Shattering Outdated Analytics Practices to Unveil Hidden Worth
In the fast-paced world of business, making informed decisions based on data analysis is crucial. However, a recent synthesis of industry insights has identified six common bad analytics habits that often hinder data teams from achieving expected outcomes in the final stages of the analytics marathon.
1. Data Spread Across Too Many Systems
When data resides in multiple disconnected tools, it creates inconsistent views and makes it difficult to measure or improve data quality comprehensively. To address this issue, centralising data access and integration can help reduce fragmentation across systems.
2. No Standard Definitions for Data Quality
Different teams having varied or conflicting rules about what constitutes "good" data leads to confusion and makes consistent quality assessment impossible. Establishing and enforcing consistent data definitions and standards across teams is essential to align understanding and measurement of data quality.
3. Poor Data Hygiene and Undefined Field Usage
Inconsistent or undefined use of fields in databases or analytics platforms results in mistrust of data and analytics outcomes. To combat this, assigning clear ownership and accountability for data stewardship, quality checks, and issue resolution responsibilities is vital.
4. Lack of Clear Ownership of Data
Without explicitly assigned roles and responsibilities for data stewardship, data issues remain unresolved and quality deteriorates. Establishing a clear ownership structure for data management is crucial to maintaining data quality.
5. Manual Processes and Limited Tool Usage
Overreliance on manual checks or outdated tools slows workflows and increases human error, limiting scalability and timeliness of insights. To improve efficiency, automating quality checks and reducing manual workflows by deploying modern data tools and validation rules within systems is recommended.
6. Low Buy-In or Awareness of Data Quality Importance
When teams do not appreciate the impact of data quality, they neglect validation steps, enter sloppy data, and fail to maintain accountability. Building a data quality culture through training, processes, and incentives can help ensure that teams recognize the business value of high-quality data.
In addition to these habits, other problem areas include human errors due to rushing or lack of standardized input controls, causing inconsistent or incomplete data entries. Lack of enforced validation rules and a data-conscious culture can result in low quality CRM or analytics databases.
To break these habits and improve outcomes, it's recommended to establish common formats for reporting, develop best practices for data storytelling, and invest in tools and training that empower business leaders to answer their questions effectively.
The impact of these habits is significant. For instance, treating data/analytics/BI teams like a dashboard factory can result in communication gaps between data and business teams, leading to working based on assumptions and data analysts answering the wrong questions. Analyzing only a portion of the available data lacks actionability and missed business opportunities. The fourth bad habit, waiting too long to act, is not specified further in the text, but it's clear that delaying action can lead to missed opportunities and undermined data culture.
Carrying out a comprehensive assessment of the current situation can help identify if the organization falls into these patterns and what specific habits are holding it back the most. Manual analysis in follow-up loops can create delays and overwhelm data teams, leading to unanswered questions and analyses that only scratch the surface.
The speed to actionable insight is becoming increasingly relevant as businesses change rapidly. Establishing a data-driven culture and addressing these bad habits can help ensure that organizations can keep up with the pace of change and make informed decisions based on reliable data analysis.
References: - Data Analytics Marathon: Why Your Organization Must Focus On The Finish by Brent Dykes on Forbes.com.
- To foster a more data-driven culture and produce actionable insights, businesses should invest in technology that streamlines data-and-cloud-computing processes and empowers careers in finance and business through automation and data- analytics tools.
- In order to avoid making poor decisions based on inaccurate data, it's important for businesses to prioritize establishing industry standard definitions for data quality, enforcing standards across teams, and providing training for better technology usage across all aspects of business operations.
