Data Data Data !!!!
Every one is running for data, collecting data and just storing data as if data is an another item to stock or like a currency with value which is on rise almost every day. Source for data is everything and its around us like how many steps one take to reach on foot or how much time and kilometers you travel in case you travel by bus/car etc. Data from other sources such as weather monitoring stations and satellites, traffic systems, industrial data, farm data, and even social media are widely used to improve day to day quality of life. Interestingly data format is not the same as data source. Organising data in particular data framework as a way for businesses to organize and understand their data assets is easy way to understand and make use of it.
Today’s data has answer for most of the things if not everything and can be quantified and tracked easily. What this means –
- What will the temperature be next day or week or even on particular day in future?
- How my followers trend may look like in next 3 months ?
- How the health of the person would be based on data and environment ?
- How much sales will be in next month ?
Last month I was at big retail store in Harare and it was a very busy day due to the fact it was month end and people got paid. Grocery shopping was in full swing, I also bought some groceries for my self. When I was in the queue for payment and collection, I saw almost every one making payment either by swiping the magic plastic card or struggling on their mobile handset by punching few numbers etc. The electronic payment queue was moving fast compared to the cash payment queue where I saw only a handful of people with just one/two small item/items.
The thought came to my mind out of this whole picture was “Whats happening here besides the payments through mobile and plastic”? Understanding the data you have is a good first step in knowing what you can do with it. Data, More Data, Lots of Data so called BIG DATA was getting generated. Customers’ attitude can be used to known customer ratings or customer comments as our data source. Artificial intelligence and big data are like 2 sides of coin, and they need each other to ring to fruition what both are promising. Big data is the key for data-driven decisions and discovery
As on date we see data almost every single person, company or any entity is just running after data. By combining all this disparate data, predictive analytics can create highly accurate models to predict pollution trends in advance allowing civic agencies to make relevant predictions and changes to prevent spikes and keep pollution levels in check. Big data and analytics can also help improve traffic management in addition to just monitoring pollution levels and the fates of artificial intelligence and big data are intertwined
Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden. A comprehensive and widespread network such as this to track the causes of pollution at source will allow government agencies to create smarter strategies to combat pollution – and when combined with predictive analytics, predictions in some cases can even be made in advance to assess your Big Data analytic needs.
World Wide Data Wrestling,The importance of data doesn’t revolve around how much data you have, but what you do with it. Analytics predictions and priorities for 2018 or in another words “The age of algorithms” is upon us already. Data is coming up as second costliest item after fuel. Data characteristics like volume, velocity, variety, variability and complexity. Concept gained momentum in the early 2000s when industry analysts articulated which is now the mainstream definition of big data as v’s and c’s. Cloud computing and other flexible resource allocation arrangements.
Data can be generated from any source to analyze for any reason for example to find answers for cost or time reductions, new product development and optimized offerings or even for smart decision making. Combining data with high-powered analytics can accomplish business-related tasks or Big Data can be used for Small Business i.e micro credit or even for micro insurance. Parallel processing, clustering, MPP, virtualization, large grid environments, high connectivity and high throughputs identify strengths and gaps in data collection efforts. Kind or type can be put up in many buckets i.e structured data or unstructured data, internal data or external data and human generated or machine generated data i.e tweets, retweets, facebook like etc.
More and more data also comes with more and more risks and keep costing industry millions or even billions of dollars. Without the right security and encryption solution in place; big data is a very big problem. A smart Big Data factory should take smart approach to this costly, sensitive and critical asset maintenance and management. Before we go further let me explain un short what is Big Data. The quality of any analysis is dependent on the quality of the data. Due to the nature of the data might be more reliable and valid than customer sentiment metrics from social media content; as a result, the use of structured data might lead to a better understanding of your data.
I am sure most of us knows the answer already; Big data is term that means a huge amount of Digital Data. This data is unorganized and unstructured because it is capture from different sources. So it is difficulty to analysis. For instance cardholder data should be managed in highly secured data vault, using multiple encryption keys with split knowledge and dual/triple control. Affordable open source, distributed big data platforms, such as Hadoop.
Big data presents a tremendous opportunity for enterprises across multiple industries especially in the tsunami like data flow industry of “Payments”. FinTech, InsureTech, MedTech are major data generating industries i.e massive group of factories. According to some data from Google it shows technology based innovative insurance companies pays $0.60-$0.65 of each dollar in claims, with the rest covering costs of admin, marketing and reinsurance. Next questions were “Who owns this data?”, “What is the use of this data?” and “How secure is this data?”.
My payment data with all my sensitive information is it secured and in safe hands? Mobile Money vs Mobile Banking difference is same with data mining and data drilling. What about privacy of my sensitive information?. Thousands of questions started spinning my head. There is a massive scope of big data security. This presents a significant opportunity for disruption. With improvements in technology which anyways happening every day without demand and this will bring reduction in each of these cost items. Consequently, the data sources being compiled need to be secured in order to address security policies and compliance mandates. It’s important to remember that the primary value from big data comes not from the data in its raw form, but from the processing and analysis of it and the insights, products, and services that emerge from analysis.
The sources for big data generally fall into one of three categories i.e Streaming data – Data to IT systems from a web of connected devices. Social media data – The data on social interactions and finally publicly available sources – Data are available through open data sources like government etc. After identifying all the potential sources of data decisions needs to be made for harnessing information i.e How to store and manage it, How much of it to analyze and How to use any insights you uncover.
Conclusion – In short, big data has transformed Artificial Intelligence, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways but for now thats for my next post. I guess my analysis is reasonable but conclusion at this time might be a bit pre-mature. The clearing houses gets real-time payment data to apply their expertise also have a vision beyond their current rails and the pockets to support it. Further, more data sources are added all the time. Start and grow businesses and prevent frauds by taking security before innovation. Transactions and data generated out of them will then be safe, quick and easy. Such data requires best technologies that helps to make the most of data and big data analytics with cheap and abundant storage, faster processors etc.
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