11 Analytics Skills You Need to Run Your Company's Data Team

Being in charge of data—whether you're the Head of Data Science or the Chief Data Officer of your organization—is no easy task. It's not always easy to know what's expected from all of the data your company collects, or how you can successfully own your role.

At the end of the day, there's no perfect playbook for running a data team. You need to have a variety of skills and tools at your disposal to be able to take control of your job—and your company's data. From understanding probabilities to communicating with your team, we'll go over some of the basic skills that any data person needs to have to succeed in working with data in the workplace.

1. Understanding when to buy vs. build

Building or buying analytics technology is a question that comes up with some regularity for companies—and whether it's backend business, onboarding flows, or data, there are pros and cons to both. Upfront, it may seem appealing to take advantage of all the open-source big data tools out there, and build your own analytics solution. It can give data science teams total control of what they collect, how it's collected, and what to do with it—right?

However, if you can outsource and still get what you need from your data, you probably should. Developing and managing data tools can quickly turn into big projects that pull engineering resources away from their core tasks. Many of them are also difficult to use for the non-data scientist end user.

If you're going to buy an analytics platform, you can explore options to decide if you want a full, robust platform—or if you might be just as well off taking a more a la carte approach, collecting data from internal tools and add-ons, stitching them together. Or, you can opt for a full-stack platform, which can offer a more thorough consolidation of your data, and quickly kickstart your work. You'll also get support integrating a team onto your new platform, which can be a great way to get different people comfortable with using data. Scuba's unique full-stack analytics platform is an example of a solution that provides flexible, powerful behavioral analytics for anyone in your company.

2. Strike early

If you're going to work with data, the sooner you can start collecting it, the better. You need to get a data pipeline in place, even if it's in the early days at your company. If you've already got things running at full speed, you need to be able to set up data collection as fast as possible, while maintaining accuracy and immediately operating at scale. Striking early also means you need to assess company needs and find the right data solution—as well as coordinating integration across teams and working with those above and below you to get a realized solution in place.

3. Understand the business context of your data

If you could just gather data and call it a day, running a data science team would be a pretty easy job. But everyone that's tried their hand at managing data knows that isn't the case. You need to use your data in the context of the goals and vision of your business. What changes do you want to see for your business in a month, a quarter, or a year? What are the metrics that will help you measure your progress to that growth? Being able to figure these questions out is what takes data from a pile of numbers to actionable insights.

4. Selective attention

Another challenge that has come with the newfound ability to track essentially anything, is discerning what deserves our attention. Especially if you have limited resources, picking and choosing what to analyze is a crucial part of building your company and achieving business goals. This builds on being able to put data and metrics in your business context. Figure out where your team and company want to go and determine KPIs that will help you get there. Even if you have a million ideas, keep your energy focused only on what you can realistically work off of. Andrew Chen, former Head of Rider Growth at Uber sums it up nicely:

 

"If you’re not going to do something about it, it may not be worth measuring. (Similarly, if you want to act to improve something, you’ll want to measure it.)"

Don't skimp on the data you want to collect, though. When in doubt, it's always better to collect more—just make sure your analytics stack can handle the load.

5. Know how to ask analytical questions

At the core of any skilled data scientist or analyst is one critical skill: knowing how to ask the right questions. Data alone isn't necessarily what will set your company apart or help build a great product and thriving business. Analytics is about drilling down into data to see what's going on under the hood. You need to be able to ask questions that will allow you to transform your information into hypotheses and theories—and ultimately, key insights and analytics. 

This doesn't mean asking the perfect question in one go—the one question that will hit upon an insight that will transform your retention or conversion. It means being able to ask analytic-driven questions of your data iteratively until you hit upon the insight you're looking for—or weren't expecting. 

While this isn't exactly a skill you can pick up at the drop of a hat, you can build upon the skill of asking intelligent, analytically-minded questions by:

• Understanding your product
• Understanding the basics of data analysis
• Being willing to think outside of the box
• Working collaboratively

These will build the foundation for great questions.

6. Build models to communicate in your organization

Data is a tool that can help anyone and everyone in your company think more deeply about your product and processes. For that to be true, everyone needs to be able to access and understand that data and know what you're doing with it.

