The amount of traffic flowing over the internet has reached 667 exabytes annually. Big Data itself is the aggregate of very large and multiple data sources. Extracting usable, insightful information from Big Data is one of the more general purposes of analytics. However, it is not the availability of data to analyze that is the issue. Having the most modern, high-powered, and expansive technologies to mine lots of disparate data is. The traditional very costly, time consuming and specialist resource intensive approach to data mining, is to ask questions of the data that translates into queries. Current analytical tools require deep expertise in statistical modelling, coding, and scripting novel algorithms. Actionable insights are often difficult and expensive to extract, as data comes from multiple sources requiring advanced skills to join and analyze the data which must first be reduced to subsets. This no longer works in solving the world's biggest problems and an entirely new paradigm is needed. One that provides an alternative to queries and automatically discovers insights from big data without ever having to ask questions. This new approach allows you to discover those hidden insights in your big complex data without you or anyone else ever needing to write a single line of code or perform queries. And that's a really good thing, because by enabling you to look holistically at your data without queries, the analysis process can be radically simplified, accelerated and the high cost of analysis reduced. And most important of all, you get your hidden insight immediately so you can take action.
These query free analytic apps make it possible to automatically make discoveries from complex data, by enabling enterprises to optimize the end-to-end analytic workflow tied to complex and expensive business problems. Using state of the art machine learning and fuzzy logic techniques combined with a branch of mathematics called Topological Data Analysis (TDA), distinct patterns or anomalies within data can be discovered. Displayed as topological networks called dendragrams and clustergrams, TDA provides high-dimensional analysis of entire data sets. TDA highlights the underlying geometric shapes of data allowing real-time interaction to produce immediate insights.
Starting with unsupervised analysis, these apps can perform computation across hundreds to millions of attributes to automatically find similarity amongst data points and surface hidden patterns and anomalies in the data. Once the insights are found, data scientists, domain experts, and business people alike can find breakthroughs to their problems and drive impactful business ROI. The tools can also automate the integration across in-house and third party applications to fully optimize the end-to-end analytic workflow.
Query-free analytics benefits many industries and organizations. Here are just a few. In medicine, biological data is high-dimensional and complex with subtle substructures, and may include gene expression data, clinical data, and data from third party sources. Biologists, geneticists, and clinical data practitioners have a wealth of big data available to them. By analyzing this big data, TDA uncovers the hidden genetic causes for specific diseases. Profound insight can be derived to develop personalized treatment approaches and also be first to market with them.
In public sector, query-free analytics provides a new approach to navigate through big data to achieve high impact outcomes. For example you can analyze big data quickly and securely, including unstructured data, from social media, consumer hotlines, transcripts, emergency call data, and other sources. By joining and integrating these outputs, crises can be averted linked to communicable diseases and pandemics by identifying signs which show up as red flags, of impending epidemic. Even video data can be analysed to transform the management of public places and optimize crowd management and improve safety and experience.
In sport, hundreds of statistics and data are gathered on athletes, sportsmen and women including tennis players and teams including soccer, rugby and cricket. This includes standard performance statistics to vector based analyses of player movement while in formula 1, racing cars radio big data back to the pits and often to the teams' headquarters. Query-free analytics is scalable and can analyze all of that big data to help sports organizations and clubs build successful teams, manage individuals' development and improve scouting. In Formula 1 for example, this is absolutely critical to a driver getting his or her car onto the pole position or being first to see the chequered flag.
When will query-free analytics technologies be available? You may be surprised to learn that they're actually available now! But only from a handful of vendors. Are any companies using them? Yes GE, Boeing, Merck and others too.