Business Case – Why Enterprise Architecture Needs to Change – Part I
The solution to moving out of the “stone age” is to use a digital end-to-end
approach for Architecture content (whether EA or SA), and provide openness and
transparency across EA, project, and reusable component
Architectures. Just like any digital approach to any business problem,
the use of structured data is key. The best-structured data language for
Architecture is arguably the ArchiMate notation which has a rich notation
covering the depth and breadth of Architecture modelling, and also a rich set
of connectors to link elements. ... Even if the new hire has significant
experience in the given industry, the new organisation’s IT platform and
processes will likely vary greatly from the person’s past experience. It
takes several months or longer for new staff to accumulate enough knowledge
about how the business and IT platform work to operate effectively without
help from other staff and operate effectively. The cost of this knowledge
gap is the new person delivering outcomes slower than other staff and
consuming time of other staff unnecessarily by simply asking questions like
‘what systems do we have?’, ‘what does the business do?’, ‘how does system X
work?’ and so on.
Why API security is a fast-growing threat to data-driven enterprises
API security focuses on securing this application layer and addressing what
can happen if a malicious hacker interacts with the API directly. API security
also involves implementing strategies and procedures to mitigate
vulnerabilities and security threats. When sensitive data is transferred
through API, a protected API can guarantee the message’s secrecy by making it
available to apps, users and servers with appropriate permissions. It also
ensures content integrity by verifying that the information was not altered
after delivery. “Any organization looking forward to digital transformation
must leverage APIs to decentralize applications and simultaneously provide
integrated services. Therefore, API security should be one of the key focus
areas,” said Muralidharan Palanisamy, chief solutions officer at AppViewX.
Talking about how API security differs from general application security,
Palanisamy said that application security is similar to securing the main
door, which needs robust controls to prevent intruders. At the same time, API
security is all about securing windows and the backyard.
Artificial Intelligence Can Enhance Banking Compliance
Technology has changed our society, and banks and other financial
institutions have digitalized their operations at a rapid pace as well.
However, the financial crime compliance units of these institutions still
rely mainly on heavy manual processes. The banking compliance units’ key
reason for their cautious approach in the utilisation of AI and automation
has been uncertainty about technology. Do regulators approve machine-based
decision-making, and is machine learning logic fair in identifying
suspicious activities? However, there is a clear need for utilising
technology in financial crime compliance. During the last number of years,
Ireland has witnessed a rise in financial crime, with illegal proceeds
making their way into the financial system, often from international
sources. Last month, data from Banking and Payments Federation Ireland
showed that over €12m was transferred illegally through so-called ‘money
mule’ accounts in the first six months of the year. When compared to the
same period last year, the quantity of bank accounts linked to the criminal
practice in Ireland almost doubled to 3,000 between January and June 2022.
Big tech has not monopolized big A.I. models, but Nvidia dominates A.I. hardware
Interest in A.I. software startups targeting business use cases also
remains formidable. While the total amount invested in such companies fell
33% last year as the venture capital market in general pulled back on
funding in the face of fast-rising interest rates and recession fears, the
total was still expected to reach $41.5 billion by the end of 2022, which
is higher than 2020 levels, according to Benaich and Hogarth, who cited
Dealroom for their data. And the combined enterprise value of public and
private software companies using A.I. in their products now totals $2.3
trillion—which is also down about 26% from 2021—but remains higher than
2020 figures. But while the race to build A.I. software may remain wide
open for new entrants, the picture is very different when it comes to the
hardware on which these A.I. applications run. Here Nvidia’s graphics
processing units completely dominate the field and A.I.-specific chip
startups have struggled to make any inroads. The State of AI notes that
Nvidia’s annual data center revenue alone—$13 billion—dwarfs the valuation
of chip startups such as SambaNova ($5.1 billion), Graphcore ($2.8
billion) and Cerebras ($4 billion).
Predictive Analytics in Healthcare
Clinicians, healthcare associations and health insurance companies use
predictive analytics to articulate the probability of their cases
developing certain medical conditions, similar as cardiac problems,
diabetes, stroke or COPD. Health insurance companies were early adopters
of this technology, and healthcare providers now apply it to identify
which cases need interventions to avert conditions and enhance health
outcomes. Clinicians also use predictive analytics to identify cases whose
conditions are progressing into sepsis. As is the case with numerous
operations of predictive analytics in healthcare, still, the capability to
use this technology to read how a case’s condition might progress is
limited to certain conditions and far from widely deployed. Healthcare
associations also use predictive analytics to identify which hospital in
patients are probable to exceed the average length of stay for their
conditions by assaying case, clinical and departmental data. This insight
allows clinicians to acclimate care protocols to observe the cases’
treatments and recoveries on track. That in turn helps cases avoid
overstays, which not only drive up expenses and divert limited hospital
resources, but also may endanger cases by keeping them in surroundings
that could expose them to secondary infections.
