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Use Case

Enterprise Data Consolidation


Enterprises that store their data in multiple places cannot consistently extract that data and mine it for insights. The results are duplicated effort and inconsistent findings. Consolidating to a single repository ensures consistency and accuracy for all users who base their decisions on data.

Bottom-Line Returns

Create a well-defined foundation that streamlines the processes of connecting data sources and extracting insights

Risk Reduction

Eliminate discrepancies from departments extracting the same data in different ways

Workforce Upskilling

Help your end users discover and share insights, instead of wasting time redoing work or questioning results

Business Problem

As enterprises make decisions on data from different sources, such as customer analytics, marketing campaign results and sales pipelines, they must connect and blend that data for a full picture. But when different users apply different methods to consolidate the same data, discrepancies arise. That can lead to different departments misunderstanding customer signals, inaccurately reporting financial results or reaching different conclusions on key performance indicators (KPIs).

Alteryx Solution

With an automated process that cleans and unifies all company data into a single repository, users are assured that they’re all looking at the same data. They can then apply the same business rules to make calculations like KPIs and pipeline status.

The main benefits of creating and enforcing a single data source are:

  • Data Governance – Enforce calculation rules before data is extracted, reducing the time needed to arrive at results and ensure everyone is working from the same data
  • Transparency – State which calculations are used at every point throughout the workflow
  • Efficiency – Enable users to find and extract their own data, thereby reducing the burden on data science and analytics teams

With Alteryx, you can:

  • Create a Designer workflow that connects to multiple data sources like Snowflake, Salesforce and MongoDB, and maintain it with Data Connection Manager (DCM)
  • Create a transparent process that blends data and applies business rules automatically
  • Schedule an extract-transform-load (ETL) process that runs hourly to populate, consolidate and update your data repository

Data Sources Simplified Workflow

1 – Data Connection

Use an Alteryx Data Connector to automate extraction from sources like MongoDB, Salesforce and Box

2 – In-Database Capabilities

Perform data prep and blend with the In-Database toolkit before extraction

3 – Prep and Blend

Easily combine data from all sources into a single data pipeline and automate application of business rules like KPI calculations

4 – Update Scheduling

Schedule your workflow to populate and update your data repository using Alteryx Server


Additional Resources


Starter Kit for Snowflake

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Starter Kit for Salesforce

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Starter Kit for Analytic Apps
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Starter Kit for AWS
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Data Preparation, Blending, and Enrichment
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