tag:blogger.com,1999:blog-132121322024-03-27T04:46:07.288+00:00Arun KottolliMarketing, Innovation & LeadershipArun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.comBlogger548125tag:blogger.com,1999:blog-13212132.post-17077392510395307962021-06-28T13:19:00.006+00:002021-06-28T13:21:11.265+00:00Types of Graph Analysis<p> </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguU4FUrJBUUVMDfNkT-acctXOJhjxrFRFIvL8czeIWbAdFKsgyecVhuA0foq3v6rDa9Hujv3W5LO0W3e4ieC1tgBlAQEWPCuE5Afnk20Q8LHCqNEPdOrpQgfvBAFIBgGFMUjxI/s1410/Types+of+Graph+Analysis.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="766" data-original-width="1410" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguU4FUrJBUUVMDfNkT-acctXOJhjxrFRFIvL8czeIWbAdFKsgyecVhuA0foq3v6rDa9Hujv3W5LO0W3e4ieC1tgBlAQEWPCuE5Afnk20Q8LHCqNEPdOrpQgfvBAFIBgGFMUjxI/s320/Types+of+Graph+Analysis.png" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><div><br /></div><div>Graph Analysis has become a groundbreaking way for organizations to look at their data and understand the relationships between them. For two years running, Gartner selected graphs as one of their top analytics and data trends because of the significant potential for value creation. </div><div><br /></div><div>Graphs capture relationships and connections between entities. The relationships and connections between the entities are used in data analysis. Knowing how the data is connected, and building a graph to understand the relationships are becoming increasingly important because they make it easier to explore those connections and made new insights. For example, understanding how a person’s buying pattern is influenced by all the entities the person is connected with. </div><div><br /></div><div><br /></div><div><b><u>Centrality analysis:</u></b> Estimates how important a node is for the connectivity of the network. It helps to estimate the most influential people in a social network or most frequently accessed web pages by using the PageRank algorithm.<br /><br /></div><div><b><u>Community detection:</u></b> Distance and density of relationships can be used to find groups of people interacting frequently with each other in a social network. Community analytics also deals with the detection and behaviour patterns of communities.<br /><br /></div><div><b><u>Connectivity analysis:</u></b> Determine how strongly or weakly connected two nodes are.<br /><br /></div><div><b><u>Path analysis:</u></b> Examines the relationships between nodes. Mostly used in shortest distance problems.</div><div><br /></div><div><br /></div><div><br /></div><p></p><div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-4118119914659396392021-06-21T09:33:00.004+00:002021-06-21T09:33:26.305+00:00Fintech Use Case for Graph Analytics<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDW5g3i6Mahn9FeLLAM82IdyoykTtDvwT8d8iqZT_QueSPavl2XTROqutP-UdlnX4QUwQp618gwBt7JoQwaus-aQHMg2yQv2IeeoKSqSHb6mmuwXs4xmjYwAeg_uIcy_4s44At/s1327/Graph+Analytics+for+Consumer+Credit+Allocations.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="793" data-original-width="1327" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDW5g3i6Mahn9FeLLAM82IdyoykTtDvwT8d8iqZT_QueSPavl2XTROqutP-UdlnX4QUwQp618gwBt7JoQwaus-aQHMg2yQv2IeeoKSqSHb6mmuwXs4xmjYwAeg_uIcy_4s44At/s320/Graph+Analytics+for+Consumer+Credit+Allocations.png" width="320" /></a></div><br /><p><br /></p><p>In my previous blog, I had written about high-level use cases for Graph Analytics. In today’s blog, lets' dive in deeper and take a look at how Fintech companies can use graph analytics to allocate credit to customers & manage risks.</p><p>Today, banks and other Fintech companies have access to tonnes of information – but their current databases and IT solutions do not have the ability to make the best use of it. Customer information such as KYC data, and other demographic data are often storied in traditional RDBMS database, while transactional data is stored in a separate database, customer interactions on web/mobile apps, customer interactions data are stored in Big Data HDFS stores, while the data from Social network or other network data about customers are often not even used. </p><p>This is where graph databases such as Neo4J or Oracle Autonomous database etc come into play. </p><p>A graph database can connect the dots between different sources of information and one can build a really cool, intelligent AI solution to make predictions on future purchases, credit needs, and risks. Prediction data can then be validated with actual transactional data to iterate and build better models. </p><p>Graph databases are essentially built for high scalability and performance. There are several open-source algorithms and libraries that can detect components and make connections within the data, you can evaluate these connections and start making predictions, which over time will only get better. </p><div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-35520136002369551672021-06-16T07:59:00.004+00:002021-06-16T07:59:53.805+00:00BFSI Use cases for Graph Analytics<p> </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEia7bcH_BbzVHSAqTINpH3xOVq_NOoYarsy5ciZFS88s9ofI7XlnMZTEPfXJxyF12F3rkF9DiJfoNMU0y0qkcNpTgQw_aCXmJhX4KsZBpCtyxb7XroKB5Su3yQn8Dz3KhropeZ-/s1257/BFSI+Use+cases+for+Graph+Analytics.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="776" data-original-width="1257" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEia7bcH_BbzVHSAqTINpH3xOVq_NOoYarsy5ciZFS88s9ofI7XlnMZTEPfXJxyF12F3rkF9DiJfoNMU0y0qkcNpTgQw_aCXmJhX4KsZBpCtyxb7XroKB5Su3yQn8Dz3KhropeZ-/s320/BFSI+Use+cases+for+Graph+Analytics.png" width="320" /></a></div><br /><p><br /></p>
<p class="MsoNormal" style="tab-stops: list 36.0pt;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Graph analytics is used to analyze relations
among entities such as customers, products, operations, and devices. Businesses
run on these relationships between customers, customers to products,
how/where/when customer’s use products, and how business operations affect the
relationships. In a nutshell, it’s like analyzing social networks and financial
companies can gain immensely by using Graph Analytics.</span><o:p></o:p></p>
<p class="MsoNormal" style="tab-stops: list 36.0pt;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Let’s see the four biggest use case of Graph
Analytics in the world of finance.</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-left: 36.0pt; tab-stops: list 36.0pt; text-indent: -18.0pt;"><o:p> </o:p></p>
<ol start="1" style="margin-top: 0cm;" type="1">
<li class="MsoNormal" style="mso-list: l0 level1 lfo1; tab-stops: list 36.0pt;"><b>Stay
Agile in Risk & Compliance</b><br />
<span lang="EN-US" style="mso-ansi-language: EN-US;">Financial services firms
today face increased regulations when it comes to risk and compliance
reporting. Rather than update data manually across silos, today's leading
financial organizations use Neo4j to unite data silos into a federated
metadata model, enabling them to trace and analyze their data across the
enterprise as well as update their models in real-time.<br /><br /></span><o:p></o:p></li>
<li class="MsoNormal" style="mso-list: l0 level1 lfo1; tab-stops: list 36.0pt;"><b>Fraud
Protection</b><br />
<span lang="EN-US" style="mso-ansi-language: EN-US;">Dirty money is passed around to blend it with legitimate funds and then turned into hard assets.
