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BPM is no more a Healthy Salad Diet of plain & bland Process & Rules. It has matured and become an amalgamation of all the disruptive technologies and digital trends. In a nutshell, it has become more of a BRUNCH Meal.
The asks of the customers and market trends are cherry picked an packaged in a BPM Product – making it a self sufficient enterprise solution [at times] but on a lighter note Bulkier too [day-by-day the size of the BPM Product Installable is getting increased] 🙂
BPM Products (these days), keep adding some new feature into their kitty to make life easy for the Business as well as IT stakeholders. With new disruptive technologies getting mushroomed everyday, BPM packaged tools provide some flavor or slice of the new technology/trend. Its no longer an a-la-carte menu kind of offering for enterprise, instead a Packaged Combo Meal serving a set of business audience or business challenges/pain-points.
The features that were the key drivers and engine of BPM a decade earlier [during its inception] are getting enhanced [no doubt about it] but at the same time getting shadowed by the competitive chaos and disruptive tech. trends.
For every customer the four-walls within which they can experiment and play around with technologies / trends are the following : [these become the key focus areas for BPM Product Vendors]
- Un-parallel Customer Experience inline with the Enterprise Ux[User Experience]
- Enterprise Architecture – Guidelines/Standards/Compliance
- Cost-Pace-Quality [CPQ] Factor
- Business Strategy & Workforce Productivity
When we are at the verge of bidding farewell to the current year and welcoming the New Year. The curious question that keeps popping / itching at this time of the year
- “What are the BPM Predictions for the Next Year?” or
- “What’s cooking for BPM, in the next year?” or
- “What the Master-Chef BPM Product vendors have to Offer?”
- “Is the coming Year going to be BPMlicious”
…Stay Tuned for the Next Post on BPM Predictions for 2017!
Please do share your comments, thoughts and suggestions !!
Happy Reading 🙂
Last Year’s Post : What are your Predictions for BPM 2016?
Image Source: Link1 [The intent of using the images was to pictorially share the thoughts and share the learning. Happy to share Credits for the photos and images] Some of the pics have been taken directly from my Breakfast Table 🙂
The Banking & the Financial services industry is the widely and wildly conquered sector where digitization and automation has been enabled to the max by IT. But in recent times, with the advancement of technology and innovations, it is important for the Banking industry to explore, pace up, transform, innovate, re-think and live in the present. The technology adoption and re-incarnation of business strategy/priorities to serve the customers will definitely play a crucial role in the Bank’s journey in making it stand tall n strong in the crowd (as a differentiator).
A nice read article on “Top Retail Banking Trends & Predictions“.
Key Highlights :
- The ‘Platformification’ of Banking
- Removing Friction from the Customer Journey
- Making Big Data Actionable
- Introduction of ‘Optichannel’ Delivery
- Expansion of Digital Payments
- Executing on Innovation
- Exploring Advanced Technologies
- Emergence of a New Breeds of Banks
- Mining New Talent
- Responding to Regulatory and Rate Changes
few additions …
- Next-Gen Customer Experience by including
- wearable device adoption
- analysis driven Next Best Actions
- ..and many more
- Developing Customer Centric Applications (primarily self service enabled)
- Welcome & Leverage Open Source Stacks (OSS)
- Crowd-Sourcing Initiatives (loan, mortgage, risk management etc)
- Blockchain technology making the financial system more decentralized
- Emergence of Banking Market Apps
- Robot Advisers that stop you from making unsound financial choices, in real time – Customer Assist
- Establishment of Banking Innovation Labs
- Beacon Technology Adoption
- Heat-Map Technology – Identifying right customer for the right product at the right time
- Automated Appointment/Scheduling/Token Management – helps avoiding unexpected crowd and long queues
- Battling with mushrooming Mobile Wallets
- DevOps driven CI/CD in IT – for streamlining Release Management
- Streamlining Operations in IT with Robotic Automation / Machine Learning techniques
Interesting Read Articles:
- 10 Branch Banking Innovation Strategies for 2016
- The Uberization of Banking
- What Makes a Great Mobile Banking App
- Digital Disruption Forces Financial Institutions to Rethink Priorities
- Selfies Transforming Mobile Banking
- With Digital Banking, Consumers Must Come First
- Internet of Things: Opportunity for Financial Services?
