Last Sunday I was at big retail store in Harare and it was a very busy day because 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”? Data, More Data, Lots of Data so called BIG DATA was getting generated.
A smart BigData factory should take smart approach to costly, sensitive, critical asset and maintenance for data management. Without the right Information-Security and encryption solution in place; big data is a very big problem. DataIntelligence and DataSecurity should be prime goals for any data factory. Before we go further let me explain in short “What is Big Data”. I am sure most of us knows the answer already; “A term used for huge amount of Digital Data and mostly in unorganised and unstructured format because it is captured from different sources”.
So it is difficulty to analysis. For instance card holder data should be managed in highly secured data vault, using multiple encryption keys with split knowledge and dual/triple control. In todays time with the help of Artificial intelligence, data security took another angle where no real data is mapped to dummy data and real data never gets into internet black hole as this data store cannot be connected to/via the internet but remains at back seat. A data thief would not be able to make use of information stolen from a database without also having multiple level of keys.
Main Story – 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? 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.
More startups are coming in to disrupt this massive and antiquated industry. Artificial intelligence helping in reducing underwriting risk using big data and machine learning; also offer secure data migration to the secured data vaults. Automating policy administration, & claims pay out to bring big smile on customer face, improving distribution via marketplaces. The wide variety of data volumes generated by FinTech, InsureTech and MedTech is inspiring for data scientists (I simply love this and would feel very happy to play with it if I ever get access to this), executives, product managers and marketers.
Leveraging on data from different platforms i.e. CRM platforms, spreadsheets, enterprise planning systems, social media feeds like facebook, twitter, instagram, linkedin, company website feed section, any video file, and any other source. Thanks to mobile devices, tracking systems, RFID, sensor networks, Internet searches, automated record keeping, video archives, e-commerce, etc. -coupled with the more information derived by analyzing all this information, which on its own creates another enormous data set.
New data sources create more data which requires new processes to handle and that in turn creates new security vulnerabilities. Over-the-air provisioning of payment credentials and applications are main reason and biggest example of potential for attackers to create vulnerable vectors for unwanted listeners to steal and misuse customer data. It is basically a process of infectious interaction with the people and their environment. In this era of dynamic digital payments and connected financial services, all businesses need to embrace evolution for growth and continuity. This isn’t just about one source of data from one Payments system. The data sources should be as many as possible to exploit full advantages of big data, companies has to leverage on various forms of data, including structured data in a range of heterogeneous applications & databases and unstructured data that comes in a number of file types.
The data warehouse can help in making more informed decisions, plans for growth, discover & design roadmap for new opportunities, opportunity for optimization, and to deliver breakthrough innovations. Today, we don’t know where new data sources may come from tomorrow, but we can have some certainty that there will be more to be contend with and more diversity to accommodate. Big data factories operating and pursuing analytics these days because it can be revelatory in spotting business trends, improving research quality, and gaining insights in a variety of fields, from FinTech to InfoTech to InsureTech to MedTech to law enforcement and everything in between and beyond.
Big data frameworks powered by Hadoop, Teradata, MongoDB, NoSQL, or another system—massive amounts of sensitive data may be managed at any given time. Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Sensitive assets don’t just live on big data nodes, but they can come in the form of system logs, configuration files, error logs, and more. within the its own environment itself—whether it’s the challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. Sources can include personally identifiable information, payment card data, intellectual property, health records, and much more.
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 facts to apply their ability 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.
An efficient domestic payment system infrastructure is key to cut costs of remittance services, especially in receiving countries. Remittances are part of an individual’s access to financial services. A good remittance product improves value to the user in the short term and access to other financial products in the long term it also increases competition and could move transactions to the formal sector. Consequently, the data sources being compiled need to be secured to address security policies and compliance mandates.
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Categories: Big Data