Practices

Projects

DataRocket provides support for data quality managers at all stages of a MDM project. You can rely on DataRocket for support at every project stage.

The improvement of (master) data quality is a complex and often long-term project in a business, particularly when it comes to applying a dynamic preventative system to master data management. Since data management is cross-functional in a business, its challenges will be complex and varied.

The factors that will typically ensure success in an MDM project are the inclusion of different departments which is the IT, staff responsible for different processes, and last but not least, the management, backing the whole process.

As the person responsible for master data and data quality in your business, you have the challenge of keeping track of data quality, managing its improvement, and most of all, identifying and measuring the additional value and advantages of good data quality.

We can support you at different stages of the project to help you maintain a high level of data quality long term and to show your strengths using key indicators.

 

Screening the Data Landscape

Situation:
Would you like to improve data quality in your business? Have you had trouble finding a concrete problem/application or measuring the added value of high-quality data?

What can be done:
We work with you to analyze your data landscape, identify quality problems and measure the additional value. This can be achieved with the help of comprehensive master data management.

Result:
You will receive a plan of action in the form of a condensed report, with guidance on how to shape your master data management project.

Database Testing

Situation:
You know what data quality problems are present in your business, but you are not sure how good the data quality is?

What can be done:
We measure the quality criteria of a data source with DataRocket (syntax test [font] + semantic test) in a two- to three-day starter project.

Result:
A data profile report on your data landscape (e.g., duplicates, Golden Records, error and plausibility problems, content, minimum and maximum values, descriptive statistics).

Cleansing Projects

Situation:
You know what the problem is and want to bring your data quality to the next level?

What can be done:
We work with you to create a set of quality criteria and key figures (for measuring additional value) that should be calculated, tailored to your needs. We put this together in the form of a data pipeline in DataRocket, which can be used to carry out workflow-optimizing cleansing.

Result:
You will receive top-quality data, continuous reporting and a structured and detailed data quality policy.

Real-Time Quality Control

Situation:
You want to automatize quality control and make sure early on that nothing can negatively impact the quality of your data – e.g., errors in data entry/dataset changes or during data migration – and to maintain a constant and continuous level of data quality in your master data so that it can be used by different departments and provide a solid foundation for reporting.

What can be done:
DataRocket acts as a central hub and other relevant systems can be connected to it. The predetermined quality policy conducts checks automatically, e.g., even during data entry, it checks the values for accuracy and duplicates.

Result:
A comprehensive master data management system. You will have a central data source with verified data (single point of truth) and a 360° view of your data. Most importantly, you will be able to take control of your master data – in short: your business will benefit big time.

Industry Sectors

DataRocket can be used as a multi-domain solution to bring added value across nearly all industries.

DataRocket is a multi-domain master data management solution. The flexible architecture and options for free modeling allows it to be applied to all industries where data plays a particularly important role.

A wide interface base and a focus on standards mean it can be used easily across all industries.

We will gladly develop interfaces for industry-specific IT systems for you. Alternatively, we can work with our partners to create specific integration solutions with DataRocket, for example, with Bosch’s inubit suite or SQL Project’s TransConnect platform. Through this, you can get a quick handle on industry-specific interfaces and standards.

Types of Data

DataRocket is a Multi-Domain MDM software that you can use to integrate and improve all kinds of data types. You can use DataRocket regardless of the type of data you have.

The customer is king. This applies also to your customer data.

The typical challenges related to customer data:

  • Syntactically inconsistent data
  • Missing contact or address data
  • Misalignment between the client management and billing systems
  • Duplications
  • Adding databases
  • Plausibility checks and validations

How would you benefit from high-quality customer data?

  • Error-free and quick processes
  • Customer satisfaction
  • Staff satisfaction
  • Better oversight and evaluation/reporting
  • Additional data for analysis and customer value calculations

How can DataRocket help you improve your customer data?

  • Establishing consistent customer master data (Golden Records)
  • Verification of addresses
  • Eliminating duplicates
  • Integrating customer data in all IT systems
  • Checking customer data quality during data entry
  • Enhancing databases
  • Accessing open data to obtain new information

You need high-quality product and material data to make sure that your supply chains function properly and that production does not come to a halt!