Detect circular money transfers to prevent money laundering via money mules. Graph Analytics discovers the network of individuals or common patterns of transfers in real-time to prevent common frauds – to detect illegal ATM transactions. Data like IP addresses, cards used, branch locations, the timing of transfers can be instantly tied to individuals to prevent fraudulent transactions.<br /><br /></span><o:p></o:p></li>
<li class="MsoNormal" style="mso-list: l0 level1 lfo1; tab-stops: list 36.0pt;"><b>Leverage
data across teams</b><br />
<span lang="EN-US" style="mso-ansi-language: EN-US;">Data is the lifeblood of finance. Companies strive to actively collect, store and use data. At the same time, financial companies are governed by laws, regulations, and standards around data. The burden of being compliant and ensuring data
privacy has become ever more complex and expensive.<br />
Graph Analytics allows tracking data lineage through the data lifecycle.
Data can be tracked and navigated, vertex by vertex, by following the edges. With graph analytics, it is possible to follow the path and find where the information originated, where it was copied, and where it was utilized. This makes it easier to remain compliant and use data for its full value.<br /><br /></span></li>
<li class="MsoNormal" style="mso-list: l0 level1 lfo1; tab-stops: list 36.0pt;"><b>Capture
360-degree view of customers</b><br />
<span lang="EN-US" style="mso-ansi-language: EN-US;">Marketing is all about
understanding relationships of their customers and their products. Knowing
the relationships between customers, customers’ transactions, and products
will build a 360-degree view of customers – which can be used for better
marketing and more effectively provide customers with what they want.</span><o:p></o:p></li>
</ol>
<p class="MsoNormal"><o:p> </o:p></p><div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-64662874088336070232021-06-15T06:09:00.011+00:002021-06-15T06:13:10.625+00:00How Banks can benefit from Blockchain Analytics?<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh63wTFoAHe9jlfwf4p5FKphVMKhV_TsgYpRktZb5E8HQh1nQ8SeYfhba2kEIbBNrguva__OLYcjC2FGYgGU3De-_9DIkf0DV4BLGBxKc0h3Xuq-9aTQnCOTwuT9-qeDmHcUFW2/s806/Analytics.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="380" data-original-width="806" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh63wTFoAHe9jlfwf4p5FKphVMKhV_TsgYpRktZb5E8HQh1nQ8SeYfhba2kEIbBNrguva__OLYcjC2FGYgGU3De-_9DIkf0DV4BLGBxKc0h3Xuq-9aTQnCOTwuT9-qeDmHcUFW2/s320/Analytics.png" width="320" /></a></div><br /> <p></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span face="Calibri, sans-serif" style="color: #323c3e; font-size: 11pt;"> </span></p>
<p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;"><br /></span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;">Blockchain is a digital and decentralized public ledger with a system that records transactions across several computers linked to a peer-to-peer network. It was originally developed for cryptocurrency assets like Bitcoin, Dogecoin, Ethereum, etc., In recent years there are several new use cases have emerged in financial services. (See: <a href="https://arunkottolli.blogspot.com/2018/06/blockchain-for-secure-healthcare-records.html" target="_blank">Blockchain for Secure Healthcare Records</a>, <a href="https://arunkottolli.blogspot.com/2021/06/how-banks-financial-institutions-can.html" rel="nofollow" target="_blank">How Banks & Financial Institutions can use Blockchain Technology</a>, <a href="https://arunkottolli.blogspot.com/search/label/blockchain" rel="nofollow" target="_blank">Blockchain use cases</a>) </span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;"><br /></span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;">As blockchain’s use cases go beyond cryptocurrency, including for government applications, healthcare, identity management, art, and IPR, the database of all blockchain transactions grow even more bigger, richer, and more valuable for banks – if they can use this data via data analytics and use these insights to build better services. The benefit of blockchain is its inherent transparency. The blockchain’s decentralized, open network allows banks to collect data from blockchain transactions. </span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;"><br /></span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;">The Rise of Data Analytics</span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;">Aside from all the aforementioned areas, blockchain also huge potential in analytics. Modern businesses have been benefiting from data analytics for several years now. Currently, the big problem with any data analytics is getting quality data from different sources and correlating them. There is the issue of whether there is enough of the right data. </span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;"><br /></span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;">This is where blockchain technology helps. Data recorded in a blockchain is irrefutable and can be easily cross verified from any node in the network. Having access to this large network that provides high quality data in a vast number of datasets is invaluable. </span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;"><br /></span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;">A good potential application will be blockchain analytics – to understand customers of cryptocurrency customers & traders. Bank’s asset & wealth management business and customer banking’s marketing organization can use these valuable analytics for future marketing campaigns and for managing cryptocurrency as an asset class in wealth management. This system can be used to forecast price movements for cryptocurrencies. </span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;"><br /></span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;">Today there are more than 100 digital assets including Bitcoin, Ethereum, ERC-20 tokens, and other crypto coins, representing over $200 billion worth of transactions per month. </span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;"><br /></span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;">Other use cases include risk analysis on crypto transactions: uncovering activities related to money laundering, terrorist fundraising, fraud, and other financial crimes. Blockchain analytics can de-anonymize funds flow by actively collecting millions of data points every week, and then implementing machine learning to its huge data pool to track flows to legitimate entities and also criminal activities.</span></span></p><p style="background: white; line-height: 19.5pt; margin: 0cm; vertical-align: baseline;"><span style="color: #323c3e;"><span style="font-size: 14.6667px;"> </span></span></p><div><br /></div><div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-8958088761975955902021-06-14T13:25:00.000+00:002021-06-15T04:41:28.172+00:00 How Banks & Financial Institutions can use Blockchain Technology<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgU3nzt5v8ZpuGIvS7YHQc-t1TdRh4BVIpO9w6ha2jbBgJWmpThPfV3oMvN3D3q3ZVKCsOY1YbIwtSVc64-bzVvwZNeqCJ9UHprHjEkOD_jNoW3M9bAX2ctFifQ5kGXO85gIuN5/s1410/Blog+-+BFSI+use+cases+for+Blockchain.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="784" data-original-width="1410" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgU3nzt5v8ZpuGIvS7YHQc-t1TdRh4BVIpO9w6ha2jbBgJWmpThPfV3oMvN3D3q3ZVKCsOY1YbIwtSVc64-bzVvwZNeqCJ9UHprHjEkOD_jNoW3M9bAX2ctFifQ5kGXO85gIuN5/s320/Blog+-+BFSI+use+cases+for+Blockchain.png" width="320" /></a></div><br /><p><br /></p><p>Apart from cryptocurrencies, there are several other important use cases for Blockchain technologies in the banking & financial sectors.</p><p><b>NFT</b> </p><p>Non-fungible tokens (NFTs) are new digital assets. In a nutshell, NFT is a crypto block – which encapsulates digital art or record. The digital art/record is tokenized – just like recording a payment transaction in a blockchain, it becomes a certified true copy – whose authenticity & Ownership can be verified by any node in the crypto-chain network. This token (also called NFT) can then be traded on the blockchain network just like any cryptocurrency.</p><p>Today, the world is experimenting with NFT for digital art or digital records. For example, Twitter CEO Jack Dorsey sold his first-ever tweet as an NFT for more than $2.9 million!