DEV Community: ramya N

Why Can’t You Start That Side Project You’ve Been Dreaming About?

ramya N — Mon, 06 Jan 2025 07:16:57 +0000

Most developers can relate to having thousands of dream projects stuck in their "brain box," never seeing the light of day. During my undergrad, I started developing small projects fueled by curiosity and motivation of the unexplored dev world. But after entering the corporate world, that spark began to fade. Despite thinking of new ideas daily, I struggled to start or complete them.
As I tried to make sense of this behavior, I noticed a pattern—small things that slowly pile up and end up derailing everything, no matter how driven you are. Let’s break down these factors and see how they can quietly creep in and throw everything off track.
It Doesn’t Need to Be the Next “Viral” Thing
When we think about creating an app or a website, it’s easy to get caught up in the idea of making it the next big hit. But out of the 3.3 million apps on the Play Store, only about 114,000 have ratings above 4 stars. That doesn’t mean the rest are failures, though.

Your project doesn’t need to attract everyone—it just needs to spark your curiosity or solve a problem that matters to you. Ironically, your chances of making something impactful actually increase when you focus on building and improving the project, instead of stressing about whether it will go viral. Once I figured this out, the pressure of starting a new project really eased up because I knew I was doing it for me, not for anyone else.

Make Your Own “Motivation”

Like a lot of people, I kick off new projects with tons of excitement—kind of like a New Year’s resolution. But by week two or three, that energy usually fizzles out. Next thing I know, I’m abandoning the project and moving on to the next one, caught in that endless procrastination loop.

To break that cycle, I came up with a reward system. For example, I’ll treat myself to a pack of my favorite noodles if I get through a module by the end of the week. It’s a small incentive, but it really helps me push through the tough spots.

Another thing that helps is having a buddy to keep me on track. A project partner or accountability buddy can give you a fresh perspective when you're stuck, remind you of your goals, and help keep you going through the rough patches. Having someone to talk to can really make a huge difference.

The “Perfect” Doesn’t Exist

Perfection is a trap. No project will ever be flawless. There will always be bugs, unfinished features, or things that don’t quite work the way you want them to—and that’s totally fine. Hesitating to start a project because you’re worried about the outcome is like refusing to try a new flavor just because you think you won’t like it. How will you know unless you try? The same goes for your dream projects.

In the beginning, I quit several good projects because I hit a roadblock or felt they weren’t “good enough.” But after failing a few times, I realized that perfection is a myth. Progress happens through consistent effort, iteration, and feedback. You’ll get closer to the “perfect” project by continually improving, not by waiting for it to be flawless from the start.

You Can Make “Dumb” Apps That Aren’t Useful

We tend to put pressure on ourselves to create something groundbreaking or super useful. But honestly, creativity thrives when there’s no pressure. I remember thinking about an idea for a simple local music app and dismissing it because it seemed “too simple.” I thought it wasn’t innovative enough.

Looking back, I realize those “dumb” ideas often lead to the most fun and unique projects. The key is just starting, whether the idea feels useful or impressive or not. It’s the process that counts.

Don’t Wait for “Holidays” to Start

Instead of waiting for the weekend or some big block of free time, try carving out a little bit of time each day for your project. Consistency is what helps you make real progress without burning out. Personally, I’ve found that breaking projects down into smaller modules and setting a goal to complete a few each week works wonders for me.

Spending just 12 minutes planning your day can save you up to two hours of wasted time. Research backs this up—taking the time to plan reduces the stress of feeling overwhelmed by the project as a whole. Breaking things down into manageable pieces really makes the whole process more enjoyable.

The Project Plan Doesn’t Need to Be the “Chosen One”

Large-scale projects often require tons of planning and teams of professionals. But when you’re working solo, you don’t need to have the perfect plan from the start. Over time, you’ll figure out a workflow that works for you as you tackle different projects—both successful and failed.

Remember, your first attempts will teach you some of your most valuable lessons. Embrace the learning process instead of stressing about starting perfectly.

Getting a side project done while balancing studies or a full-time job is a pretty big deal. But honestly, it's more about enjoying the process than stressing over the outcome. It’s not about creating the next viral hit; it’s about working on something that excites you and helps you learn. In the end, you’ll be glad you started, even if it’s messy. So, why wait? Take that first step today!!

