Go Clean to Be Lean: Data Optimization for Improved Business Efficiency

21 Jun 2024


The question I’m posing here comes from the age-old dilemma that dates back to at least the 7th century BC when the first coins appeared. Should I buy it, provided I have the coin, or should I do it myself?

If we transfer this dilemma to business, it’s either having your own accounting or sourcing it to one of the Big Four. And let’s be honest, we don’t always get it right.

But which should you choose for your business data needs?

In this case, it comes down to raw data vs clean data. Are you willing to pay for the latter? Are you willing to do the cleaning yourself? No matter your choice, extra time and money expenses are involved.

However, clean data can help save resources in other areas. Let me tell you how.

What is clean data?

First things first. If you’re reading this, chances are you have already bought or want to purchase some web data. Putting different vendors, prices, and data characteristics aside, you can basically choose from raw or cleaned data.

As the name implies, raw data is unrefined, and in addition to beneficial records, it also contains low-value, duplicate, and irrelevant entries. Also, it often has stylistic tags in < > brackets and other code meant for machines only. This makes it less human-readable and more difficult to ingest.

The raw dataset is also very large, requiring more storage space and time to transform into actionable insights. It takes a team of data engineers to handle it properly and extract that business value.

In contrast, clean data can be defined as a refined and often AI-enriched version of raw data. It has all sorts of rubbish thrown out, making the dataset up to four times lighter, with a much higher signal-to-noise ratio.

Moreover, enriched clean information has extra data points that are not present in the original raw dataset. Data providers add valuable information from other public sources or use LLMs to extract or transform information, creating new, relevant bits of data. It might even blend multiple data sets.

Defining the definitions

The data industry has yet to precisely agree on what clean data means. Also, using adjectives like filtered and enriched interchangeably doesn’t make things easier.

To shed some light on terminology, filtered data has various errors and worthless data points have been removed. This means that the provider has set some rules according to which the whole data set was combed.

However, without enrichment, it only contains the data from the original database.

In the meantime, “enriched” refers to something that wasn’t on that raw data set. These are usually some extra data points that increase the value of the main database.

However, if it’s not filtered, it can contain weeds you’ll need to pluck out with your very own hands.

At this moment, you should already start having an idea of which data product you’re leaning toward. If not, the next chapter will help you decide.

Who can benefit from clean data?

Raw data is a good choice for larger companies with strong data departments. Smaller businesses would have trouble handling the volume of information, which wouldn’t be cost-efficient. In contrast, clean data is good for companies of all sizes, requiring less data preparation efforts.

With clean data, you don’t need a big team of data people to make sense of that raw gibberish you just bought. The information you get is already filtered and enriched, requiring way less professional attention.

And even if you do have the necessary capabilities to handle raw data, is this the best way to use data analysts who should be, well, analyzing?

How data people (should) spend their time

Data scientists often complain that they spend most of their time gathering, cleansing, and structuring data instead of looking for insights. The (in)famous 80/20 rule used by data people says that 80% of the time is spent on collection and preparation, with just 20% dedicated to actual analysis.

Gartner seems to agree with this statement. According to their 2021 report, data preparation is one of the major investment areas. Luckily, it can take less time to automate some of the tasks with data preparation tools. I can only add that those tools must be AI-based to impact performance significantly.

Other data scientists refer to the 1-10-100 rule, which was laid down by George Labovitz and Yu Sang Chang back in 1992. According to them, it takes $1 to verify a record while entering it, $10 to cleanse and deduplicate it later, and, if no one notices the error, it costs $100 in both time and resources.

This is another argument for why cleaning with AI can be more cost-effective than writing algorithms on your own. They take longer to write and execute the task and are more error-prone. Sometimes, it’s simply impossible to write the proper code for cleaning data.

You can benefit from business data

Some businesses think there’s no way they can benefit from big data. That couldn’t be further away from the truth.

Data, especially its clean version, can help businesses of various types. We can start with company data, which is great for checking on your competitors and your market. Publicly available professional data related to employees can be invaluable for your HR department when selecting a new workforce.

Let’s not forget about your sales department. Having the necessary data can be that step from cold-calling to presenting the right option for the right client that’s ready to buy.

If that wasn’t enough, funding data gives valuable insights about funding trends. It helps you track company performance, identify newly funded businesses, and check the actual amount of investors. In short, funding data allows you to make better decisions in investment, lead generation, and market research.

