1. Leveraging analytical expertise
In this video, we’ll be exploring how data-fluent individuals collaborate with data experts to leverage more complex analytical techniques to solve business challenges.
2. Beyond the skill of exploring and describing data
Methods that go beyond basic exploratory analysis or methods to describe data can be called "complex" analytical methods. Such methods include performing analysis with large and diverse datasets, which can be handled using querying or programming languages. Building complex dashboards that can be used by multiple stakeholders and include the KPIs to track performance or even delving into predictive and prescriptive analytics such as developing models to automate processes and look at future outcomes.
3. Being aware of the techniques
Unless they have these skills as part of their specialization, data-fluent individuals do not need to master the intricacies of complex analytical methods, especially the business users. Nevertheless, knowing the existence and understanding the potential of such methods empowers data-fluent individuals to request support and collaborate with data experts such as data analysts and data scientists who possess the necessary technical proficiency. Subsequently, this collaboration results in the application of more effective analytical solutions.
4. Identifying use cases for advanced analytics
Data-fluent individuals may not build predictive models, but they play a crucial role in driving their application. They possess the ability to identify business use cases where advanced analytics can provide value. Utilizing their deep understanding of the business context, they can formulate questions that advanced analytics can answer. For instance, a marketing manager might recognize the potential of predicting customer churn to target retention strategies. Without a predictive model, the manager would be forced to rely on historical patterns to identify customer churn. This reactive approach means that they would only become aware of churn after it has already occurred, limiting their ability to intervene and retain customers in real time.
5. Collaborating with data experts
Collaboration between data-fluent business individuals and data experts is the key to success. It fosters an exchange of insights and knowledge. Business individuals, armed with domain expertise can outline the problem, provide context, and define the desired outcomes.
On the other hand, data experts can bring their technical knowledge to the table. They possess an in-depth understanding of analytical techniques, machine learning algorithms, and statistical methods. Their role is to select the appropriate analytical approach that aligns with the problem and data availability. Awareness of advanced analytics fosters cross-functional discussions and breaking down silos between business and analytical teams. This collaborative environment nurtures innovation, as business insights and technical expertise converge to solve complex challenges.
6. Understanding the limitations
Data-fluent individuals understand the limitations of advanced analytical methods. For instance, they can comprehend that the effectiveness of those methods hinges on the quality of input data. They are aware of the importance of data preprocessing, ensuring data cleanliness, and handling outliers to prevent inaccurate or biased results. They're aware that implementing advanced analytical methods may require specialized skills, computational resources, and time. Data-fluent individuals understand the trade-offs between complexity and feasibility, ensuring that the chosen methods align with available resources.
7. Example: balancing accuracy and time constraints
For example, an operations manager at a retail store requests the development of a demand forecast to help make informed restocking decisions. He is aware that a complex model can potentially yield higher accuracy but may demand extensive time and resources. Given the tight timeline to make restocking decisions, he collaborates with the data science team to choose a simpler analytical approach that would take less time and can still provide the required level of accuracy. This choice offers a good balance between accuracy and consideration of time constraints.
8. Let's practice!
Now, let's check your understanding with some practical exercises!