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Data Quality Improvement with SAP Data Services


First things first, what is data quality and why should I care?

Data quality is crucial to operational and transactional processes and to the reliability of business analytics and business intelligence reporting. You can’t make good decisions with bad data. Data quality is essentially ensuring that all of your data coming in and all of your loaded data is of high quality.

Well, what is “high quality?”

High quality data is:

  • Accurate
  • Complete
  • Up to date
  • Relevant
  • Consistent across data sources
  • Reliable
  • Accessible

So what happens when you realize your data doesn’t meet these criteria? How do you get started when you want to implement a data quality initiative? The first two steps are what this blog is about: preparing your data and standardizing your data.


Preparing Your Data

 

Ideally, you start with a Data Prep Phase. This is the process of collecting, cleaning, and consolidating your data into one place for use. Gartner estimates that up to 80% of the work in data analytics is done during the prep phase! So this isn’t an area to downplay the importance of.

Questions to ask yourself during this phase:

  • Where is my data? Where does it live? What is the data source?
  • Who uses the data? There will be stakeholders in varying business and functional areas to consider and involve; be sure to seek experts who not only understand the data, but also the business processes.
  • Is the data any good? Is it usable?
  • What is the best way to consolidate the data?


Standardizing Your Data

 

Once you’ve completed the prep phase, you’re ready to move on to the Data Cleansing and Standardizing Phase.

Data standardization is the next step to ensuring that your data is shareable across the enterprise. You want to make sure your data is the same across the organization. If not, sales figures may not match up, your detail reports may not confirm your summary reports, addresses will not be valid. These types of situations result in wasted time, additional overhead, bad decisions and a lot of frustration.

SAP Data Services is a great tool for getting to “one version of the truth.” SAP Data Services:

  • Cleanses and standardizes customer data such as name, addresses, emails, phone numbers, and dates; prevents incorrect data such as invalid contact information
  • Manages international data for over 190 countries and reads and writes Unicode data
  • Removes errors to uncover the true content of the database
  • Improves integrity of data to identify matches
  • Ultimately creates a single customer view

SAP Data Services can also help you apply and enforce data quality rules whenever data is created, updated, or moved. It also allows you to perform data quality checks anytime, in real-time, on data sets before analyzing, moving, or integrating data.

SAP Data Services helps your organization move toward that “one version of the truth” and stave off hours of wasted time and rehashed problems. Your departments will have the same definitions and terms to work with, correct data and clean information.

Standardization is the cornerstone of business intelligence.

For more information and to see some examples of how SAP Data Services transforms data, you can listen to a pre-recorded webinar I gave called “Expert Guidelines for Building a Data Quality Assessment & Improvement Initiative.”

Expert Guidelines for Building a Data Quality Assessment and Improvement Initiative

You can also read more about Data Quality and other SAP Resources in my other blog series:

Getting Started with SAP BusinessObjects Data Quality

Bruce Labbate HeadshotAbout Bruce Labbate
Bruce is a business intelligence consultant specializing in data warehousing, data quality, and ETL development. Bruce delivers customized SAP Data Services solutions for customers across all industries. With Decision First Technologies, A Protiviti Enterprise, Bruce utilizes Data Integrator, Data Quality, Information Design Tool, and a variety of database technologies, including SQL Server, DB2, Oracle, Netezza, and HANA.

 

Categories: Data Services
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