Tips Every CFO Should Consider For Implementing Tech Solutions
Conduct a cost assessment to pinpoint areas where tech upgrades may be needed
and determine if these upgrades will add value to your financial operations.
Remember, newer doesn’t necessarily mean better. Therefore, you must invest in
tech solutions and upgrades that improve efficiency across the board. By
taking the initiative and identifying areas where tech solutions can solve
specific pain points, CFOs can help ensure a seamless transition when
implementing new technology. ... While many organizations today jump at the
opportunity to implement updated solutions to replace legacy systems, an
overhaul doesn’t have to be made just because new technologies become
available. ... The key is fully understanding why you’re switching to and
implementing new technology. Just because certain tasks and processes can be
done using advanced tech tools doesn’t necessarily mean your company needs new
software.
The power of data management in driving business growth
Effective data management means business leaders can stay abreast of the
ever-surging tide of data, as well as deploying new services quickly, and
scaling faster. It can deliver insights which lead to new business streams or
even the reinvention of the entire company. Data management comes in multiple
forms, encompassing both hardware and software. Solutions include unified
storage, which enables organisations to run and manage files and applications
from a single device, and storage-area networks (SANs), offering network
access to storage devices. ... As well as data management, the Data Leaders
thrive in two other key areas: data analytics and data security. These three
elements are interdependent. Data management naturally works hand-in-hand with
data analytics, and data security is increasingly important as business
leaders hope to share data with partners securely. It’s impossible for leaders
to thrive when it comes to data management if they haven’t harnessed data
security, or to adopt data analytics without mastering data
management.
Zero Trust: Beyond the Smoke and Mirrors
Despite misleading marketing, a lack of transparency into the available
technologies, the limited scope of the technologies themselves, mounting
privacy concerns, as well as a complete question mark when it comes to price
and deployment, trust in zero trust remains. Organizations know they need to
embrace it– and preferably yesterday. ... Despite this enhanced savviness and
market maturity around zero trust, major barriers to implementation remain.
These include:Damn you, marketers. Some vendors may use misleading marketing
tactics to promote their zero-trust solutions, overstating their capabilities
or making false claims about their performance. See through the noise the best
you can. Most tools let you test things out first. Take vendors up on
that. What the hell does this cost? Implementing zero trust security
solutions can be expensive, especially for organizations with large IT
infrastructures. Chances are, the more devices, networking gear, locations,
and compliance standards you need to adhere to…the more this will
cost. Complexity is almost always guaranteed. Zero trust can also be
complex to deploy, especially across distributed, multi-vendor networks.
Technical Debt is Inevitable. Here’s How to Manage It
Technical debt is a threat to innovation, so how can we mitigate it? Well, if
you don’t already do so, it’s a good idea to build technical debt into your
budgeting, planning and ongoing operations, said Orlandini. “You have to
manage it, expect it and be responsible with your technical stacks in the same
way you are responsible with your financial stacks,” he said. Here are a few
other ways to manage the debt you have and avoid accumulating more. Consider
using AI to refactor legacy code. Generative AI could be leveraged to reactor
legacy code into more modern programming languages. This could help
automatically convert PEARL code, for instance, into JavaScript. Today’s large
language models (LLMs) could help solve many of today’s problems. However,
since they are built on a pre-existing body of work, they will use less trendy
languages and might cause some technical debt in the process, cautioned
Orlandini. Don’t over-rely on new DevOps processes as a cure-all. DevOps can
accelerate the time to release features, but it does not, by its nature,
eliminate technology changes, said Orlandini.
Cloud repatriation and the death of cloud-only
IT analyst firm IDC told us that its surveys show repatriation as a steady
trend ‘essentially as soon as the public cloud became mainstream,’ with around
70 to 80 percent of companies repatriating at least some data back from public
cloud each year. “The cloud-first, cloud-only approach is still a thing, but I
think it's becoming a less prevalent approach,” says Natalya Yezhkova,
research vice president within IDC's Enterprise Infrastructure Practice. “Some
organizations have this cloud-only approach, which is okay if you're a small
company. If you're a startup and you don't have any IT professionals on your
team it can be a great solution.” While it may be common to move some
workloads back, it’s important to note a wholesale withdrawal from the cloud
is incredibly rare. ... “They think about public cloud as an essential element
of the IT strategy, but they don’t need to put all the eggs into one basket
and then suffer when something happens. Instead, they have a more balanced
approach; see the pros and cons of having workloads in the public cloud vs
having workloads running in dedicated environments.”
