A system of insight ecosystem enables individuals to locate and access data, build visualizations and create analytical models that can help improve the operations of organizations. See how the data reservoir reference architecture describes the technical capabilities necessary for a system of
The data warehouse has never been more relevant than it is now. The DW’s role in the big data universe appears likely to grow. What the DW does, above all else (and this is far from its only role in many organizations) is serve as hub for governing your system-of-record data to be delivered into
Among all the data-related opportunities and concerns of this year, one that is demanding increased attention is information governance. Governance is a concern because the growing business appetite for insights puts pressure on systems to deliver data rapidly, whether it is well-governed or not.
In an environment where lack of trust is common, the data warehouse challenge is not just finding the best warehouse technology, but also determining how to create a warehouse that instills confidence among the business users who receive the output of analysis.
1.8 trillion gigabytes of information is available in today’s digital world, and there are great rewards for those who are truly prepared to leverage this opportunity to significantly improve organizational decision making using data. With the right data governance, businesses can tap into big data
There is a growing trend among larger companies to develop mature and repeatable processes. One such growing trend is the use of data masking factories where repetitive processes are executed as a centralized service for large numbers of applications.
What are the opportunities of data refinement from the IT point of view? Does self-service data refinement devalue IT? Does it simply create more work for IT, cleaning up after business users who have run amuck?
Data refinement is one of the most important revelations in the big data market. The idea is simple: you want to take advantage of and use all sources of big data. But when each individual user needs only information relevant to them, what’s needed is a data refinery. It automatically cleans,
Before business users can start to analyze data and consider the next best actions to improve results, it is typically required to submit a request for the data. Depending on the backlog of requests to IT, the business user might have to wait days, weeks or more before moving ahead with analysis
It seems that everyone these days is interested in big data: using more data, more quickly and making better decisions from it. How does your company interact with data? Or, more specifically, how do business users interact with data?
What makes customers tick? Why do they buy, how do they prefer to shop and, the big question, why do they switch? These are all important questions that every organization faces. Understanding the socially connected customer (having a 360 degree view) makes it possible to provide a truly
In the world of big data, the elephant is king. Hadoop, whose elephant logo has become the face of big data, has been joined in the big data jungle by many friends: Pig, Jaql and even a ZooKeeper to keep them all in line. The entire big data jungle will be making a trip to Las Vegas in October for
Gartner has recently released their first ever Magic Quadrant for Structure Data Archiving Application Retirement. In this report, Gartner evaluates vendors based on their capabilities and offerings for structured data archiving and application retirement. IBM has been recognized as a leader in the
Research indicates that business and IT professionals spend more than 70 percent of their time finding data, validating it or defending it, rather than focusing on what they find most important: analyzing the data. With too little time spent focusing on data analysis, organizations derive sub-
Big data does not make data quality redundant. As long as there is a need to make sound decisions based on information and insights, there will be a need for data quality and governance. Data quality function will adapt to the changing business needs and play a crucial role in building confidence