The goal of pervasive BI applications is to take the data that produced the back office ROI of more than 400% and deliver it to front-line employees in a form appropriate to their job functions – with similar results. There are thousands of business process steps in a typical enterprise where pervasive BI insights can be added. What are leading companies doing with pervasive BI?
Often, the insurance underwriters stay late that last day of the month to enter new policies. This means that IT staff have a very small window to execute the applications critical to successful and error-free closing of the accounting books. IT staff had to run and baby sit the applications – one application requiring manual operation took over three hours to complete, another that uploads premium and claim information to the data warehouse took up to six hours.
No matter the vintage or sophistication of your organization’s data warehouse (DW) and the environment around it, it probably needs to be modernized in one or more ways. That’s because DWs and requirements for them continue to evolve. Many users need to get caught up by realigning the DW environment with new business requirements and technology challenges. Once caught up, they need a strategy for continuous modernization.
Published By: Pentaho
Published Date: Jan 16, 2015
If you’re considering a big data project, this whitepaper provides an overview of current common use cases for big data, from entry-level to more complex. You’ll get an in-depth look at some of the most common, including data warehouse optimization, streamlined data refinery, monetizing your data, and getting a 360 degree view of your customer. For each, you’ll discover why companies are investing in them, what the projects look like, and key project considerations, including tools and platforms.
Published By: Matillion
Published Date: May 12, 2011
This whitepaper explains the importance of Business Intelligence to midsize companies, examines the challenges found in BI projects and explains how to be successful in improving visibility in your business.
Good analysis and benchmarking of hotline data helps organisations answer crucial questions about their ethics and compliance programme.
Comparing internal data year over year to help answer these questions is important. But getting a broader perspective on how your performance matches up to industry norms is critical. To help, each year NAVEX Global takes anonymised data collected through our hotline and incident management systems to create these reports. This particular report is the second NAVEX Global benchmark report we have published that focuses specifically on the status of ethics and compliance hotline services in the EMEA and APAC regions. This benchmark only takes reporting data from organisations that has its data warehoused in Europe—a subset of the data used in our global hotline report.
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications.
Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios.
Since starting to work with this technology
In today’s competitive on-line world, the speed of change in customer behaviour is increasing. In addition, in industries such as retail banking, car insurance and to some extent retail, the Internet has become the dominant way in which customers interact with an organisation.
Yet in many data warehouses today, being able to analyse customer on-line behaviour is often not possible because the clickstream web log data needed to do this is missing. It is a key point because customer access to the web has made loyalty cheap.
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse.
The Accenture Oracle data team has more than 20,000 professionals, who aid in delivering 50 billion transactions a day across more than three exabytes of data for clients globally. Accenture Oracle data specialists recently put the Oracle Autonomous Data Warehouse to a rigorous performance test to provide a real-life application usage experience. The data was then extrapolated and expanded to nine years’ worth of data to test the performance.
Learn directly from Accenture experts about testing methodology and results that enable them to deliver more data intelligence faster to the enterprise and transform the way people live and work.