Big Data: Data Data Data! Organizing data in a particular data framework as a way for businesses to organize and understand their data assets is an easy way to understand and make use of it. Today’s data has answers for most things, if not everything, and can be quantified and tracked easily. What this means
Data – A New Fuel
In the modern digital age, data has become the lifeblood of businesses, flowing from every corner of the online world, customer interactions, and operational processes. The proliferation of information has given rise to what is commonly referred to as “Big Data.” This vast and ever-growing pool of data presents immense opportunities for businesses seeking to gain a competitive edge and drive advancement. However, with the sheer volume and complexity of data available, cracking the code to make sense of Big Data has become a critical challenge that can determine the success or failure of organizations.
- 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 a big retail store in Harare, and it was a very busy day due to the fact that it was month-end and people got paid. Grocery shopping was in full swing, and I bought some groceries. When I was in the queue for payment and collection, I saw almost everyone making payment either by swiping the magic plastic card or struggling on their mobile handset by punching a few numbers, etc. The electronic payment queue moved faster than the cash payment queue, where I saw only a handful of people with just one or two small items.
The thought that came to mind out of this whole picture was “What’s happening here besides the payments through mobile and plastic”? Understanding the data you have is a good first step to knowing what you can do with it. Data, More Data, Lots of data—so-called big data—were getting generated. Customers’ attitudes can be used as a data source in conjunction with known customer ratings or customer comments. Artificial intelligence and big data are like two sides of the same coin, and they need each other to bring to fruition what both are promising. Big data is the key to data-driven decisions and discovery.
Data – Kind Of Currency
As of today, almost every single person, company, or 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 sensors 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 the source will allow government agencies to create smarter strategies to combat pollution, and when combined with predictive analytics, predictions can even be made in some cases 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 other words, “The age of algorithms,” are upon us already. Data is coming up as the second-costliest item after fuel. Data characteristics like volume, velocity, variety, variability, and complexity The concept gained momentum in the early 2000s when industry analysts articulated what 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, optimized offerings, or even smart decision-making. Combining data with high-powered analytics can accomplish business-related tasks or Big Data can be used for Small businesses, i.e., micro-credit or even 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 into 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 likes, etc.
Data – Uncover the Covered Truth
More and more data also comes with more and more risks, which keep costing the 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 a smart approach to this costly, sensitive, and critical asset maintenance and management. Before we go further, let me explain briefly what big data is. The quality of any analysis is dependent on the quality of the data. Due to the nature of the data, it 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 know the answer already: Big data is a term that means a huge amount of Digital Data. This data is unorganized and unstructured because it was captured from different sources. So it is difficult to analyze. For instance, cardholder data should be managed in a highly secured data vault using multiple encryption keys with split knowledge and dual or 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?”.
The Power of Big Data: A Wealth of Insights
Big Data refers to the vast and diverse set of structured and unstructured data that inundates businesses on a daily basis. This data encompasses customer behavior, social media interactions, website traffic, financial transactions, and much more. The potential value of Big Data lies not merely in its size but in the valuable insights it holds. Businesses can uncover patterns, correlations, and trends that were previously invisible, gaining a deep understanding of their customers, operations, and markets.
My payment data, with all my sensitive information, is it secured and in safe hands? Mobile Money vs. Mobile Banking: The difference is the same with data mining and data drilling. What about the privacy of my sensitive information? Thousands of questions started spinning in my head. There is massive scope for big data security. This presents a significant opportunity for disruption.
Improvements in technology, which are anyway happening every day without demand, will bring a 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 that analysis.
Organizing the Data: Building Frameworks for Clarity
The sources for big data generally fall into one of three categories: 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 the government, etc. After identifying all the potential sources of data, decisions need 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.
As technology continues to evolve, the potential for data science to unlock further innovations in payment intelligence is boundless, promising even more advancements and benefits for businesses and consumers alike. Amidst the ocean of data, organizations must navigate the challenge of organizing and structuring the information to extract meaningful insights.
This is where data frameworks come into play. Data frameworks provide a systematic and organized approach to processing, storing, and analyzing data, allowing businesses to make sense of the vast information available.
Data frameworks serve as the backbone of data management, offering a structured and consistent way to capture, store, and retrieve data. These frameworks can include data warehouses, data lakes, and data integration platforms, each tailored to handle different types of data and business needs.
Books + Other readings Referred
- Research through Open Internet – NewsPortals, Economic development report papers, and conferences.
- Personal experience of @AILabPage members (Self-learner group)
Points to Note:
it’s time to figure out when to use which tech—a tricky decision that can really only be tackled with a combination of experience and the type of problem at hand. So if you think you’ve got the right answer, take a bow and collect your credits! And don’t worry if you don’t get it right.
Feedback & Further Questions
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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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