Creating a World-Class Data And Analytics Team For Your Company – A How-to Guide

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    If you don’t have good data driving your business strategy, you won’t complete the important things. Instead, you’ll chase business goals that might no longer be relevant in an ever-changing market.

    But a data analytics team is a huge investment. Not only is that talent going to demand a high salary, but the whole effort could be disappointing if you don’t implement it effectively. Let’s go over what you need to do to set up your company’s first data and analytics team for success.

    What are data and analytics?

    A clear business strategy created with up-to-date information is essential to your business’s long-term success. Not only does it determine how you respond to new challenges in the moment, but it also informs new business ideas the team may or may not decide to pursue.

    As businesses have integrated more technology into their operations, they’re generating reams of data every minute. Tech companies like Amazon Web Services and Stripe are using your data to improve their services – from big product roadmap decisions to simple test coverage metrics – so why shouldn’t you take advantage of it too?

    A data and advanced analytics team takes information from all over your business and turns it into valuable insights. This enables you to make big strategic decisions based on sound information your competitors don’t have access to.

    Business intelligence software like Clicdata’s dashboards and integrated reports can take you a long way, but becoming a data-driven company requires a team. In this article, we’ll cover what that team might look like and how you can go about building and growing it.

    How to build your data and analytics team

    Creating a world-class data and analytics team will be a long process, but we can break it down into six smaller parts.

    1. Define goals

    A data analytics team can be powerful, but that won’t make a difference if you don’t know how to use them. Before you start making hires, think about what business goals you want the data team to achieve and how that plays into the larger strategy.

    This will involve some high-level talks in the c-suite. It’ll also involve a detailed look at your everyday business processes to find opportunities to improve in the long term.
    If you’re formally documenting these processes for the first time, you should think more generally about bpm and automation. Business process management (BPM) gives you a chance to clarify best practices and chains of responsibility, and by outlining each step of the process in writing you can see opportunities to automate needless data entry work.

    2. Find a structure that fits

    If you’re not thinking about structure now, you risk painting yourself into a corner where your data and analytics team isn’t performing as well as it could. There are broadly three ways data teams are structured: centralized, decentralized, and a mixture of the two.

    In a centralized model, you’ll have a single data team reporting to an executive. They’ll work together on whatever is asked of them, from improving financial reports to streamlining inventory management. One benefit of this is that it signals, to both employees and potential data hires, that data is going to be just as important as sales or marketing at your company.
    But there’s a risk that the data team becomes siloed and bureaucratic if left to its own devices. If the team gets into a set way of working, it might be hard to respond quickly to new problems. Amending a spreadsheet shouldn’t be as big a deal as changing an ISO accreditation, but if the team depends on the data model looking the way it does, it could be hard to change quickly.

    In a decentralized model, you start by hiring a few data specialists for teams like sales and marketing. These staff are embedded in the team, and they report to the managers working in service of those teams’ business goals. The data staff work independently of each other, but might report to an executive responsible for data across the company.

    This is more practical for smaller companies, but if it’s not managed well, it can create problems with coordination. 

    For example, one data specialist might be working on email onboarding for the marketing department while another is segmenting the same audience for the sales team’s bottom-of-the-funnel efforts. Both are doing pretty much the same job, and the company is going to end up with two different sources of truth on how their audience should be split up.

    This lack of coordination leads to poor team task management which is essential to keeping people aligned on the same goals, especially in remote-work settings where you can’t just see over your colleague’s shoulder. Business intelligence and task management software can help keep everyone on track, but team structure is the biggest factor determining who works on what.

    3. Define roles

    Job titles like ‘Chief Data Officer’ or ‘Data Scientist’ can mean different things in different companies. To build a successful data team from scratch you’ll need a clear idea of what business needs the data team needs to solve.

    A Chief Data Officer might not be a data specialist, but you need one person responsible for building the company’s data capabilities. They’ll be the point of contact between the data team and the executive suite, responsible for keeping their work aligned with long-term business strategy.

    Data engineers will be the front-line staff responsible for taking raw data generated from business activities and preparing it for analysis. Data scientists are usually responsible for turning that data into real insights. While engineers are focused on building infrastructure to get different parts of the business talking to each other, scientists are more focused on business insights.
    People in a ‘data translator’ role might not be doing that full-time. They’ll be very technically fluent and understand topics like the finer points of the TikTok algorithm your marketing department is being paid to play, but they don’t need to be elbow-deep in code every week.

    4. Recruit

    The key to your data-driven transformation is not data, but people. “People, ideas, machines – in that order!” as military strategist John Boyd used to say. Once you’ve defined roles and skills as in the step above, you’ll have everything you need to start writing a job description that will attract the right people to your company.

    You’ll have departments like sales and marketing which urgently need strong analytics talent, so you might consider filling these embedded, business-facing roles first. If the new hire is as good as they said they were, you’ll quickly see a real impact on your profit margins in these roles. As the value of data and analytics staff proves itself, you can fill up the ‘hub’ roles like Chief Data Officer, which will enable more high-level work.

    5. Upskill the whole company

    The ultimate goal of a data-driven transformation is ‘data democratization.’ This means data-driven decision-making is not limited to a specific data team, but is enabled in every single part of the company.

    Not only will this improve the decisions employees are making, using tools like dashboards and embedded analytics, it’ll also empower them to ask questions and build their own quick reports without asking your data team for help every time they have a question.
    This will free up your data team from day-to-day firefighting and enable them to work on long-term strategic problems which will take your business to the next level. Working on the business, not in it.

    6. Retain and develop talent

    Data and analytics talent is highly sought-after, and the richest companies in the world are happy to pay well for it. No surprise then that data science professionals stay with their employers for just under two years on average. A speedy turnover rate means you’re losing out on skills that haven’t really sunk into the company, and that means your business isn’t consistently improving over time.

    To fight this, you’ll need to create an environment where the best talent is happy to spend at least a few years of their career.

    If you commit to a data-driven culture very seriously, you’ll naturally create opportunities for context, challenge, and collaboration. Your data team will be able to see the fruits of their labor, they’ll enjoy challenging work that expands their skillset, and they’ll spend their days working with people who share the same commitment to constant improvement they do.

    If data team members stick around, they’ll have time to help you transfer those skills to the rest of the company. In time you’ll be able to recruit from within your own workforce as the time comes to grow the team or fill vacancies.

    The power of data analytics

    Building a world-class data and analytics team with a few talented new hires is quite a job. But the real long-term goal to keep your eye on is transforming the way the whole company works.

    A successful data team can use end-to-end business intelligence software such as ClicData to put data in front of every employee who needs it to make better decisions every day.

    About the author

    Alister Esam is the CEO and Founder of Process Bliss, a work management software that is reinventing how businesses execute day-to-day tasks. He is an expert in strategic planning, business process management, and business process optimization. With more than 15 years of experience in helping improve business processes, Alister has dedicated his career to make work easier, with tools such as  and more motivating for managers and employees alike. Here is his LinkedIn. He has also written content for AirDroid and Recruitee.