Import Sales Report Into A New Tab Named Sales Rep ✓ Solved

Import Sales Report.txt into a new tab named Sales Rep

Import Sales Report.txt into a new tab named Sales Report and import Sales Rep ID.accdb into a tab named Sales Rep ID. Create a data model relating the workbook tabs and design pivot tables and charts as follows: Sheet1 — create a pivot showing Sales as values, Sales Rep Name as columns, Dates as rows; group Dates by Month; add a slicer for Company Name (select first four firms); set the values field to show Average Sales; format numbers and labels; add a complementary pivot chart, suppress the value field button on the chart, and add a clear title. Name this sheet Monthly Sales. Sheet2 — place Company Codes in the filter, filter to the first ten company codes, create a column pivot chart, and name this sheet Sale by Rep. Dashboard — copy the three pivot charts to a new tab named Dashboard (unfilter company codes in Sale by Rep first); add slicers for Company Codes (connected to Monthly Sales and Sale by Rep charts) and Sales Reps (connected to Monthly Sales chart only); select the first three choices in each slicer. Answer the following analysis question: considering the business analytics logical data flow 'octopus' (Figure 8.1), explain how an analysis system can gather data from multiple protected sources, with facts and examples. Format the response in APA style.

Paper For Above Instructions

Introduction

This paper explains the step-by-step process to import provided files into Excel, build a data model, create the required pivot tables and charts, construct a dashboard with slicers, and addresses how an analytics system can securely gather data from multiple protected sources (the “octopus” data flow). The technical steps use Excel modern data tools (Power Query, Power Pivot) and standard dashboard best practices. Security and integration strategies reference established patterns and technologies (Hohpe & Woolf, 2003; Kimball & Ross, 2013).

Step 1 — Importing and Modeling Data

1. Import the files: Use Data > Get Data > From Text/CSV to load Sales Report.txt into a new sheet named "Sales Report" (Microsoft, 2023a). Use Data > Get Data > From Database > From Microsoft Access Database to import Sales Rep ID.accdb into "Sales Rep ID" (Microsoft, 2023b). Preserve the original .XLS tab (existing sheet).

2. Transform and clean: Open each source in Power Query to ensure column types (Date, Text, Number) are correct and to trim whitespace, remove duplicates, and normalize company codes and rep IDs (Microsoft, 2023a).

3. Create data model and relationships: Load cleaned queries into the workbook data model (Power Pivot / Data Model) and create relationships on keys (e.g., Company Code and Rep ID) using Manage Data Model > Diagram View (Kimball & Ross, 2013). This enables pivot tables that aggregate across tabs.

Step 2 — Monthly Sales (Sheet1)

1. Create pivot from the data model: Insert > PivotTable > Use this workbook’s Data Model. Place Sales in Values, Sales Rep Name in Columns, and Dates in Rows.

2. Group Dates by Month: Right-click a date row > Group > select Months (and Years if desired) to show month-level subtotals (Microsoft, 2023c).

3. Configure slicer and filters: Insert > Slicer > Company Name, position it to the right of the pivot, and select the first four firms in the slicer to filter the pivot.

4. Set aggregation to Average: In the PivotTable Fields, click the Values field > Value Field Settings > Average. Format numeric cells via Home > Number to currency and edit labels for clarity.

5. Add a complementary PivotChart: With the pivot selected, Insert > PivotChart and choose an appropriate chart (e.g., clustered column or line for trends). To suppress field buttons, use PivotChart Analyze > Field Buttons > Hide All (or PivotChart Options > Display > Show field buttons unchecked) to declutter the visual (Microsoft, 2023d). Add a clear chart title describing the metric and period.

6. Rename the sheet "Monthly Sales."

Step 3 — Sale by Rep (Sheet2)

1. Create or adjust the second pivot to include Company Code in the Filters area; set the filter to the first ten company codes.

2. Insert a column-type PivotChart for this pivot and format appropriately (axes titles, data labels if needed).

3. Name this sheet "Sale by Rep."

Step 4 — Dashboard Construction

1. Create a new tab named "Dashboard". Copy the three pivot charts (the two above plus the third pivot chart requested in initial materials) onto the Dashboard sheet. Before copying, ensure Company Codes are unfiltered in the Sale by Rep pivot so the dashboard starts unfiltered for that visual.

