Strategizing for Big Data Integration and Governance Challenges

Businesses are overwhelmed by a proliferation of data sources, types, and stores. The abundance of information and tools is increasing the challenge of combining data into meaningful, valuable insights.

While companies today are investing heavily in initiatives to increase the amount of data at their disposal, most information workers are spending more time finding the data they need rather than actually putting it to work.

The need for faster and smarter data integration capabilities is growing. At the same time, to deliver business value, people need information they can trust to act on, so balancing governance is absolutely critical nowadays, especially with new regulations.

DBTA recently held a webinar featuring Kevin Petrie, senior director and technology evangelist, Attunity, Danny Sandwell, director of product marketing, erwin, Inc., and Jake Freivald, vice president, Product marketing, Information Builders, who discussed the key technologies and best practices for overcoming big data integration and governance challenges.

The various problems with the normal data integration process include:

  • Data modeling - Too much time spent coping with slight changes in our business data
  • Business/IT alignment - Data architects, DBAs, and others can’t communicate with businesspeople
  • Processes - Too much detail lost by handing off responsibility for business data to different people

According to Freivald, MDM was the starting point. Business need to unifying data quality with MDM, align business users with mastered subjects, and capture transactional subjects in MDM store.

The omni-gen approach includes:

  • Immediate capture in automatically generated data hub
  • Master data: business-user-oriented, subject-oriented
  • Rapid, integrated data quality rules
  • Mastered and transactional subjects
  • Rapid cycle times to keep the business engaged
  • Support and automatically apply best practices

Companies should start reevaluating their analysis, planning, justification, and prioritization, Sandwell explained.

erwin offers a platform that Underpinned by a comprehensive metadata capability that is  automated, efficient, iterative, agile and multi-contextual.

The platform is enriched by data models, which helps with a business oriented approach to visualizing data elements/structure for the use cases, capturing and organizing data requirements, and identifying and depicting business rules.

Modern analytics requires modern data integration, according to Petrie. The Attunity platform can help enterprises reach their goals.

The Attunity platform can deliver delivering efficiently and in real-time to data lake, streaming, and cloud architectures. The platform consists of three solutions including Attunity Replicate, Attunity Compose, and Attunity Enterprise Manager.

An archived on-demand replay of this webinar is available here.