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We Need Information, Not Data
It is important to emphasize the importance of transitioning from viewing data as the final product to recognizing the necessity of information. We discuss the classification of content, the biases involved, and how Advanata utilizes information in analytics for more effective problem solving. By prioritizing information, businesses can better address challenges.
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Three Steps to Success with Advanata
Practical analytics problems can often be solved in three simple steps, focusing on reframing research into business issues. Analysts refine parameters for effective solutions using data, while Advanata’s AI optimizes outcomes for immediate action. The approach simplifies complex challenges, making analytics accessible and impactful. This demonstrated in a marketing budget example.
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You Don’t Need Another Dashboard
Dashboards often distract from effective problem-solving in business operations, focusing on data volume rather than actionable insights. While useful for reporting and transparency, their complexity can hinder decision-making. Analytics, like those enabled by Advanata, are essential for addressing real business challenges and fostering survival in an evolving marketplace.
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Smarter Analytics: Less Work, Bigger Impact
The analytics sector faces a significant disconnect between customers and analysts, resulting in confusion and inefficiencies. Customers seek clarity, while analysts provide depth. Advanata addresses this by employing a top-down approach that simplifies processes and enhances usability, enabling more effective analytics and maximizing value without unnecessary complexity or waste.
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KPI Theater Needs a Final Curtain
KPIs are often misused, diverting attention from complex problems to simpler, measurable ones, leading to detrimental outcomes. They should be indicators, not targets, with real goals aligned to customer business needs. Properly defined objectives are essential for effective analytics, making sure actions focus on meaningful results rather than misleading metrics.
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Don’t Fall for the Correlation Trap
Correlation does not imply causation; many analysts erroneously conflate the two. Collaboration between data analysts and customers, who possess domain expertise, is crucial for accurate conclusions. Employing an inductive approach allows for clearer identification of causation, improving efficiency and outcomes in analytics. This balance enhances the analysis process and results.
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Redefine Analytics with Advanata
Everyone should be using data analytics to help solve their business challenges. To help overcome difficulties in adopting data analytics Advanata reduces complexity through the use of a business problem framework, involves customers through the use of the inductive approach, and generates actionable outputs using the data effectiveness methodology.
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Cut Complexity with the Inductive Approach
Traditional analytics often struggles with speed and cost due to excessive data focus and insufficient use of domain knowledge. Advanata employs an Inductive approach that leverages this knowledge to define business problems and thus craft actionable solutions, aligning with customer needs for better outcomes and efficiency.
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Business Problems: What are they?
Business problems deliver the optimal actions a customer needs to reach their goals without any excess data or analysis. Most real-world problems are in fact of this type. Business problems are a structured form of analytic problems with the additional identification of goals, options and resources.
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