As 500 milhas de Indianápolis e a Análise de Dados2013 May 25
Neste post do Doug Laney ele mostra como as equipes de corrida participantes da Formula Indy utilizam a análise de dados para ajustes nos carros, bem como realizar predições relativas ao compostamento do carro com níveis de acurácia de 90%.
- Are you sufficiently monitoring key business processes, systems and personnel using available sensors and instrumentation?
- Are your data streams collected frequently enough for real-time process adjustments (i.e. complex event processing)?
- Do your business processes support real-time or near real-time inputs to adjust their operation or performance?
- Can you anticipate business process or system failures before they occur, or are you doing too much reactive maintenance?
- Do you centrally collect data about business function performance?
- Do you make use of advances in high-performance analytics such as in-memory databases, NoSQL databases, data warehouse appliances, etc.?
- Do you gather important external data (e.g. weather, economic) to supplement and integrate with your own data?
- Do you synchronize, align and integrate data that comes from different streams?
- Do you make your data available to key business partners, suppliers and customers to help them provide better products and services to you?
- Do you have a common, sophisticated analytics platform that includes the ability to establish new analytic functions, alerts, triggers, visualizations?
- Can you run simulations on business systems while they’re operating and also between events to adjust strategies?
- Does your architecture support multiple users around the world seeing real-time business performance simultaneously?
- Do you have teams of business experts, product/service experts and data scientists collaborating on making sense of the data?
- Do you modify your products or services as frequently as you could or should based on available data?
- Do you also use data you collect to develop new products or services as frequently as you could or should?
E uma motivação a mais para este tipo de raciocínio que emula a realidade das pistas é colocada pelo próprio Laney:
Racing teams are able to invest in advanced analytics because millions of dollars and euros are on the line from hundreds of sponsors. Hopefully your own big data project sponsors appreciate that big money is on the line for your business as well. Winning the race in your industry now probably depends on it.