Common challenges faced by a data team
Poor data quality
One of the most common challenges faced by a data team is data quality. Poor data quality can lead to inaccurate results and ultimately, incorrect decisions. In turn, these incorrect decisions can then lead to a number of negatives for your business, including poor customer service for clients, poor use of resources, and increased equipment downtime. However, your team can use data cleaning tools to ensure the accuracy and consistency of the data they are working with. These tools can help identify and correct errors, anomalies, and inconsistencies in data, making it easier to produce reliable results.
Data integration
Data integration can present a challenge too. This task involves combining data from different sources and formats into a single, unified view, and it can be a complex process, especially when dealing with large and diverse datasets. However, data integration tools – something that we’ll talk about later in this blog – can help automate this process.
Data analysis
Data analysis can also pose a challenge. Data analysis involves examining the data that has been collected to identify patterns, trends, and insights that can be used to better inform decision makers, and help them make better decisions. Data analysis can be time-consuming and complex, which is a problem – however, data analysis tools can again help automate this process, making generating useful insights easier, and faster.
Essential data tools that every data team needs
Tools for data visualisation
A data team needs data visualisation tools in order to show data in a way that is easier for stakeholders to understand. Patterns and trends that might not be instantly obvious to senior management when presented in a table or spreadsheet can be easily seen with the aid of visualisation tools. Additionally, these tools can aid in disseminating results to stakeholders who might lack technical knowledge.
Tools for cleaning data
For data to be accurate and consistent, data cleansing measures are essential. Data cleaning tools can be used to find and fix data mistakes, anomalies, and inconsistencies. As a result, it becomes easier to generate trustworthy results and make defensible choices.
Tools for integrating data
When it is important to combine data from various sources and formats into a singular, unified view, data integration tools are needed. By automating this procedure, it can be made quicker and more precise – with the added bonus of freeing up your team’s time and resources so they can work on other tasks.
Tools for data processing
Tools for data analysis are crucial when looking at data to find patterns, trends, and ideas. Data processing tools can assist in automating this procedure, facilitating and accelerating the generation of insights. Data teams may be able to make better choices as a result of these insights, and add even more value to the organisations that they work for.
Collaboration tools that every data team needs
Tools for project administration and management
A data team needs project management tools – such as Trello, Asana, and Jira – because they make it possible for the members of a team to assign or delegate tasks quickly, and easily understand who is working on what. Using them, teams can track progress, assign duties, and work together more effectively.
Tools for communication
Because they help teams interact more effectively, communication tools are a crucial aid to the work carried out by a data team. Teams can collaborate and share knowledge more readily with the aid of communication tools, and some popular communication tools include Slack, Microsoft Teams, and Zoom.
Tools for documentation
A data team needs documentation tools because they make it easier for teams to record their work. Teams can benefit from using documentation tools to organise their work, exchange knowledge between team members, properly document their work, and through that proper documentation, guarantee the reproducibility of their results. Some popular documentation tools include GitHub, Google Docs, and Confluence.
Ways to measure data team effectiveness
KPIs
Key performance indicators (KPIs) are essential for measuring team effectiveness. KPIs can be used to track progress, identify areas for improvement, and ensure that a data team is meeting their objectives. Some common KPIs used to evaluate a data team include data accuracy, data completeness, and data processing time.
Surveys and feedback forms
Surveys are a great way for a data team to get feedback from stakeholders about their work. Surveys can be used to identify areas for improvement, gather feedback on specific projects or tasks, and ensure that teams are meeting the needs of their stakeholders.
User testing
User testing is a great way for a data team to ensure that their work is meeting the needs of their stakeholders on a practical level. User testing involves gathering feedback from users who have hands-on experience regarding a specific project, about what the data team produced for them as part of that project.
Questions such as if the information was presented in a clear and concise way, if they understood the key takeaways and if the information provided was useful and provided insight that informed their decision making process can be answered through user testing. Furthermore, this feedback can also be used to identify areas for improvement and again ensure that a team is meeting the needs of their stakeholders.
What are the differences between having an in-house team and outsourcing?
Both hosting internal data teams and outsourcing data work to an external team have benefits and drawbacks. Let’s look at those pros and cons now:
Outsourcing data work
Since businesses can access expertise without having to employ full-time staff, outsourcing can be more cost-effective. Additionally, it may give access to a larger pool of knowledge and abilities. Outsourcing, however, can also be less adaptable and less responsive to shifting requirements.
Hosting are in-house team
Since they are focused on serving the needs of the company, internal data teams can offer more control and freedom. Due to their closer integration with the company, in-house teams can also forge stronger bonds with stakeholders. However, building and maintaining internal teams may be more costly, and it may require more resources.
Conclusion
A well-supported data team is essential for businesses that want to really harness the power of business intelligence, and make informed data-driven decisions about their business. However, managing such a team can be challenging, especially when it comes to ensuring productivity, efficiency, and collaboration.
However, by using the must-have tools and resources discussed in this article, via a well supported data team businesses will be able to maximise their efficiency and provide more value to their organisations. Whether you are outsourcing your data team or building an in-house team, it is essential to focus on data governance, data security, and data quality, while also staying up-to-date with the latest trends and best practices.
Further support from TouchstoneBI
Of course, providing support in this area is one thing that the TouchstoneBI team will be able to handle for you. For more information on how we can help your business meet its individual, unique needs, for personalised advice regarding things like implementation, use of the cloud, and the development of a sound data strategy for your business, or for advice on how best to support the people charged with enacting that strategy, reach out to us today using the details on our contact us page.
