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Data Quality at a Glance Conference

Posted by Lucie Felixova on April 21, 2010

Javlin a.s., producer of CloverETL, took part in a Data Quality at a Glance Conference held on April 20th, at PriceWaterhouseCoopers's premises in Prague. This conference was organized by IDG and Javlin served there as the professional supervisor partner.

Javlin together with other conference partners PriceWaterhouseCoopers, SAS and Ataccama each held presentations on Data Quality topics.

The first presentation was held by Mr. Snytr. It discussed Personal data and its quality from the view of the Office for the Protection of Personal Data.

Mr. Maly, senior manager at PriceWaterhouseCoopers talked in his presentation about Optimizing business processes. According to him, data quality is closely related to quality processes. Poor data has an impact on strategic decision-making and can cause a loss of business opportunity and/or profit. Therefore, it is important to find the source of data errors and set the right data processes. Mr. Maly emphasized that it is essential to cleanse data continuously.

Mr. Kyjonka from SAS held a presentation on One version of truth for all or MDM in business life. He highlighted importance of Data integration, Data Warehousing and MDM for getting the right data on time. He showed 3 important parts of MDM - System of Record, MDM Hub and Integration infrastructure. Further it was shown that 4 different types of MDM solutions can be used for various purposes - Registry style, Transaction style, Hybrid style, and Consolidation style (ETL). Choosing the right style of MDM solution depends on budget and how much time the company has. Quality cleansed data, technical infrastructure, Data Governance program, willingness to share data, etc are some of the important factors for MDM.

Mr. Matous, Javlin’s consultant had a presentation on Data Cleansing. According to him data quality is the process of detection, reporting and correction of the invalid or missing values in data. Mr. Matous made it clear how important it is to do data audits and why to use a data quality scorecard. A data quality scorecard tracks the financial impact of poor data and estimates the return on investment into Data Quality activities. It helps managers determine whether or not to invest into data cleansing tools. Mr. Matous talked about several Data Quality benefits in business. Some of them include increased efficiency of marketing campaigns, early warning system, increased credibility and reputation among customers. It was also shown how data cleansing could be done using CloverETL.

Mr. Vojtek, VP of Engineering at Javlin, discussed a specific case study where Javlin had undertaken data quality improvements. He emphasized how data quality is essential for quality business results. It was shown what kinds of pitfalls could be experienced in a data quality implementation in multinational companies.

The last presentation of the conference was hold by Mr. Kyjonka from SAS. He discussed a case study named The clever way to cleanse data. He argues that when companies think they have only 10% of poor or bad data, they usually have about 40% bad data. In his particular case 90% of the data had to be corrected during the data cleansing process.

Live from the Conference - @CloverETL on Twitter. Follow the tag #DQPWC.

Data integration software and ETL tools provided by the CloverDX platform (formerly known as CloverETL) offer solutions for data management tasks such as data integration, data migration, or data quality. CloverDX is a vital part of enterprise solutions such as data warehousing, business intelligence (BI) or master data management (MDM). CloverDX Designer (formerly known as CloverETL Designer) is a visual data transformation designer that helps define data flows and transformations in a quick, visual, and intuitive way. CloverDX Server (formerly known as CloverETL Server) is an enterprise ETL and data integration runtime environment. It offers a set of enterprise features such as automation, monitoring, user management, real-time ETL, data API services, clustering, or cloud data integration.