Finding your path to data science

Full disclosure, I’m a machine learning engineer by trade. I love data. I believe the world would be a better place if we can truly unlock the power of data.

So, it may not surprise you when I tell you there are many paths you can take with your data strategy. And I’m OK with whatever option you choose. That is, so long as you find a way to unlock the power of your data…

When does your company need data science?

If you’re exploring a data strategy, you’re likely already using data to help your business in at least simple ways. Yet you might be finding that expanding business needs are outgrowing your current approach. Or maybe you believe there must be more value hidden in your data.

Regardless of what might be prompting your exploration, here are a few signs that investing in data science could be worthwhile:

• The business demands more predictability
• Real-time intelligence would enable capturing more opportunities
• Decision making should be more data-driven
• Data is increasingly complex and difficult to interpret
• Data appears to be under-leveraged

Two viable paths to data science

Building a data science team might seem like the next logical step for companies that have invested in gathering data. However, creating and maintaining an in-house data science team can be difficult, time-consuming, and expensive.

Another option is to use a SaaS application that leverages your data to deliver insights and guidance for your organization. For instance, we here at Tingono are using data science to make it easy to retain and expand revenue.

Our product is powered by AutoML technology. We are providing customized prediction and analysis models fit to each individual business. And these models continually learn and update to fit the needs of the business over time.

Evaluate AI-based SaaS apps carefully

With the proliferation of data science, machine learning, and deep learning, it’s getting easier to find helpful SaaS apps built on these technologies.

However, many companies now claim their solution is built on AI when it’s not really. Often these solutions are little more than a complex rules-based system.

You can usually spot these faux-AI systems by asking how the system gets set up. It should be a big warning if you and a team of people are required to initiate the system by manually creating rules based on your domain knowledge.

A system like this will then generate results from your experience without learning from your data. This approach will produce a hard-coded, brittle solution that breaks as your business grows.

Another warning sign is whether the system is dynamic or static. A data-driven system will dynamically adapt with respect to the change in your business data.

If the system is static, it might not be a data-driven system. This means it will easily become out of date unless you keep manually updating the rules.

Building an in-house data science team

If you just can’t find a data-driven SaaS solution that is customized to your business and grows with your business, you might decide to build in your own data science team.

Your first step to building a team is hiring a Data Science Team Lead. An outstanding team lead will help build the remaining team, develop data-driven products, and even shape a data-driven culture for the company. But while easily said, this is not as easily done…

Senior data scientists and machine learning engineers who know their craft well are extremely popular. And those who have experience managing are even more scarce. You will likely be competing with big tech companies and unicorns to hire them.

Given this, to hire a Data Science Team Lead you’ll need to bring both your A-game and a convincing offer! And some patience; this could take a few months.

However, hiring the rest of the data team should be relatively straightforward with your Team Lead’s guidance. Just be aware that hiring a data scientist or machine learning engineer is not the same as hiring a software engineer. Standard domain knowledge and experience questions just are not enough.

It’s important your data scientist has both general domain and case-specific knowledge.  Additionally, with recent advancements in data science, you’ll want to confirm your candidates are keeping up with new techniques.

Finally, I’d be remiss if I didn’t mention that hiring data scientists alone often isn’t enough. Most companies will need to consider hiring software engineers, data engineers, DevOps engineers, business analysts, and product managers to complete a fully functional data science team.

Build the data-driven world you want to live in

Once your data science team is in place, the next step is gathering the tools they need to build your data-driven company. Your data science team has many options, so this is not an easy task.

Your team should assess and select appropriate tools based on your specific use cases. Then they will need to learn how to use them, implement them, and continually monitor their impact.

This raises another important point. Managing a data science team is different than a standard software development team.

Data science is, after all, still a relatively young field. Since there are not yet standard approaches for many common data science issues, your team will have to conduct many experiments and thorough research. This requires a unique management approach for success.

So, yes, you have data strategy options

Identifying the right data strategy for you will depend entirely on what option best meets your business needs. So long as you find a way to leverage data in your business, you win. And I love that.

In the meantime, if your business is looking for a way to make it easy to retain and expand revenue, please contact us. We’d love to share with you how we’re using data science to solve this problem.