About eighty percent of the data in your enterprise is in a multi-structured format, and that means it’s not in a relational format and is hard to analyze. Big data implies different analytics, different data structures, and diversity to the analytics that we need to deliver.
Big data generates value from very large data sets that cannot be analyzed with traditional computing techniques. Organized, it can help many organizations to understand people better and allocate resources more effectively. It is moving from customer transactions to customer interactions and not just understanding the transactional data (such as what they buy, when they bought it, how much they paid for it, what’s the demographic, where they live, what’s the frequency) and enhancing that into all the interactions that we’ve had with a customer whether it be on our website, mobile device or other aspects.
Big data analytics is a knowledge management process and an extension to the analytics and the applications that can be delivered. This enables companies to extend their traditional data warehouse, lots of data that can be structured, and extend that into a new world of sensor data, machine data, spatial data, and weblog data to get a better understanding of the business.
Where does multi-structure data come from?
Various sources feed multi-structure data. Here are a few:
- Call center log interactions from customer service support
- Social media websites, things people are saying online
- It comes from your website, in the form of web logs
- It comes from the wealth of sensors, deployed by our businesses to track location by gps or telematics
Big data challenges and characteristics
There are many challenges to dealing with big data. Due to these challenges, many organizations have little choice but to ignore or delete large quantities of potentially valuable information.
- Where to put your data
- Can you keep it secure
- Thinking fast can lead to false positives
- Complexity. The more data you have, the harder to find true value from it
- Incorrect findings. Big data may not give exactly what you are looking for
- Business marketing optimizations
- Risk analytics
- Hardware optimizations
3 V’s – Volume, Velocity, Variety
Big data is often characterized using the 3 v’s
- Volume: Volume poses both the greatest challenge and the greatest opportunity as big data. It can help many organizations to understand people better and to allocate resources more effectively, however traditional computing solutions by relational databases are not scalable to handle data of this magnitude
- Velocity: Big data velocity raises a number of issues with the rate at which data is flowing into many organizations, exceeding the capacity of their IT systems. Users increasingly demand data and streaming real-time and delivering this is also a challenge.
- Variety: Variety of data types to be processed is becoming increasingly diverse. Gone are the days when data centers only have to deal with documents, financial transactions, stock records, and personnel files. Today photographs, audio, video, 3d models, compact simulations, and location data are being piled into corporate data silos; they are unstructured and not easy to categorize let alone process with traditional computing techniques
Due to these challenges, many organizations have little choice but to ignore or delete large quantities of potentially valuable information. There are now various software available to help with these challenges. Some of these features to look for include:
- Distribution of storage and processing of large data sets across groups or clusters of server computers.
- Distributed file system which permits high bandwidth cluster based storage
- MapReduce (google search technology) distributes or Maps last data sets across multiple servers each server then creates a summary of the data at speed allocated. Information is then aggregated and allows extremely large your data sets to be rapidly distilled before more traditional data analysis tools are applied
- For organizations who cannot afford an internal big data infrastructure cloud-based Big Data Solutions are available where public big data sets need to be utilized. Running everything in the cloud also makes a lot of sense as data does not have to be downloaded.
Looking further ahead quantum computing may greatly improve big data processing. Quantum computers store and process data using quantum mechanical states and well in theory excel at the massively parallel processing of structured data. Big data holds the promise of giving enterprises deeper insight into their customers, partners, and businesses. In time big data may also help farmers to accurately forecast bad weather and crop failures. Meanwhile governments may use big data to predict and plan for civil unrest or pandemics. Studies estimate billions of dollars in efficiency and quality savings each year by leveraging big data.
Originally posted 2016-08-26 15:21:02. Republished by Blog Post Promoter