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Big Data

Every day, 2.5 quintillion bytes of data are created. This data comes from digital pictures, call recordings, posts to social media sites, intelligent sensors, purchase transaction records, cell phone GPS signals, website tracking data, RFID, Machine to Machine data, text, geo-spatial to name a few. This is Big Data.

Big data is defined as "high Volume, Velocity and/or Variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision- making, and process automation."
Volume is an issue when the quantity of information to be processed exceeds the capacity of the existing technology to do so. Velocity also raises a number of key issues. For a start, the speed at which data is flowing into most organizations is increasing beyond the capacity of their IT systems to store, process, analyse, decide and act upon. Finally, Big Data is characterised by its Variety; significant business value can be derived by combining data sets of one type with data sets of another type.All of this means that a great deal of effort is required in complex pre-processing and data cleansing before any meaningful analysis can be carried out.

There is a great interest in all kind of sectors: in commercial, in public and in the research communities. Analyzing Big Data becomes a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus.

Big Data provides opportunities for business users to ask questions they never were able to ask before. How can a financial organization find better ways to detect fraud? How can an insurance company gain a deeper insight into its customers to see who may be the least economical to insure? How does a Telco find its most at-risk customers—those who are about to churn? They need to integrate Big Data techniques with their current enterprise data to gain that competitive advantage.

Many organizations lack the skills required to exploit Big Data — this is pointed out in the entry for the Data Scientist, which is an emerging role encompassing a wide range of skills. These include: Skills to work with business stakeholders to understand the business issue and context. Analytical and decision modeling skills for discovering relationships within data and proposing patterns. Data management skills are required to build the relevant dataset used for the analysis.

To conclude, it is important to remember that the only reason to pursue any Big Data initiative is to deliver against a business objective. Cronos is in an unique position to help the customer to ask the right questions and use the right technologies to get to the answers.

Cronos Big Data Services Offering

Big data is more than just the size of your dataset; it's an opportunity to find new insights and change your business. Big Data can help organizations become more agile and find answer to new and unknown questions. Imagine if you had the ability to lose all assumptions about your data and could think outside the box for new and emerging results from your data.

As the leading e-business integrator on the Benelux market our consultants have the experience and expertise required to support customers during all phases of a Big Data project, including

Use Case Discovery Workshops

The use case workshop is an organized brain-storming meeting. The group needs to contain people with different background, knowledge and experience. Business requirements are gathered and these serves as input for use case modeling. Deliverable is a Vision Document defining the stakeholders view of the product to be developed specified in key needs and features.

Proof of Concepts

With our industry leading experience in conventional BI systems and emerging big data technologies we define the architecture and blueprints to ensure the proper alignment of architecture components to deliver the foundation for enterprise information need. We transform the strategy into blueprint including how to optimally acquire, process and store data for large-scale experimentation and visualization for business consumption. Our architecture encompasses both, the emerging big data technologies and existing BI systems to empower each other’s capabilities and in turn elevate the overall capabilities.

Implementation Services

Typically, big data projects start with a specific use-case and data set. Over the course of implementations, we have observed that organization needs evolve as they understand the data – once they touch and feel and start harnessing its potential value. Use agile and iterative implementation techniques that deliver quick solutions based on current needs instead of a big bang application development. When it comes to the practicalities of big data analytics, the best practice is to start small by identifying specific, high-value opportunities, while not losing site of the big picture.

Big Data Analytics

Defining an architecture for big data and implementing it to store and retrieve data is just one side of the big data puzzle. Although great benefits can be gained from this, the other side of the coin (and the reason for excitement around big data) is the possibility of gaining deep insights into one’s business using exploratory and predictive analytics. Our team of data scientists can help the customer  with building analytical models for problems such as recommendation engines, fraud detection and customer churn prediction.