My Data Science journey starts at CERN where I finished my master thesis in 2009. CERN, the European Organization for Nuclear Research, is the home of the Large Hadron Collider (LHC) and has some questions to answer: like how the universe works and what is it made of. CERN collects nearly unbelievable amounts of data – 35 petabytes of data per year that needs analysis. After submitted my thesis, I continued my Data Science research at CERN.
Today companies have realized that Business Analytics needs to be an essential part of their competitive strategy. The demand on Data Scientists grows exponentially. To me, Data Science is more about the right questions being asked than the actual data. The MBA enabled me to understand that data does not provide insights unless appropriately questioned. Delivering excellent Big Data projects requires a full understanding of the business, developing the questions, distilling the adequate amount of data to answer those questions and communicating the proposed solution to the target audience.
“The task of leaders is to simplify. You should be able to explain where you have to go in two minutes.” – Jeroen van der Veer, former CEO of Royal Dutch Shell
How about some visual takeaways from the IMF’s World Economic Outlook? Recently I prepared two nifty data visualizations with Tableau that I like to share with you.
These visualizations allow you to explore plenty of economical data, including IMF staff estimates until 2020. Don’t forget to choose “Units” after switching “Subject” on the right-side bar. A detailed description on each subject is displayed below.
There are four key issues to overcome if you want to tame Big Data: volume (quantity of data), variety (different forms of data), velocity (how fast the data is generated and processed) and veracity (variation in quality of data). You have to be able to deal with lots and lots, of all kinds of data, moving really quickly.
That is why Big Data Analytics has a huge impact on how we plan CERN’s overall technology strategy as well as specific strategies for High-Energy Physics analysis. We want to profit from our data investment and extract the knowledge. This has to be done in a proactive, predictive and intelligent way.
The following presentation shows you how we use Big Data Analytics to improve the operation of the Large Hardron Collider.
The raw data from the experiments is stored in structured files (using CERN’s ROOT Framework), which are better suited to physics analysis. Transactional relational databases (Oracle 11g with Real Application Clusters) store metadata information that is used to manage that raw data. For metadata residing on the Oracle Database, Oracle TimesTen serves as an in-memory cache database. The raw data is analysed on PROOF (Parallel ROOT Facility) clusters. Hadoop Distributed File System (HDFS), however, is used to store the monitoring data.
Just as in the CERN example, there are some significant trends in Big Data Analytics:
Descriptive Analytics, such as standard business reports, dashboards and data visualization, have been widely used for some time, and are the core applications of traditional Business Intelligence. This ad hoc analysis looks at the static past and reveal what has occurred. One recent trend, however, is to include the findings from Predictive Analytics, such as forecasts of sales on the dashboard.
Predictive Analytics identify trends, spot weaknesses or determine conditions for making decisions about the future. The methods for Predictive Analytics such as machine learning, predictive modeling, text mining, neural networks and statistical analysis have existed for some time. Software products such as SAS Enterprise Miner have made these methods much easier to use.
Discovery Analytics is the ability to analyse new data sources. This creates additional opportunities for insights and is especially important for organizations with massive amounts of various data.
Prescriptive Analytics suggests what to do and can identify optimal solutions, often for the allocation of scarce resources. Prescriptive Analytics has been researched at CERN for a long time but is now finding wider use in practice.
Semantic Analytics suggests what you are looking for and provides a richer response, bringing some human level into Analytics that we have not necessarily been getting out of raw data streams before.
As these trends bear fruit, new ecosystems and markets are being created for broad cross-enterprise Big Data Analytics. Use cases like the CERN’s LHC experiments provide us with greater insight into how important Big Data Analytics is in the scientific community as well as to businesses.
About a year ago, I had a first try with Tableau and some survey data for a university project. Last week, I finally found time to test Tableau with High Energy Physics (HEP) data from CERN’s Proton Synchrotron (PS). Tableau enjoys a stellar reputation among the data visualization community, while the HEP community heavily uses Gnuplot and Python.
I was using an ordinary CSV file as data source for this quick visualization. Furthermore, Tableau can connect to other file types such as Excel, as well as to databases like Microsoft SQL Server, Oracle, and Postgres.
I’m also quite impressed by the ease and speed with which insightful analysis seems to appear out of bland data. Even though your analysis toolchain is script-based (as usual at CERN where batch processing is mandatory), I highly recommend using Tableau for prototyping and for ad-hoc data exploration.
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