- Banks Need to Make Mobile Apps An Experience, Not An Add-On
- Article : The derivative effect: How financial services can make IoT technology pay off (link : pdf)
- 7 Habits Of A Highly Successful Digital Bank
- Differentiation in Banking Requires Better Data Insights
- New Customer Onboarding Goes Beyond Slick Marketing (link: pdf)
- Mobile Banking Satisfaction Drops As Digital Expectations Rise
- Banks Expand Innovation Investments to Battle Fintech
- How Banking Can Survive Digital Disruption
- Three Barriers to Banking Innovation
- Digital Thinking for Bank 1.0
- Becoming a Smarter Bank
What are your top trends & predictions in the retail banking space?
Happy Learning!! 🙂
The power to Think, Decide and Act based on situation, emotion and person is something that makes Human a very unique species in the ecosystem.
Machine Learning is like building the Human type of behavior in a Non-liven Object/Machine/System based on some highly rich and complex algorithms and techniques.
Machine Learning helps me what I should take an fits my taste before I myself realize and ask for the same.
Before deep-diving into the details and granularity of the Machine Learning features. Let’s get a feel and heads up – where in our day-to-day real life Machine Learning is important and makes sense :
- Banking / Retail / Telecommunication
- Prospective Customers and Partners
- Satisfactory index of the Customer (based on relationship, transaction, marketing campaign etc.)
- Fraud, Waste and Abuse of Claims
- Forecasted Credit Risk and credibility of the customer
- Effectiveness of a Marketing Campaign
- Eg: How many accepted the offer and how many rejected it. Any decisive factors leading to acceptance.
- Cross Selling and Recommendations
- Eg: Ecommerce sites : People who purchased this product also purchased this
- Contact Center (helps the CSR to engage the customer during the call with the relevant data)
- Eg: We have seen that you have ordered the cheque books to a separate address (different from the registered address) – would you like to change your Address Details
- Healthcare & Life sciences
- Scanning & Screening – Biometrics
- Drug Discovery based on the component mix
- Diagnosis and remediation based on the Symptoms, Patient Record and Lab Reports
- AECP – Adverse Event Case Processing Scenarios based on drug, patient, geo, climatic conditions, past history, food intake etc.
- Handwriting to Text or Speech (Identification & Learning Graphology Techniques)
- Debugging, Troubleshooting and Solution Wizard
- Email filtering based on Spam
- Text & Mail categorization/recommendation
- Support Issues and enriching KeDBs (Knowledge Error Databases)
- Friends and Colleagues Recommendation – via Facebook, LinkedIn, Twitter etc.
- Self-Driving Cars – by building artificial intelligence and algorithms
- Image Processing
- Handwriting / Signature / Fingerprint / Iris / Retina Verification
- Face Recognition
- DNA Pattern Matching
(Feel free to suggest and help me enrich this list based your experience and the real time scenarios you have witnessed)
- Building something with a machine/non-living object which is a replica of human brain and that caters to meet the brains of millions and billions of users – is NOT an EASY job.
- A Rich Volume, Quality Data and Flawless Algorithm is very critical for building/training a Machine Learning Model to think/decide/act like humans do.
- With millions/billions of non-stop data processing – human mind can get fatigue/tired. At times the Human factor also creates a “Dependency“. This is where Machine Learning Algorithms play a crucial role. Build once and automate it.
- In simple words : “Big Data + Machine Learning = Deadly Duo“
Stay Tuned and Watchout for this space for Detailed Blogs/Notes/Links on Deep Diving Machine Learning. Till then Thanks for Stopping By.
Happy Learning!! 🙂
Big Data – A Visual History (Timeline)
Source Site : http://www.winshuttle.com/big-data-timeline/
Learning Mongo DB!!
Started to gear up and lean a NoSQL DB – Mongo DB. During the course of navigating and visiting multiple learning / training links – am collating the same for reference.
Please do post/comment and share any useful links you came across while kichstarting to learn MongoDB
- Mongo DB – Wiki
- A MongoDB Shell in your browser Just enough to scratch the surface
- Mongo DB Tutorial
- Getting Started with MongoDB
- How I Learned MongoDB – Rackspace
- Learn Mongo
- MongoDB University
- Getting Started with MongoDB – Part 1
- Getting Started with MongoDB – Part 2
- Mongo DB Tutorial
- Introduction to MongoDb
- Java MongoDB Tutorial
- Getting Started with MongoDB DrDobbs
- Learn MongoDB Step-By-Step
- Twitter Handle – Learn Mongo
- MongoDB tutorial video
- MongoDB Documentation
- MongoDB – An introduction and performance analysis
- MongoDB Operations Best Practices
- REFERENCE CARDS for MongoDB
- MongoDB Quick Reference Cards
- MongoDB Booklet
- Mongo DB Blogs
Happy Learning 🙂