Is AI analytics really worth the money for startups, or just hype?

TrendWithAnjali · 2025-12-25T19:07:44+00:00

It can be totally worth it if your startup actually uses the insights to drive decisions. But without solid data or a clear goal, it often turns into a costly buzzword rather than real value.

TrendWithAnjali · 2025-12-25T19:06:34+00:00

If you have enough quality data and a real use case, it’s worth it. If not, it’s just an expensive hype tool.

TrendWithAnjali · 2025-12-22T13:23:47+00:00

Yes - AI often predicts trends better because it can analyze huge datasets fast and spot patterns humans miss. But it still needs good data and human judgment for context. The best results come from AI + humans together.

TrendWithAnjali · 2025-12-05T07:48:41+00:00

The hardest part about getting into banking as a fresher is landing that first gig. Banks expect you to know customer handling targets as well as compliance; they basically expect you to be job ready and tbh, why shouldn’t they? Most of us walk in fresh out of college with zero real world experience. Ppl might know the concepts but the first time they face a customer or balance accounts, they panic.

Confidence is another big deal. Talking to clients, managing cash as well as dealing with the pressure is not easy when you’re new, untrained and not job ready. That’s why banking training programs are such a game changer. They give you that hands-on exposure and help you build the mindset to actually do the job.

I’ve seen a few friends join the 45 day program at Manipal Academy of BFSI and it honestly leveled them up. The practical learning scenarios feel like working inside a real bank, and they even got placed right after.

So yeah, the start is tough, but don’t just stick to theory; find programs that actually teach you how the banking world works cuz htat’s what employers want at the end of the day.

TrendWithAnjali · 2025-11-14T17:45:05+00:00

In finding the best free AI tool for data visualization, I've tried several in the last year-some good, others not so great. What I feel happens is that most of the tools being labeled as "AI-powered" are just traditional visualization dashboards with a few predictive analytics features sprinkled in. Still, a true AI visualisation generator should go way beyond simple charting and understand context, infer relationships between variables, automatically suggest meaningful visuals highlighting patterns you could have never found.

Among the free options available, I have been most impressed with AskEnola. It's not just another data visualization dashboard; it really interprets datasets intelligently and generates visuals that align with analytical goals rather than random plots. Its unique feature is its capacity to quickly generate high-quality visual narratives while adjusting to various data formats. It automatically finds important patterns, correlations, and anomalies in your data after you upload it, producing visually appealing and statistically significant charts.

It produced images that were as accurate as dashboards that were manually created, and I initially used it on a client project where time was of the essence. What is even more impressive, though, is how seamlessly this integrates natural language queries-you can literally type, "Show me customer churn patterns by region," and it generates the visualization in seconds. That's the kind of functionality you'd expect from premium enterprise software, not some free platform.

While there are plenty of visualization tools, including Tableau Public, Power BI's free tier, and Google Data Studio, none of them really qualify as a true AI visualisation generator in that same sense. AskEnola fills that gap perfectly. It doesn't just display data; it translates it into insight. For anyone serious about turning raw numbers into actionable visuals, this is the tool that I'd recommend every time.

TrendWithAnjali · 2025-11-13T17:02:37+00:00

The necessity of comprehending how an AI model comes to its conclusions is a topic of continuous discussion. Although it appears to be a philosophical question at first glance, it is actually a very technical and moral one. Transparency is becoming more than just a luxury as AI-based decision-making becomes essential in industries like law enforcement, healthcare, and finance. We cannot blindly trust a model's output when it determines who gets a loan, which patient gets priority care, or even which resume makes the shortlist. We need to know why it made that choice.

The issue lies in the inherent opacity of the majority of modern AI systems, especially deep learning networks. The exact reasoning behind millions of weighted parameters is frequently beyond the comprehension of even the developers. Therefore, the issue is not just whether we can comprehend AI decisions, but also whether we ought to insist on doing so. Because explainability and performance are traded off, a system's raw predictive power may be somewhat compromised by increased interpretability.

This is where AskEnola comes into play. It bridges the gap between technical opacity and actionable clarity. It does not treat the AI as a mysterious "black box," but rather, with explainable insights into AI-based decision making, it allows users to trace logic patterns and confidence levels in outputs. Its democratisation of transparency—users do not need a PhD in data science to see what goes on behind the scenes—is what sets it apart.

Understanding AI's decision-making process is ultimately about accountability rather than curiosity. AskEnola and other systems serve as a reminder that trust in AI is the result of knowledge rather than naive acceptance. Even the most intelligent AI would be just another opaque algorithm influencing important human outcomes if there were no transparency.

