Filtering: The Initial Step To Turn Raw Data Into Meaningful Metrics

A decade ago, the challenge was obtaining sufficient data for analysis. Today, businesses face a different issue: data chaos. The proliferation of large Data Lakes has resulted in a daunting mountain of contradictory data that is noisy, biased and disjointed.

Leading with Decisions helps streamline the identification of necessary metrics and data sources essential for painting a comprehensive and clear business picture. Incorporating multiple metrics that capture various aspects of each decision is crucial to achieve a holistic view. Consequently, transforming these essential data sources into Meaningful Metrics accurately reflects the true drivers of business performance, thereby preventing unnecessary headaches for executive decision-makers.

The Challenge of “Dirty” Data

In conversations with executives and data users, a common sentiment about raw data is: “It’s noisy.” This noise often leads to results that are questioned by stakeholders, eroding confidence in findings. Consequently, more time is spent debating credibility rather than making value-added decisions.

While cleaning small volumes of data manually is feasible, managing a full Data Lake requires more sophisticated solutions. Fusion Analytics has developed a suite of advanced data filtering techniques powered by AI and Machine Learning.

Fusion’s Filtering Data Enrichment Engine focuses on:

• Identify Statistically Proven Signals: In noisy data environments, distinguishing true trends and insights is challenging. Our statistical engine evaluates data movements to identify the highly probable signals, remove unnecessary noise and enhance accuracy.
• Maximize Value of Each Data Component: Often, time-series data may appear inadequate at face value (e.g. no correlations with other sources). However, breaking down time-series data into components, such as 1) baseline, 2) seasonality, 3) short-term growth and 4) long-term growth, can unlock its full potential, as a data source might be good for one component but not another. Thus, breaking down to components can maximize the value and usefulness of each data source.

Example of Raw vs. Filtered Data:

Benefits of Fusion’s Filtering AI Engine

• Informed Decision-Making: Base strategic choices on accurate, unbiased insights that you can trust.
• Eliminate Second-Guessing: Remove the fear of flawed data that can undermine critical business decisions.
• Increased Efficiency: Avoid wasting time and resources addressing data discrepancies, enabling focus on analysis and action.
• Peace of Mind: Rest assured knowing your decisions stand on firm data-driven grounds.

Unifying to One Version of the Truth

Filtering serves as the initial step in transforming raw data into Meaningful Metrics. The next step involves unifying disparate data sources to establish One Version of Metric. By expertly Unifying information from multiple sources, this eliminates blind spots and fosters a comprehensive, 360-degree view of the business. This will enable teams to collaborate effectively towards the common goal for the business.

If you’re tired of battling unreliable data and its negative consequences, Fusion Analytics is here to assist. Let’s collaborate and uncover meaningful insights that foster confident decision-marking.

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