The future of work is human-AI synergy
AI and humans can work in sync by capitalising on their respective strengths.
AI's ability to automate routine tasks liberates human workers to focus on more
complex and nuanced responsibilities, where their human touch is indispensable.
This dynamic significantly amplifies productivity and allows employees to
dedicate their time to strategic thinking and fostering innovation. AI's
application in Big Data Analytics equips human workers with invaluable insights,
enabling them to make quicker, more informed decisions with heightened
precision. For instance, financial institutions employ AI analytics to rapidly
evaluate loan applications, while healthcare professionals use AI algorithms to
swiftly diagnose serious illnesses from patient data. However, it's crucial to
emphasise that AI serves as a valuable tool rather than a replacement for human
workers. The efficiency and productivity gains result from the synergy between
human intelligence and AI capabilities.
10 Strategies for Simplified Data Management
Centralization means creating a unified, accessible, and authoritative store for
all of your organizational data. Users and processes can then leverage and
manage otherwise distinct data in a convenient, coherent fashion. The two main
approaches here are data lakes and data warehouses. A data lake is a large
repository of different kinds of data - all stored in their original format.
This provides a valuable resource, as we can apply any kind of transformations
and aggregation we need for analysis. A data warehouse differs from a data lake
in the sense that it is stored in a format and structure that’s defined for a
specific purpose. This is useful if we need to carry out similar analytical
operations on a large scale. ... As we said earlier, an enterprise data model is
a detailed account of all of the data assets that are involved in core business
processes - along with where each of these is sourced from, what they’re used
for, and how they relate to each other. This is effectively a data-centric
representation of how your business works. In turn, an effective data model
brings along several important benefits.
Data Quality Assessment: Measuring Success
A Data Quality assessment will move along more efficiently and provide better
results if a list of concerns and goals is created before the assessment. When
creating this list, be aware of the organization’s long-term goals, while
listing short-term goals. For example, the long-term goal of making the business
more efficient can be broken down into smaller goals, such as fixing the system
so the right people get the right bills, and that all the clients’ addresses are
correct, etc. This list can also be presented to a board of directors as a
rationale for initiating and paying for Data Quality assessment software or
hiring a contractor to perform the assessment. The basic steps for creating the
list are presented below.Start by making a list of Data Quality problems that
have occurred over the last year. Spend a week or two observing the flow of data
and determine what looks questionable, and why. Share your observations with
other managers and staff, get feedback, and adjust the results using the
feedback.
Test Architecture: Creating an Architecture for Automated Tests
The test architecture is important, especially when you are dealing with a
complex project or expecting the project to grow in the near future. The test
architecture helps to reduce the risks and eliminate the assumptions before
delivery. As you are aware, anything you do randomly may not help in a better
outcome. The test architecture streamlines the entire process of testing. Unlike
other testing activities, it’s not focused on a single testing activity rather
it is focused on the entire testing and the testing team aims to deliver a
high-quality product. ... The test architect works with multiple teams such as
development, DevOps, testing, and business/product team. So the test architect
is responsible for communication with stakeholders. If there are any challenges
from the development team he should be able to work with them and get them
resolved. The complexity of the test architecture for automation depends on the
tool you choose. Because some tools require creating the framework, some come
with a framework ready. Not all the tools require coding, so the activities that
are involved in defining the coding standards and setup will be
reduced.
7 Cybersecurity Questions That Can Transform Your Business
Anyone who has spent any time thinking about cybersecurity knows how
multifaceted and complicated our digital supply chains are today. That means
we need to empower people who are working directly with the different touch
points in the supply chain and elevate their cybersecurity thinking. They need
all the information and resources available to ensure they only push secure
software to customers. ... You may have a long list of audits and other
compliance procedures in progress currently. This is where I ask you to
remember that the point of a canvas like this is that it is one page! While
that may not give you all the room to include every initiative, that may be a
good thing. Instead of starting new small-scale initiatives, consider, for
instance, adopting or enhancing a DevSecOps approach that could transform
your security efforts. ... When we talk about costs here, we mean actual
costs. This includes external consultant fees, CISO office salaries, MDR
subscriptions, security training and platform subscriptions. When confronted
with these numbers, we can make decisions that aren’t only guided by whims or
immediate needs.