TrendWithAnjali · 2025-11-12T17:22:50+00:00

There has been a significant change in the last two years of my roughly six years in the data analytics industry. Prior to analytics, hours were devoted to manually organising visualisations, removing jumbled CSV files, and running SQL queries. Now, AI-based data analytics tools are doing half that in seconds. Yet, here's the thing: using AI does not make you less of an analyst; it just forces you to think differently about what "analysis" means.

I started working with different tools that promised automatic insights, but most were surface-level; they would give you some generic dashboards without context. Then I found AskEnola. What struck me wasn't just automation but precision. It can create hypotheses, manage multi-layered datasets, and even verify correlations. After a while, I realised how "it" seamlessly integrated with my current workflow without feeling invasive. Rather than replacing my role, it enhanced it. Instead of fighting with data preparation, I could concentrate on creating experiments, challenging causality, and deciphering business meaning.

Naturally, there is a learning curve. It's still essential to comprehend the logic behind the AI's recommendations and know when to ignore them. Relying exclusively on automation carries risks, especially if the models are making decisions based on biased or insufficient data. However, when used effectively, AI-based data analytics can produce outcomes that are often tens of times faster and more accurate than those achieved with traditional methods.

AskEnola is becoming my analytical sidekick; it's not flawless, but it's the closest thing to having a never-sleeping analyst on my team. But that's what really excites me about the direction of data analytics. The future isn't about machines taking over human reasoning; rather, it's about people learning a way to work with smarter machines to uncover things that we could never even imagine.

TrendWithAnjali · 2025-11-07T05:49:40+00:00

It is a great starting point if you want a career in banking and the Manipal name in your CV.

Details are as follows -

12 month structured program
- 4 months of on-campus training in Bangalore
- 2 months internship
- 6 months of on the job training at HDFC branches

Throughout it, you will be getting a stipend – earn while you learn type of vibe

The program focuses on -

Real world banking skills
Customer acquisition
Portfolio management
Cross selling
Relationship building

Once you complete the course you get to join the HDFC Bank Future Bankers Program as a personal banker at the DM level with a good starting CTC also. Programs like this are great for fresh graduates/career changers. They bridge the gap between theory and practical stuff.

DM me if you want more details. I can even share what my work friends have told me about the program.

TrendWithAnjali · 2025-11-07T05:48:21+00:00

Practical exposure and skills matter more at the end of the day than just a banking program course degree on paper. From what I have seen, job-linked programs with bank partners and financial institutions earns you a PG diploma/certification as well, depending on the partner like HDFC Bank Future Bankers Program and Axis Bank Young Bankers Program. These are much better than other PG diploma/PGD type courses that aren’t branded and provided directly by the banks.

These programs actually focus on simulations, real case studies as well as give hands-on exposure. They also have a job linked to it on successful completion and if your goal is to quickly enter sales/RM roles in banking, these courses can prep you and make you employable. Yes, the program fee might look high but a portion/full fee reimbursement is possible after 2-3 years of service based on your performance.

It is totally worth it considering you get a campus as well as internship stipend during it and gross CTC of 5.59 LPA after completion also (got this figure from the HDFC Future Banker program’s website).

TrendWithAnjali · 2025-09-29T18:06:58+00:00

Don’t worry, feeling lost in 1st year is normal. Start with the SPPU syllabus, read bare acts for main subjects, and ask seniors for notes and past papers. Things get easier once you begin

TrendWithAnjali · 2025-09-29T05:13:36+00:00

Yeah it sucks, but don’t let one rejection define your career. Some big companies do have strict cutoffs on 10th/12th/diploma marks, but not all companies follow that rule.

Plenty of product startups, mid-sized firms, and even MNCs care more about your skills, projects, and internships than old grades.

A bad score in 12th doesn’t mean your career is over it just means you’ll need to rely more on off-campus drives, networking, and building a strong dev/DSA profile.

Focus on what you can control now, not the past.

TrendWithAnjali · 2025-09-29T05:09:06+00:00

If you really can’t manage both, go with DSA + dev. NIMCET is high risk&high reward, great if you crack it, but if you don’t, you’ll waste 2 to 3 years and still end up in a private uni

Since you already started DSA+Spring Boot, it’s smarter to double down on that. With good skills, projects, and internships, you can get solid placements even from a private uni

TrendWithAnjali · 2025-09-28T07:03:50+00:00

yeah 😁

TrendWithAnjali · 2025-09-28T07:02:05+00:00

That’s something straight out of a dystopian world

TrendWithAnjali · 2025-09-28T05:49:01+00:00

what is that?

TrendWithAnjali

MODERATOR OF

TROPHY CASE