Generative AI Gets a SaaSy Touch
Although pure-play Generative AI startups, Rephrase.ai, Blend, ProbeAI, Fasthr.AI to name a few, are not that common yet in India, it has set foot into many industries such as healthcare, music and art, finance, advertising and marketing, gaming and entertainment, among others. This has opened up huge opportunities for the SaaS (software as a service) sector. Tapping into the opportunity are India's leading SaaS companies, who are following the footsteps of global software giants like SAP, Salesforce, and IBM and investing in Generative AI like never before. ... Other prominent SaaS companies are also investing heavily in Generative AI. Freshworks recently unveiled reddy Self-Service, Freddy Copilot and Freddy Insights to make AI more accessible to every workplace. The new predictive and assistive generative AI capabilities embedded within Freshworks solutions and platform are said to go beyond content generation and help support agents, sellers, marketers, IT teams and leaders become more efficient with a revolutionary new way to interact with their business software.
When low-code and no-code can accelerate app modernization
“Software architecture requires broadening our perspective and considering the
larger system, a wider range of possibilities, and a longer time scale,” says
Andrew Davis, senior director of methodology at Copado. “Legacy applications are
living proof that a significant portion of the cost of software is in its
long-term maintenance. Low-code and no-code applications are designed to reduce
complexity and thus increase maintainability over time.” Low-code or no-code
platforms can help accelerate application modernization, but not in every case.
You need a good match between the application's business requirements, user
experience, data, architecture, integrations, security needs, compliance
factors, and reporting with a low or no-code platform’s capabilities. ... Some
types of applications and use cases are better candidates for low-code and
no-code. Applications used for departmental business processes such as approval
workflows, onboarding, content management, work queues, request processing,
budget management, and asset tracking are high on the list.
The CISO’s toolkit must include political capital within the C-suite
Where a security leader sits in a company’s pecking order or to whom they report
“is fundamentally irrelevant, because every organization sees things
differently,” according to John Stewart, president of Talons Ventures and a
former chief security and trust officer at Cisco. “The relevant piece is access,
support, authorities, and accountability,” Stewart tells CSO. Stewart has
cautioned CISOs many times to be careful of the “I need to report to the CEO to
be effective” instinct. “That suggests either the business, the culture, or the
individual are ineffective.” A more effective approach should be, according to
Stewart: “I need access to the CEO with their support and a clear understanding
of my responsibilities and authorities that is backed up with action.” This is
pretty much in line with the thinking of Malcolm Harkins, former CISO at Intel
and other entities, who tells CSO that it is “unimportant” to whom an individual
CISO reports. “The CISO is the one who should be responsible and accountable for
mitigating risk,” he says.
Is starting with a startup the right choice?
When presented with the choice between failing with a plan or succeeding without
one, many startup founders and teams lean towards the latter. The prevailing
belief is that success is the ultimate goal, regardless of the presence or
absence of a plan. However, this perspective fails to consider the reliability,
repeatability, and scalability of such success. Without a plan, it becomes
uncertain whether the success can be attributed to one's own efforts, sheer
luck, or the misfortune of competitors. Relying on hope alone is not a sound
strategy. In the fast-paced world of startups, the constant action and hustle
often mask personal weaknesses and systemic deficiencies. It is only when the
economic tide recedes that the true vulnerabilities of startups are exposed, as
astutely pointed out by Warren Buffet's observation, "Only when the tide goes
out do you discover who's been swimming naked." During a favorable economic
climate, many startups may appear successful without a well-defined strategy.
They simply need to be in the right place at the right time.
Office workers feel AI is better than a human boss
Looking at employee concerns around AI bosses, softer skills and capabilities
is the key area respondents think a robot would lack. In addition, just under
half of employees think they would struggle to see a robot as an authoritative
figure. Despite this, over one in five (22%) admit they would feel more
comfortable talking about their frustrations at work to a robot over their
boss. According to Business Name Generator, this may be due to not wanting to
cause conflict or emotional distress that is part of human interaction. The
survey found that 18% would trust a robot boss over their current one. The
office workers polled said that a lack of appreciation is the biggest
frustration they feel about their bosses, with 14% experiencing this
currently. Micromanagement, being a “know it all”, and lacking patience are
among the frustrations featured in the top 10, with over one in 10 respondents
experiencing this. Poor management skills such as bosses who are disorganised
or have unclear expectations also featured in the top 10 of complaints.