Why Cloud Native Expertise Is so Hard to Hire for, and What to Do Instead
Fortunately, there are alternatives for organizations looking to develop their
cloud native expertise. One of the most popular options is to work with a
third-party provider that specializes in providing cloud native services — so
you don’t have to. This is a core component of what entails “ZeroOps,” or
rather, the notion of freeing your own employees to take their time back, and
letting someone else do the time-consuming, bothersome stuff. Working with a
third-party provider can provide organizations with high levels of expertise
and resources while allowing your team to focus on their core business —
innovating, creating, and making a measurable impact. This can result in
significant cost savings and increased efficiency, as the provider takes on
the responsibility of managing complex cloud native solutions. Many providers
can offer comprehensive services, ranging from architecture to software
engineering and deployment, and can tailor their services to an organization’s
unique requirements — of which we know there are many.
The CISO Carousel and Its Effect on Enterprise Cybersecurity
“There is still a prevalent perception that CISOs are viewed as scapegoats in
serious breach events,” adds George Jones, CISO at Critical Start. “This is
based on a general lack of understanding, high expectations, and
accountability associated with the role. When a breach occurs, it’s easy to
point the finger at the person responsible for cybersecurity.” It’s the
effect, says Yu, of “accountability without authority”. Making the CISO a
scapegoat is a common but not blanket response to cybersecurity incidents.
Agnidipta Sarakar, VP and CISO advisory at ColorTokens, points out,
“Organizations who are mature tend not to blame the CISO unless the security
program is actually not good enough.” But less mature organizations with
weaker programs or negligent security oversight will readily activate the
scapegoat effect. ... Globally, there are many companies where cybersecurity
is both prioritized and supported, but these tend to be among the larger and
more mature organizations. There remains a large underswell of newer and
smaller companies where growth is often prioritized over security.
Closing the skills gap in the AI era: A global imperative
To tackle this reskilling challenge on a large scale, we require a combined
effort from the government, education, and private sector. This can be
achieved through the following ways: Make learning achievable: Instead of
diving into the deeply technical aspects of AI, companies can begin by
introducing the workforce to tools that require no-code or low-code experience
Further, citizen development programs can be implemented. These programs
encourage employees to be innovative problem solvers and foster a sense of
ownership as they witness the direct impact of their work on business outcomes
using no-code/low-code tools. These programs allow them to savour initial
automation successes almost immediately and to envision greater possibilities
for bots to help them in the future. Take advantage of existing partnerships:
Companies should leverage the knowledge of their existing technology partners
to quickly roll out skilling programs. The National Health Service in the UK,
for example, was able to offer its 1.7 million employees automation training
via the help of its technology partner.
Could APIs undermine Zero Trust?
APIs come in various shapes and flavours. As well as being internal or public
facing, they might interface in numerous ways, from a single API providing
access to a service mechanism, to aggregated APIs that then use another as the
point of entry, to APIs that act as the go-between between various
non-compatible applications, or partner/third party APIs. They are also
problematic to monitor and secure using traditional mechanisms. Segmentation
and deep inspection technology at the layer 7 network level can miss APIs
completely, resulting in those shadow APIs, while application level 4
protection methods such as web application firewalls (WAFs) which use
signature-based threat detection will miss the kind of abuse that typically
leads to API compromise. Often, APIs are not’ hacked’ as such, but their
functionality is used against them in business logic abuse attacks and so it’s
the behaviour of the API request and resulting traffic that needs to be
observed. Yet it’s clear that APIs must be included in ZTA.
Transforming Decision-Making Processes
GenAI has quickly become a part of everyday conversations from the boardroom
to the kitchen table. One specific topic of interest is the role genAI can
play in enhancing and improving an organization’s decision-making paradigm.
Organizations should look for AI engines that combine the power of artificial
intelligence, machine learning, and generative AI to further advance the
democratization of analytics. This can reduce the time required to derive
insights from data. With AI and cloud-native analytics automation, the power
and scale of better decision-making is at everyone’s fingertips. While it is
still the early days for genAI, we see this newer capability accelerating the
path for organizations to become more insights driven in their
decision-making. Natural language processing translates insights into business
language that can be shared broadly and leveraged by all. GenAI and large
language models (LLMs) eliminate tedious tasks, leverage best practices from
millions of workflows in production, automatically document workflows, and
free up time for humans to focus on more strategic challenges.
Quote for the day:
"People often say that motivation
doesn't last. Well, neither does bathing - that's why we recommend it
daily." -- Zig Ziglar
No comments:
Post a Comment