How to build a data-driven team?

Every captain should pull the ropes when it is needed to help their crew.

This is me above, helping my crew and teaching them how they should perform docking. In every movie, captains sit in their room sipping coffee in their shiny uniforms and giving orders, but in reality, they are many times on deck hands full of work, working side by side with their crew.

That being said, the captain should never lose themselves with tying ropes and tiding the boat, but make sure that always has one eye on the weather coming to his boat and the other eye on the direction his boat is moving.

How does that have to do with building and leading a team, you ask? A lot more than you think!

In the era of covid or post-covid, depending on whom you ask, where economies are shifting towards using online business more than ever, data-driven teams are the central pivot point in a data-driven organization.?

The idea that data should serve as a fundamental building block for every company hasn't been the accepted view for long. Companies of all sizes and across many industries realize only now that they've been sitting on top of goldmines of helpful information for years.?

Data-driven teams use data insights across the whole company to make decisions and take action faster while maximizing success rates overall. They help companies generate revenue (higher conversion rates), minimize risk (better customer experiences), and optimize time management (more efficient scheduling).

A few years ago, I was asked by the small business CEO to help them build a data-driven team. The company had been using analytics for its marketing campaigns to some degree, but before this point, they weren't entirely sure how valuable it could be or if they would even need one. He gave me two weeks and $25,000 to get started.

I was given the task to retrain three employees from different backgrounds (math major, social science major, and economics major) and an admin assistant and form a data team with them. My first day on the job presented challenges: not only did the analyst have little experience with any coding language, but my admin assistant had never used Excel in her life! I admit that at first, we were clumsy, but over time we became more adept at data science.

One of the biggest challenges was data access. The data was not clean, and it wasn't all in one place--we had to track down many data sources throughout the organization. Once we found data, we needed a way to turn that data into something meaningful. We wrote Python scripts to parse data from different sources, and I created an internal blog for us, so our findings were transparent for everyone to see. As more people became interested in what we were doing, business stakeholders asked if they could get involved with data analysis. We set up a weekly meeting in which individuals could join the conversation based on their interests and skill sets (and now there are four data-driven teams in that small business).

The company's efforts have been successful. They can make data-driven decisions, and they use data insight across the whole company.

If you're interested in a data-driven team but don't know where to begin or how to get started, here are some things I found useful:

Look at data science as an investment

Data is everywhere; it exists in every part of your organization. Some data may have already been analyzed, but much of it remains untapped and unprocessed. If approached as an investment rather than a cost center, data science can be used strategically throughout your organization--from marketing campaigns to product development and even customer support processes--to increase revenue or retain customers. Although there will always be challenges when adopting data science, data is a powerful asset for all organizations.

Build data products that solve strategic business problems

Data products are the final step in data science efforts, and they can be used to make data-driven decisions across your organization. Building these data products allows you to think about your work in terms of impact (i.e., how useful will this be for my team?), rather than adhering to standard data science techniques that lack practical application in the real world (i.e., what is the most sophisticated way to do this data-analysis technique? ).

Identify data sources and data access points

In order to have a team, you need to have a goal to solve. To do so, one of the first steps would be to identify which data sources and data access points your team will use in the future.

In every organization, some data sources can be used to make data-driven decisions; frequently, they are stored in disparate locations throughout your company. The data spread throughout different data management systems might not even be considered valuable by the organization as a whole (for example, an aging product or a previously discontinued business unit). By first identifying data sources, you'll have the opportunity to see where your most relevant information is located and how you can get it into one place. Data scientists may need support from IT, data warehouse teams, and other departments within your organization. Without their input, any new purchasing or implementation measures will be difficult to implement.

Hire data scientists with varying data backgrounds and data skill sets

You should have at least one data scientist on your team with a background in statistics, coding, or data analytics. As a lead data scientist, it is important that you can communicate data insights effectively (whether through presentations or written reports) and quantify the benefits of data analysis for business stakeholders. Aside from this analyst role, hiring people whose interests align with your organization's goals will make building a data-driven culture easier; for example, if you want to improve customer experience by improving conversion rates on your website, hire someone who understands marketing efforts and optimization techniques. Their knowledge of these areas can help build up and improve data-driven efforts.

Develop data modeling skill sets throughout your organization

Data modeling is a data science skill that will prove valuable to your data analysis efforts. The concept behind data modeling is simple: You build a model based on the questions you want to answer with the best data you could have collected at the time. Then test the model by making predictions with new data and already-defined expectations.

Data scientists often do this work, but data analysts can also build predictive models POCs and test their robustness before deployment together with the data science team. Like this Data, Analyst can test their hypothesis based on their business knowledge and be enabled to create new hypotheses based on the prediction results they got from the models.

Suppose you want to build better data-driven teams. In that case, everyone must understand how to use predictive analytics techniques when possible so they can make informed decisions rather than relying solely on recommendations from analysts or data scientists - it's essentially employee empowerment through data science!

