Making well-informed strategic and operational decisions is key to business success. Nevertheless, leaders often rely on intuition, experience, or instinct lacking data insights. This raises failure risks substantially. According to the experts over at ISG, companies must adopt more scientific data-driven decision making through the latest data analytics solutions to outsmart competition.
Gather Expansive Data
The raw material for smart decisions is data itself. Companies must extensively track key performance indicators across sales, marketing, operations, finance, human resources, technology, etc. Capture customer response metrics along the entire journey from initial interest to repeat purchases. Gather wide-ranging data on all website visits too. The broader the data capturing, the higher the input reliability for decisions.
Clean Up Data Quality
Before analysis can happen, data scrubbing is essential to remove errors and inconsistencies creeping because of multiple sources. Data cleaning identifies gaps, fixes outliers and places data accurately into structured databases organized by metrics groups relevant to specific decisions. High quality cleaned data ensures decision tool output accuracy.
Benchmark Externally
While internal data reveals operational baseline, it insufficiently signals competitive standing or potential stretches to aspire for. Licensing specialized benchmarking data provides anonymous performance comparison across companies of similar size or industry for key metrics. Does company X generate 10% higher sales conversion than category average from its website traffic through superior web design? External benchmarking provides such strategic input to step up game.
Get Leadership Buy-In
Executives play two pivotal roles in data-based decisions: visibly prioritizing it as part of culture and actively using insights for choices. When leadership insists on data justification for investments, cross-functional teams get cues to gather decision-ready metrics. As executives reject intuitively suggested projects lacking data support, adherence naturally propagates across organization. Leadership adopting evidence-based decisions catalyzes company-wide adoption.
Invest in Analytics Platform
Transforming vast raw data into decisions requires sophisticated analytics solutions with modeling tools and algorithms to generate, visualize and interpret relevant performance insights. Identify data patterns, forecast scenarios, highlight correlations that humans cannot always easily discern from scattered excel reports alone. User-friendly analytics platforms like self-service business intelligence make metrics accessible enterprise wide.
Build Analytical Capability
Making data-backed decisions ultimately relies on the analytical and critical thinking skills of staff themselves. Develop and continuously upgrade data and analytics skills across roles through internal and external training to ask the right questions, correctly interpret insights and apply findings contextually.Â
Democratize Access to Data
Curated reports by select teams often hamper wider usage of analytics. Democratizing access by integrating systems and dashboards makes reliable metrics available easily across levels and functions. Sales staff fix conversions issues quicker with performance data visibility. Customer support tracks complaint resolutions better.Â
Simplify Data Storytelling
Analytics output means little unless presented in a compelling way. Using simple charts, images, and analogies instead of just data tables and jargon makes communication sticky across diverse audiences. Build a centralized library containing visual assets and presentation templates as resources for teams. Training analysts in simplifying data storytelling and visualization instills understanding the power of context while sharing insights.
Keep Improving Models
Analytics models provide decision support based on historical patterns. But business contexts keep evolving dynamically, modifying interrelationships within data. Continuously stress testing models against emerging data detects relevance cracks early, helping refine algorithms to sustain reliability. Annually reviewing analytics models improves rigor and predictions accuracy over time as new variables get incorporated.
Conclusion
Data democratization, analytical talent development and leadership commitment drive evidence-based decision-making maturity across organizations. Just like individual health benefits from regular diagnostics, companies stand smarter avoiding blind spots when systematically leveraging data analytics solutions with the right context to spot risks, identify solutions and track outcomes.
