Data Management & Analysis at LinkedIn
LinkedIn is the world’s largest professional network. It has over 187 million members from over 200 countries. Its members include everyone from freelancers to CEOs of Fortune 500 companies. The company started out from California Mountain View in 2003 with the mission to connect the world’s professionals and it surely has achieved that in the last 10 years.
Today LinkedIn earns $252 million in revenues every year and employs over 3200 people worldwide. It has become the go to resource for HR executives whenever they have to look for someone to fill up a position. The profiles of members are their online resume that every employer can see and access. It also provides opportunities for people to connect with the right persons to take their career ahead.
All this is possible because of data collection and management. All information provided by a member in their profile is collected, analyzed and sorted so that whenever anyone wants to access it, they can do so quickly and effortlessly. This data management enables LinkedIn to provide hiring solutions, marketing solutions and networking opportunities to its members.
Not only is the data invaluable to employers but individuals too can use it to search for talent matches, similar jobs, interesting events and networking opportunities. This huge amount of data also allows LinkedIn to customize products through out the world.
LinkedIn uses data scientists to analyze the data collected by it so that they can rapidly make some sense out of it and use it to recognize opportunities and take advantage of these opportunities. These data scientists are usually qualified to analyze data and statistics and also need business skills and knowledge to make sense of this data.
LinkedIn’s success can be attributed to its decision to develop its own data management application. The company used market solutions and customized them for their own particular use to collect, sort and analyze data. It stores data online using Oracle and Expresso. It uses services such as Voldemort, Zoie, Bobo, Sensei, D-Graph, Kafka and Databus. The offline data store uses Hadoop for machine learning, ranking & relevance, Teradata etc. It also uses MapReduce Analytics, Clickstream for A/B site testing etc.
Corporations and business use LinkedIn to search for people to fill up key positions and people with social influence to test new products. Analyzing viral marketing results and recommendation engine optimization are two other services that LinkedIn offers to businesses. It helps create specialized marketing services for different businesses.
LinkedIn’s value creation is based on this data management and making their analysis of the data to key players in a short amount of time. As long as they have the ability to analyze and manage all this data, they’ll continue to grow and market new and customized products. In order to maintain their edge LinkedIn needs to find ways to handle this ever growing stream of data and also improve the quality of their data analysis.
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