Predictive Analytics on Big Data
Four elements are important for developing advanced analytical capabilities
Data, Tools, People and Intent
Companies need a strategic plan for collecting and organizing data, one that aligns with the business strategy of how they will use that data to create value. In our analytics survey, 56% of the companies didn’t have the right systems to capture the data they needed or weren’t collecting useful data, and 66% lacked the right technology to store and access data. A good data policy identifies relevant data sources and builds a data view on the business in order to—and this is the critical part—differentiate your company’s analytics capabilities and perspective from competitors. A critical aspect of good data policy is to focus on identifying relevant sources of data. For example, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends; however, keeping detailed logs of requests that were easily handled might be less valuable.
Advanced analytics and Big Data tools are developing so rapidly. Tools and platforms like Hadoop, Spark and NoSQL are providing insights and statistical novelties in ways that were not possible even as recently as a year ago. These analytical visualization and data management tools are provided by a wide range of vendors and an even larger community of open-source community.
Successful analytics teams build capabilities by blending data, technical and business talent. Think of a band as the model: a team with different but overlapping skills that knows how to effectively and efficiently communicate and collaborate. Successful Big Data and analytics efforts need:
- Data scientists: Data scientists who provide expertise in statistical, mathematical, predictive modelling as well as business strategy skills to build the algorithms necessary to ask the right questions and find the right answers
- Data Engineer: Data Engineer who who understands how to apply technologies to solve big data problems and to develop innovative big data solutions. Expert knowledge building data processing systems with Hadoop and Hive using Java or Python should be common knowledge to the big data engineer;
- Technical specialists: Technical specialists who help manage the hardware and software solutions needed to collect, clean and process the data;
- Business analysts: Business analysts who identify and prioritize the problems worth solving and the business relevance of data anomalies and patterns identified by the data scientists;
Companies need a strategic plan for collecting and organizing data, one that aligns with the business strategy of how they will use that data to create value. A good data policy identifies relevant data sources and builds a data view on the business in order to—and this is the critical part—differentiate your company’s analytics capabilities and perspective from competitors. A critical aspect of good data policy is to focus on identifying relevant sources of data. For example, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends; however, keeping detailed logs of requests that were easily handled might be less valuable.
Why you should work with us
Data Engineering
Vivomente builds Big Data applications and solutions based on Hadoop, Spark, Kafka, NoSQL and other leading platforms. We can help with:
- Device behavioural data for improved customer service and proactive maintenance
- Detailed parametric and test data to improve manufacturing yield
- Improved insight into consumer behavioural data
- Adoption of Analytics as a competitive weapon
Analytics on Spark and Hadoop
Everyone seems to be talking about Apache Spark for big data analytics on Hadoop. However, while Spark has plenty of buzz, there are still growing pains around this nascent technology. To succeed in using Spark for analytics, you’ll need to work with a partner that has the right skills, experience, and approach to make your implementation a success.
Data Science on Spark
Vivomente specializes in big and high velocity data using our experienced consultants to provide predictive analytics for clickstream, Internet of Things, customer insights, content recommendations and more.
A PROVEN APPROACH
Collaboration, agility and creativity – we’re focused on helping you identify high-value use cases that help you take the lead or close the gap with competitors