In today’s data-driven world, businesses rely more and more on data and analytics to drive strategy, measure performance and optimise operations. As a result, the demand for professionals with data and analytics skills has skyrocketed in recent years. In fact, according to LinkedIn data, demand for data professionals has jumped by nearly 50% over the last two years alone. Demand for data professionals increases because many businesses have begun to understand the importance of data, AI, and other emerging analytic technologies to drive better business outcomes. In response to this trend, organisations have started creating dedicated data and analytics teams to support these departments within their respective businesses.
Creating a data and analytics team from scratch is no easy feat. It’s not always as simple as hiring for specific roles and hoping for the best. Many companies struggle when first implementing their new data team. They might not have the processes to support them; they might not understand what skills are needed, or they might not know how to source candidates effectively.
Fortunately, a handful of experts have been there before. With that in mind, we’ve compiled 5 top pieces of advice on how you can build a compelling data and analytics team from scratch:
- Engage with experts to strongly define your goals and expectations
As with any other function, it’s essential to understand your goals and expectations for a data and analytics team. The knowledge will allow you to have meaningful and targeted conversations with hiring managers and other decision-makers. Business leaders, hiring managers, and others involved in hiring for analytics roles may not understand your business goals and objectives as well as you do. In most cases, hiring managers will have limited exposure to data and analytics. Communicating your goals and expectations will create more meaningful and impactful hiring conversations and help you steer clear of unwanted and unhelpful requests.
- Develop solid data foundations with analysts and data engineers
Once you’ve hired your first few data analysts and data engineers, you’ll want to ensure that they lay the right data foundations required for success in the future.
Focus on developing and providing the following;
⦁ Define your data architecture and governance: Data governance and architecture are essential aspects of any organisation and must be defined.
⦁ Robust data architecture will set the foundation for success by allowing the team to scale with ease, create more accurate and reliable analysis, and provide better service to the organisation.
⦁ Building an R&D library and model repository
⦁ Build a library of test cases and sample models: These structures will save your team time and resources, focusing on building better models and increasing the return on investment.
⦁ Establishing a data onboarding process: The data onboarding process is critical for any data engineering project. It’s the first step your team must take to collect, analyse, and act on data.
Without a clear and defined process, your team will be inefficient, and their projects will take longer to complete.
- Deliver small and incremental wins
Deliver some small, incremental wins that will help create buy-in and momentum across your organisation. These wins will help make positive change and momentum while boosting team morale and strengthening relationships. Begin by mapping out your organisation’s key performance indicators (KPIs) and data sources. Then, work with your data team to create basic visualisations and reports. These visualisations and reports should be tied to your key performance indicators. They should provide more insight into the data that drives these metrics. The insights will allow the business to make more informed decisions using this data.
- Establish a cross-disciplinary team with business SMEs and programming experts
Along with hiring data analysts, many organisations create data and analytics teams that combine data analysts, data engineers, and other data-related specialists. This type of cross-disciplinary team is often very effective at delivering value to an organisation. In fact, at a certain point within an organisation, it makes sense to bring together data analysts, data engineers, and other data specialists on a single team. Allow data and business SMEs to help your team by providing insight into the needs of the business and the data that drives them. They also help your team better understand the business and its data. These individuals on board will help your team navigate more effectively and efficiently. Programming expertise will allow your team to automate tasks, ultimately saving time and money. It will also help your team move more quickly and efficiently through their work.
- Understanding value delivery is better than machine learning projects
As you work to establish a cross-disciplinary data and analytics team, you’ll encounter opportunities to implement a data science project. The next few years will probably see an increased focus on machine learning and other data science projects. However, before you start implementing machine learning projects, you’ll want to take the time to understand the value delivery process. The value delivery process helps your team understand collecting, analysing, and applying data to create value for the organisation. Having this process defined will help your team better understand the importance of data and how they can use it to create value for the organisation. It will also help your team avoid being sidetracked by machine learning projects that don’t provide value to the business.
The data and analytics team is an increasingly important component of many modern businesses. With the right talent on board and the right processes in place, the team can help organisations make smarter decisions and understand their customers better. To build a successful data and analytics team, organisations will first need to create hiring plans and processes that attract the best talent in the industry. Then, they’ll need to onboard new hires, build robust data foundations, and deliver small, incremental wins. Finally, these organisations must establish a cross-disciplinary team and understand the value delivery process.
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