Do you know your data portfolio?

A Collection of gummibears colorsorted

Do you have an understanding of the data in your organization? Do you identify the typical challenges that different types of data pose to your organization? Do you know how to overcome them to get the most value from your data? In the following article, we will introduce you to the different types of data and show you how to maximize the potential of your data.

The customer is king - and that includes customer data.

a handshake of two persons

Close customer relationships and the correct handling of customer data are particularly important in the age of GDPR. Make sure you have transparent data collection processes, auditable documentation, and high data quality in your CRM systems. The better the quality of your customer data, the more effective your marketing campaigns will be.

Guess what your customers want, address them personally, remember their birthdays, and solve their problems quickly and easily. In addition, well maintained customer data is the basis for your customer analysis and customer value calculation.

Typical customer data challenges include

  • Syntactically inconsistent data

  • Missing contact or address information

  • Lack of coordination between customer management and billing systems

  • Elimination of duplicates

Solutions for improving customer data quality

  • Checking customer data quality at data entry

  • Enrich data assets

  • Access open data to gain new information

  • Validate address data

  • Leverage software tools to create a single customer record (golden records)

E-commerce retailers, large and small, face the same problem: Supplier data

Blick in einen Frachhafen in dem ein großes Schiff mit Containern beladen liegt

To fill their stores with products, they need to process large amounts of data from many different sources. In particular, the provision and updating of product data from their suppliers leads to high data preparation costs. Often, an entire team of category managers is responsible for manually reviewing and correcting the data to get it into their target ERP, store, or PIM systems. In this way, companies do their best to meet the high expectations of their customers: a smooth user experience (e.g. perfect search results) and purchase processing (correct shipping) - both of which depend on maximum data quality.

Typical challenges associated with supplier data:

  • Multiple suppliers and logistics partners with different data formats, structures, and interfaces

  • Inconsistent information quality due to lack of standards

  • Extensive data transfer and integration into the ERP or shop system

  • Manual data cleansing

  • High customer data quality requirements: timeliness & accuracy

Solutions to optimize the data quality of supplier data

  • Creation of a consistent reference data structure

  • Rule-based text generation

  • Definition of quality gates

  • Create interfaces to standard systems

  • Use software tools to automate data transfer and data quality checks

The efficiency of supply chain management depends on product and material data

a collection of miniature toy cars

The efficiency of supply chain management depends on the flow of goods and information. Only reliable product and material data can ensure a smooth supply chain and valid and powerful reporting. On the other hand, ambiguous data such as duplicates lead to incorrect inventory and distorted inventory results. It also prevents you from taking advantage of larger procurement volumes, ties up capital, and increases process costs.

Typical product and material data challenges include

  • Incorrect planning parameters: lot sizes, reorder levels, discounts, quantities

  • Incorrect safety stocks and lead times

  • Duplicates (ambiguous data)

  • Dummies

  • Product group assignment

  • Classification problems

  • Value checks

  • Data entry and maintenance responsibilities

Solutions for improving the quality of product and material data

  • Create a consistent reference data structure

  • Create a departmental or enterprise-wide data validation policy

  • Integrate data quality control into existing approval workflows

  • Assign data cleansing tasks to specialized departments (e.g., warehouse)

  • Leverage software tools to tag dummies and establish a consistent material record (golden records)

An organization-wide view of existing technical infrastructure data

Einblick in einen U-Bahn-Tunnel mit einer entgegenkommenden U Bahn in Berlin

An organization-wide view of existing technical infrastructure data is the starting point for service-oriented IT, facility, and organizational management. Technical infrastructure data is a collection of maintenance, facility management, and information technology data. It includes building plans, room data, cabling plans, storage capacity plans, and building equipment and infrastructure. Optimizing data quality simplifies invoice verification and recoupment. It also provides the basis for cost control and re-licensing.

Typical challenges associated with engineering infrastructure data include

  • Lack of visibility - large volumes of Excel lists

  • Questionable up-to-dateness of the database (data quickly becomes outdated)

  • Large amount of automatically generated data, especially for machine data

  • Often numerical records without clear structure and semantics

Solutions to optimize the data quality of technical infrastructure data

  • Mapping of a reference data model as a basis

  • Create a unified directory across all data sources (including Excel)

  • Indexed full-text search of all data

  • Access and value rules for employee-maintained data

  • Integration with SCCM and DMS systems

  • Integration with Active Directory and name servers (e.g. LDAP)

The key to effective logistics and resource planning: geospatial data

a map with markers

As data with a direct or indirect reference to a specific location or geographic area, this type of data serves the right location. Geospatial data describes an object either directly (by coordinates) or indirectly (by zip code, landscape, or position in space). Geospatial data can be linked together through its spatial reference to create detailed queries and analyses. They can be used to plan precise routes and avoid detours. They can also be used to visualize primary data (customer or material data).

Typical challenges associated with geospatial data

  • Incorrect geodata such as coordinates (X / Y values)

  • Incorrect mapping of base geodata to attributes / metadata descriptions (e.g. POI)

  • Incorrect metadata descriptions (use of a property)

  • Conceptual, format, value, topological, geometric consistency

  • Positional accuracy (internal + external) and raster data accuracy

  • Timeliness

Solutions to optimize the quality of geospatial data

  • Geographic Information System (GIS) integration

  • Taking into account the multi-dimensionality of the data 2D / 2.5D / 3D / 4D

  • Validation and enrichment of geospatial data sets using open source data


Organizations face a common problem across all types of data. Almost all data landscapes have a certain percentage of duplicates. Duplicates are ambiguous data records, some of which exist in multiple database systems. Ideally, a software tool is used to identify and clean up duplicates. It checks your entire database across all systems based on configurable criteria. The cleansing process is then performed automatically, or the user is guided through a simple process for manual cleansing. The result is a record - the golden record - in which the data of all duplicates has been correctly and completely merged.

DATAROCKET Coreis our tool for sustainable master data management. Data quality and data analysis take place in one place, and new data records are directly created in a quality-checked process. This way, your employees work with the best data quality and you can make better business decisions.

Sound good?

Core is the Master Data Management software for enhanced data quality.

Manage your product data in DATAROCKET Shuttle. The product information is uploaded by your suppliers to a one-stop supplier portal, checked against your data quality rules and cleansed. You only transfer secured data into your systems. You can also onboard your suppliers directly via DATAROCKET Shuttle - including certificate checks and direct communication.

Easy onboarding of your suppliers: Offer your suppliers easy delivery of product data and only transfer verified data into your systems.

DATAROCKET Guide is the data governance tool for the representation of your enterprise data. Discover your enterprise data and assign responsibilities, standards and business terms.

Are you ready to capture and transparently map your enterprise data knowledge?

The tool for implementing data governance processes - Gather knowledge about your company's data and make rules, responsibilities and policies transparent.

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