Consulting

    Data and Business Analytics

    Data and Business Analytics provide insight to strategic variables that are not visible at the operational level of the organization.  In some cases a lack of visibility to how the data is being applied can cause a breakdown in how your teams gather, stratify and present it.  Widely accepted best practice in this space calls for “visibility” and context around the use of Data and Business Analytics.  In fact, business analytics should be the focal point of any organization when they think about “big data.”  The AMS approach to business analytics starts by connecting the purpose of the data collection to the driving desire to have it collected.  This internal education often times results in collaborative improvement and a higher degree of engagement throughout the process.    Many times data collection is automated and then stratified via an algorithmic platform such as AI and machine based activity.  In this case, AMS will help to ensure the variable control points are coded in sequence and relevant to the output.

    Solution Implementation Considerations

    1. Technology infrastructure
    2. Identification of critical data points
    3. Strategic alignment of measurable data points
    4. Finite skill sets around data collection, stratification and presentation
    5. Use of AI/AR and machine involvement with data
    6. Validation of wanted, needed, and critical data
    7. Business modeling of Integration of data output
    8. Environmental data impact points
    9. Continuous data refinement (CDR)

    Best Practice Summary 

    AMS can build a methodology and train your staff to leverage data and apply it to create strategic advantage.  The most effective way to leveraging our best practice is derived from a cross functional application of validating the requested data, modeling its application and validating its value.  Many times organizations are burdened by to much data and ultimately gain little to nothing from what they have. It is the AMS belief that in any data project we must first determine the viability of what you are looking at, why you are looking at it, and  then refine the vision with added perspective.

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