Data preparation and self-service for analytics: is the party over?


“IT does not deliver, the department does not know what it wants by way of data today or tomorrow.”

This is a regular complaint, and usually results in departments turning to self-service for their information requirements. Information workers are still around, but they no longer have all the answers. Instead, departments are finding their own solutions. You might see SAP systems, Excel, database(s), local sources, the internet, and unstructured data. Does this sound familiar?

The way in which data management works in companies has changed in recent years. Data warehousing and ETL are established by IT departments, but cannot cope with the fast-moving digital assembly line. Specialist areas need answers to previously unpredictable questions, and always faster than last time. In practice, data management tasks have long since become part of the day-to-day business. So why not recognise this? The IT department is still as busy as ever, because the carefully-built data warehouses always need more attention. They cannot take on any more.

It is time to react to changes in digital. After all, today’s iPhone is nothing like the iPhone of 10 years ago. I believe that it is time that business areas took on responsibility for efficient and modern data management. The departments would then be in a position to provide good quality data and could also provide feedback to the IT department.

Finding the right balance

Reducing complexity and increasing flexibility are not mutually exclusive. Modern tools can be used to adjust processes, to add to the existing mandatory requirements. Data preparation and self-service for analytics are a key success factor for business departments in the digital age, and therefore also for IT and, indeed, the entire company. Providing a consistent response to changes in business and searching for new fields are not disadvantages.

Of course some companies take the position of “But we have always done it like this”, preferring tradition to progress. Many previously market-leading companies have taken this approach, and many are no longer relevant today. But these companies provide important lessons in how not to do it. What are your experiences, whether positive or negative?

How do you address the challenge of providing departments with more agility in data management and bring data governance and regulatory requirements into line with self-service techniques? And how do these fit with existing data warehousing and business intelligence processes?

Join the discussion

Why not join our #saschat on Twitter on Friday 27 January at 15:00 CET and share your experience with experts and sufferers alike? We will discuss around these questions:

Q1: What factors have most influenced trends in analytics self-service over the past 12 months?

Q2:  How is the challenge of providing departments with more agility in data management best handled?

Q3: How have self-service approaches kept pace with data governance and regulatory requirements?

Q4: What determines the role of data quality in data preparation?

Q5: How do self-service techniques fit with existing data warehousing and business intelligence processes?


About Author

Torsten Beck

Sr Solutions Architect

Torsten Beck arbeitet bei SAS als Senior Solutions Architect im Center of Excellence Data Management und ist in Deutschland, Österreich und Schweiz aktiv. Seit mehr als 15 Jahren ist er in der Informationstechnologie tätig, darunter mehrere Jahre als SAP-Berater. Zu seinen Arbeitsschwerpunkten gehören, die Integration von SAP-Systemen, Datenmanagement insbesondere Datenqualität sowie Themen im Zusammenspiel mit Hadoop.

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