Data Quality Is to a Company as Oil Is to an Engine

Data Quality Is Something You Can See

Data Quality with InfoZoom

Data quality management is complex and affects all divisions of a company, but not everyone recognizes its importance. Yet it is worth an investment because high data quality is crucial to the success of a company.

To ensure high data quality, you have to identify inaccurate data, remove duplicates, standardize spellings, detect formatting errors, conduct plausibility checks, and more. If you only have inflexible analyses to work with, it will take you hours to complete these tasks. But with InfoZoom, you are done with everything in just a couple of minutes.

  • Carrying out plausibility checks
  • Master files management
  • Data Cleansing and updating data, including:
    • Selecting and correcting inaccurate data
    • Identifying filling levels at a glance
    • Standardizing spellings
    • Checking data formats
  • Detecting duplicates
  • Reconciling data in various tables and data sources
  • Defining rule sets
  • Harmonizing data structures
  • The basis for data mining
  • Reliable and transparent data migration/integration

Name Normalization
using InfoZoom

Address Normalization using InfoZoom

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sales@humanit.de

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Solution: Transparency for ALL ─ Checking Data Quality at a Glance

  1. The “Destination” attribute contains missing values. They are represented by dashes in the overview of data.
  2. Formatting issues in an attribute are marked with exclamation points.
  3. Data can also be displayed in value lists. They are sorted by frequency. Charts can be created at the push of a button.
  4. Users immediately detect different spellings (example: year specified as 2013 and 13).
  5. The width of a line indicates the frequency distribution.

Effects of Poor Data Quality

There are as many error sources as evaluation criteria.

  • Reporting which draws on inaccurate or incomplete data is imprecise and might lead to decisions with fatal consequences.
  • Negative impacts on business processes significantly reduce productivity.
  • Poor customer service leads to low customer satisfaction.Poor_Data_quality

Can You Rely On Your Data and Analyses?

High Data Quality for Reliable KPIs

Example

Schreibweisen-vereinheitlichen-datenqualitaet

Every time a customer calls your hotline, their data are recorded. Your employees enter the customer data in a mask and save them. At the end of the month, you want to know how many times a specific customer called, and why.
Which feature do you select for your analysis? Are the results reliable?

Entering data is not as easy as it seems. Variant spellings make it difficult to enter last names correctly. Not to mention typos.

It can even be tricky to spell the name of a city, as shown in the example.
This section of a data pool contains more than 90 (!) different spellings of “Stuttgart”.

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