Can you imagine making a business decision purely based on your “gut feeling”?
A big no, right? You want to back every major decision with some data.
Now imagine making a decision using data that’s old, messy, or full of mistakes. That’s even worse than making gut-based decisions. Because now you’re likely to make poor decisions that could cost you precious dollars.
This is where data engineering services come into the picture, ensuring that every decision you make for your business is validated by high-quality data.
Let’s start from the basics to understand how better data is directly related to better business choices.
Are You Working with Clean Data?
To check if you’re working with clean data or not, you need to consider these parameters. See if your data is:
Accurate – Your data should offer correct real-world information. Inaccurate data can make everything else dependent on data, like forecasts, performance reports, and predictive models, unreliable.
Valid – Each entry should meet the requirements and constraints set for the respective data field. Invalid data can break automated workflows and trigger reporting errors.
Complete – The extent to which all the necessary data is available will determine its completeness. All the required fields should be populated.
Consistent – The data values should be uniform across systems and databases. Inconsistency becomes a problem when multiple systems are working independently. Harmonizing data in such cases is important for clarity.
Timely – You should be able to access data that reflects the current state of business operations. It wouldn’t matter how accurate your data is if it’s outdated. You need reliable access to fresh data as and when needed.
Unique – There shouldn’t be any duplicate or redundant entries in your database. Duplication can throw off your reports and create confusion, affecting the systems that depend on it.
You must fine-tune your data engineering pipelines so that your data can deliver quality decisions.
Why Ignoring Data Quality is a Huge Mistake
Bad data isn’t just about small reporting mistakes — it can send shockwaves through your entire organization. It can mislead strategies and cause problems you might not even notice until much later. Productivity is not the only thing you lose here. Customer experience and brand credibility are also at stake here.
If you’re planning on using AI and predictive analytics, you can’t afford to have flawed data. Using high-quality data is a must to train your ML models.
Well-designed data engineering pipelines ensure that you can trust all the data-driven reporting to take decisions. Data powers business decisions that decide the future course for your organization. The last thing you want is going down the wrong road, all because of bad data.
How did We Help a Client Save $250K Annually?
One of our clients, a reputed brand in medical compression therapy and wearables, wanted to completely revamp how their sales and financial data were managed, right from the time it was collected to when it was analysed. Because they had operations spread all over the world, they had data pouring in from different sources. And there was no way to consolidate it all. They relied on manual data entry methods which was just slowing them down.
Here’s how we helped them solve this problem with our data engineering services.
We created a centralized web application where users could enter the data. Once it passed the validation checks, the data was moved to a central data warehouse, a consolidating point for all the data. Users could also generate reports easily through Microsoft Power BI integration.
The results were game-changing.
The client was able to cut back on a significant amount of time spent in manual reporting, saving 15 man-days a month. While earlier it took two weeks to bring all of the data together to get insights out of it, now it can be done in real time. And thanks to the reduction in the tedious manual effort, they were able to save an astounding $250K in a year.
Check out this case study to learn more about how our data engineering services helped our client achieve faster, cleaner, and smarter data management.
What Can you Do for Clean Data?
Here are a few suggestions for you to implement in your organization.
Determine Clear Rules for Everyone
One of the easiest ways you can keep data consistent is by laying down some common ground rules. For example, how everyone should enter, name, and store data. Make sure all the people working with data are on the same page regarding these rules.
Make Data Audits a Regular Habit
Old records, blank fields, and mismatched values can slowly pile up without anyone noticing. So, review and clean your data from time to time. Use tools that can automatically flag any issues with the data.
Integrate Automation as Much as Possible
With manual data entry, there tend to be problems like typos and inconsistent formats. So, try and automate much of your data processes. Opt for automated data validation tools, integrations, and data pipelines. This way you can reduce human errors and keep your data clean in real time.
It’s Time to Get Smarter with your Data
Data handling can no longer be left on the back burner.
If you’re looking for data engineering consulting services that offer you the strategy and expertise needed to improve your data processes, Arna Softech is here for you.
Let us know the problem you’re trying to solve, whether it’s scattered data or slow processes, and we’ll tailor a decision-ready system for you.
