Analytics projects range in scale and complexity, but every analyst eventually runs into the same problem. Regardless of the kind of data you’re working with or the size of the project, from a mid-sized manufacturer to a project that truly is Big Data, businesses look to analytics and BI tools because the scope and complexity of the data is obscuring critical information. Organizations spend a great deal of time and resources creating models and running queries trying to get a more complete picture of the data set. BI and visualization tools give a visual representation of the data and make the search process faster and more efficient.
Regardless of the tools that are available or the problems that need to be solved, what Big Data is missing is the bigger picture. Data sets don’t come with road maps, so a good deal of every analytics project is data exploration. Each query tells you a little bit more about the data, filling that picture in bit by bit until you find what you are looking for. It’s possible that a combination of luck and a good understanding of your data will get you to an answer quickly, but more often than not much digging is done before results emerge. Modern tools help the user to ask questions faster, but they don’t tell you where to search. The true costs of that searching are difficult to calculate, but recent news stories claim that many organizations are not realizing the expected returns from their Big Data projects.
Modern tools help the user to ask questions faster, but they don’t tell you where to search. The true costs of that searching are difficult to calculate…
In a perfect world, every data set would come with a map outlining the most relevant sections and isolating the noisy and disconnected data that obscure meaning. Instead, most recent advancements in analytics have simply improved our ability to ask more questions of the data. Companies enlist hundreds of data scientists and invest in multiple tools to dig through data for valuable insights, but asking more questions faster is a brute force approach.
The future of analysis of big and complex data sets is going to be when that exploratory step is automated, allowing analysts and data scientists to spend less time exploring data and more time interpreting answers. Emcien’s pattern detection engine is on the cutting edge of this concept. With over a decade of applying graph analytics algorithms to complex business problems, Emcien now offers a full suite of analytics solutions powered by the pattern detection engine. Organizations using pattern detection begin their analysis with a ranked list of relevant and interesting patterns automatically calculated, outlining where to look for strong connections in the data or where the data is very disconnected or noisy.
Beginning each project with a ranked list of the patterns that make up your data is a novel approach, but once analysts see automatic pattern detection in practice the advantages are clear. Time once spent in an open-ended search can then be spent putting real and valuable insights to work achieving analytics success.