How to handle a multicloud migration: Step-by-step guide
The first order of business is to determine exactly what you want out of a
multicloud platform; what needs are in play, which functions and services should
be relocated, which ones may or should stay in house, what constitutes a
successful migration, and what advantages and pitfalls may arise? You may have a
lead on a vendor offering incentives or discounts, or company regulations may
prohibit another type of vendor or multicloud service, and this should be part
of the assessment. The next step is to determine what sort of funding you have
to work with and match this against the estimated costs of the new platform
based on your expectations as to what it will provide you. There may be a
per-user or per-usage fee, flat fees for services, annual subscriptions or
specific support charges. It may be helpful to do some initial research on
average multicloud migrations or vendors offering the services you intend to
utilize to help provide finance and management a baseline as to what they should
expect to allocate for this new environment, so there are no misconceptions or
surprises regarding costs.
Intro to blockchain consensus mechanisms
Every consensus mechanism exists to solve a problem. Proof of Work was devised
to solve the problem of double spending, where some users could attempt to
transfer the same assets more than once. The first challenge for a blockchain
network was thus to ensure that values were only transferred once. Bitcoin's
developers wanted to avoid using a centralized “mint” to track all transactions
moving through the blockchain. While such a mint could securely deny
double-spend transactions, it would be a centralized solution. Decentralizing
control over assets was the whole point of the blockchain. Instead, Proof of
Work shifts the job of validating transactions to individual nodes in the
network. As each node receives a transaction, it attempts the expensive
calculation required to discover a rare hash. The resulting "proof of work"
ensures that a certain amount of time and computing power were expended by the
node to accept a block of transactions. Once a block is hashed, it is propagated
to the network with a signature. Assuming it meets the criteria for validity,
other nodes in the network accept this new block, add it to the end of the
chain, and start work on the next block as new transactions arrive.
Data’s Struggle to Become an Asset
Data’s biggest problem is that it is intangible and malleable. How can you
attach a value to something that is always changing, may disappear, and has no
physical presence beyond the bytes it appropriates in a database? In many
organizations, there are troves of data that are collected and never used. Data
is also easy to accumulate. Collectively, these factors make it easy for
corporate executives to view data as a commodity, and not as something of value.
Researchers like Deloitte argue that data will never become an indispensable
asset for organizations unless it can deliver tangible business results:
“Finding the right project requires the CDO (chief data officer) to have a clear
understanding of the organization's wants and needs,” according to Deloitte.
“For example, while developing the US Air Force’s data strategy, the CDO
identified manpower shortages as a critical issue. The CDO prioritized this
limitation early on in the implementation of the data strategy and developed a
proof of concept to address it.”
In The Face Of Recession, Investing In AI Is A Smarter Strategy Than Ever
Many business leaders make the mistake of overspending on RPA platforms,
blinded by the promise of some future ROI. In reality, due to the need to
customize RPA to every client, these decision-makers don’t actually know how
long it will take to begin reaping the benefits—if they ever do. I, myself,
have made this mistake in the past, spending far too much time and money on a
tedious RPA solution that was intended to solve a customer success back-office
function, only to find that after the overhead of managing it, the gains were
marginal. If business leaders want to fully maximize their investments and
reap quicker benefits, they’ll go one giant leap beyond automation, landing in
the realm of autonomous artificial intelligence (AI). True AI solutions, which
continually learn from a company’s data to become increasingly accurate with
time, are the holy grail of ROI. Finance leaders are in a great position to
lead the way within their own companies by implementing AI solutions in the
accounting function. Across industries, these teams are sagging under the
weight of endless, tedious accounting tasks, using outdated, ineffective
technology and wasting significant time fixing human errors.
Top 8 Data Science Use Cases in The Finance Industry
Financial institutions can be vulnerable to fraud because of their high volume
of transactions. In order to prevent losses caused by fraud, organizations must
use different tools to track suspicious activities. These include statistical
analysis, pattern recognition, and anomaly detection via machine/deep learning.
By using these methods, organizations can identify patterns and anomalies in the
data and determine whether or not there is fraudulent activity taking place. ...
Tools such as CRM and social media dashboards use data science to help financial
institutions connect with their customers. They provide information about their
customers’ behavior so that they can make informed decisions when it comes to
product development and pricing. Remember that the finance industry is highly
competitive and requires continuous innovation to stay ahead of the game. Data
science initiatives, such as a Data Science Bootcamp or training program, can be
highly effective in helping companies develop new products and services that
meet market demands. Investment management is another area where data science
plays an important role.
