What is the 9-box talent review? A matrix for identifying top performers
The first step in using a 9-box grid is to assess an employee’s performance,
which is typically done by evaluating performance reviews or using talent
management systems. Managers are tasked with ranking employees based on
performance and behavior, and then those rankings are passed onto upper
management and leaders who can then identify and rank employees for their
leadership potential. Employees can rank as low, medium, or high performance
depending on how well they meet the requirements of their role. Low-performing
employees are those who do not complete job requirements and regularly fail to
meet assigned KPIs or other benchmarks. Employees who fall into the medium
category are those who meet expectations part of the time and complete job
requirements half of the time. High-performing employees reach all their
necessary benchmarks and job duties, often surpassing them. Despite the fact
that the 9-box grid puts an emphasis on the highest and lowest performers, it’s
not designed to pit workers against one another or to make them feel as if
they’re being ranked.
Approach cloud architecture from the outside in
Outside in moves in the opposite direction. You begin with the specific business
requirements, such as what the business use cases are for specific solutions or,
more likely, many solutions or applications. Then you move inward to
infrastructure and other technologies specifically chosen to support the many
solutions or applications required, such as databases, storage, compute, and
other enabling technologies. Most cloud architects move from the inside out.
They pick their infrastructure before truly understanding the solution’s
specific purpose. They partner with a cloud provider or database vendor and pick
other infrastructure-related solutions that they assume will meet their specific
business solutions requirements. In other words, they pick a solution in the
wide before they pick a solution in the narrow. This is how enterprises get
solutions that function but are grossly underoptimized or, more often, have many
surprise issues such as the ones discussed earlier. Discovering these issues
requires a great deal of work and typically requires the team to remove and
replace technology solutions on the fly.
The future of the internet: Inside the race for Web3’s infrastructure
The fastest way to provide reliable infrastructure to power DApp ecosystems is
for centralized companies to set up a fleet of blockchain nodes, commonly housed
in Amazon Web Services (AWS) data centers, and allow developers to access it
from anywhere for a subscription. That is exactly what a few players in the
space did, but it came at the price of centralization. This is a major issue for
the Web3 economy, as it leaves the ecosystem vulnerable to attacks and at the
mercy of a few powerful players. ... Decentralization is a key tenet of the Web3
economy, and centralized blockchain infrastructure threatens to undermine it.
For instance, Solana has suffered multiple outages due to a lack of sufficient,
decentralized nodes that could handle spiking traffic. This is a common problem
for blockchain protocols that are trying to scale. ... Even more importantly,
decentralized infrastructure competition results in greater decentralization of
the Web3 economy. This is a good thing, as it makes the economy more resilient
against attacks and censorship.
Enterprise architecture is based on business strategy, is it not?
Interestingly, many attempts to develop actionable plans out of business
strategy to enable it are precluded, first of all, by the symbolic and elusive
nature of strategy itself. For example, a rather common industry situation with
business strategy can be vividly illustrated by the following jocular quote of
Jeanne Ross, a former principal research scientist at MIT Sloan Center for
Information Systems Research (CISR): ‘I remember IBM saying, “Our strategy is,
we’re gonna raise share price to $11 per share”, and I thought, “Who the heck is
gonna enable that strategy?”’. In fact, decades of research on information
systems planning have long identified a broad spectrum of problems associated
with business strategy as a basis for acting. Strategy can be vague, ambiguous
and interpreted differently by different people (e.g. ‘become number one’ or
‘provide best services’). Strategy can be purely aspirational and consist of
mere motivational slogans. Strategy can comprise various objectives and
indicators offering no actionable hints, especially for IT. Strategy can be
market sensitive, deliberately obscure and surrounded by secrecy.
6 Best Data Governance Practices
People, procedures, and technology are all critical aspects of data management.
Keep all three elements in mind when developing and executing your data plan.
However, you don’t have to improve all three areas simultaneously. Start with
the essential components and work your way up to the final image. Begin with
people, progress to the procedure, and conclude with technology. Before any
component may proceed, it must build on top of the preceding ones for the whole
data governance plan to be well-rounded. The process won’t work without the
correct individuals. If the people and procedures in your company aren’t
managing your data as you intended, no cutting-edge technology can suddenly
repair it. Before developing a process, search for and hire the proper people.
... It is critical to track progress and display the effectiveness of your data
governance strategy, just as it would be with any other shift. Once you’ve
acquired executive buy-in for your business case, you’ll need evidence to
support each stage of your transition. Prepare ahead of time to establish
metrics before implementing data policies so that you can build a baseline based
on your current data management strategies.
