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Prepare Data for Analysis

This page applies to developers and data stewards. It describes how to prepare process data for analysis in Process HQ.

Overview

Business processes come in all shapes and sizes; so does the data you collect about those processes. To get that data ready for analysis, you need to transform it into a simplified, cleaned, and standardized format.

Preparing process data for analysis is often one of the most time-consuming and costly aspects of a traditional process mining project. It typically requires the involvement of specialists who understand what makes good data for process analysis—and perhaps another set of specialists who can perform the high-code transformations needed to get the existing data ready for that analysis. Even if transformation scripts are reusable, that work those specialists do results in static data that typically needs to be repeatedly exported from the source and imported into the mining tool.

In contrast, Process HQ sources the data it analyzes directly from your data fabric and takes advantage of sync-enabled features to automate this transformation. As a result, process analysis is continuous and always up-to-date, and data preparation is one low- or even no-code step that lasts for the lifecycle of a process. What's more, Process HQ provides an accelerated path to data enrichment that allows business users to quickly iterate on production data with no-code transformations, leading to better, more actionable insights. This is the true power of having a unified platform for both your business applications and analysis.

If your process data supports your analytical goals in its current form, no further configuration is needed before deploying your app to the production environment.

However, if you want to enrich or clean your data, use this page to review the different types of transformations developers and data stewards can do to prepare data for process insights.

How to prepare data

For the most part, developers can take advantage of record type relationships, custom record fields, and source filters to perform most data preparations.

Once your record types have been deployed to production, data stewards can perform any additional transformations to the production data, without the need for re-deployment.

The following table describes what data preparation can be performed by a developer or data steward, and where the data preparations should occur:

What

How

Where

Developer

Keep data under the synced row limit.

Configure sync options

Development

Remove ad hoc events

Add source filters on the record type

Development

Standardize timestamps

Add custom record fields

Development

Standardize activities (or event types)

Add custom record fields

Development

Standardize automation type

Add an automationId field to the event history record type and capture the type of automation that completes an event in a process model

Development

Extract text from a field

Add a custom record fields

Development

Merge multiple event history record types

Create a records-backed record type

Development

Extract case data or event data from an existing record type

Create a record-backed record type

Development

Data steward

Provide user-friendly names for attributes

Rename attributes

Production

Hide sensitive data

Add custom attributes

Production

Group the difference between two dates

Add custom attributes

Production

Use data from a one-to-many relationship

Add custom attributes

Production

Open in Github Built: Fri, Jun 14, 2024 (05:47:37 PM)

Prepare Data for Analysis

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