5 Challenges Data Analysts Face & How to Overcome Them

In today’s fast-paced, data-driven world, analysts are the gatekeepers. They have the power to deliver critical insights that drive business growth. This especially applies to mobile games and apps. But with great power comes great responsibility.

Maybe you’re trying to generate accurate, real-time insights. Maybe you need to handle big data. Or maybe you’re integrating data from various sources. The point is that many data analyst challenges can slow down decision-making and harm your business’s bottom line. 

However, identifying these issues early can help you overcome them. All you need is the right strategies and tools. Let’s dive into some data to better understand these challenges.

Data Analyst Challenges: Key Statistics

The following statistics shed some light on the most pressing challenges that data analysts regularly face:

• According to Sigma, 85% of data analysts struggle to interpret data in some capacity.
• 35% of data analysts are worried about doing incorrect analysis and making decisions based on inaccurate insights.
• 27% of data analysts find large data sets too difficult to curate and analyze.
• In this study, 70% of respondents are overwhelmed by collecting and interpreting large volumes of data.
• 49% of companies surveyed by Harvard Business Review can’t consistently convert big data into actionable insights.

These figures show that collecting big data, interpreting it, and maintaining accuracy are the most common challenges among data analysts.

Why Identifying Data Challenges Early Matters

There are many reasons why data analysts must be vigilant when facing challenges. These include:

• Better decision-making: The earlier you identify these problems, the more time you have to improve acquisition, engagement, and retention.
• Optimized resource management: Addressing challenges early reduces the chances of wasted time and costs on incorrect data.
• Quicker problem-solving: Data analyst challenges can spiral out of control very quickly. It’s easier to solve these problems the earlier they are detected.
• Improved user experience: Analysts can focus more on their app or game’s performance, providing users with more seamless experiences.

5 Challenges Data Analysts Face (and How to Address Them)

By addressing the following data analyst challenges, you start to leverage your data properly and put your game/app back on the right track.

1 – Handling Large Data Volumes

Mobile apps and games are constantly growing in detail and sophistication. So their large data sets can overwhelm traditional data analytics tools.

For analysts, generating actionable insights on user behavior patterns is a real pain. Nearly half of companies are overwhelmed by big data collection, and struggle to convert it into actionable insights. As a result, app optimization can be ill-informed. 

Take drop-off points in user journeys, for example. Analysts often need multidimensional frameworks to get a holistic view of the many variables of churn analysis. 

How to overcome it:

• Use relevant platforms for real-time data visualization to identify trends quickly.
• Machine learning models can be implemented for predictive analytics and data filtering.
• Tools: Snowflake, Google BigQuery.

2 – Incomplete, Inaccurate Data

Gaps in data can set off a chain of negative outcomes. In this order, it can:

• Generate inaccurate insights
• Lead to ill-informed marketing strategies
• Cause a dip in user engagement.

Poor cross-platform integration and a lack of event oversight can lead to incomplete, and inaccurate data. 

For developers, these gaps can cause missed opportunities for identifying key user behaviors. This creates a ripple effect through an app’s development cycle. For example, the wrong features might end up being prioritized. 

This can also impact a product’s marketing acquisition and retention methods. Also, a lack of accurate data can damage the ability to personalize an app’s user experiences.

How to overcome it:

• Invest in new analytics tools with high-speed databases to reduce inaccuracies across platforms.
• Conduct routine audits to pinpoint data gaps and optimize event tracking.
• Tools: Adjust, Talend.

3 – Data Integration and Fragmentation

The average analyst has to collect data from multiple, diverse data sources. These include marketing platforms, in-game analytics, and user behavior solutions.

Technically speaking, this data can stem from CSV, SQL databases, AWS, XML, Excel (XLSX), and many other locations.

The result is likely to produce extremely fragmented datasets. This lack of cohesion will only provide isolated insights, and not a holistic overview to fully understand user journeys. 

Other results of this typical data analyst challenge include:

• Poorly optimized marketing funnels
• Low engagement
• High churn
• Misinformed retention strategies.

How to overcome it:

• Focus on APIs and cross-platform compatibility to streamline data sharing between systems.
• Tools: Tableau, Google Analytics, Firebase, Zapier, Google Cloud Pub/Sub. These are all ideal tools for improving data integration and visualization. Not only do they integrate multiple data streams, but they lead to more accurate decision-making.

4 – Visualizing Data Correctly

Visualizing data in a way that’s instantly understandable for all relevant stakeholders is crucial. However, analysts can often struggle to find the right way to visualize their data. This can either be by choosing the wrong formats or by trying to present too much data in one chart (or not enough).

How to overcome it:

• Tailor the visualization to your audience: Your CEO might want different information from a CTO. So you need to keep the recipient of the data in mind when visualizing it.
• Find the right story and message you want to tell with your data.
• Keep the visualization simple: Avoid clutter. Apply clear titles and labels. And make the color palette as limited as possible for readability.
• Adopt a data analytics solution that can generate accurate data visualizations and reports. Effective visualization tools include Looker, Power BI, and Tableau.

5 – Managing Real-Time Data

Mobile game/app analysts and developers often work under tight schedules and need to make quick decisions. Real-time data demands this. 

However, processing limitations can often impact the accuracy of these decisions. The consequences can be devastating for both development strategies and performance optimization.

For example: Using skewed, real-time data to optimize gameplay can harm user experience.

How to overcome it:

• Combine real-time insights with periodic batch processing to identify user behavior trends.
• Use system validation tools and error-checking techniques to maximize data accuracy.
• Tools: Datadog, Kafka (Apache).

Hello New Tools, Goodbye Old Data Problems

There are many challenges that data analysts face, such as lacking domain expertise and data security issues. 

However, our findings highlighted the five mentioned above: handling big data, maintaining accuracy, integrating data sources, visualizing data, and managing real-time data. 

By adopting streamlined strategies and advanced tools, analysts can overcome these challenges and unlock the full potential of their data. This reinforces app and game optimization efforts, driving improved user experiences and engagement.