Do you know your data portfolio?
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.
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
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
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
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
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
Conclusion
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.