</p><p><b>Ownership & Transfer of Financial Instruments: </b></p><p>One of the biggest use cases of NFT in the financial world is recording ownership of financial records: Bond/stock certificates, insurance policies, etc can be tokenized to record the ownership of financial assets, and then these NFTs can be traded on the crypto network. </p><p><b>Payments, Remittance & Reconciliation</b></p><p>Unlike cryptocurrencies, banks can create & issue crypto tokens that are tied to a fixed value – which can then be transferred over the network for instantaneous payments and remittances without the need for a central bank’s approval. JPCoin is a good example of this.</p><p><b>Servicing of Instruments</b></p><p>Once the ownership of financial assets is tokenized, Payments as per financial contracts such as Bond coupons or dividends can be made programmatically to the current owner of the Financial instrument accurately. </p><p><b>Storing KYC information & Anti-Money Laundering Registers</b></p><p>KYC information is nonfungible data that can be tokenized so that these records cannot be altered by hackers, and also be used for rapid ID identification across the network for rapid transactions. As the pace of transactions increases, the current data warehousing systems impose certain limitations and the use of NFT for KCY AND AML Registers can speed up global financial transactions.</p><p><b>Regulatory Reporting</b></p><p>Regulatory Reporting should be a nonfungible report as it has major implications. Countries and participating Banks can use the crypto network to get, store and use all regulatory reporting data. These reports can then be shared securely in the crypto database with multiple regulators and other governing bodies. </p><div><br /></div><div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-5031664214575162172020-04-10T09:44:00.000+00:002020-04-10T09:46:02.609+00:00AI for Consumer Banking in the age of Covid-19<div dir="ltr" style="text-align: left;" trbidi="on">
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-51253686491691345652018-08-30T08:03:00.002+00:002018-08-30T09:05:33.944+00:00Interesting Careers in Big Data<div dir="ltr" style="text-align: left;" trbidi="on">
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<br />
Big Data & Data analytics has opened a wide range of new & interesting career opportunities. There is an urgent need for Big Data professionals in organizations.<br />
<br />
Not all these careers are new, and many of them are remapping or enhancements of older job functions. For example, a Statistician was formerly deployed mostly in government organization or in sales/manufacturing for sales forecast for financial analysis, statisticians today have become center of business operations. Similarly, Business analysts have become key for data analytics – as business analysts play a critical role of understanding business processes and identifying solutions.<br />
<br />
<br />
Here are 12 interesting & fast growing careers in Big Data.<br />
<br />
<b>1. Big Data Engineer</b><br />
Architect, Build & maintain IT systems for storing & analyzing big data. They are responsible for designing a Hadoop cluster used for data analytics. These engineers need to have a good understanding of computer architectures and develop complex IT systems which are needed to run analytics.<br />
<br />
<b>2. Data Engineer</b><br />
Data engineers understand the source, volume and destination of data, and have to build solutions to handle this volume of data. This could include setting up databases for handling structured data, setting up data lakes for unstructured data, securing all the data, and managing data throughout its lifecycle.<br />
<br />
<b>3. Data Scientist</b><br />
Data Scientist is relatively a new role. They are primarily mathematicians who can build complex models, from which one extract meaningful analysis.<br />
<br />
<b>4. Statistician</b><br />
Statisticians are masters in crunching structured numerical data & developing models that can test business assumptions, enhance business decisions and make predictions.<br />
<br />
<b>5. Business Analyst</b><br />
Business analysts are the conduits between big data team and businesses. They understand business processes, understand business requirements, and identify solutions to help businesses. Business analysts work with data scientists, analytics solution architects and businesses to create a common understanding of the problem and the proposed solution.<br />
<br />
<b>6. AI/ML Scientist</b><br />
This is relatively a new role in data analytics. Historically, this was part of large government R&D programs and today, AI/ML scientists are becoming the rock stars of data analytics.<br />
<br />
<b>7. Analytics Solution Architects</b><br />
Solution architects are the programmers who develop software solutions – which leads to automation and reports for faster/better decisions.<br />
<br />
<b>8. BI Specialist</b><br />
BI Specialists understand data warehouses, structured data and create reporting solutions. They also work with business to evangelize BI solutions within organizations.<br />
<br />
<b>9. Data Visualization Specialist</b><br />
This is a relatively new career. Big data presents a big challenge in terms of how to make sense of this vast data. Data visualization specialists have the skills to convert large amounts of data into simple charts & diagrams – to visualize various aspects of business. This helps business leaders to understand what’s happening in real time and take better/faster decisions.<br />
<br />
<b>10. AI/ML Engineer</b><br />
These are elite programmers who can build AI/ML software – based on algorithms developed by AI/ML scientists. In addition, AI/ML engineers also need to monitor AL solutions for the output & decisions done by AI systems and take corrective actions when needed.<br />
<br />
<b>11. BI Engineer</b><br />
BI Engineers build, deploy, & maintain data warehouse solutions, manage structured data through its lifecycle and develop BI reporting solutions as needed.<br />
<br />
<b>12. Analytics Manager</b><br />
This is relatively a new role created to help business leaders understand and use data analytics, AI/ML solutions. Analytics Managers work with business leaders to smoothen solution deployment and act as liaison between business and analytics team throughout the solution lifecycle.<br />
<div>
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</div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-11877716815418286882018-08-29T08:28:00.000+00:002018-08-29T16:02:31.033+00:00Customer Journey Towards Digital Banking<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgLZzz-PpguRcdG9QKcmMfIoBOcJlszraKocpSPMpkoWooon0cTep5iuaCNFwnq_v_0ksdnWMxPK3000M9bBTI4zKueHnLA4udjfsA25t94V6EwQjCyG8R5ZLzT7OVZBszVdRh1/s1600/Customer+Journey+towards+Digital+Banking.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="669" data-original-width="1275" height="167" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgLZzz-PpguRcdG9QKcmMfIoBOcJlszraKocpSPMpkoWooon0cTep5iuaCNFwnq_v_0ksdnWMxPK3000M9bBTI4zKueHnLA4udjfsA25t94V6EwQjCyG8R5ZLzT7OVZBszVdRh1/s320/Customer+Journey+towards+Digital+Banking.png" width="320" /></a></div>
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<br />
The bank branch as we know it with tellers behind windows and bankers huddled in cubicles with desktop computers, is in need of a massive transformation.<br />
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Today. most customers now carry a bank in their pockets in the form of a smart phone app, and visit an actual branch is not really needed. But banks all over the world are still holding on to the traditional brick-and-morter branches.<br />
<br />
Though many banks are closing these branches. <a href="https://www.business-standard.com/article/finance/thousands-transferred-at-sbi-many-branches-closed-yet-no-protest-117081500513_1.html" target="_blank">In 2017 alone, SBI, India's largest bank closed 716 branches!</a><br />