That doesn't mean you simply distribute the final result of your analysis, or dump large volumes of raw data and assume your company will understand it as you do. Democratizing your data is key to building better analyses, teams, and processes. It means taking the information you have and making it accessible by simplifying it so that everyone can see, understand, and manipulate what you're collecting.

7. Basic mathematical probabilities

Simple mathematical concepts can confound even the brightest of mathematicians when presented with counterintuitive situations. That's because our gut feeling and the data are often pointing in different directions, and that makes it surprisingly hard to discern what is actually correct. Part of combatting this dilemma is continually refreshing your basics—and going back to them when you feel like the data is telling you something that doesn't match up with your intuition.

This can help you avoid biases like Twyman's Law, for instance. Twyman's Law is that any statistic that looks unusual is wrong. That is, if you see something that's much more interesting than you expected, there's something behind it—for example, websites with no traffic from 2-3 AM in March might simply be seeing the effects of daylight savings time. Being aware of the way that data is sometimes tricky for our brains to handle can stop you from making a mistake or drawing incorrect insights.

8. Gauging success

For all the quantitative work you're doing in analytics, it can be easy to forget that you should be measuring your own success, too—especially with KPIs, which vary by department and teams. Your sales team might need to evaluate how their CRM tool tracks customers, and whether it's working to help them meaningfully follow up with leads. Your data science team might need to figure out whether their analytics on new user retention is having an effect, or how many daily (DAU) and monthly active users (MAU) your product has.

Encourage teams to take a critical eye to their own processes and set their own metrics for success. For example, it might be useful to explore the objectives and key results (OKRs) framework to focus on how tools are helping team goals. Setting aside a regular time to meet with your team and review tools can also save you from wasting money on ineffective tools or processes in the long run. Don't implement a tool or new process based on your data without having a plan in place to track its success—and don't hesitate to replace or iterate on tools or metrics that aren't working. It will save you time and build your long-term success.

9. Look at the “why” and not just the “what”

Your data is generated by actual people (or devices or other things). If you only review data points, you might miss a key component of a customer's journey: the why.

Behavioral analytics can help you craft powerful hypotheses about why users of your product are doing what they're doing. Sometimes, it can be just as valuable to go straight to the source. To keep a holistic view of your data and a comprehensive process for your analysis, you can use:

• Customer research: Whether you bring in and talk to the customers using your product, ask them questions via Zoom, or conduct comprehensive surveys, hearing unfiltered customer experiences is one of the best and most direct ways to get insight as to why they behave the way they do.
  
  
• Customer journeys: Tools that allow you to see what a customer is actually doing with your product are invaluable for figuring out the 'why'. By watching the beginning of their journey sessions of those users, data teams can stitch together the many paths of users on your site—from product usage to the checkout process. Finding points of friction, stickiness or even drop-off can allow data teams to uncover what's driving these trends—and how to act upon them. Build your commitment to understanding how your customers use your product by going straight to the source instead of just relying on numbers.

10. Tell a story

Companies are full of stories. They build narratives around their internal values, they tell their customers a story that shapes their brand, and their data should tell stories, too. Stories all around us are informed by data—sports, news, politics—and data can be a great foundation for explaining things. But when you're using your data to tell stories about your customer and your company, you aren't just using data as a foundation. You're using narrative to hook people into what your data is telling them. It's human nature to listen to stories. If you want people to pay attention to your metrics and insights, you need to be able to tap into that to make it happen.

11. Encourage adoption internally

Many data efforts fail because analytics tools don't always get adopted across a company. Part of this comes with making data accessible, as we discussed earlier. Leverage tools that unify and democratize data, so folks without technical skills can also dive into data. Empowering team members across your company fosters curiosity, initiative, and even unexpected insights.

Never stop developing

This list is chock-full of interconnected skills that take effort to keep sharp. Mastering them—and mastering data—is an ongoing pursuit. There are always going to be places where you need to improve. And, as the field continues to develop rapidly alongside our technology, the people in charge of data will need to improve their processes and skills, too. At Scuba Analytics, our customer intelligence platform enables teams—from marketing to data science—to dive deep into data, draw meaningful insights, and tell powerful customer stories.

Explore our demo or reach out to an expert today.