Big Data hype or how not to get into a trap

The ‘big data’ matter has lately been widely discussed within the various businesses and industries, such as IT,  chemical, pharmaceutical, metallurgical, financial, oil and gas, consumer, high technology, retail etc. No wonder it even managed to create a significant hype, the promises of big data acquisitions are very tempting, but the failure rate is high as well.

We tried to analyze different researches regarding ‘big data’ and form our own opinion on the subject.

“Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it”

Let’s start with the definition of ‘big data’. ‘Big data’ is a term used for data sets so large or complex that traditional data processing software is unable to capture, curate, manage, and process data within a tolerable elapsed time. Challenges also include analysis, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.

Challenges or common mistakes in the area of big data usage, analytics and decision making:

Asking wrong questions. Data science is very complex and requires various sector specific skills and knowledge. Technology alone won’t solve the problem. In fact, most failed data analysis efforts derive from asking the wrong questions and using wrong data.

Solutions and suggestions to avoid getting in the big data failure:

The proliferation of so-called ‘big data’ and the increasing capability and reducing cost of technology are very seductive for retail financial services organisations seeking to improve their customer engagement and operational performance. But many simply do not appreciate the real costs – in terms of money and time – that burden ‘big’ approaches to big data programmes. And very few understand that the strength and quality of customer engagement bear little relation to the tools that have been bought. Rather than rushing into big data programmes, organisations need to invest in a ‘lean’ approach to data and analytics, which will align all business capabilities, including strategy, people, processes and technology, towards a more socially connected customer.

Big data. Time for a lean approach in financial services” by Deloitte

Oleksandr Topachevskyi

Oleksandr Topachevskyi

FOUNDER / HEALTH ECONOMIST

Oleksandr is a health economist with a strong background in economic evaluations of healthcare programmed and health economics modelling, epidemiology, statistics, patient reported outcomes data collection and software solutions development. From 2009 to 2014 he worked at in GlaxoSmithKline Vaccines HQ at a position of a global health economics manager.

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