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Harnessing Big Data

The success of any analytics initiative depends on how much impact it brings to the business. It’s not about collecting raw data. It’s about turning it into actionable insights that drive increases in revenue, efficiency, and cost savings.


Former chief scientist at Amazon, Andreas Weigand, once said: “Data is the new oil... Oil needs to be refined before it can be useful.” A precious component - imperative to economies and residents alike, it has to be sourced, collected and managed before it reaches a valuable state. This, is a perfect illustration of data’s role in IT; just as the global industrial economy runs on oil, the information economy runs on data that has been refined and analysed for effective decision making, efficient business operations, and maximum business impact.


As you may be aware, it takes a lot of skills, expertise and knowledge to turn “raw” data into something useful. You have to access and collect, sort and prepare, discover and profile, transform and extract data of all types,  anytime, anywhere. This is why it takes 80% of a data scientist’s time to simply prepare the data for analysis. Whilst data can be rapidly collected, such scientists need to carefully extract and identify the most valuable data; carving out various indicators, anomalies and trends to feed back the most informative data values.


With the evolution of data, comes the evolution of innovation in the analytics sector. This can not only provide huge benefits to data scientists, but also poses some serious problems. With evolution comes complexity, and it is becoming a key issue which scientists in the sector are trying to overcome. The last several years have seen the rise of many new analytical computing platforms, ranging from analytics appliances to in-memory databases to agile business intelligence tools. These next-generation analytics solutions have evolved to address an ever-growing variety of data and an ever-widening range of analytics problems.


Given all this, how can you automate the work of data preparation so that you can get down to the business of analysis and decision making? How can you take advantage of all the technology innovations in big data analytics, without having to retrain your entire staff? How can you quickly move from a pilot project to production?


You need an information platform that supports all styles of analytics, including the latest flavors of big data technologies. A platform that understands the differences in the underlying analytical computing platforms and languages, so you don’t have to. Tapping into all types of data, rapidly discover insights, and innovate faster in the age of big data.


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