Datameer

The 2012 U.S. Women’s Olympic cycling team was looking for a competitive edge after a disappointing finish in the World Championships. They turned to Olympic cyclist Sky Christopherson, who had used the quantified-self movement in his training to break a world record. Christopherson established an experimental project to help the team record and analyze relevant data that could reveal actionable insights for optimizing their athletic performance. The team faced the challenge of recording relevant data, integrating it, analyzing it, and visualizing all of these data points to reveal insights they could incorporate into training. The sheer amount of data, and with each device producing different types of data (often in unstructured formats) meant that traditional database and business intelligence technologies were not an option.

The Detroit Crime Commission (DCC) was created to combat the high crime rates in Detroit, which have been exacerbated by severe budget cuts to law enforcement agencies. The DCC's main effort has been to identify individuals known to be engaged in dangerous criminal activities. They compiled terabytes of proprietary and public crime-related data related to these individuals' activities. However, they needed a way to quickly and easily aggregate and analyze this data to identify and prevent ongoing or planned criminal activity. The DCC had tried other data collection and analysis tools but found them lacking. These tools were only good at collecting a sample of the data, and they did not perform the relevant analyses needed by the Commission staff.

Vivint, the largest home automation company in North America, was facing a challenge with their analytics infrastructure platform, Hadoop. The team was spending too much time on mundane, technical tasks preparing and integrating the data rather than doing actual, value-added analysis. They were looking for a solution that could make their staff more efficient and seamlessly integrate with their Hadoop analytics platform. Another key consideration was the ability to integrate and analyze not just row data but also streaming data, which is a key component to their smart home analytics solution to big data for Internet of Things.

Sophos, a company that has been producing antivirus and encryption products for nearly 30 years, was facing a challenge with the increasing complexity of IT networks and the sophistication of threats and attacks. The company's products examine billions of events per day to detect malicious files, with over 300,000 new potentially malicious files reported to SophosLabs daily for analysis. The volume and complexity of the data grew to a point where their old analytic infrastructure could not keep pace. Another challenge was the cloud telemetry data consisting of billions of lookups for website and file information. A particular aspect of the analysis – correlating patterns across previous analysis – had become too complex for their SQL-based database and analytic tools to manage. Sophos investigated NoSQL technologies available at the time and selected Hadoop for big data analytics needs related to telemetry and threat correlation. However, out-of-the-box Hadoop was lacking any enterprise-ready tools for creating analytic reports, dashboards, data access controls or mechanisms to easily import or export data in and out of various storage systems.

Yapı Kredi, the fourth largest private bank in Turkey, wanted to become a more data-driven company to increase business agility, reduce operating expenses, and improve the overall customer experience. However, they faced the challenge of deriving value from their vast amount of data, most of which was structured and stored in a traditional relational data warehouse. Their traditional business intelligence tools were too inflexible and forced a waterfall approach, which was time and resource-consuming. The rigid data schemas required before moving to the analysis step every time made the process laborious and slow. Yapı Kredi needed a more agile toolset for the iterative process of data discovery that’s important for any analysis.

Cancer diagnosis is complicated due to the uniqueness of each case, and treatment outcomes vary greatly from patient to patient. DKFZ, the largest biomedical research Institute in Germany, is working to understand the mechanisms of cancer, identify risk factors, and find new ways to prevent people from getting cancer. A key focus of DKFZ’s medical researchers is genomic data research. However, due to the massive volumes of genomic data involved in this research, DKFZ faced huge challenges on the data and analytics front. Their analytic systems were overwhelmed by many petabytes of data, and analyzing an entire patient data set took weeks and even months to complete. These huge bottlenecks greatly slowed research and frustrated staff.

SHOEPASSION.com had disparate systems running different parts of its business operations, which was hindering their aggressive expansion plans. With data in silos from their ecommerce, analytics, and ERP systems, they wanted to add in data from Google Ads, Google reports and docs, and excel spreadsheets to gain better customer and operational insights. On the marketing side, SHOEPASSION.com wanted to increase the yield in marketing activities by analyzing customer orders, revenue, and churn. It was also important to analyze their costs and ROI from various marketing channels. On the operational side, SHOEPASSION.com faced challenges in managing product inventory, distribution, and delivery. To minimize their costs, they needed to keep the minimal level of inventory to satisfy their customer demands in the various geographies.