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Today, despite all the modern mobile technologies, physical branches remain an essential part of banks' operations and customer advisory functions. Brick-and-mortar locations are still one of the leading sales channels, and even in digitally advanced European nations, between 30 and 60 percent of customers prefer doing at least some of their banking at branches.<br />
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While banks like to move customers to the mobile banking platform, changing customer behavior has become a major challenge. The diagram shows the 5 distinct stages of customer behavior and banks must nudge customers to go along this journey.</div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-16675949883074662402018-08-24T09:52:00.000+00:002018-08-24T09:52:38.230+00:00Four Key Aspects of API Management<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhCspJLw_c1b7zDDtjA7nbfWBt3Rjr0crkhyphenhyphen9qwAHUF9SJP85kEEwG9KHF3zpGIkNo8CcEershngctS1rB0T8z2inLquUPeBHsymHybjGYRO4tMzt1IroAOnpT-ZudD1KLnx51E/s1600/Four+Key+Aspects+of+API+Management.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="668" data-original-width="1382" height="153" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhCspJLw_c1b7zDDtjA7nbfWBt3Rjr0crkhyphenhyphen9qwAHUF9SJP85kEEwG9KHF3zpGIkNo8CcEershngctS1rB0T8z2inLquUPeBHsymHybjGYRO4tMzt1IroAOnpT-ZudD1KLnx51E/s320/Four+Key+Aspects+of+API+Management.png" width="320" /></a></div>
Today, APIs are transforming businesses. APIs are the core of creating new apps, customer-centric development and development of new business models.<br />
<br />
APIs are the core of the drive towards digitization, IoT, Mobile first, Fintech and Hybrid cloud. This focus on APIs implies having a solid API management systems in place. <br />
<br />
API Management is based on four rock solid aspects:<br />
<br />
<b>1. API Portal</b><br />
Online portal to promote APIs.<br />
This is essentially the first place users will come to get registered, get all API documentation, enroll in an online community & support groups.<br />
In addition, it is good practice to provide an online API testing platform to help customers build/test their API ecosystems.<br />
<br />
<b>2. API Gateway</b><br />
API Gateway – Securely open access for your API<br />
Use policy driven security to secure & monitor API access to protect your API’s from unregistered usage, protect from malicious attacks. Enable DMZ-strength security between consumer apps using your APIs & internal servers<br />
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<b>3. API Catalog</b><br />
API lifecycle Management Manage the entire process of designing, developing, deploying, versioning & retiring APIs. <br />
Build & maintain the right APIs for your business. Track complex interdependencies of APIs on various services and applications.<br />
Design and configure policies to be applied to your APIs at runtime.<br />
<br />
<b>4. API Monitoring</b><br />
API Consumption Management<br />
Track consumption of APIs for governance, performance & Compliance.<br />
Monitor for customer experience and develop comprehensive API monetization plan<br />
Define, publish and track usage of API subscriptions and charge-back services<br />
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com1tag:blogger.com,1999:blog-13212132.post-67213363924642836622018-08-23T05:47:00.001+00:002018-08-23T05:47:34.487+00:00Common Options for Disaster Recovery<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJZUlMwpSM9TjyTBJvX5_UCyFPfIJK7TybqkxcyU4UuG0iszumGwl2JeHH8BXDR08QR1ulay6ool5LsuzLSQ3gmnfAxop_LfzVaAnelBaM6VUnaDfGT3mg0wdwY2akrl4a3LI1/s1600/Common+Options+for+Disaster+Recovery.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="681" data-original-width="1355" height="160" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJZUlMwpSM9TjyTBJvX5_UCyFPfIJK7TybqkxcyU4UuG0iszumGwl2JeHH8BXDR08QR1ulay6ool5LsuzLSQ3gmnfAxop_LfzVaAnelBaM6VUnaDfGT3mg0wdwY2akrl4a3LI1/s320/Common+Options+for+Disaster+Recovery.png" width="320" /></a></div>
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Disaster recovery (DR) is based on three standard DR sites.<br />
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In this article, lets take a look at the differences in hot site vs. warm and cold sites in disaster recovery.<br />
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<b>Hot site </b><br />
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In a hot site approach, the organization duplicates its entire environment as the basis of its DR strategy — an approach which, as you’d expect, costs a lot in terms of investment and upkeep. Even with data duplication, keeping hot site servers and other components in sync is time consuming. A typical hot site consists of servers, storage systems, and network infrastructure that together comprise a logical duplication of the main processing site. Servers and other components are maintained and kept at the same release and patch level as their primary counterparts. Data at the primary site is usually replicated over a WAN link to the hot site. Failover may be automatic or manual, depending on business requirements and available resources. Organizations can run their sites in “active‐active” or “active‐ passive” mode. In active‐active mode, applications at primary and recovery sites are live all the time, and data is replicated bi‐directionally so that all databases are in sync. In active‐ passive mode, one site acts as primary, and data is replicated to the passive standby sites.<br />
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<b>Warm site </b><br />
<br />
With a warm site approach, the organization essentially takes the middle road between the expensive hot site and the empty cold site. Perhaps there are servers in the warm site, but they might not be current. It takes a lot longer (typically a few days or more) to recover an application to a warm site than a hot site, but it’s also a lot less expensive.<br />
<br />
<b>Cold site </b><br />
<br />
Effectively a non‐plan, the cold site approach proposes that, after a disaster occurs, the organization sends backup media to an empty facility, in hopes that the new computers they purchase arrive in time and can support their applications and data. This is a desperate effort guaranteed to take days if not weeks. I don’t want to give you the impression that cold sites are bad for this reason. Based on an organization’s recoverability needs, some applications may appropriately be recovered to cold sites. Another reason that organizations opt for cold sites is that they are effectively betting that a disaster is not going to occur, and thus investment is unnecessary. <br />
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-8972608042173615832018-08-21T11:18:00.000+00:002018-08-21T11:18:43.661+00:00Fundamentals of Data Management in the Age of Big Data<div dir="ltr" style="text-align: left;" trbidi="on">
In the age of GDPR and when new data regulations are being put in place, companies now have to be prudent and cautious in their data management policies. <br />
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Data management, data privacy & security risks pose a great management challenge. In order to address these challenges, companies need to put proper data management policies in place. Here are eight fundamental policies of data management that needs to be adhered to by all companies.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFhZ-qzbnraYt0OlBhptcZLnCi1Nuxvh9xfffkhwkuIJnx_VkVpR6uvuROCx6D9UGsF66vHd_KvvldUquxOjm036nv_gYf6lUStiZCj6vCds0dYnCmwLzgEFnHh3ydrxx3FXvX/s1600/Fundamentals+of+Data+Management+in+the+Age+of+Big+Data.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="852" data-original-width="1422" height="190" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFhZ-qzbnraYt0OlBhptcZLnCi1Nuxvh9xfffkhwkuIJnx_VkVpR6uvuROCx6D9UGsF66vHd_KvvldUquxOjm036nv_gYf6lUStiZCj6vCds0dYnCmwLzgEFnHh3ydrxx3FXvX/s320/Fundamentals+of+Data+Management+in+the+Age+of+Big+Data.png" width="320" /></a></div>
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-27216813379050121832018-08-17T06:57:00.000+00:002018-08-18T08:08:57.018+00:004 Types of Data Analytics<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjE1vs5fMxxe6DrWX26TH9HNXUB1hek-hmQR-B7L1yFJayuJVsQk94WOjirhi2SIkj7lA5PtH3hzvca-YTud5lEsoaFaamKc_oHAQccvtfv-qVeWeXGq-JhMvJcVgnHSlbBBT5S/s1600/4+Types+of+Data+Anaytics.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="787" data-original-width="1391" height="181" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjE1vs5fMxxe6DrWX26TH9HNXUB1hek-hmQR-B7L1yFJayuJVsQk94WOjirhi2SIkj7lA5PtH3hzvca-YTud5lEsoaFaamKc_oHAQccvtfv-qVeWeXGq-JhMvJcVgnHSlbBBT5S/s320/4+Types+of+Data+Anaytics.png" width="320" /></a></div>