2. Add slicers: Insert slicers for Company Codes and Sales Reps. To connect slicers to specific charts, right-click the slicer > Report Connections (or Slicer Connections) and check the pivot charts to which the slicer should apply. Connect Company Codes to Monthly Sales and Sale by Rep charts; connect Sales Reps to Monthly Sales only. Use slicer selections (first three choices) to demonstrate filtering behavior.

3. Align and label visuals with descriptive headings, ensure consistent color and number formatting, and confirm interactivity works across connected visuals. This approach adheres to dashboard usability best practices (Eckerson, 2011).

Securely Gathering Data from the 'Octopus' Data Flow

The “octopus” data flow shows an analytics engine ingesting data from many protected sources. Secure, legal, and reliable collection requires a layered approach: authenticated connectors, secure transport, governed staging, and auditing.

1. Authenticated, least-privilege connectors: Use vendor APIs and database connectors that require strong authentication (OAuth2, API keys, or Kerberos/AD integration). Tools such as Power Query, SSIS, Informatica, or managed connectors (Fivetran, Stitch) support these authentication flows (Microsoft, 2023a; Hohpe & Woolf, 2003). For example, Salesforce exposes scoped OAuth tokens that allow analytics systems to read objects without broader account access.

2. Secure transport and encryption: Use TLS/HTTPS for REST APIs and encrypted channels (SSL/TLS) for database connections to protect data in transit. At rest, staging areas or data warehouses should use encryption and access controls (NIST, 2020).

3. On-premises data gateway and data virtualization: For protected internal systems that cannot be directly exposed, use a secure gateway or virtualization layer. Microsoft’s On-premises Data Gateway, or data virtualization platforms (Denodo), allow queries while keeping sensitive data inside the corporate perimeter and enforcing access policies (Denodo Technologies, 2020; Microsoft, 2023e).

4. ETL/ELT pipelines and change data capture (CDC): Use ETL/ELT processors to extract authorized subsets, apply transformations and masking/anonymization, and load to a governed analytics store. CDC reduces load by only moving changed records. Managed services (e.g., Azure Data Factory, Fivetran) provide secure pipelines and connectors (Kimball & Ross, 2013).

5. Governance, auditing and compliance: Implement role-based access control, data lineage, and audit logging so that data access is tracked and compliant with regulations (GDPR, HIPAA). NIST and enterprise security frameworks provide controls for monitoring and incident response (NIST, 2020).

By combining secure connectors, encrypted transport, on-prem gateways or virtualization, controlled ETL, and governance, an analytics system can safely aggregate data from multiple protected systems while minimizing risk and preserving compliance (Hohpe & Woolf, 2003; Denodo Technologies, 2020).

Conclusion

Following the steps above will result in a workbook with an integrated data model, correctly configured pivot tables and charts, an interactive dashboard with slicers, and a robust understanding of secure data ingestion from multiple protected sources. Practical tools include Power Query for ingestion, Power Pivot for modeling, and slicers/pivot charts for dashboard interactivity; secure integration requires authenticated connectors, encrypted transport, gateways/virtualization, ETL/CDC pipelines, and governance (Microsoft, 2023a; Kimball & Ross, 2013).

References

  • Denodo Technologies. (2020). Data virtualization best practices and architecture. Denodo. https://www.denodo.com
  • Eckerson, W. (2011). Performance Dashboards: Measuring, Monitoring, and Managing Your Business. Wiley.
  • Hohpe, G., & Woolf, B. (2003). Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions. Addison-Wesley.
  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.
  • Microsoft. (2023a). Get data from Text/CSV in Excel using Power Query. Microsoft Docs. https://learn.microsoft.com
  • Microsoft. (2023b). Import data from Access into Excel. Microsoft Docs. https://learn.microsoft.com
  • Microsoft. (2023c). Group dates in a PivotTable. Microsoft Docs. https://learn.microsoft.com
  • Microsoft. (2023d). PivotChart options and hide field buttons. Microsoft Docs. https://learn.microsoft.com
  • Microsoft. (2023e). On-premises data gateway overview. Microsoft Docs. https://learn.microsoft.com
  • NIST. (2020). NIST Special Publication 800-53, Security and Privacy Controls for Information Systems and Organizations. National Institute of Standards and Technology. https://nvlpubs.nist.gov

Related Assignments