights your data team collects are worthless if your leadership team doesn't use them. If the leadership team does understand the value of what the data reveals, it can make an enormous difference to your business trajectory.