Learn from data, especially when data conflicts with logic or intuition

It's important to understand that data can contradict common sense or established practices at times. Although you might be tempted not to trust data if its insights don't align with what you already know, it's these moments where data is the most valuable--they force you and your team to think beyond your knowledge and biases to see things in a different light. Even if data results in conflict with established practices, take time to verify whether those practices are grounded in data or simply have been passed down as fact within an organization (i.e., "we did this last year, so we need to do it again this year").

Make data an open-source resource for your entire data team

Sharing data and data insights also helps promote cross-pollination of knowledge across teams - this is especially important when you have a growing team made up of new data science members but eager to learn! It can be easy to keep data insights in silos within data-driven teams; data scientists often hoard their data and analysis efforts so they can reap the benefits when it comes time to publish academic papers, while data analysts may keep data modeling scripts private because they are worried about competitors seeing them. Although there is value in developing proprietary data science techniques that you want to protect as intellectual property to generate consistent revenue streams, if you want to build better data-driven teams by using a more open approach, you need to create a culture where everyone feels comfortable sharing their data findings with others on the team. For example, data scientists can explain data analysis techniques to data analysts, or data analysts can share data modeling scripts with data scientists.

Encourage different ways of approaching the same problems

As data collection evolves (e.g., more data sources emerge, and data analytics tools become easier for nontechnical users), the opportunities for how companies can use data increase exponentially in terms of both quantity and quality. However, an abundance of options isn't necessarily a good thing: Any company needs consistency to execute its data strategy effectively, and banning data tools or methods is not the answer. Instead, data-driven organizations need to find ways for data methodologies to coexist in a way that does not disrupt company workflow yet still leverages data insights. For example, part of your data team's role is to evaluate which data analytics tools will work best for a given data problem; this doesn't mean you should insist on using only one tool but rather that your goal should be to use multiple tools and then decide whether those data points can be integrated into an all-encompassing "canned solution" that answers a business question (i.e., could we build a model using data from three different sources instead of just one source?).

Integrate data analytics into every decision-making process

For data-driven teams to be successful, data insights must be an integral part of all data-related decisions. This can't just happen by chance; data science and data analysis techniques need to be explicitly baked into the very fabric of your organization. For example, having a data analytics dashboard that summarizes key metrics in a data-driven team's report--such as how many new users signed up per day or cost per conversion--and then displaying this dashboard on the computer monitors of decision-makers will help ensure that data insights influence every business move made within your company.

Make it easy for non-technical people to work with data

Although data analytics has become more user-friendly over the years, data science still requires a certain level of technical aptitude; data modeling scripts typically need to be written using programming languages, and data visualization software is complex enough to be challenging for non-technical users to create data visualizations. If you want your data team to have a successful organizational impact--and if you want data professionals, in general, to work better with business teams--you need to create an environment where even data novices can easily work with data. One way you can do this is by providing intuitive (and hopefully pre-built) tools such as Tableau Public or DataRobot's Workbench that anyone within your organization can use. This way, data insights are accessible to data scientists and nontechnical data analysts alike. However, keep in mind that data visualizations can be misleading if they're not designed properly, so make sure your data team also has the expertise and know-how to build effective data visualizations.

Invest in data education and data literacy (including data visualization) across the organization

To be successful, data-driven teams need data analytics experts who can not only develop data models but also communicate these insights to other members of their company--and this means that data science professionals must possess strong communication skills as well as the ability to teach data science techniques and highly effective data modeling tools like R or Python. Before you hire a data scientist, make sure she knows how to code and can develop complex machine-learning algorithms; however, don't forget that it's equally important for your data team members to know how best to tell their stories using Effective Data Visualization.

Teaching and data literacy is data professions in and of themselves, but data science professionals need to be great at communicating data insights. This means they must know how to tell stories through data visualization. They must also have the knowledge needed to teach data storytelling techniques to other company members, including data analysts and key decision-makers such as marketing or finance managers.

Encourage team members to share data insights

The data analytics world is moving ever closer towards open-source technologies and open-access data tools; instead of using proprietary code, more and more data scientists adopt open-source methodologies that let them collaborate openly with others who share a common interest in data science. Data science is a data-driven team sport. By sharing data insights with fellow data scientists, data professionals can exchange knowledge and build better data models; even if the data scientist does not build upon her colleagues' work, she will at least be able to incorporate key data modeling techniques learned from others into her own data analyses. Open-source data science tools, such as R or Python, are now commonly used by data teams across industries; however, just because you're utilizing a common programming language doesn't mean your company should share all of its proprietary data sets with its competitors. The best strategy for building collaborative relationships between your company and other organizations in your industry that use open-access software is to collaborate on non-proprietary data sets while only sharing proprietary data through data tools that are under your company's ownership.

Hire data team members who can think like a data scientist

The most effective data-driven teams have data scientists who possess strong communication skills as well as the ability to teach data science techniques and machine learning models to nontechnical users; but even though you want your data scientists to possess the aforementioned qualities, you also want them to have one more skill: they need to be able to think like a data scientist.