Check out my blog here

Why Can’t You Start That Side Project You’ve Been Dreaming About?

ramya N — Fri, 03 Jan 2025 10:13:00 +0000

As I tried to make sense of this behavior, I noticed a pattern—small things that slowly pile up and end up derailing everything, no matter how driven you are. I tried to summarize all those things and give essential tips that worked for me.

It Doesn’t Need to Be the Next “Viral” Thing
When we think about creating an app or a website, it’s easy to get caught up in the idea of making it the next big hit. But out of the 3.3 million apps on the Play Store, only about 114,000 have ratings above 4 stars. That doesn’t mean the rest are failures, though.

Make Your Own “Motivation”

Click here to read the full article.

Why Data Quality Dimensions Are the Secret Ingredient for Data-Driven Success

ramya N — Tue, 31 Dec 2024 11:50:23 +0000

In today’s fast-paced world of data collection, we are generating and accumulating more data than ever before. Much of this data remains unorganized and poorly defined, preventing business professionals from extracting valuable insights from data applications. Let’s explore the need for data quality dimensions in this article.

Identification of the Dimensions

Organizations should start their data quality journey by identifying the need and understanding the problems they are facing as a whole. Deciding on the data quality dimensions is the most important part of the roadmap. It sets the tone for the insights and how we understand our data from the statistics provided.

There are generic data dimensions that can fit into any of the data quality (DQ) solutions, which we will discuss below. Not all dimensions need to be applied to all datasets. For example, there is a dimension called Timeliness, but it cannot be applied to data entities that do not pull data from the sources frequently. Domain experts should help with the identification of the dimensions respective to sources, considering load frequency, type of data, data requirements, etc.

Data Quality Dimensions

Some of the commonly used dimensions include:

Completeness measures the fully contained data in the given data entities. It states how much data can be used. If a column contains many null values, it indicates a lack of data. In the worst cases, the data procurement and ETL process must be analyzed to find the root cause.
Timeliness measures how frequently the data is loaded. It can involve checking recently updated timestamps or logs to verify the last update time.
Uniqueness ensures that a column has no duplicate values to maintain the integrity of the table. It is often used with primary keys and other unique keys existing in the provided table.
Conformity helps to verify whether the data imported is valid or not. Sometimes, data may contain unsupported or garbage symbols that could cause significant issues. This dimension ensures the data matches the required format.
Accuracy assesses how well the data aligns with the provided requirements. For instance, if a table contains an address and a country code, and the country code does not align with the address, it is considered inaccurate. This check ensures the data is true and usable.
Connectedness verifies the referential integrity of the table. It checks whether a foreign key exists in the reference table. It ensures the integrity of the relationships between the tables as a whole.

Other dimensions include consistency, validity, reliability, usability, availability, etc. The dimensions should be selected based on business requirements.

How dimensions affect the Data Quality Score

There are instances where many of these dimensions might not apply well. For such datasets, specific dimensions must be identified and applied. Otherwise, it might result in a very low DQ score on average. For example, a field collecting symbols based on user input cannot be evaluated under typical conformity rules, as it would result in a very low DQ score.

By carefully selecting and applying the appropriate data quality dimensions, organizations can ensure their data is reliable, actionable, and ready to drive impactful decisions.

Overlapping of Dimensions

While calculating the conformity score for a field, the results might be skewed if null values are included in the denominator of the data quality (DQ) score. To address this, overlapping dimensions can provide a more accurate DQ score.

For instance, consider a field labeled Optional that contains many null values because it is not mandatory. When calculating the conformity score for this field, null values should be excluded to avoid artificially lowering the score. Instead, we should calculate conformity based only on non-null values.

Here’s an example table:In this table, the completeness DQ score would be 3/5 (three non-null values out of five total entries). The conformity score, calculated without overlapping, would be 2/5 (two valid entries out of five total entries). However, if we overlap dimensions by excluding null values, the conformity score becomes 2/3 (two valid entries out of three non-null entries), providing a more accurate representation of the data’s quality.

Conclusion

Data quality dimension identification and the order of execution result in different data quality scores. Identification of the DQ dimensions is equivalent to crossing half of the ocean. Once the dimensions are identified, organizations can surf through the metadata and gain valuable insights.

In this article, we explored data quality in terms of dimensions and highlighted the importance of tailoring DQ checks to specific datasets. By understanding and applying these dimensions effectively, organizations can ensure their data is reliable, actionable, and ready to drive impactful decisions. Start by assessing your data quality strategy today and take the first step toward unlocking the true potential of your data.

Check out my blog at here