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Data analytics can be classified into 4 types based on complexity & Value. In general, most valuable analytics are also the most complex.<br />
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<b>1. Descriptive analytics</b><br />
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Descriptive analytics answers the question: What is happening now?<br />
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For example, in IT management, it tells how many applications are running in that instant of time and how well those application are working. Tools such as <a href="https://www.cisco.com/c/en/us/services/acquisitions/appdynamics.html" target="_blank">Cisco AppDynamics</a>, <a href="https://www.solarwinds.com/network-performance-monitor" target="_blank">Solarwinds NPM </a>etc., collect huge volumes of data and analyzes and presents it in easy to read & understand format.<br />
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Descriptive analytics compiles raw data from multiple data sources to give valuable insights into what is happening & what happened in the past. However, this analytics does not what is going wrong or even explain why, but his helps trained managers and engineers to understand current situation.<br />
<br />
<b>2. Diagnostic analytics</b><br />
<br />
Diagnostic Analytics uses real time data and historical data to automatically deduce what has gone wrong and why? Typically, diagnostic analysis is used for root cause analysis to understand why things have gone wrong.<br />
<br />
Large amounts of data is used to find dependencies, relationships and to identify patterns to give a deep insight into a particular problem. For example, <a href="https://www.emc.com/collateral/software/.../h8918-emc-service-assurance-so.pdf" target="_blank">Dell - EMC Service Assurance Suite </a>can provide fully automated root cause analysis of IT infrastructure. This helps IT organizations to rapidly troubleshoot issues & minimize downtimes.<br />
<br />
<b>3. Predictive analytics</b><br />
<br />
Predictive analytics tells what is likely to happen next.<br />
<br />
It uses all the historical data to identify definite pattern of events to predict what will happen next. Descriptive and diagnostic analytics are used to detect tendencies, clusters and exceptions, and predictive analytics us built on top to predict future trends.<br />
<br />
Advanced algorithms such as forecasting models are used to predict. It is essential to understand that forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires a careful treatment and continuous optimization.<br />
<br />
For example, <a href="https://infosight.hpe.com/" target="_blank">HPE Infosight</a> can predict what can happen to IT systems, based on current & historical data. This helps IT companies to manage their IT infrastructure to prevent any future disruptions.<br />
<br />
<br />
<br />
<b>4. Prescriptive analytics</b><br />
<br />
Prescriptive analytics is used to literally prescribe what action to take when a problem occurs.<br />
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It uses a vast data sets and intelligence to analyze the outcome of the possible action and then select the best option. This state-of-the-art type of data analytics requires not only historical data, but also external information from human experts (also called as Expert systems) in its algorithms to choose the bast possible decision.<br />
<br />
Prescriptive analytics uses sophisticated tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage.<br />
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For example, IBM Runbook Automation tools helps IT Operations teams to simplify and automate repetitive tasks. Runbooks are typically created by technical writers working for top tier managed service providers. They include procedures for every anticipated scenario, and generally use step-by-step decision trees to determine the effective response to a particular scenario.</div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-65393586213457454212018-08-16T11:46:00.000+00:002018-08-16T11:46:11.768+00:00Successful IoT deployment Requires Continuous Monitoring<div dir="ltr" style="text-align: left;" trbidi="on">
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Growth of the IOT has created new challenges to business. The massive volume of IoT devices and the deluge of data it creates becomes a challenge — particularly when one uses IoT as key part of their business operations. These challenges can be mitigated with real-time monitoring tools that has to be tied to the ITIL workflows for rapid diagnostics and remediation. <br />
<br />
Failure to monitor IoT devices leads to a failed IoT deployment.<br />
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-88657813097409995892018-08-16T06:15:00.001+00:002018-08-16T11:20:47.271+00:00Steps in Cloud Adaption at Large Enterprises<div dir="ltr" style="text-align: left;" trbidi="on">
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Large enterprises have bigger challenges when it comes to migrating applications to cloud. Migration to cloud is often an evolutionary process in most large enterprises and is often a 4 step process - but not necessarily a sequential process, and can happen in sequence or on parallel.<br />
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Moving to cloud requires a complete buy-in from all business & IT teams: developers, compliance experts, procurement, and security.<br />
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The first step is all about becoming aware of cloud technologies and its implications. IT team will need to understand:<br />
<br />
1. What are benefits - Agility, cost savings, scalability, etc.<br />
2. What is the roadmap for moving to the cloud?<br />
3. What skills each team member will need?<br />
4. How does the legacy applications work in the future?<br />
5. Who are the partners in this journey?<br />
<br />
The second step is all about experimentation and learning from those small experiments. These are typically PoC projects which demonstrates the capability & benefits. The PoC projects are needed to get key stake holder buy-in.<br />
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Third step is essentially a migration of existing apps to cloud. For example moving emails to cloud or moving office apps to Offce365 cloud etc. These projects are becoming a norm for large enterprises - which have a rich legacy.<br />
<br />
Fourth step demonstrates the final maturity of cloud. In this stage, companies now deploy all new apps on cloud and these are cloud only apps.</div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-64156593828772650152018-07-26T17:15:00.000+00:002018-07-26T17:15:28.229+00:004 Stages of Developing a Data Lake<div dir="ltr" style="text-align: left;" trbidi="on">
Companies generally go through the following four stages of development when building a data lake:<br />
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-91124355825334608712018-07-25T12:20:00.000+00:002018-07-25T12:20:13.468+00:00Why Edge Computing is critical for IoT success?<div dir="ltr" style="text-align: left;" trbidi="on">
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Edge computing is the practice of processing data near the edge of your network, where the data is being generated, instead of in a centralised data-processing warehouse.<br />
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Edge computing is a distributed, open IT architecture that features decentralised processing power, enabling mobile computing and Internet of Things (IoT) technologies. In edge computing, data is processed by the device itself or by a local computer or server, rather than being transmitted to a data centre.<br />
