Thursday, July 19, 2018

5 Pillars of Data Management for Data Analytics

Basic Data Management Principles for Data Analytics

Data is the lifeblood for Big data analytics and all the AI/ML solutions built on top.

Here are 5 basic data management principles that must never be broken.



1. Secure Data at Rest

  • Most of the data is stored in storage systems which must be secured.
  • All data in storage must be encrypted  


2. Fast & Secure Data Access 

  • Fast access to data from databases, storage systems. This implies using fast storage servers and FC SAN networks.  
  • Strong access control & authentication is essential


3. Manage Networks for Data in Transit

  • This involves building fast networks - a 40Gb Ethernet for compute clusters and 100Gb FC SAN networks
  • Fast SD-WAN technologies ensure that globally distributed data can be used for data analytics.


4. Secure IoT Data Stream

  • IoT endpoints are often in remote locations and have to be secured.
  • Corrupt data from IoT will break Analytics.
  • Having Intelligent Edge helps in preprocessing IoT data - for data quality & security


5. Rock Solid Data backup and recovery

  • Accidents & Disasters do happen. Protect from data loss & data unavailability with a rock solid data backup solutions.
  • Robust disaster recovery solutions can give zero RTO/RPO.


Wednesday, July 18, 2018

Business Success with Data Analytics


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.

Here are 10 key points that must be followed to succeed.


  1. Understand how Analytics can disrupt your industry
  2. Define ways in which Analytics can create value & new opportunities
  3. Top managers should learn to love metrics and measurements
  4. Change Organizational structures to enable analytics based decision making
  5. Experiment with data driven, test-n-learn decision making processes
  6. Data Ownership must be well defined & Data Access must be made easier
  7. Invest in data management, data Security & analytics tools
  8. Invest in training & hiring people to drive analytics 
  9. Establish Organizational Benchmarks for data analytics
  10. Layout a long term road map for business success with Analytics

Friday, July 06, 2018

5 AI uses in Banks Today





1. Fraud Detection
Artificial intelligence tools improve defense against fraudsters and allowing banks to increase efficiency, reduce headcount in compliance and provide a better customer experience.
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.

2. Chatbots
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.
Recently Google demonstrated its  AI chatbot that could make table reservation at a restaurant.

3. Marketing & Support
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
Also see: https://www.techaspect.com/the-ai-revolution-marketing-automation-ebook-techaspect/

4. Risk Management
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.

5. Algorithmic Trading
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.


Thursday, July 05, 2018

Importance of Fintech to India

On 8 November 2016, the Government of India announced the demonetisation 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 future of India's economic growth.

Here are 10 reasons why Fintech is very important to India.



1. Economic Growth
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
Each 1% increase in electronic payment produces ~$104 B in consumption of goods & services

2. Financial Inclusion
Fintech opens up opportunities for previously unbanked population to access modern financial instruments. For people living in poverty or at the fringes of economy, Fintech lowers costs of Financial transactions: Lower cost of credit and other banking services.

3. Speed & Quality of Innovation
Fintech drives improvements in traditional financial services – which will replace legacy systems. Eg: Peer-to-peer lending, Robo advisors, Hi-frequency trading

4. Business Sustainability & Scalability
Fintech has made businesses sustainable & Scalable. The entire e-commerce economy was built on e-payment systems and new business models such as Ride sharing: OLA, UBER, Metro Bikes etc were developed on Fintech e-payment systems – which allows these businesses to scale and grow rapidly

5. Transparency & Audits
All digital transactions are inherently auditable hence bringing in greater transparency into the system. Data sharing in real time across banks & financial institution reduces fraud risks and reduces cost of regulatory processes.

6. New Value Streams
New fintech technologies are creating new business opportunities. Bitcoin & other crypto currencies have spawned a whole new businesses.

7. Market Curation & Structural Transformation 
Fintech technologies is transforming other industries. For example, healthcare record management, Real estate, land registration with Blockchain etc. This is bringing structural reforms to businesses which were in the fringes of regulated economy into mainstream economy.

8. Collaborative Culture
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.

9. The Scale of the Industry
Fintech has grown from being a niche to mainstream. Today Fintech companies are collectively worth more than $500Billion and directly employees millions on men.

10. Borderless Innovation
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.

Wednesday, July 04, 2018

Skills Needed To Be A Successful Data Scientist

Data Scientist, the most demanded job of 21st century, requires multidisciplinary skills – mix of Math, Statistics, Computer Science, Communication & Business Acumen.