Measuring consumer sentiment, optimising supply chains, detecting fraud – Big Data is powerful. But to harness that power, organisations must hire data scientists, craft complex algorithms, and make massive investments in infrastructure and software. That leaves business leaders and the IT professionals supporting them wondering: Is it possible to make Big Data useful for business users?

Making Big Data Relevant for Business Users

Big Data’s value can be unleashed for business users by condensing it and intelligently presenting only what is relevant and contextual to the problem at hand. For example, an executive might be interested in summary data across the company’s product lines, while a manager of a specific product or geography might need more detail, but only for the areas that he or she oversees. In certain instances, such as a customer support manager who needs to investigate one specific customer’s call records, it may be necessary to view the raw data. IT professionals are challenged with not only providing the infrastructure but also to help provide meaning to the Big Data.

Tame Big Data with QlikView

  • Consolidate relevant data from multiple sources, including Big Data repositories
  • Choose the method most relevant for you and your IT infrastructure
  • Leverage existing investments in big data infrastructure or data warehouses
  • Access Big Data without complex data modeling or programming
  • Explore associations between Big Data and traditional data
  • Visualise Big Data with engaging, state-of-the-art graphics
  • Access and analyse Big Data from mobile devices
  • Enable social decision-making through real-time collaboration

QlikView Offers Choices

Big Data is a relative term and the data requirements of every organisation is different. QlikView offers two approaches to handling Big Data that both deliver the same great user experience. Switching between the two is a trivial task so customers are not locked into either approach.

Analyse Big Data with QlikView’s 100% In-Memory Architecture

QlikView’s patented in-memory data engine compresses typical data by a factor of 10, meaning a single 256GB RAM server can load upwards of 2TB of uncompressed data. This represents billions of rows while offering response rates only possible with in-memory architectures. Other QlikView features such as document chaining and binary load further accelerate the exploration of very large data sets. This is the path chosen by many QlikView customers to analyse terabytes of data stored in data warehouses or Hadoop clusters and similar repositories.

QlikView Direct Discovery does three things:
  • Query data from Big Data repositories on-the-fly
  • Cache query results in memory for faster recall
  • Maintain associations among all the data, regardless of where it is located
This hybrid approach delivers these business user benefits:
  • Tapping into Big Data without knowledge of programming
  • Adding meaning and context to Big Data
  • Drilling down to granular details when necessary