10 Data Analytics Problems Your Team Faces

(and How to Handle Them)

As sports fans, several of us at Webapper have read and appreciate Moneyball by Michael Lewis. This spellbinding book from the early days of analytics changed sports forever. With baseball entering playoffs and football season kicking off, analytics tend to come to the forefront of broadcasting. It got us thinking about the common problems that data analytics teams face. The old adage “garbage in, garbage out” certainly applies, but there are a host of other issues to deal with too. Today we’re going to look at those issues and talk about how to handle them.

The statistics were not merely inadequate; they lied.

–– Michael Lewis, Moneyball

1. Data Quality Issues

“Garbage in, garbage out!” Incomplete, inaccurate, or inconsistent data can hinder data analysis. Issues include missing values, duplicate entries, or outdated information.

Solution: Implement comprehensive data validation processes, data cleansing techniques, and data profiling. Establish data governance practices within your organization to ensure data quality is consistently maintained.

2. Data Integration

Data from various sources may not be easily integrated, leading to silos. The result is often disjointed insights and limited access to valuable data.

Solution: Utilize ETL (Extract/Transform/Load) processes and data integration tools to merge data from disparate sources. Develop a data warehouse or data lake for centralized storage and harmonization of data.

3. Resource Constraints

We all deal with resource constraints. Limited budgets, compute power, and staffing can constrain analytics projects, impacting scope and scale.

Solution: Prioritize projects based on ROI and resource availability. Consider leveraging cloud-based solutions that offer scalability and cost-effectiveness.

4. Data Security and Privacy

We almost always mention security in our articles… Handling sensitive data while adhering to privacy regulations can be challenging and risks legal implications if managed improperly.

Solution: Implement robust data security measures, including encryption, access controls, and anonymization techniques. Stay informed about and compliant with data privacy laws.

5. Lack of Domain Knowledge

Understanding the specific domain you’re analyzing is essential for accurate insights, but you may be short on expertise in some areas.

Solution: Collaborate with subject matter experts within your organization. Conduct domain research and use knowledge-sharing sessions to remedy knowledge gaps.

6. Visualization Complexity

Effective data visualizations convey insights clearly and concisely. Doing so can be complex, especially when dealing with large, complex datasets.

Solution: Leverage specialized visualization tools like Tableau and Power BI, or use Python libraries like Matplotlib and Seaborn. To effectively convey data analytics insights to non-technical stakeholders, work on improving data storytelling and communication skills.

7. Modeling

Complex machine learning models may lack interpretability — how accurately a machine learning model can associate a cause to an effect. With certain models, it can become challenging to explain results that gain trust in model outputs.

Solution: When appropriate, choose interpretable models over complex ones. Utilize techniques like SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-agnostic Explanations) to explain model predictions and increase transparency.

8. Scalability

As data volume grows, your analytics infrastructure may struggle to keep pace, leading to performance bottlenecks.

Solution: Consider adopting distributed computing frameworks like Apache Spark for handling large datasets. Alternatively, deploy cloud-based solutions that can auto-scale based on demand.

9. Data Access and Availability

Accessing real-time or near-real-time data for timely analysis can be challenging without the right infrastructure.

Solution: Invest in data streaming solutions and real-time data pipelines using technologies like Apache Kafka or AWS Kinesis to ensure access to the most up-to-date information.

10. Change Management

Stakeholders in your organization may be resistant to data-driven decision-making due to fear, lack of understanding, or cultural factors.

Solution: Communicate the value of modern analytics and data-driven insights to stakeholders (maybe buy them a copy of Moneyball!). Provide training & support to make data-driven processes more accessible. Then you can more easily promote a culture of data-driven decision-making.

Overcoming Data Analytics Problems

Analytics teams face many challenges that impact the quality and effectiveness of their insights. In addition to the solutions prescribed above to effectively address these challenges, continuous learning, keeping up with industry trends, and adapting to emerging technologies are essential. Additionally, fostering effective communication and collaboration with colleagues and stakeholders remains crucial in overcoming these issues. As Moneyball hero Billy Beane said, “No matter how successful you are, change is always good.”