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Edge computing enables data-stream acceleration, including real-time data processing without latency. It allows smart applications and devices to respond to data almost instantaneously, as its being created, eliminating lag time. This is critical for technologies such as self-driving cars, and has equally important benefits for business.<br />
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Edge computing allows for efficient data processing in that large amounts of data can be processed near the source, reducing Internet bandwidth usage. This both eliminates costs and ensures that applications can be used effectively in remote locations. In addition, the ability to process data without ever putting it into a public cloud adds a useful layer of security for sensitive data.<br />
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-75806853095257001322018-07-23T08:50:00.001+00:002018-07-23T08:50:49.070+00:008 Key Points in a Product Plan<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7mKatX16LsLOBhRw7dMJ5pm4JCQUqUUmUtmMLj_rui118llCjcAEuW5OwIhZTW39PqaOIjIB6GI6qGmxMTlQLFvynuc9q8lFq0xG3DXf684wYfLTUdUfE_brgOiCn_CvDi0Zc/s1600/8+Key+Points+in+a+Product+Plan.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="695" data-original-width="1466" height="151" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7mKatX16LsLOBhRw7dMJ5pm4JCQUqUUmUtmMLj_rui118llCjcAEuW5OwIhZTW39PqaOIjIB6GI6qGmxMTlQLFvynuc9q8lFq0xG3DXf684wYfLTUdUfE_brgOiCn_CvDi0Zc/s320/8+Key+Points+in+a+Product+Plan.png" width="320" /></a></div>
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<br />
Developing a great product is not an accident. It takes careful planning upfront in developing a Product Requirement Document (PRD).<br />
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A good PRD addresses 8 main points which are listed here. This document defines what the product will be, what problem it solves, when it will be ready and how much it will cost. There is no limitation on number of pages the document contain, but it could be comprehensive & concise.<br />
<br />
The key to building a great product is to keep this PRD document true to its core intentions. This implies a lot of upfront work, but is absolutely essential for success. If the product is well planned, then only one can build a great product. Oftentimes, it makes sense to develop a user guide as part of the proposed solution – as this helps in developing the product. The amount of work that needs be done upfront is huge – but it aids in every step of product development. Some companies even go into great depths of defining each small step in the project plan with weekly timelines.<br />
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<b><i>"One can achieve greatness with 10000 small steps!"</i></b></div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-78770120306526590732018-07-19T02:52:00.000+00:002018-07-19T02:52:35.116+00:005 Pillars of Data Management for Data Analytics<div dir="ltr" style="text-align: left;" trbidi="on">
Basic Data Management Principles for Data Analytics<br />
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Data is the lifeblood for Big data analytics and all the AI/ML solutions built on top.<br />
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Here are 5 basic data management principles that must never be broken.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOEKKif8OOnrwiqro15GTm8d9Gsgad_WS9VyWp9MHNn_H5KHhr13DCN6PhdbfBPsUdSKUjCAmvjPps5c41tkBJwQ5pq6i5xJE-1ur36xdh0UL0_os-ALIQxeUaY_0qLIKMq7lc/s1600/5+Pillars+of+Data+Management+for+Data+Analytics.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="831" data-original-width="1435" height="184" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOEKKif8OOnrwiqro15GTm8d9Gsgad_WS9VyWp9MHNn_H5KHhr13DCN6PhdbfBPsUdSKUjCAmvjPps5c41tkBJwQ5pq6i5xJE-1ur36xdh0UL0_os-ALIQxeUaY_0qLIKMq7lc/s320/5+Pillars+of+Data+Management+for+Data+Analytics.png" width="320" /></a></div>
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<b>1. Secure Data at Rest</b><br />
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<ul style="text-align: left;">
<li>Most of the data is stored in storage systems which must be secured.</li>
<li>All data in storage must be encrypted </li>
</ul>
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<br />
<b>2. Fast & Secure Data Access </b><br />
<br />
<ul style="text-align: left;">
<li>Fast access to data from databases, storage systems. This implies using fast storage servers and FC SAN networks. </li>
<li>Strong access control & authentication is essential</li>
</ul>
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<br />
<b>3. Manage Networks for Data in Transit</b><br />
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<ul style="text-align: left;">
<li>This involves building fast networks - a 40Gb Ethernet for compute clusters and 100Gb FC SAN networks</li>
<li>Fast SD-WAN technologies ensure that globally distributed data can be used for data analytics.</li>
</ul>
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<br />
<b>4. Secure IoT Data Stream</b><br />
<br />
<ul style="text-align: left;">
<li>IoT endpoints are often in remote locations and have to be secured.</li>
<li>Corrupt data from IoT will break Analytics.</li>
<li>Having Intelligent Edge helps in preprocessing IoT data - for data quality & security</li>
</ul>
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<br />
<b>5. Rock Solid Data backup and recovery</b><br />
<br />
<ul style="text-align: left;">
<li>Accidents & Disasters do happen. Protect from data loss & data unavailability with a rock solid data backup solutions.</li>
<li>Robust disaster recovery solutions can give zero RTO/RPO.</li>
</ul>
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<div>
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</div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-77754692613150006722018-07-18T08:27:00.000+00:002018-07-18T08:27:08.065+00:00Business Success with Data Analytics<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh6I3s6as40ugItCIHjU94GluR8v04V6leqpxilvir-apMiX5SN17K2msZiVCXNPXKK5G632QXPc02HslSimaY5V6uZ9Ew3k0zhByUA9QHX_US_niclwbgRfjHDSdl5v154kISn/s1600/Business+Success+with+Data+Analytics.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="775" data-original-width="1493" height="166" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh6I3s6as40ugItCIHjU94GluR8v04V6leqpxilvir-apMiX5SN17K2msZiVCXNPXKK5G632QXPc02HslSimaY5V6uZ9Ew3k0zhByUA9QHX_US_niclwbgRfjHDSdl5v154kISn/s320/Business+Success+with+Data+Analytics.png" width="320" /></a></div>
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Data and advanced analytics have arrived. Data is becoming ubiquitous but several organizations are struggling to use data analytics in everyday business process. Companies who adapt data analytics in the truest and deepest levels will have a significant competitive advantage, ; those who fall behind risk becoming irrelevant. Analytics has the potential to upend the prevailing business models in many industries, and CEOs are struggling to understand how analytics can help.<br />
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Here are 10 key points that must be followed to succeed.<br />
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<br />
<ol style="text-align: left;">
<li>Understand how Analytics can disrupt your industry</li>
<li>Define ways in which Analytics can create value & new opportunities</li>
<li>Top managers should learn to love metrics and measurements</li>
<li>Change Organizational structures to enable analytics based decision making</li>
<li>Experiment with data driven, test-n-learn decision making processes</li>
<li>Data Ownership must be well defined & Data Access must be made easier</li>
<li>Invest in data management, data Security & analytics tools</li>
<li>Invest in training & hiring people to drive analytics </li>
<li>Establish Organizational Benchmarks for data analytics</li>
<li>Layout a long term road map for business success with Analytics</li>
</ol>
</div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-44330838767716955702018-07-06T07:58:00.001+00:002018-07-06T07:59:16.949+00:005 AI uses in Banks Today<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhwxxFDMaMXbFQmH_RYK7PuRRtEujlQhT5qglnDkmSDpP7DAXcWxDzSO6SgEQEVjj4x-GkdJuahunHDX_cyZ08stTF1_VAmlcl9j_vjjT_hkaRvFuartqTFgbuPqkoDmXZ2mxdx/s1600/5+AI+uses+in+Banks+Today.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="783" data-original-width="1279" height="195" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhwxxFDMaMXbFQmH_RYK7PuRRtEujlQhT5qglnDkmSDpP7DAXcWxDzSO6SgEQEVjj4x-GkdJuahunHDX_cyZ08stTF1_VAmlcl9j_vjjT_hkaRvFuartqTFgbuPqkoDmXZ2mxdx/s320/5+AI+uses+in+Banks+Today.png" width="320" /></a></div>