A Bridge Over Troubled Data: Giving Enterprises Access to Advanced Machine Learning
Thankfully, the smart data fabric concept removes most of these data troubles,
bridging the gap between the data and the application. The fabric focuses on
creating a unified approach to access, data management and analytics. It builds
a universal semantic layer using data management technologies that stitch
together distributed data regardless of its location, leaving it where it
resides. A fintech organisation can build an API-enabled orchestration layer,
using the smart data fabric approach, giving the business a single source of
reference without the necessity to replace any systems or move data to a new,
central location. Capable of in-flight analytics, more advanced data management
technology within the fabric provides insights in real time. It connects all the
data including all the information stored in databases, warehouses and lakes and
provides the vital and seamless support for end-users and applications. Business
teams can delve deeper into the data, using advanced capabilities such as
business intelligence.
Why You Should Start Testing in the Cloud Native Way
Consistently tracking metrics around QA and test pass/failure rates is so
important when you’re working in global teams with countless different types
of components and services. After all, without benchmarking, how can you
measure success? TestKube does just that. Because it’s aware of the definition
of all your tests and results, you can use it as a centralized place to
monitor the pass/failure rate of your tests. Plus it defines a common result
format, so you get consistent result reporting and analysis across all types
of tests. ... If you run your applications in a non-serverless manner in the
cloud and don’t use virtual machines, I’m willing to bet you probably use
containers at this point and you might have faced the challenges of
containerizing all your testing activities. Well, with cloud native tests in
Testkube, that’s not necessary. You can just import your test files into
Testkube and run them out of the box. ... Having restricted access to an
environment that we need to test or tinker with is an issue that most of us
face at some point in our careers.
Why IT leaders should prioritize empathy
It’s simple enough to practice empathy outside of work, but IT challenges make
practicing empathy at work a bigger struggle. Fairly or unfairly, many
customers expect technology to work 100 percent of the time. When it doesn’t,
it falls on IT leaders to go into crisis mode. Considering many of these
applications are mission-critical to the customer’s organizational
performance, their reaction makes sense. An unempathetic employee in this
situation would ignore the context behind a customer’s emotional response.
They might go on the defensive or fail to address the customer’s concerns with
urgency. A response like this can prove detrimental to customer loyalty and
retention – it takes up to 12 positive customer experiences to make up for one
negative experience. Every workplace consists of many different personality
types and cultural backgrounds – all with different understandings of and
comfort toward practicing empathy. Because of this diversity, aligning on a
single company-wide approach to empathy is easier said than done. Yet if your
organization fails to secure employee buy-in around the importance of empathy,
you risk alienating your customers and letting employees who aren’t
well-versed in empathetic communication hold you back.
What devops needs to know about data governance
Looking one step beyond compliance considerations, the next level of
importance that drives data governance efforts is trust that data is accurate,
timely, and meets other data quality requirements. Moses has several
recommendations for tech teams. She says, “Teams must have visibility into
critical tables and reports and treat data integrity like a first-class
citizen. True data governance needs to go beyond defining and mapping the data
to truly comprehending its use. An approach that prioritizes observability
into the data can provide collective significance around specific analytics
use cases and allow teams to prioritize what data matters most to the
business.” Kirk Haslbeck, vice president of data quality at Collibra, shares
several best practices that improve overall trust in the data. He says,
“Trusted data starts with data observability, using metadata for context and
proactively monitoring data quality issues. While data quality and
observability establish that your data is fit to use, data governance ensures
its use is streamlined, secure, and compliant. Both data governance and data
quality need to work together to create value from data.”
The Power of AI Coding Assistance
“With AI-powered coding technology like Copilot, developers can work as
before, but with greater speed and satisfaction, so it’s really easy to
introduce,” explains Oege De Moor, vice president of GitHub Next. “It does
help to be explicit in your instructions to the AI.” He explains that during
the Copilot technical preview, GitHub heard from users that they were writing
better and more precise explanations in code comments because the AI gives
them better suggestions. “Users also write more tests because Copilot
encourages developers to focus on the creative part of crafting good tests,”
De Moor explains. “So, these users feel they write better code, hand in hand
with Copilot.” He adds that it is, of course, important that users are made
aware of the limitations of the technology. “Like all code, suggestions from
AI assistants like Copilot need to be carefully tested, reviewed, and vetted,”
he says. “We also continuously work to improve the quality of the suggestions
made by the AI.” GitHub Copilot is built with Codex -- a descendent of GPT-3
-- which is trained on publicly available source code and natural language.
Quote for the day:
"Great Groups need to know that the
person at the top will fight like a tiger for them." --
Warren G. Bennis
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