While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.
Despite the advances in data integration and the blurring of the lines between batch (ETL) and real-time (EAI) integration, the process of managing and blending third-party or externally public datasets continues to be a burden for IT, and a cost for business, both in dollars and missed opportunities. Identifying and
If you didn’t already know, let me be the first to break the good news to you: data on the Indian data analytics sector is very encouraging. Today, India’s BI (Business Intelligence) and Data Analytics industry is worth a princely $10 billion and is expected to skyrocket to $26.9 billion
Where do I get started with data-driven engineering? How can the 3 I’s of data-driven engineering help me get off to a running start? How can I avoid the common pitfalls of data-driven engineering? What are the 3 I’s? The 3 I’s of data-driven engineering are insights, indicators and investments.
Want to innovate faster? Then it’s time to get your head into the cloud. The true value of the cloud software is not that it’s a cheaper alternative to hardware, but that it is a fast and flexible environment where innovation can flourish – for both traditional companies and companies
Big data breaches aren’t going away any time soon and the recent revelation that Russia’s criminal underworld is in possession of account data for 272.3 million people is a testament to that. Initially announced by Reuters at the start of May but constantly referred to by other media outlets ever
Every day, human beings create 2.5 quintillion bytes of data – data that is generated, accessed and shared on laptops, mobile devices and social media. In the political realm, data drives today’s campaigns – from studying voter demographics and conducting outreach to tailoring and testing specific messages. Data, Data, Everywhere
The “unreasonable effectiveness” of data for machine-learning applications has been widely debated over the years (see here, here and here). It has also been suggested that many major breakthroughs in the field of Artificial Intelligence have not been constrained by algorithmic advances but by the availability of high-quality datasets (see
Platforms used for big data are a bit of a conundrum. Big data and data science are two of the biggest business buzzwords, and the biggest companies around the world are hard at work to get ahead of the data curve. Normally, when it comes to big money opportunities, the
The rise of data science in the last decade has been driven by the ease of access to deep data and significant reductions in the costs associated with processing it. These days anyone with a credit card can now setup a cloud-based data warehouse and tracking system within minutes, but
Data lakes are based on a simple idea: You can store and analyze massive amounts of raw data at scale. But why data lakes? Here are five reasons why IT leaders are excited about this idea: Unfortunately, lots of companies end up with a data swamp instead of a data