Quantifying the Impact of Self-Service Analytics

Much has been made about the promise and business value that self-service analytics solutions can provide. By removing the onus from IT of constantly responding to analytics requests from the line-of-business and empowering individual business users to perform the analysis they need, self-service analytics can often present an attractive investment. However, influencers seeking to make investments in such solutions find difficulty in quantifying downstream business benefits. To facilitate this evaluation process, Blue Hill Research provides the following framework for assessing the relevant value that such investments might have on their organization. In building this framework, Blue Hill Research draws upon deep qualitative and quantitative interviews with 21 end-user organizations across a range of size and industry.
Get Whitepaper

2015 Gartner Magic Quadrant for ITSSM

IT service support management tools are vital for infrastructure and operations organizations to manage support and delivery of IT services. This Magic Quadrant research profiles key vendors of enterprise ITSSM tools to help I&O leaders make better selections.
Get Whitepaper

Enhancing Your Application with Mobile BI – Five Easy Steps

Business intelligence is a technology that has held a spot within the consciousness of nearly all IT department decision-makers and professionals for the past several decades. More recently, as innovations have given rise to mobile technology in the enterprise sector, software and applications developers are looking to include mobile BI functionality into their existing offerings.
Get Whitepaper

Data Visualization: When Data Speaks Business

See why organizations are increasingly recognizing that to be competitive, they need to be data-driven, for which they need not only strong analytics and BI capabilities at all decision levels, but also an effective way to transform data into information and ensure its optimal delivery.
Get Whitepaper

Follow the Money: Big Data ROI and Inline Analytics

Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
Get Whitepaper