Typical challenges regarding product and material data:

  • False MRP parameters: lot sizes, reorder points, discounts, quantities
  • Incorrect details on safety stocks and replacement times
  • Duplicates (ambiguous data)
  • Dummies
  • Article group assignment
  • Classification problems
  • Value checks
  • Responsibility assignments for data

How can you benefit from high-quality product and material data?

  • Exact inventories and order volumes
  • Lower procurement and warehousing costs
  • Identification of dead inventory
  • Higher customer and supplier satisfaction as a result of more reliable information on the global supply chain
  • Transfer of higher-quality data as a service to the product
  • Validation and performance reporting
  • Lower capital intensity
  • Simplifies the reusability of components
  • Readiness for the Internet of Things and Industry 4.0

How can DataRocket help you improve your product and material data?

  • Creation of a consistent reference data structure
  • Formation of a consistent material master (Golden Record)
  • 360° view of your product and material data
  • Creation of a departmental or company-wide policy on data verification
  • Identification of duplicates
  • Locating dummies
  • Integrated product and material data across all IT systems
  • Integration of DQ control into existing approval workflows
  • Assignment of data checks to different departments (e.g., warehouse)
  • Automatized creation of quality, Web-based reports

Having the correct location information is the key to effective logistics and resource planning.

The typical challenges regarding geodata are:

  • Incorrect geobase data like coordinates (XY values)
  • Incorrect assignment of geobase data to attributes/meta descriptions (e.g., POI)
  • Incorrect meta data descriptions (land use)
  • Doubled-up locations (duplicates)
  • Conceptual, formatting, value, topological and geometric consistency
  • Positional accuracy (internal and external) and raster data accuracy
  • Time accuracy
  • Classification of geodata

 

  • Exact route planning
  • Correct positioning
  • Detours are avoided
  • Simplified, location-based resource planning
  • Correct business figures for different regions and locations
  • Improved address data
  • Optimized space and demand planning
  • Lower costs through the use of freely available data

How can DataRocket help you improve your geodata?

  • Identification of duplicates
  • Visualization of primary data (customer or material data) with the help of geodata
  • Integration of geographic information systems (GIS)
  • Working with geometric forms (shape)
  • Attention paid to multiple dimensions of data (2D / 2.5D / 3D / 4D)
  • Validation and enrichment of geodatabases using open source data

An organization-wide overview of existing technical and infrastructural data is the starting point for service-oriented IT and organizational management.

Typical challenges relating to technical and infrastructural data:

  • A lack of oversight – uncontrolled proliferation of Excel lists
  • The data is often managed by the staff themselves
  • Mainly comprising numbers without clear structure or semantic consistency
  • Ensuring the data is up to date (fast administration of the data)
  • A high proportion of the automatic data comprises automatically generated data
  • Redundant data in a wide variety of formats
  • No clear data basis

How do you benefit from high-quality technical and infrastructural data?

  • Improved organization-wide IT service management
  • Foundation for the implementation of ITIL
  • Optimization of facility, IT and organizational management
  • Establishing knowledge management
  • Standardized IT and organizational processes
  • Decentralized support for customers
  • Optimized roll-out planning
  • Foundation for cost-controlling and relicensing
  • Simplified invoice verifications and charges

How can DataRocket help you improve you technical and infrastructural data?

  • Mapping of a referential data model as a foundation
  • Creation of a consistent directory on all the data sources (also Excel)
  • Indexed search of the complete text for all of the data
  • 360° view of your data
  • Access rules and value policies for data that is managed by staff
  • Identification of duplicates
  • Connecting SCCM and DMS systems
  • Connecting to the active directory and name server (e.g., LDAP)
  • Technical support in consolidating the data through workflow processes and staff participation

Standards

The eCl@ss classification standard and the data standard GDSN of GS1 (Global Standards One) – the leading standards for coherent databases in manufacturing and trade – are supported by DataRocket.

DataRocket can be used with the leading product data standard for the classification and description of products and services. eCl@ss makes digital sales of classified products involving multiple partners simple. The standard acts as a reference data model, a common denominator for the exchange of data, as well as classification. DataRocket offers functions and algorithms that help you classify your material master records according to eCl@ss guidelines, and to reduce your costs significantly.

The Global Data Synchronization Network (GDSN) creates a standardized data storage and makes consistent data accessible to all participating trade partners in the value chain. DataRocket can be used throughout the value chain and acts as a hub and central point of standardization that everyone can access. GDSN and GS1 must be taken into consideration.

DE | EN