Reframing Analytical Routines to Uncover Hidden Worth
In today's fast-paced business environment, breaking free from outdated analytics practices is essential for driving value and achieving expected outcomes. Here are six common bad habits that hinder data teams from delivering value and how to address them.
Neglecting Data Quality and Integrity
Many teams rely on manual data cleaning, reducing efficiency and risking inaccurate analysis. Poor data quality leads to flawed insights and customer dissatisfaction. To address this issue, implement end-to-end automated data quality validation and cleaning processes, establish clear data entry and storage standards, enforce strong data governance, and regularly monitor data quality to maintain accuracy and trustworthiness.
Treating Cost-Saving Measures as Best Practices in Data Handling
Practices such as data sampling, aggregation, short retention periods, and data deletion are used as coping mechanisms rather than true best practices. These approaches discard valuable data, limiting analysis and long-term insights. Challenge these habits by investing in scalable systems capable of handling large data volumes and retaining data longer to preserve its analytical value.
Insufficient Investment in Education, Tooling, and Processes for Data Democratization
Underestimating the resources needed to enable business users to self-serve data leads to failures in making data accessible and usable beyond the data team. To address this, allocate ongoing resources for education, tailored tooling, process redesign, and support to empower a broader user base to generate insights independently.
Lack of Operational Agility
Rigid adherence to initial plans and poor resource allocation impede a team's ability to adapt strategies as data needs evolve. This lack of flexibility hinders ongoing value delivery and scalability. To foster organizational agility, allow pivoting strategies, reassigning resources dynamically, and scaling solutions to meet growing or changing requirements.
Inadequate Data and Metrics Strategy
Without a coherent strategy covering data acquisition, storage, analysis, and regular impact measurement, organizations struggle to derive meaningful value or course-correct digital initiatives as needed. Develop comprehensive data strategies encompassing metrics to guide decisions and ensure alignment with business objectives through continuous reviews.
Weak Data Governance and Access Controls
Lack of proper governance can allow unauthorized changes and integration errors that degrade data quality and usability. Establish strict data governance policies, control access, and train teams on the importance of data quality and security from the outset.
Addressing these habits requires a combination of cultural shifts toward valuing data quality, investment in appropriate technology and education, and flexible strategic planning to create sustainable analytics value and meet expected outcomes.
Business stakeholders need quick and actionable insights to make informed decisions. Assessing the current situation, prioritizing, and making incremental changes can help break bad analytics habits and improve the value of analytics. The last mile of the analytics marathon comprises data analysis, insight communication, and taking action.
Analyzing only a portion of the available data can lead to missed insights and opportunities. To break this habit, go beyond the available slices and dices on dashboards and start analyzing all actionable dimensions in your data, and go deeper by looking at combinations of multiple factors and leveraging different types of data.
Investing in tools and training that empower business leaders to answer their questions can help break the follow-up loop. Treating data/analytics/BI teams like a dashboard factory leads to communication gaps between data and business teams, resulting in working based on assumptions and data analysts answering the wrong questions. To break this habit, involve data teams earlier in the process, foster strong collaboration between data and business teams, develop processes to make people reflect on the why, and consider embedding analysts into the business teams.
The speed to actionable insight is increasingly important as businesses change rapidly. Manual analysis of diagnostic analytics using dashboards can create follow-up loops and overwhelm data teams. To address this, establish common formats for reporting and developing best practices for data storytelling to break this habit and ensure quick, actionable insights.
Waiting too long to act can result in delayed critical decisions. Many companies fail to achieve the expected outcome due to falling out of the race before tackling these last three steps. Breaking these habits is crucial for businesses to stay competitive and make the most of their data-driven potential.
- To drive success in today's fast-paced business environment, it's essential to invest in data-and-cloud-computing technologies that facilitate analyzing all actionable dimensions in your data, going deeper by looking at combinations of multiple factors and leveraging different types of data.
- In careers related to finance and business, breaking away from the follow-up loop involves treating data/analytics/BI teams not just as dashboard factories, but as partners who should be involved earlier in the process, fostering strong collaboration, developing processes for reflection on 'why', and considering embedding analysts directly into the business teams.