5 Ways to Implement AI During Information Risk Assessments
The problem is that there is no such thing as a perfectly secure system; there
will always be vulnerabilities that an IT team is unaware of. This is why IT
teams perform regular penetration tests – simulated attacks to test a system’s
security. ... By turning this task over to AI, companies can run automated
penetration tests at any time. These AI models can work in the background and
provide immediate alerts the moment a vulnerability is found. Better still,
the AI can classify vulnerabilities based on the threat level, meaning if
there’s a vulnerability that could allow for a system-wide infiltration, then
that vulnerability will be prioritized above lesser threats. ... AI-powered
predictive analytics can be an incredibly powerful tool that allows an
organization to estimate the results of a marketing campaign, a customer’s
lifetime value, or the impact of a looming recession. But predictive analytics
can also be used to predict the likelihood of a future data breach.
13 Cloud Computing Risks & Challenges Businesses Are Facing In These Days
Starting with one of the major findings of this report, we can see that both
enterprises and small businesses cite the ability to manage cloud spend as the
biggest challenge, overtaking security concerns after a decade in place one.
This can be the consequence of economic volatility, where organizations keep
spending and innovating with multiple cloud services to keep up with the
digital world in an unstable environment. ... Proper IT governance should
ensure IT assets are implemented and used according to agreed-upon policies
and procedures, ensure that these assets are properly controlled and
maintained, and ensure that these assets are supporting your organization’s
strategy and goals. In today’s cloud-based world, IT does not always have full
control over the provisioning, de-provisioning, and operations of
infrastructure. This has increased the difficulty for IT to provide the
governance, compliance, risks, and data quality management required. To
mitigate the various risks and uncertainties in transitioning to the cloud, IT
must adapt its traditional IT control processes to include the cloud.
When are containers or serverless a red flag?
Limited use cases mean that containers and serverless technologies are
well-suited for certain types of applications, such as microservices or
event-driven functions. But they do not apply to everything new. Legacy
applications or other traditional systems may require significant
modifications or restructuring to run effectively in containers or serverless
environments. Of course, you can force-fit any technology to solve any
problem, and with enough time and money, it will work. However, those
“solutions” will be low-value and underoptimized, driving more spending and
less business value. Complexity is a common downside of most new technology
trends. Container and serverless platforms introduce additional complexity
that the teams building and operating these cloud-based systems must deal
with. Complexity usually means increased development and maintenance costs,
less value, and perhaps unexpected security and performance problems. This is
on top of the fact that they just cost more to build, deploy, and operate.
Vector Databases: What Devs Need to Know about How They Work
Unsurprisingly, a vector database deals with vector embeddings. We can already
perceive that dealing with vectors is not going to be the same as just dealing
with scalar quantities. The queries we deal with in traditional relational
tables normally match values in a given row exactly. A vector database
interrogates the same space as the model which generated the embeddings. The
aim is usually to find similar vectors. So initially, we add the generated
vector embeddings into the database. As the results are not exact matches,
there is a natural trade-off between accuracy and speed. And this is where the
individual vendors make their pitch. Like traditional databases, there is also
some work to be done on indexing vectors for efficiency, and post-processing
to impose an order on results. Indexing is a way to improve efficiency as well
as to focus on properties that are relevant in the search, paring down large
vectors. Trying to accurately represent something big with a much smaller key
is a common strategy in computing; we saw this when looking at hashing.
Understanding Data Mesh Principles
When an organization embraces a data mesh architecture, it shifts its data
usage and outcomes from bureaucracy to business activities. According to
Dehghani, four data mesh principles explain this evolution: domain-driven data
ownership, data as a product, self-service infrastructure, and federated
computational governance. ... The self-service infrastructure as a platform
supports the three data mesh principles above: domain-driven data ownership,
data as a product, and federated computational governance. Consider this
interface an operating system where consumers can access each domain’s APIs.
Its infrastructure “codifies and automates governance concerns” across all the
domains. According to Dehghani, such a system forms a multiplane data
platform, a collection of related cross-functional capabilities, including
data policy engines, storage, and computing. Dehghani thinks of the
self-service infrastructure as a platform that enables autonomy for multiple
domains and is supported by DataOps.
Quote for the day:
"The level of morale is a good
barometer of how each of your people is experiencing your leadership." --
Danny Cox
No comments:
Post a Comment