The age of internet has brought banking at our finger tips. Traditional banking has undergone a major makeover. There is no more waiting in line to transfer, deposit or withdraw cash. Customers do not have to fill in lengthy forms to carry out a transaction. Online transactions offer the luxury of doing business even on holidays and weekends. Banking has been made much more convenient. But what exactly does this change mean for consumers? How safe are the data centers? With data thefts increasing at a rapid rate, how committed are the banks and insurance company to protect sensitive information?In February 2015, Anthem, the second largest health insurer in the US faced a tough time dealing with a cyberattack that resulted in the theft of personal information of more than 78 million existing and previous customers. The total cost of the breach is expected to cross $100 million. This is just one instance of how vulnerable today’s banking scenario is. Continue reading
Big Data has witnessed enormous growth in the recent past giving businesses a new window into valuable streams of information. The ability of Big Data to transform massive volumes of unstructured data in to valuable insights has helped explore various possibilities leading to better consumer experience across sectors.
Need for Big Data Risk Management
One of the most important concerns of several IT companies has been risk management. The data breach landscape also does not paint a pretty picture with statistics highlighting a staggering increase in the percentage of companies having experienced data breaches over the past two years. Despite security measures in place, proper monitoring and reporting of risks continue to be an issue urging companies to explore the potential profitability of Big Data in risk management. Predictive analysis in Big Data helps companies to assess and report potential risks well in advance. Data Architecture and IT systems have realized the importance of investing in sophisticated data management tools to monitor and manage a broad spectrum of risks. According to a report by Celent, a research body, spending on Big Data in risk management is estimated to grow from $470 million in 2014 to $730 million in 2016 as tools mature and firms deploy more enterprise wide solutions.. Some of the benefits of Big Data risk management is listed below:
- Rational and data-driven decisions help in eliminating risks as opposed to applying ones gut feeling to anticipate potential risks strengthening the security measures
- Reporting of potential risks help in taking counter measures help prevent the adverse effects of a financial crisis
- Assessment and evaluation of potential risk can be identified better using predictive analysis employed by Big Data risk management
- Advanced methods of Big Data Risk Management is pivotal in achieving sustainable business growth
- Specific tools and tailored defense mechanisms in big data risk management enables risk mitigation strategies at a much faster rate than traditional methods of risk management
The current rate at which data is created and captured in the IT industry calls for Big Data risk management. While there have always been apprehensions in the adoption of new technologies, the benefits outweigh the disadvantages as far as Big Data risk management is concerned. In an era of ever increasing security and compliance threats, the trend of Big Data risk management will not wither away for years to come.
Big Data may just be the next big thing given the considerable amount of buzz it is generating — In a 2012 survey of 209 North American and European companies, 77% of companies accepted that big data was important to them, about 19% of companies already had a live Big Data application, and a further 20% were slated to go live by end 2012.
But, even as businesses seek to improve their ROIs on it, Big Data advocates are pushing for greater interactions between organizations’ Master Data Management (MDM) systems and Big Data itself. Why?
While Big Data – data produced by the Web or Sensor Logs for example — is all about really high volumes, it is way too much for what current databases are geared to handle. Consider this, “a jet airliner generates a massive 20TB of diagnostic data an hour! In 2003, the largest data warehouse in the world was 30TB in size but, by late 2012, Teradata had 25 customers with petabyte-sized data warehouses, an increase of over 30 times in a decade.” Continue reading