DataRocket

What is DataRocket?

DataRocket is a new generation of master data management

DataRocket is multi-domain master data management software. It is unique in its range of functions – including analysis, data-cleansing and reporting – and its super simple operability. You can individually define the criteria to measure your data quality. DataRocket will become the centerpiece for data quality management in your company.

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See in 2 minutes and 29 seconds how simple data management can be with DataRocket – clearly explained!

Our clients

We are proud to present successfully implemented projects in the fields of data migration, data governance and data quality with our customers from various industries.

 

Functions

DataRocket comprises four modules: the cockpit, designer, analyzer and browser.

From the Cockpit, you get an overview of what is happening. You can see how data quality has changed over the preceding few days and follow changes that were recently made.

  • Overview of the workflow coordination of the data quality improvement measures (e.g. approval processes and quality gates)
  • Individual reports on data quality in all data sources and data criteria
  • Long-term measures of success through a system of definable indicators
  • Activity and change history (audit trail) for all activities
  • Access to the user management system (management of roles and rights)

 

With the designer, you can choose your data model, create links between data sources and define your policy for measuring data quality.

  • Easy creation of data models using ones generated automatically or chosen from a pool of references
  • Connect (importing/exporting) data to hub interfaces
  • Define relationships between data sources
  • Assign datasets from different data sources (e.g., choosing leading attributes, data pre-processing)
  • Define the overall policy for securing data quality
  • Apply individual quality criteria in the editor by choosing components using the Lego Principle
  • Freely configurable computation paths for complex quality criteria (data pipelines)
  • Select and integrating external reference data sources for data validation/enrichment (e.g., addresses, phone numbers, open data)
  • Apply industry standards (e.g., eClass or ISO8000) as a templates

In the Analyzer, you can assess the evaluation of the data pipelines (quality criteria) over time. You define your Golden Records (defined datasets) and set the upper values of data quality with key figures that are measureable.

  • Analyze the databases using the defined quality criteria
  • Identify duplicates and consolidate them
  • Identify and cleanse inconsistent datasets
  • Update databases
  • Data validation/plausibility checks
  • Export datasets and quality reporting
  • Improve of data in real time or by batch
  • Assess data quality over time
  • Display the monetization potential (e.g., by integrating activity-based costing)
  • Cleansing tool that supports workflow

In the browser, you have a 360° overview of your data. Search and filter your data in real-time.

  • 360° view of your data and data quality
  • Search and edit across your databases from a centralized location
  • Filter and visualize your databases
  • Transfer reports in a collaborative setting (e.g., SharePoint)
  • Dynamic visualizations of data (e.g., heat maps, visualization on a map)
  • Excel-like views and input masks for processing data

DataPipeline

Analyze your data quality with predifined components - step by step in our DataPipeline!

The DataPipeline is a process- and component-based function that allows you to define and measure and controll the data quality. It will greatly improve your data quality step by step through a unique method. You can go directly to the errors and, over time, measure and observe the effect of data quality improvement activities. Component by component, you can build up your own unique quality pipeline.

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We show how the DataPipeline works by a simple example:

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Step 1: Choice of database: here, we have a database of 10 product data sets named “Components”.
Step 2: Filter by category: out of the 10 entries, 2 are excluded. The result: 8 filtered product data sets.
Step 3: Recognition of duplicates: a duplicate is identified and two datasets are consolidated. Data volume: 7 product data sets.
Step 4: Definition of quality attributes: here, we check for completeness. 2 data sets do not meet the criteria – 5 entries are complete.
Step 5: Calculation of key figures, e.g., order values based on deficient data.
Step 6: Showing the results using the diagram component.

 

Interfaces

Choice of existing interfaces and Web Service connectivity for DataRocket

Databases

  • MongoDB
  • SQLite
  • Oracle DB
  • MySQL
  • MS SQL
  • MS Access
  • PostgreSQL
  • ODBC

Files

  • MS Excel
  • CSV
  • XML
  • EDIFACT

API-Interfaces (REST / SOAP)

  • SAP ERP (IDOC)
  • Salesforce
  • SugarCRM
  • MS Dynamics
  • Atlassian JIRA
  • Atlassian Confluence
  • OpenStreet MAP
  • AS 400

 

We offer further interfaces through our integration partners. We love to create solutions for our clients for client-specific systems or for use with their own applications.

Prices

Our licensing model is simple!

You can rent DataRocket monthly to receive the following services:

Access to all modules:

Cockpit
Widgets
Designer/Integration
Analyser
Workflow

Unlimited number of:

Users
Data sources
Data pipelines
Reports
Updates

As an option, you can choose the Enrichment Service and get access to the reference data models. The monthly rent includes a service fee for software care and maintenance.

You can enquire about pricing for DataRocket by speaking to Dr. Tobias Brockmann.

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Live Demo

Get an idea of how DataRocket works in a free, 30-minute live demo

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