Melbourne, Australia-based Yellowfin aims to ease the chore of analytics and remove analysis fatigue from the world of big data. The company has introduced Yellowfin Signals, a platform designed to automatically discover and surface the most important changes in data, in real time. Related: Business Intelligence as a Managed Service: Ripe Opportunity? One of the biggest challenges facing businesses today is how to interpret critical data and bring forth insights from all of the noise. Yellowfin has recognized the shortcomings of traditional BI dashboards and reporting systems, which often fail to identify the relevance of certain data feeds and apply those to projects at hand.
Data is the new language today. Data leads to insights, and insights help organizations to make actionable business decisions. However, sourcing the data and preparing it for the analysis is one of the tedious tasks organizations face these days. Analysts devote a lot of time in searching and gathering the right data. According to a research firm, analysts spend around 60 to 80 percent of their time on data preparation instead of analysis.
Organizations of all sizes and industries strive to be more data-driven, but with a worldwide shortage of trained data scientists, these businesses are empowering their business analysts and other domain information experts with the tools and support they need to become citizen data scientists. By doing so they are creating what is arguably the most exciting and critical role in organizations today. Citizen data scientists typically reside in a line of business such as Sales, Marketing, Finance, or HR, and have deep domain knowledge of the business challenges their department faces. Armed with powerful software, they can perform detailed diagnostic analysis and create analytic models that leverage predictive analytics to supplement the work that previously required a data scientist, statistician, or other analytics experts. Citizen data scientists work in tandem with data scientists in most artificial intelligence (AI)-driven organizations.
The business intelligence (BI) assembly line is broken, with adoption or utilization rates of only 30 percent in a typical organization, according to Gartner. These adoption rates include all the users of the BI system – administrators who manage the system, analysts who build reports, and business users who consume reports for better decision making. This underutilization ignores employees outside the BI system who could be using valuable data to make better decisions. No one can argue that business performance and innovation are negatively impacted when data assets are not fully leveraged for decision making. How does this BI assembly line contribute to poor adoption?
Many definitions on the topic of big data focus on a bottom-up view, using the 3 Vs of the data -- volume, variety & velocity. You may start with a general question, one your traditional descriptive analytics has revealed. Big data analytics lets you explore the deeper diagnostic questions -- some of which you may not have thought about asking -- to reveal a new level of insight and identify steps to take to improve business performance. Many organizations have spent years generating descriptive analytics -- answering what happened questions. This information is valuable, but only provides a high-level, rearview mirror view of the business performance.