Even if your company has multiple roles that include the word "data" in their title, not everyone on your company's data team will be using these same tools--and this is where data science specialization comes into play. Data scientists need to have a data-driven mindset that allows them to think like data scientists; this means they must be able to look at a data set and see data patterns in the same way data analysis tools do--and they can only learn how to do this if they're working alongside data analysis tools that possess artificial intelligence algorithms that can teach data science techniques and machine learning models for non-technical users.

Hire data team members who are not only experts in data analytics but also experts in their specific industry

Data science is a data-driven team sport. The effectiveness of your company's entire data team depends on every member's ability to understand his specific data project's strategic goals and data insights. Suppose every data team member understands the project's data strategy. In that case, he will also be able to identify data patterns that reveal important data insights: if a data scientist is unaware of the project's needed data results or his data-driven mission, he may miss key data insights--and then his work may get discarded.

Data science skills are only useful when applied correctly; this means you want to hire data science experts who understand their specific industry more than they know how to use statistical analysis software packages. A solid understanding of your company's business model can help a data scientist think like a businessperson as well as a data scientist; in order for data analysts to think like data scientists, they must first be encouraged to think like data team members that understand the data strategy of their project.

Data team members are data-driven by updated data models and data analytics software

Your company's data science projects should be data-driven in the sense that all data analysts on your data team have access to improved new versions of advanced machine learning models; however, you also want a data-driven company culture where everyone on your entire business team is constantly going through updates in algorithms and data analysis tools (without sacrificing company resources). If every employee has time during his daily routines to use online resources available at no cost for professional development training, he will begin thinking like a data scientist.

The data-driven company culture you want includes data analysts who understand data science techniques, platforms that make data analytics easier to do, and data transparency (data sharing) tools that allow data team members to work together: all of these resources can be used by your data team members to discover more efficient methods for performing data analysis. You need open-access software platforms that have automated data analysis tools capable of working alongside human data analysts; this gives your company's data team access to new versions of advanced machine learning models over time.

To data-driven data analysts, this means they can only learn data science techniques from data analytics tools that possess artificial intelligence algorithms capable of teaching data science to data scientists.

Expanding a company's data analytics platforms to include employees with no previous data analysis training is a sign of having an ambitious data strategy

Data democratization, or the availability of data insights to many within a company, will only be successful if employees have data skills—the ability to understand data and use it as a decision-making tool. Having data skills is important because it allows companies to use data strategically across all areas of their business, such as marketing, operations, finance, and more.

Many companies are looking for ways to foster data literacy (providing access to data) within their organizations while trying to avoid redundancy (having too many people with similar roles). One way they do this is by having employees learn from each other. For example, some might teach classes using presentations such as slideshows or videos. Others might send regular emails or have discussions around topics related to data and data analytics. Still, others might send data-related articles or white papers.

Another way organizations can foster data literacy is by providing ways for employees to practice what they learn about data analytics, data visualization, and other data-related skills through self-guided exercises in areas such as data cleaning, working with data sets (such as getting dirty data into a usable format), creating effective visualizations, pulling insights from data by connecting the dots from various spreadsheets, freeing up time spent on administrative tasks related to data management like reporting and analysis, avoiding common pitfalls when analyzing information, and more. These practices should be easy enough so that anyone can implement them with little training. They also need to be clearly tied to business goals so that data literacy leads to data-driven decisions that drive business value.

Encouraging your team to learn continuously.

To bring growth, development, and a data-driven team to success in an organization you have to continuously encourage your team to learn, grow and innovate. This is possible at every level - right from the top management down to the very bottom of the pyramid. The growth of an organization depends highly on how each individual in the organization is given the opportunity to grow irrespective of their current position or designation.

It is important for each and every employee in the organization as it is for both managers and employees or upper management themselves who are running the company day by day.

The growth may be one's growth in technology expertise by hiring a technology consultant or by assigning someone to a new project that involves exploring new technologies, innovations, etc. Growth can also mean getting one extra project from another development team or project.

It is important for development & innovation to move the organization forward and for development teams to be able to run in-house projects - new projects, development of newer technologies, tools, platforms, etc. This can help you get a competitive advantage over your competitor!

You need to inspire the team to stay curious and shift their mindset towards learning each day by exploring new things every day. When you're able to try new things and think outside the box, it will also have an impact on your career as well as your personal development.

It is important to encourage everyone on your entire business team to follow trends in big data technology--even if it means you have to give them some time away from their day jobs. Encourage every member of your company to learn new skills designed to make him a more valuable asset (not just a less expensive data team member).?

The data-driven company of tomorrow will welcome data team members eager to develop their skills, even if it means they have to use less software and data than they might otherwise be able to get in a company like yours. Suppose you want your company's data analytics platform to keep pace with new advanced machine learning models. In that case, you will need a highly engaged (and flexible) human data analysis workforce capable of thinking without access to every piece of data at their disposal. Suppose you don't give every member of your business team an opportunity for continuous professional development training. In that case, they can't continue offering value as data team members in a data-driven company.

Growing a successful team is always a unique experience every trial has its own unique challenges and blessings. Stay curious and humble before everything else and expect the best to happen!