A Walkthrough of Adopting Infrastructure as Code
The world of cloud infrastructure is a bit daunting. Pulumi supports over 100
clouds. AWS has over 200 services with over 1,000 individual resources and
over 300,000 configurable properties across all of them. Pulumi is a
multicloud tool, but “multicloud” does not mean “lowest common denominator.”
Instead, Pulumi exposes all those individual clouds, resources and properties
in their raw, unadulterated form. The benefit of this is that you have the
entire capabilities of all of these clouds right at your fingertips. The
downside is that to use them you need to understand these clouds and how to
use them properly. As a result, you’ll probably quickly find that you want a
starting point, rather than a blank page. Pulumi Templates are a good way
to get started. They represent over a dozen of the most common application and
infrastructure architectures on the most popular clouds. They were built to be
simple enough to be understandable at a glance but complete enough to be
useful in practice.
Data Governance in Higher Ed is Critical. Here’s How to Achieve and Sustain It.
“It’s important to remember, however, that data is an institutional asset,”
she says. Divided as institutions are by different schools and academic
departments — not to mention professional departments like IT, enrollment or
student life — silos are often unavoidable in higher education. Those silos
often are one of the biggest barriers to developing and implementing an
effective data governance strategy across an institution. That’s because data
governance works best as a Venn diagram than in silos, says Matthew Hagerty, a
consultant who specializes in IT, efficiency and analytics, and faculty
engagement at EAB, a Washington D.C.-based education consulting firm. “Make
sure the right people are in the room to craft that policy,” Hagerty says. He
says that many times during initial data governance meetings, “maybe halfway
through, someone will raise their hand and ask, “‘Wait a second. Why isn’t Bob
from finance here? Who’s representing human resources in this committee?’”
Hate being more productive? Ignore AI agents
Business school professor and technologist Ethan Mollick offers what I’ve
found to be very useful framing for how to think about generative AI: “It is
not good software, [rather] it is pretty good people.” And rather than
thinking about AIs as people who replace those already on the payroll, treat
them like “eager interns” that can help them be more productive. This metaphor
can help on two fronts. First, it keeps the need for human supervision front
and center. Just as hiring and productively managing interns is a valuable
competency for an organization, so too is using ChatGPT, Microsoft’s CoPilot,
or Google’s Bard. But you would no more blindly trust this class of model than
you would even the most promising intern. Second, and as important: IT isn’t
responsible for hiring interns in Finance and HR. Likewise, Finance and HR
(and every other function) must build their own competency i figuring out how
to use these tools to be more productive. The job to be done is closer to
answering domain-specific staffing questions than IT questions.
Top Tips for Weeding Out Bad Data
Bad data often really means low quality data. In this case, it’s up to the
data owner to define the acceptable level of quality in terms of relevance,
accuracy, age, or other criteria. “But bad data can also mean inappropriate
data, in which case “appropriate” would need to be defined,” says Erik
Gfesser, director and chief architect at business advisory firm Deloitte
Global. One enterprise’s highly useful data might be meaningless to another.
Since many use cases aren’t particularly demanding, data quality doesn’t
always have to adhere to the same standards. “As such, judgment often needs to
be used to determine what’s appropriate,” he explains. It’s also important to
check for duplicate records, which can be caused by data entry errors or
identical data being retrieved from multiple sources. “A clearly defined data
governance program and an enterprise-level data pipeline design that’s shared
enterprise-wide are the best ways to prevent duplicate records,” Shah
recommends. It’s possible to identify outliers and detect anomalies by
comparing values that appear to be significantly different from the rest of
the data or by running statistical tests, such as regression analysis,
hypothesis testing, or correlation analysis, to identify patterns in data,
Shah says.
Choosing the Right Data Architecture
"If it turns out that none of those references is close to your scale, doing
what you want to do, then you know you're well beyond the frontier of the
vendor’s product." If that’s the case, then you need to conduct tests to help
control and manage your risk. "The best kind of test is a full-scale,
realistic benchmark, and the best case is where you have more than one
credible vendor." Winter recommends testing two or three solutions and
comparing the results. You can see if any vendor can demonstrate they have the
capability to meet your most critical requirements. If multiple vendors pass
this test, then examine differences in cost, complexity, and the agility of
the solution. "These differences can be very revealing. Once you've
illuminated what's going on via testing, you can get into much deeper
conversations with the vendor about what you're seeing in the behavior of the
system. We've had remarkable experiences doing this, even with the most modern
systems in the cloud."
Quote for the day:
"Leaders are more powerful role models
when they learn than when they teach." -- Rosabeth Moss Kantor
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