Data is the currency of business in todays digital business era. The data authority companies helping customers leverage and manage their data wherever it resides in the cloud, in their data centers, or at the edge as part of the Internet of Things (IoT).
Data Data Data !!!!
Organising data the Concealed-Weapon in particular data framework as simple but time taking task. For businesses to organise and understand their data assets is easy to make use. Sadly most of the time data is even not touched. Today’s data has answer for most of the things if not everything. Data of today can be quantified and tracked easily as it has human elements. 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 ?
The Live Experience
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 by moving fingers on their smart phones. The electronic payment queue was moving fast compared to the cash payment queue. Even though it had 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 and habit can be known by 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.
Generate Data & Collect Data
As on date we see data almost every single person, company or any entity is just running after data. With combination of all disparate data and predictive analytics one can create highly accurate data models. Depending upon data nature relevant prediction can be made. For example if data was related to pollution trends then in advance allowing civic agencies can make relevant predictions and changes to prevent pollution spikes and keep its level in order.
Big data and analytics can also help improve traffic management in addition to just monitoring pollution levels. Fates of artificial intelligence and big data are intertwined though.
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 – Concealed-Weapon
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 or Concealed-Weapon for better name can be generated from any source to analyze for any reason. To find answers for cost or time reductions, new product development, optimized offerings or even for smart decision making. Combining data with high-powered analytics can accomplish business-related tasks. 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.
DataIntelligence – Biggest source of security breach
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.
Data Sources – Social & Other Platform
The sources for big data generally fall into one of three categories i.e Streaming data – Data as a Concealed-Weapon 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.
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.
As organizations undertake data-driven digital transformation. To achieve the same they are increasingly turning to powerful artificial intelligence and deep learning capabilities to drive competitive advantage. Fully enabling that opportunity requires the ability to easily integrate and manage data at scale from diverse sources, and to support the demanding performance requirements of AI/DL applications.
Robots can fail; as reason is very simple and known which is machine learning models are not sufficiently accurate or can’t be accurate without lots of data and lots of training. Artificial Intelligence with cloud computing add advancements using new use cases to improvise the systems developed so far.
Disclaimer – All credits if any remains on the original contributor only.
Books + Other readings Referred
- Open Internet – Research Papers and ebooks
- Personal hand on work on data & experience of @AILabPage members
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Conclusion – In my personal opinion and hand on experience Data helps business and works as biggest influencer for Blue Ocean shift strategy. So I say Data is the angle to show you best of your business’s hidden potential. In short, big data as Concealed-Weapon 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.
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Categories: Big Data