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Clever data portfolio management

Berlin, February 6, 2020 | Do you have an overview of which data exists in your company? Do you know the typical challenges that different types of data pose to your organization? Do you know how to master them skillfully in order to generate the maximum benefit from your data? In the following article we will introduce you to the different types of data and illustrate how you can make the best possible use of your data potential.

Data type: Customer dataCustomer data

The customer is king – this also applies to the customer’s data. Close customer relationships and the correct handling of customer data are particularly important in times of the GDPR. Therefore, pay attention to transparent data collection procedures, auditable documentation and high data quality in your CRM systems. The better the quality of your customer data, the more effectively you can launch your marketing campaigns.

Guess your customers’ desires, enable a correct personal addressing, remember your customers’ birthday and solve their problems with providing a quick and easy solution. In addition, well-maintained customer data is the basis for your customer analysis and customer value calculation.

Typical challenges related to customer data:

  • Syntactically inconsistent data
  • Missing contact or address information
  • Missing consultation between customer management and billing system
  • Eliminating duplicates

Solutions for optimizing data quality of customer data:

  • Checking customer data quality when entering data
  • Enrichment of data stocks
  • Access to open data to gain new information
  • Validation of address data
  • Use of software tools to determine a uniform customer data set (golden records)

Data types: Supplier dataSupplier data

E-commerce retailers in particular –  regardless of whether they are large or small – face the same problem. In order to fill their shops with products they have to process large amounts of data from many different sources. Especially the provision and updates of product data from their suppliers lead to high expenditures in the preparation of the data. A whole team of category managers is often in charge to manually check and correct the data in order to pass it on to their target ERP, shop or PIM systems. This is how companies do their best to meet their customers’ high expectations: a smooth user experience (e.g. perfect search results) and purchase processing (correct shipping) – both dependent on maximum data quality.

Typical challenges related to supplier data:

  • Plenty of suppliers and logistics partners with different data formats, structures and interfaces
  • Inconsistent information quality due to lack of standards
  • Extensive data transfer and integrations in the ERP or shop system
  • Manual data cleansing
  • High demands on data quality provided by the customer: timeliness & correctness

Solutions for optimizing data quality of supplier data:

Data types: Product and material dataProduct and material data

The efficiency of supply chain management depends on the flow of goods and information. Only reliable product and material data can guarantee a smooth supply chain as well as valid and high-performance reporting. On the other hand, ambiguous data such as duplicates lead to a wrong inventory and falsify inventory results. In addition, it prevents benefits from larger procurement volumes, generates capital commitment and increases process costs.

Typical challenges related to product and material data:

  • Wrong disposition parameters: lot sizes, reorder levels, discounts, quantities
  • Faulty safety stocks and lead times
  • Duplicates (ambiguous data)
  • Dummies
  • Product group assignment
  • Classification problems
  • Values ​​tests
  • Responsibilities in data entry and maintenance

Solutions for optimizing data quality of product and material data:

Data types: Technical-infrastructural dataTechnical-infrastructural data

An organization-wide overview of existing technical-infrastructural data is the starting point for service-oriented IT, facility and organizational management. Technical-infrastructural data summarize data sets from maintenance, facility management and information technology. Included are building plans, room data, cabling plans, storage capacity plans or plant and infrastructure equipment for buildings. Optimizing the data quality results in simplified invoice verification and onward billing. It also creates the basis for cost controlling and re-licensing.

Typical challenges related to technical-infrastructural data:

  • Missing overview – huge amounts of Excel lists
  • Up-to-dateness of the data basis questionable (data quickly outdated)
  • Large number of automatically generated data, especially with machine data
  • Often numerical registration without clear structure and semantics

Solutions for optimizing data quality of technical-infrastructural data:

  • Illustration of a reference data model as a basis
  • Creation of a uniform directory across all data sources (including Excel)
  • Indexed full-text search across all data
  • Access and value rules for data maintained by employees
  • Connection of SCCM and DMS systems
  • Connection to Active Directory and name server (e.g. LDAP)

Data types: geo dataGeo data

The key to effective logistics and resource planning is consistent geospatial data. As data with a direct or indirect reference to a specific location or geographical area this data type serves the correct location. Geo data describe an object either directly (by coordinates) or indirectly (e.g. by postcode, a landscape or by its position in space). Geospatial data can be linked to one another via their spatial reference in order to be able to create detailed queries and analyzes. Your benefit lies in the exact route planning and the avoidance of detours. They can also be used to visualize primary data (customer or material data).

Typical challenges related to geo data:

  • Incorrect geospatial data such as coordinates (X / Y values)
  • Incorrect assignments of geospatial base data to attributes / meta descriptions (e.g. POI)
  • Incorrect metadata descriptions (use of a property)
  • Conceptual, format, value, topological, geometric consistency
  • Position accuracy (inner + outer) and raster data accuracy
  • Correctness in time

Solutions for optimizing data quality of geo data:

  • Integration of the geographic information system (GIS)
  • Consideration of the multidimensionality of the data 2D / 2.5D / 3D / 4D
  • Validation and enrichment of geo data sets using open source data

Conclusion:

Companies are faced with one specific problem across all types of data. Almost all data landscapes have a certain percentage of duplicates. These are ambiguous data records, some of which are entered in several database systems. A software tool is ideally used to identify and clean up duplicates. It checks your entire data base across all systems based on configurable criteria. The cleansing process then takes place automatically or the user is guided through the manual cleanup in a simple process. As a result, you will receive a data record – the so-called golden record – in which the data from all duplicates has been correctly and completely merged.

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