I always make the point that data is everywhere.
Big data is often boiled down to a few varieties including social data, machine data, and transactional data. Social media data is providing remarkable insights to companies on consumer behavior and sentiment that can be integrated with CRM data for analysis, with 230 million tweets posted on Twitter per day, 2.7 billion Likes and comments added to Facebook every day, and 60 hours of video uploaded to YouTube every minute (this is what we mean by velocity of data).
Machine data consists of information generated from industrial equipment, real-time data from sensors that track parts and monitor machinery (often also called the Internet of Things), and even web logs that track user behavior online. At arcplan client CERN, the largest particle physics research center in the world, the Large Hadron Collider (LHC) generates 40 terabytes of data every second during experiments. Regarding transactional data, large retailers and even B2B companies can generate multitudes of data on a regular basis considering that their transactions consist of one or many items, product IDs, prices, payment information, manufacturer and distributor data, and much more. Major retailers like Amazon.com, which posted $10B in sales in Q3 2011, and restaurants like US pizza chain Domino’s, which serves over 1 million customers per day, are generating petabytes of transactional big data.The thing to note is that big data can resemble traditional structured data or unstructured, high frequency information.
In defining big data, it’s also important to understand the mix of unstructured and multi-structured data that comprises the volume of information.
Unstructured data comes from information that is not organized or easily interpreted by traditional databases or data models, and typically, it’s text-heavy. Metadata, Twitter tweets, and other social media posts are good examples of unstructured data.
Multi-structured data refers to a variety of data formats and types and can be derived from interactions between people and machines, such as web applications or social networks. A great example is web log data, which includes a combination of text and visual images along with structured data like form or transactional information. As digital disruption transforms communication and interaction channels—and as marketers enhance the customer experience across devices, web properties, face-to-face interactions and social platforms—multi-structured data will continue to evolve.
Industry leaders like the global analyst firm Gartner use phrases like “volume” (the amount of data), “velocity” (the speed of information generated and flowing into the enterprise) and “variety” (the kind of data available) to begin to frame the big data discussion. Others have focused on additional V’s, such as big data’s “veracity” and “value.”
One thing is clear: Every enterprise needs to fully understand big data – what it is to them, what is does for them, what it means to them –and the potential of data-driven marketing, starting today. Don’t wait. Waiting will only delay the inevitable and make it even more difficult to unravel the confusion.
Once you start tackling big data, you’ll learn what you don’t know, and you’ll be inspired to take steps to resolve any problems. Best of all, you can use the insights you gather at each step along the way to start improving your customer engagement strategies; that way, you’ll put big data marketing to work and immediately add more value to both your offline and online interactions.
There are some of many sources of BigData:
- Sensors/meters and activity records from electronic devices:These kind of information is produced on real-time, the number and periodicity of observations of the observations will be variable, sometimes it will depend of a lap of time, on others of the occurrence of some event (per example a car passing by the vision angle of a camera) and in others will depend of manual manipulation (from an strict point of view it will be the same that the occurrence of an event). Quality of this kind of source depends mostly of the capacity of the sensor to take accurate measurements in the way it is expected.
- Social interactions: Is data produced by human interactions through a network, like Internet. The most common is the data produced in social networks. This kind of data implies qualitative and quantitative aspects which are of some interest to be measured. Quantitative aspects are easier to measure tan qualitative aspects, first ones implies counting number of observations grouped by geographical or temporal characteristics, while the quality of the second ones mostly relies on the accuracy of the algorithms applied to extract the meaning of the contents which are commonly found as unstructured text written in natural language, examples of analysis that are made from this data are sentiment analysis, trend topics analysis, etc.;
- Business transactions: Data produced as a result of business activities can be recorded in structured or unstructured databases. When recorded on structured data bases the most common problem to analyze that information and get statistical indicators is the big volume of information and the periodicity of its production because sometimes these data is produced at a very fast pace, thousands of records can be produced in a second when big companies like supermarket chains are recording their sales. But these kind of data is not always produced in formats that can be directly stored in relational databases, an electronic invoice is an example of this case of source, it has more or less an structure but if we need to put the data that it contains in a relational database, we will need to apply some process to distribute that data on different tables (in order to normalize the data accordingly with the relational database theory), and maybe is not in plain text (could be a picture, a PDF, Excel record, etc.), one problem that we could have here is that the process needs time and as previously said, data maybe is being produced too fast, so we would need to have different strategies to use the data, processing it as it is without putting it on a relational database, discarding some observations (which criteria?), using parallel processing, etc. Quality of information produced from business transactions is tightly related to the capacity to get representative observations and to process them;
- Electronic Files: These refers to unstructured documents, statically or dynamically produced which are stored or published as electronic files, like Internet pages, videos, audios, PDF files, etc. They can have contents of special interest but are difficult to extract, different techniques could be used, like text mining, pattern recognition, and so on. Quality of our measurements will mostly rely on the capacity to extract and correctly interpret all the representative information from those documents;
- Broadcastings: Mainly referred to video and audio produced on real time, getting statistical data from the contents of this kind of electronic data by now is too complex and implies big computational and communications power, once solved the problems of converting “digital-analog” contents to “digital-data” contents we will have similar complications to process it like the ones that we can find on social interactions.
What kind of datasets are considered big data?
The uses of big data are almost as varied as they are large. Prominent examples you’re probably already familiar with including social media network analyzing their members’ data to learn more about them and connect them with content and advertising relevant to their interests, or search engines looking at the relationship between queries and results to give better answers to users’ questions.
But the potential uses go much further! Two of the largest sources of data in large quantities are transactional data, including everything from stock prices to bank data to individual merchants’ purchase histories; and sensor data, much of it coming from what is commonly referred to as the Internet of Things (IoT). This sensor data might be anything from measurements taken from robots on the manufacturing line of an auto maker, to location data on a cell phone network, to instantaneous electrical usage in homes and businesses, to passenger boarding information taken on a transit system.
By analyzing this data, organizations are able to learn trends about the data they are measuring, as well as the people generating this data. The hope for this big data analysis are to provide more customized service and increased efficiencies in whatever industry the data is collected from.