Four ways to optimize costs with data

By now, it should already be clear that clean data has many advantages over traditional raw data. But how exactly it can help you save money? Here are my four examples.

1. Shortened onboarding and time to value

Shortened onboarding is a big plus for clean data. In most cases, setting up the delivery process between you and your provider takes over a month. In the meantime, you’re paying for a product you can only use later.

With its simplified and refined structure, clean data makes the setup much less complicated. Naturally, this decreases time to value when you don’t have to wait to put your newly purchased dataset to use. But how much?

The shortest and most unamusing answer is “It depends.” For some, it can be days, while for others, it can be months. And while data processing is quicker because you have less data, it’s never a major factor. Let’s explore one example.

Suppose you have an experienced data team ready to clean the hell out of that raw B2B data you bought. However, it’s doubtful that all your data analysts are experts in data cleaning. They finish the job, and after a while, you get a complaint from your sales team that some data is missing.

Now, confused and infuriated, you get back to your data professionals and order them to fix the issue ASAP. They apologize semi-honestly and start all over. Remember that your sales department has no leads all of this time.

You ask them to return to cold-calling, which is accepted lukewarmly with low enthusiasm, resulting in a low conversion rate. Finally, your data analysts upload the second version. Now, do you know it is finally fixed, or do you hope so?

So, the story's moral is that clean data lets you switch from hoping to knowing.

2. Faster data processing

I mentioned this already, but I want to reiterate that your data team is not the only factor that impacts the speed of data processing. It’s also the machine and the scripts that they wrote. And this process can take days.

When more and more companies have ready-to-use data, everyone can come up with the same insights. Now, speed is the competitive advantage you want to have.

With the help of AI, data processing will happen in real time. Data itself will become fast and actionable, and its quality will play a major part. So investing in clean data today can be invaluable tomorrow when raw data will be a thing of the past, just like fax.

3. More time for analysis

This one is probably the most important in terms of optimizing costs. Your employees can focus on other high-value areas instead of routine data operations.

Naturally, clean data can result in a smaller workforce. Your data processing becomes more efficient, and salary savings will likely pay for the more expensive clean database.

Since there’s no way to analyze and process data simultaneously, you can’t start using it right after you spend money on it. Only when the preparation is done can you start looking for valuable insights?

With clean data, your team can quickly jump right into the analysis phase. From now on, it’s highly unlikely that your data department will become a bottleneck for using the information to generate revenue.

4. Reduced dataset size

Storing information requires space. Storing big data requires big space. Newsflash – the amount of information you’ll need to analyze will not get smaller anytime soon. Here’s where clean data comes in handy.

After filtering all unnecessary data points, you are left with what is really worth storing. And it takes four times less data storage. Now, you can downgrade your cloud subscription from the Enterprise to the Pro plan.

Sure, some businesses have their own servers and might keep data there. That’s fine, but you still need a lot of space for raw data.

Finally, the future of data processing is inevitably in the cloud, done with the help of machine learning (ML). And this applies to smaller companies as well. So wouldn’t it be nice to transfer with a four times smaller need for storage before ordering a long-term deal?

When should you clean your data yourself?

There aren’t many scenarios where the best option is to clean the data yourself. The first one is having a big data team at your disposal. This way, part of it can clean, filter, and enrich the dataset while the others are analyzing the already cleaned data.

Another case is when you have no need for huge datasets. If there are thousands of records instead of millions or billions, cleaning it yourself can be cheaper, provided you already know how to do that. Then again, if you’re not planning to buy any raw data in the foreseeable future, gaining data-cleaning skills will pay off for your employees but not for you.

This leads us to the final scenario when you only occasionally need clean data. In this case, having your data analysis people learn the ropes to filter and enrich new datasets later is beneficial. Just ensure the knowledge doesn’t leave your company with one of your senior data scientists.

Finally, let’s not forget that clean data is often prepared with the help of AI. If you plan to do it manually, it is no longer feasible due to the proprietary knowledge of using AI (LLM) technology for this purpose.


No matter how you feel about raw or clean data, the latter option is the new norm. The question here is whether you have enough hands to clean, filter, and enrich your datasets in a timely manner.

It might be an option for now, but with the amount of raw data expanding, at some point, a small team of data scientists won’t be able to handle it cost-effectively. Therefore, the future belongs to actionable and fast data that is ready for analysis right away.

I hope my article has shown you the ways to optimize your business costs with clean data. And with the data prices increasing yearly, the best option might be to become an early adopter.