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<br />
<br />
<b>1. Fraud Detection</b><br />
Artificial intelligence tools improve defense against fraudsters and allowing banks to increase efficiency, reduce headcount in compliance and provide a better customer experience.<br />
For example, if a huge transaction is initiated from an account with an history of minimal transactions – AI can shop the transactions until it is verified by a human. <br />
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<b>2. Chatbots</b><br />
Intelligent chatbots can engage users and improve customer service. AI chatbot brings a human touch, have human voice nuances and even understand the context of the conversation.<br />
Recently Google demonstrated its AI chatbot that could make table reservation at a restaurant.<br />
<br />
<b>3. Marketing & Support</b><br />
AI tools have the ability to analyze past behavior to optimize future campaign. By learning from prospect’s past behavior, AI tools automatically select & place ads or collateral for digital marketing. This helps craft directed marketing campaigns<br />
Also see: https://www.techaspect.com/the-ai-revolution-marketing-automation-ebook-techaspect/<br />
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<b>4. Risk Management</b><br />
Real time transactions data analysis when used with AI tools can identify potential risks in offering credit. Today, banks have access to lots of transactional data – via open banking, and this data needs to be analyzed to understand micro activities and access the behavior of parties to correctly identify risks. Say for example, if the customer has borrowed money from a large number of other banks in recent times.<br />
<br />
<b>5. Algorithmic Trading</b><br />
AI takes data analytics to the next level. Getting real time market data/news from live feeds such as Thomson Reuters Enterprise Platform, Bloomberg Terminal etc., and AI tools can use this information to understand investor sentiments and take real-time decisions on trading. This eliminates the time gap between insights & action.<br />
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-21148781082997698402018-07-05T04:33:00.002+00:002021-06-15T06:49:48.711+00:00Importance of Fintech to India<div dir="ltr" style="text-align: left;" trbidi="on">
On 8 November 2016, the Government of India announced the demonetization of all ₹500 and ₹1000 banknotes, it set off a wave of Fintech growth in India. Fintech is now mainstream and a critical segment for the future of India's economic growth.<br />
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Here are 10 reasons why Fintech is very important to India.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhZcgiPG-oNsq42cAOeT6bn0I78HenxuJ3NCxcDlAR_5atg1Ek-u9u5mNMTFUHYOoyFUntRHi01xGhUxOWNJ36VErVN25SO2wYJrDEVxAYCN_10_dvlHLrA8lz3ya17iCNvZTMM/s1600/Importance+of+Fintech+to+India.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="821" data-original-width="1493" height="174" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhZcgiPG-oNsq42cAOeT6bn0I78HenxuJ3NCxcDlAR_5atg1Ek-u9u5mNMTFUHYOoyFUntRHi01xGhUxOWNJ36VErVN25SO2wYJrDEVxAYCN_10_dvlHLrA8lz3ya17iCNvZTMM/s320/Importance+of+Fintech+to+India.png" width="320" /></a></div>
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<br />
<b>1. Economic Growth</b><br />
The payment segment has been a major enabler of economic growth. Electronic payments systems added $300B to GDP in 70 countries between 2011-2015, which resulted in ~2.6Million Jobs/Yr<br />
Each 1% increase in electronic payment produces ~$104 B in consumption of goods & services<br />
<br />
<b>2. Financial Inclusion</b><br />
Fintech opens up opportunities for the previously unbanked population to access modern financial instruments. For people living in poverty or at the fringes of the economy, Fintech lowers costs of Financial transactions: Lower cost of credit and other banking services.<br />
<br />
<b>3. Speed & Quality of Innovation</b><br />
Fintech drives improvements in traditional financial services – which will replace legacy systems. Eg: Peer-to-peer lending, Robo advisors, Hi-frequency trading<br />
<br />
<b>4. Business Sustainability & Scalability</b><br />
Fintech has made businesses sustainable & Scalable. The entire e-commerce economy was built on e-payment systems and new business models such as Ridesharing: OLA, UBER, Metro Bikes, etc were developed on Fintech e-payment systems – which allows these businesses to scale and grow rapidly<br />
<br />
<b>5. Transparency & Audits</b><br />
All digital transactions are inherently auditable hence bringing greater transparency into the system. Data sharing in real-time across banks & financial institution reduce fraud risks and reduces the cost of regulatory processes.<br />
<br />
<b>6. New Value Streams</b><br />
New fintech technologies are creating new business opportunities. Bitcoin & other cryptocurrencies have spawned whole new businesses.<br />
<br />
<b>7. Market Curation & Structural Transformation </b><br />
Fintech technologies are transforming other industries. For example, healthcare record management, Real estate, land registration with Blockchain, etc. This is bringing structural reforms to businesses that were on the fringes of the regulated economy into the mainstream economy.<br />
<br />
<b>8. Collaborative Culture</b><br />
New Fintech businesses are built in collaboration with other businesses. For example, Blockchain is based on open collaboration between members who host the shared ledger.<br />
<br />
<b>9. The Scale of the Industry</b><br />
Fintech has grown from being a niche to mainstream. Today Fintech companies are collectively worth more than $500Billion and directly employ millions of men.<br />
<br />
<b>10. Borderless Innovation</b><br />
Technological innovations in Fintech can be quickly adapted across borders, creating new competition and new opportunities for existing players. This rapid innovation is bringing whole new financial hubs and opening new markets.<br />
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</div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-2798422257861580572018-07-04T09:26:00.002+00:002018-07-04T09:27:25.705+00:00Skills Needed To Be A Successful Data Scientist<div dir="ltr" style="text-align: left;" trbidi="on">
Data Scientist, the most demanded job of 21st century, requires multidisciplinary skills – mix of Math, Statistics, Computer Science, Communication & Business Acumen.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgd49oJ2m7JZt8pJ4BdEpno_xt5-oKUeaVLPiW-zYVFJ3LJbUlM_fLDiVrgBlGlZZ8jul9R6gtxreJgb94S6faTDC-426YXxaJPHG8zDpHcW2Ba4Zma-zaTtR9HbTjOh6Tl6bsA/s1600/Skills+needed+to+be+a+Successful+Data+Scientist.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="685" data-original-width="1308" height="167" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgd49oJ2m7JZt8pJ4BdEpno_xt5-oKUeaVLPiW-zYVFJ3LJbUlM_fLDiVrgBlGlZZ8jul9R6gtxreJgb94S6faTDC-426YXxaJPHG8zDpHcW2Ba4Zma-zaTtR9HbTjOh6Tl6bsA/s320/Skills+needed+to+be+a+Successful+Data+Scientist.png" width="320" /></a></div>
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-20542648232756281802018-07-04T08:45:00.001+00:002018-07-04T08:45:33.446+00:00Top Challenges Facing AI Projects in Legacy Companies<div dir="ltr" style="text-align: left;" trbidi="on">
Legacy companies which have been around for more than 20 years have been always slow to embrace new technologies & the case is also very true with embracing AI technologies.<br />
<br />
Companies relutcantly start few AI projects - only to abandon them.<br />
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Here are are the top 7 challenges AI projects face in legacy companies:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi2YFU0xoQFCJsfolYVAPs9oJr-GiDwqgf7shYy0bv-fw3_viau4CMy52u5-YhPxYlPcIPVUwAWwg2UnCUPRE1lkmcMqVtgnz7I7mS4M60pPGKKEafkejxpBxlfG81b6vFNyUX5/s1600/Top+Challenges+Facing+AI+projects+in+Legacy+Companies.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="756" data-original-width="1579" height="153" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi2YFU0xoQFCJsfolYVAPs9oJr-GiDwqgf7shYy0bv-fw3_viau4CMy52u5-YhPxYlPcIPVUwAWwg2UnCUPRE1lkmcMqVtgnz7I7mS4M60pPGKKEafkejxpBxlfG81b6vFNyUX5/s320/Top+Challenges+Facing+AI+projects+in+Legacy+Companies.png" width="320" /></a></div>
