Implement ready to use digital solutions and change internal processes instead of starting from scratch to build solutions to cater to its processes. Don’t shy from exploring global solutions, you would most likely get a great product which may not be expensive. Insist on following the Methodology of “Pay as you Use or Pay as you Grow” instead of incurring significant implementation charges and license fees. Explore working with StartUps who are hungry for businesses and will go out of the way to build great solutions. A robust database for sending relevant, targeted and personalized communications Make a beginning and take baby steps. Focus on 90% of your requirements. Lot of time and energy is spent on addressing 10% of requirements which can be done manually or there could be a work around We are at the cusp of a brave, new world that demands self-sufficiency, and it is becoming rapidly clear that greater digital freedom will play a pivotal role in making the Industry more effective, scalable and enduring on this uncharted road ahead. Firms that deploy these tools fast will attract clients and survive. The Industry has always been one to shy away from digital transformation.
In earnest, the difficulty of recruiting diverse candidates reflects the fact that the networks the banking industry typically relies upon to attract and recruit talent do not reach diverse pools of talented candidates. This network gap is insidious too, leading to a lack of diversity in other aspects of business, like vendor procurement and investment. Once, Mitt Romney spoke of “binders full of women” when running for president. While his wording was inartful, he seemed to recognize that he needed to make a deliberate effort to build his network of talented women in order to be able to appoint numbers of qualified women. So, what deliberate steps can banks take to close the network gap and find talented people of color? Here are a few things any bank can do to turn intention into impact, and close the network gap. Begin with reflection: Why are you not tied to diverse networks? Do you know where to find black and brown civil society? Learning why your company may not be a cultural fit for certain demographics is nothing new for banks. Gender is probably the most recent example. Understanding that women bring different and needed experience to leadership creates an impetus for more diversity.
The first step is demystification. All of the abstract terms – even the word “architecture” – should be modified or replaced with words and phrases that everyone – especially non-technology executives – can understand. Enterprise planning or Enterprise Business- Technology Strategy might be better, or even just Business-Technology Strategy (BTS). Why? Because “Enterprise Architecture” is nothing more than an alignment exercise, alignment between what the business wants to do and how the technologists will enable it now and several years out. It’s continuous because business requirements constantly change. At the end of the day, EA is both a converter and a bridge: a converter of strategy and a bridge to technology. The middle ground is the Business-Technology Strategy. EA – or should I say “Business Technology Strategy” – isn’t strategy’s first cousin, it’s the offspring. EA only makes sense when it’s derived from a coherent business strategy. For technology companies, that is, companies that sell technology-based products and services – the role of EA is easier to define. Who doesn’t want to help technology (AKA “engineering”) – the ones who build the products and services – build the right applications with the right data on the right infrastructure?
Amazon says you can specify a flight path, map your house, locate points of interest, and generally instruct the eye of Skynet where to fly. Cyberdyne, uh, Amazon also says the device has built in obstacle avoidance. Let's think about that for a minute. Will the device be able to avoid hanging lamps or plants? What about objects high up on shelves? Will it be able to stand back when a sleep-addled adult gets up in the middle of the night to do middle of the night business? Why would it be out and about at that time anyway? And what about the downdraft? How close can it fly to bookshelves and knickknacks without air-blasting them to the ground? How much will it freak out your pets? My spouse? Your spouse? Just how creepy would it be for it to hover over the kids beds because you're too lazy to get off the couch to see if they're asleep? Every rational fiber of my being tells me this is wrong on every level. ... The Always Home Cam is primarily meant as a remote security cam. If you're out and you get an alert from a Ring doorbell or other security device (I wonder if this will work with other trigger devices), you can virtually fly around your house and see what's happening.
Project InnerEye has been working closely with the University of Cambridge and Cambridge University Hospitals NHS Foundation Trust to make progress on this problem through a deep research collaboration. Dr. Raj Jena, Group Leader in machine learning and radiomics in radiotherapy at the University of Cambridge, explains, “The strongest testament to the success of the technology comes in the level of engagement with InnerEye from my busy clinical colleagues. For over 15 years, the promise of automated segmentation of images for radiotherapy planning has remained unfulfilled. With the InnerEye ML model we have trained on our data, we now observe consistent segmentation performance to a standard that matches our stringent clinical requirements for accuracy.” The goal of Project InnerEye is to democratize AI for medical image analysis and empower developers at research institutes, hospitals, life science organizations, and healthcare providers to build their own medical imaging AI models using Microsoft Azure. So to make our research as accessible as possible, we are releasing the InnerEye Deep Learning Toolkit as open-source software.
Data analysts and business analysts rely heavily on a fit-for-purpose data environment that enables them to do their jobs well. These environments allow them to answer questions from management and different parts of the business. These same professionals have expertise in working and communicating with data but often do not have deep technical knowledge of databases and the underlying infrastructure. For instance, they may be familiar with SQL and bringing together data sources in a simple data model that allows them to dig deeper in their analysis, but when the database performance degrades during more complex analysis, the depth of infrastructure reliance becomes clear. The dreaded spinner wheel or delays in analysis make it difficult to meet business needs and demands. This can impact critical decision making and reveal underlying weaknesses that get in the way of other data applications, such as artificial intelligence (AI). These indicators of poor performance also show the need for scaling the data environment to accommodate the growth of data and data sources.
I think FAIR has really codified a way of thinking about data that's incredibly aspirational and resonates with people. One of the biggest challenges we're facing in this field right now is findability of the data—search is a hard problem. Then let's say you manage to find some data that you're very interested in; a lot of the time it's not clear whether or not those data are accessible to you or to the public. There's been a large push over the last decade to make everything reproducible, to make the data accessible, to have a data management plan. A lot of that effort isn't necessarily resourced, so just because you have a data management plan doesn't mean that you have a clear place where you can actually put data. We're lucky that the Sequence Read Archives exist and that the NIH continues to fund it, because that's become one of these major focal points for collecting the data. But even more than that, when you're in the middle of collecting data for a very specific question, you're not necessarily thinking about what other information to collect to make these data useful to other groups or other labs. That's not a part of the thought experiment that you're going through in that moment.
Quote for the day:
"A company is like a ship. Everyone ought to be prepared to take the helm." -- Morris Wilks