Running a data-driven organization necessitates more than just technology and processes; you must also have an effective data analytics team in place to guarantee that the proper technology and processes are employed, as well as best practices, to extract the most value out of your data.
According to Gartner, the most significant impediment to the adoption of emerging technologies and business transformation is a lack of trained talent. Businesses are struggling with a variety of disturbances, but none is more prevalent than the overworked staff. It’s difficult to locate people who are qualified and skilled in data and analytics—and you don’t want to recruit the incorrect person. You must strategically grow your data analytics team in order to be prepared for the future.
Fortunately, a few specialists have already been there. With that in mind, we’ve prepared 5 key pieces of advice on how to develop a strong data and analytics team from the ground up:
1. 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.
2. 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 organization 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 organization.
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, analyze, and act on data.
Without a clear and defined process, your team will be inefficient, and their projects will take longer to complete.
3. Deliver small and incremental wins
Deliver some small, incremental wins that will help create buy-in and momentum across your organization. These wins will help make positive change and momentum while boosting team morale and strengthening relationships. Begin by mapping out your organization’s key performance indicators (KPIs) and data sources.
Then, work with your data team to create basic visualizations and reports. These visualizations 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.
4. Establish a cross-disciplinary team with business SMEs and programming experts
Along with hiring data analysts, many organizations 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 organization. In fact, at a certain point within an organization, 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.
5. 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, analyzing, and applying data to create value for the organization. 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 organization. It will also help your team avoid being sidetracked by machine learning projects that don’t provide value to the business.
Many modern firms are seeing an increase in the importance of their data and analytics teams. With the appropriate people on board and the correct processes in place, the team can assist firms in making better decisions and better understand their consumers.
To construct a successful data and analytics team, companies must first develop hiring strategies and processes that attract the top people in the market. Then they must onboard new staff, establish solid data foundations, and achieve tiny, incremental successes. Finally, these firms must form a multidisciplinary team and comprehend the value delivery process.