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<b>1. Management Reluctance</b><br />
Fear of Exacerbating asymmetrical power of AI<br />
Need to Protect their domains<br />
Pressure to maintain statusquo<br />
<br />
<b>2. Ensuring Corporate Accountability </b><br />
Internal Fissures<br />
Legacy Processes hinder accountability on AI systems<br />
<br />
<b>3. Copyrights & Legal Compliance </b><br />
<br />
<ul style="text-align: left;">
<li>Inability to agree on data copyrights</li>
<li>Legacy Processes hinder compliance when new AI systems are implemented</li>
</ul>
<br />
<br />
<b>4. Lack of Strategic Vision</b><br />
<br />
<ul style="text-align: left;">
<li>Top management lacksstrategic vision on AI</li>
<li>Leaders are unaware of AI's potential</li>
<li>AI projects are not fully funded </li>
</ul>
<br />
<br />
<b>5. Data Authenticity</b><br />
<br />
<ul style="text-align: left;">
<li>Lack of tools to verify data Authenticity</li>
<li>Multiple data sources</li>
<li>Duplicate Data </li>
<li>Incomplete Data</li>
</ul>
<br />
<br />
<b>6. Understanding Unstructured Data</b><br />
<br />
<ul style="text-align: left;">
<li>Lack of tools to analyze Unstructured data</li>
<li>Middle management does not understand value of information in unstructured data</li>
<li>Incomplete data for AI tools</li>
</ul>
<br />
<br />
<b>7. Data Availability</b><br />
<br />
<ul style="text-align: left;">
<li>Lack of tools to consolidate data </li>
<li>Lack of knowledge on sources of data</li>
<li>Legacy systems that prevent data sharing </li>
</ul>
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<br /></div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-61264740406005815942018-07-02T15:50:00.001+00:002018-07-02T16:00:19.411+00:00Benefits of Aadhaar Virtual ID<div dir="ltr" style="text-align: left;" trbidi="on">
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Use Aadhaar Virtual ID to Secure your Aadhaar Details<br />
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Considering the privacy of the personal data including the demographic and biometric information mentioned on the Aadhaar card, UIDAI has recently decided to come up with a unique feature, termed as Aadhaar Virtual ID.<br />
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The Aadhaar Virtual ID offers limited KYC access providing only that much information which is required for verification rather than offering complete details of an individual's Aadhaar card.<br />
<br />
<b>What is an Aadhaar Virtual ID?</b><br />
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<span style="text-indent: -0.04in;">The Aadhaar Virtual ID consists of 16-digit random numbers that is mapped to an individual's Aadhaar card at the back end. An Aadhaar card holder using the virtual ID need not submit his Aadhaar number every time for verification purpose, instead he can generate a Virtual ID and use it for various verification purposes like mobile number, bank and other financial documents.</span><br />
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The Aadhaar Virtual ID gives access to the biometric information of an Aadhaar card holder along with the basic details like name, address and photograph that are sufficient for the e-KYC. Unlike in the past, the agency will not know the 12-digit Aadhaar number and other personal details.<br />
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<b>Benefits of Aadhaar Virtual ID</b><br />
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<ol style="text-align: left;">
<li>Complete Privacy of personal Data<br />eKYC can now be done without sharing Aadhaar number<br />All private information: biometric, DOB, address are private<br /> </li>
<li>User has complete control on sharing Aadhaar ID details<br />Only the Aadhaar card holder can generate virtual ID<br />
Only the Aadhaar card holder can share virtual ID<br />
Aadhaar Virtual ID expires after a pre-set time, preventing misuse<br /> </li>
<li>Automates all eKYC verification process in the backend<br />Simplifies agencies task of individually verifying KYC data<br />Web Based verification system is fast and reliable for real time business applications</li>
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<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0tag:blogger.com,1999:blog-13212132.post-31274281111178834692018-07-02T10:21:00.000+00:002018-07-02T10:21:17.794+00:00Big Data Analytics for Digital Banking<div dir="ltr" style="text-align: left;" trbidi="on">
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Big Data has a huge impact on banking, especially in the era of digital banking.<br />
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Here are six main benefits for data analytics for banks.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-qHcCwPwHdsLZzIHHxd2CSF5aOmFp0rW30XpkzrfrqakILnjcqWXYmJPGoR9sK1Cmcf_f-rU-aDRODh__jv_1tnKn_5bfUjPZd5VjZiOOwwlMwAR6QvWK20WpOZCHijv7d6ST/s1600/Big+Data+Analytics+for+Digital+Banking.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="736" data-original-width="1522" height="154" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-qHcCwPwHdsLZzIHHxd2CSF5aOmFp0rW30XpkzrfrqakILnjcqWXYmJPGoR9sK1Cmcf_f-rU-aDRODh__jv_1tnKn_5bfUjPZd5VjZiOOwwlMwAR6QvWK20WpOZCHijv7d6ST/s320/Big+Data+Analytics+for+Digital+Banking.png" width="320" /></a></div>
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<b>1. Customer Insights</b><br />
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Banks can follow customer's social media & gain valuable insights on customer behavior patterns<br />
Social media analysis gives a more accurate insights than traditional customer surveys<br />
Social media analysis can be near real time, thus helping understand customer needs better<br />
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<b>2. Customer Service</b><br />
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Big data analysis based on customer's historical data, current web data can be used to identify customer issues proactively and resolve them even before customer complains<br />
Eg: Analyzing customers geographical data can help banks optimize ATM locations<br />
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<b>3. Customer Experience </b><br />
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Banks can use big data analytics to customize website in real time - to enhance customer experience.<br />
Banks can use analytics to send real time messages/communications regarding account status etc.,<br />
With Big Data analytics, Banks can be proactive to enhance custoemr service.<br />
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<b>4. Boosting Sales</b><br />
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Social media analysis gives a more accurate insights into customer's needs and help promote the right banking products to customers. For e.g., customers looking at housing advertisements and discussing housing finance in social media - are most likely in need of a housing loan.<br />
Data analytics can accurately acess customer's needs & banks can promote right types of solutions.<br />
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<b>5. Fraud Detection</b><br />
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Big Data analysis can detect fraud in real time and prevent it<br />
Data from third parties and banking networks holds valuable information about customer interactions.<br />
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<b>6. New Product Introduction</b><br />
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Big Data analysis can identify new needs and develop products that meet those needs<br />
Eg: Mobile Payment services, Open Bank APIs, ERP Integration gateways, International currency exchange services etc are all based on data analytics</div>
<div class="blogger-post-footer">Arun Kottolli
arunkottolli.blogspot.com</div>Arun Kottollihttp://www.blogger.com/profile/03340453577675308779noreply@blogger.com0