How to Set Yourself Up For Success As a New Data Science Consultant With No Experience
The key is to know what you’re good at and focus on it. Going out on your
own as a consultant is scary enough — ensure that you’re going to be
marketing and using skills that you’re comfortable with. Having confidence
that you can successfully produce results using your tools and skills of
choice goes a long way to becoming a successful consultant. Additionally,
do some market research to see where your niche could lay. While they say
that data scientists should all be generalists in the beginning, I believe
that consultants should focus on specializing themselves in niches that
complement their skills and their alternative knowledge. For example, I
would focus on becoming a data science consultant who specializes in
helping companies solve their environmental problems — this would combine
my specialized skills (data science) with my alternative knowledge and
educational background in environmental science. Companies love working
with consultants who have first-hand experience in their sector, so it
can’t hurt to play to your strengths, past employment, education, or
interest background.
The future of employment in IT sector
Whilst the companies keep up with the changing economic climate, what’s
become undeniable is the war for recruiting good talent, now more than
ever. There has been a significant change in employees’ needs and
priorities. Cream talent is re-evaluating their careers based on aspects
like flexibility, career growth and employee value proposition. Companies
must therefore invest in ‘Active Sourcing’ to create a rich pipeline and
not only recruit them but also train them for the upcoming 4th industrial
revolution. It needs to invest in their skills and holistic development,
not forgetting to create a safe, healthy work environment to retain the
talent. As dynamic as it is, one cannot deny the menace of tech burnout.
This blog describes it perfectly, ‘Tech burnout refers to the extreme
exhaustion and stress that many employees in the technology sector
experience. While burnout has always been an issue in many industries, 68%
of tech workers feel more burned out than they did when they worked at an
office.’ Technology is the most rapidly evolving industry with a
challenging work environment.
On the Psychology of Architecture and the Architecture of Psychology
Most of our intelligence, however, consists of patterns that we execute
efficiently, automatically and quickly. Some of these are natural
elements, which are fixed: e.g. a propensity to communicate and use tools,
to perform ‘mental travel’ — memory, scenarios, fantasy — and all of
it based on pattern creation and reinforcement. Some of these elements may
even be genetic (like basic strategies such as wait-and-see versus
go-for-it you can observe in small children), but most of it is probably
learned. All of this is part of Kahneman’s ‘System 1’. We learn by
employing our capability to employ logic and ratio and our
copying-and-being-reinforced capability — and while we do a lot more of
the latter two than the former, culturally, we tend to believe that the
reverse is true. Learning by reinforcement also includes learning by
doing. Chess grand masters have very effective fast ‘patterns’ in the
‘malleable instinct’ part of their brains, and the difference between
grand master and good amateurs is not their power of logic and ratio —
calculating, thinking moves ahead — but their patterns that identify
potential good moves before they start to calculate , and these patterns
come from playing a lot of games. You also have to maintain your patterns:
it is ‘use it or lose it’.
7 Common Data Quality Problems
Data inconsistencies: This problem occurs when multiple systems are
storing information without using an agreed upon, standardized method of
recording and storing information. Inconsistency is sometimes compounded
by data redundancy. ... Fixing this problem requires the data be
homogenized (or standardized) before or as it comes in from various
sources, possibly through the use of an ETL data pipeline. Incomplete
data: This is generally considered the most common issue impacting Data
Quality. Key data columns will be missing information, often causing
analytics problems downstream. A good method for solving this is to
install a reconciliation framework control. This control would send out
alerts (theoretically to the data steward) when data is missing. Orphaned
data: This is a form of incomplete data. It occurs when some data is
stored in one system, but not the other. If a customer’s name can be
listed in table A, but their account is not listed in table B, this would
be an “orphan customer.” And if an account is listed in table B, but is
missing an associated customer, this would be an “orphan account.”
Building IT Infrastructure Resilience For A Digitally Transformed Enterprise
At a minimum, resiliency means having stable operations, consistent
revenue, manageable security risks, efficient workflows, and an informed
and agile employee base. Having visibility over the operating systems of
network devices can reduce network downtime and open doors to further
efficiencies. If a business is resilient, it can maintain stable network
operations, drive down IT costs and deliver a more robust service at a
lower cost. Overall, when businesses can dramatically lower IT expenses
and have better visibility, they can expend resources on separate projects
that improve the quality of service—a win for all. From a regulatory
perspective, regulators now want to see everything documented. Take mobile
banking, for example; regulators want to know everything, including what
code is being used on which servers as well as which people and processes
have access to which services. Intelligently automated network operations
can allow enterprises to be better equipped to answer the questions that
regulators ask, such as how they're validating and how often they're doing
a failover.
Quote for the day:
"A good general not only sees the way to victory; he also knows when
victory is impossible." -- Polybius
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