Data quality can make or break efforts to bring artificial intelligence to IT operations
The success of AIOps is inexorably tied to "data, data, data, and how well you
can handle and process the data," Krishnamsetty agrees. One of the most vexing
issues is data access and acquisition, he points out. "You want to pull data
from your AWS environment, or your application performance monitoring tools, or
your log analytics tool. But all this data is in different formats." RDA
addresses the data challenges associated with AIOps, Krishnamsetty continues.
"If you don't have the proper data, it's garbage-in, garbage-out. However,
powerful your machine learning algorithms are, if your data quality is poor, you
are not going to get good insights and analytics." For example, "if you look at
any raw alerts coming from any of your management or monitoring systems, you
will know how sparse the data is," he illustrates. "A human can't make a quick
decision on it unless it is automatically enriched. The data is incomplete. What
application, what infrastructure, and so forth." RDA also helps address the
skills gap, which is in short supply for assuring the quality of data that is
fed into AI systems, he continues.
How Crypto Lending Platforms will revolutionize the Fintech Industry moving in 2022
The transformative role that crypto based lending platform can have cannot be
understated. It gives the power to each person to become their own bank. Not
only can they borrow from others at rates and conditions more favorable than
traditional financial institutions, but they can also borrow against their own
assets. For example, one could deposit their crypto assets and take out a loan
against their cryptocurrency. SO, when it appreciates, they have an increased
asset position, plus the ability to meet urgent need for liquidity. Credit
forms the backbone of any healthy economy and the access to that credit
determines it success in the global markets. Credit helps businesses and
individuals grow in the backdrop of a growing economy. It provides business
much needed capital to expand, maintain inventory, spend on research and
development, and sustainably pay wages. Without easy access to credit,
business is often placed under a glass ceiling which hampers their ability to
grow. Thanks to the internet the world has gone global much faster than air
travel ever connected us.
Do You Need a Semantic Layer?
Most organizations don’t trust their data, leading to slow decisions or no
decisions at all. In fact, according to the recent Chief Data Officer
Survey, 72% of data and analytics leaders are heavily involved in or leading
digital business initiatives, but they are uncertain how they can build a
trusted data foundation to accelerate them. It’s not hard to see why a lack
of trust in analytics outputs is so pervasive. Conflicting analytics outputs
are all but assured when multiple business units, groups, business users,
and data scientists prepare their analytics using their own business
definitions and their own tools. A semantic layer can drive trust in data by
empowering data self-service while ensuring the consistency, fidelity, and
explainability of analytic outputs. With the fast pace of today’s business
climate, waiting for a centralized data team to produce analytics for the
business is a thing of the past. The self-service analytics revolution was
born in response to the need for businesses to free themselves from the
constraints of IT.
Do CBDCs Need Blockchain? Growing Number of Central Banks Say No
It’s still too early to say that blockchain provides any definitive
benefits, Dinesh Shah, the Bank of Canada’s director of FinTech research,
told crypto industry news outlet The Block last week. Blockchain “is not a
given but it’s still on our list of potentials,” when it comes to designing
a CBDC, said Shah, who has expressed skepticism about the technology crypto
is built on in the past. That is roughly where MIT’s researchers came down
in a February test of technologies performed with the Federal Reserve Bank
of Boston, which found that in a head-to-head test of a barebones CBDC
design, a blockchain-based platform was far inferior. The blockchain-based
platform was capable of only 10% of the scalability of a non-DLT system
because of bottlenecks created by the need for a single and complete record
of transactions in the order in which they were processed. Shah said that’s
especially noteworthy because the Bank of Canada is collaborating with the
Boston Fed and the Bank of England — also an MIT partner — on this
research.
Test Case vs. Test Scenario: Key Differences to Note for Software Developers
It’s worth noting that test cases often form part of a test scenario. A test scenario is focused on an aspect of the project — for instance, "test the login function." Test cases are your means of checking if that aspect works as intended — in this case, that would be detailing the steps to take. ... Because test scenarios usually have one simple goal, the means of getting to that goal is more flexible than in test cases (where the process is more specific). The test documents will reflect these differences. A test case document will have specific guidelines for every case: the test case name, pre-conditions, post-conditions, description, input data, test steps, expected output, actual output, results, and status fields will all be laid out in the case document. ... In contrast, a test scenario document is open to interpretation by the team. They should identify the most important goal of the project and then design tests around reaching that goal. Test case scenarios allow for creativity on the part of the testers.
Quote for the day:
"Don't be buffaloed by experts and
elites. Experts often possess more data than judgement." --
Colin Powell
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