Data scrubbing, also commonly known as data cleaning is a process that refines your data by removing duplicates and fixing unstructured content.
If you’ve been handling data, you would know that dirty, duplicated data is a problem that organizations have been struggling to manage for ages. While data formats and structures a few decades ago were quite simple, they are now extremely complex.
With the emergence of apps, metadata collected via devices, multiple third-party platforms like social media and marketing platforms – organizations are literally drowning in data. Most of it is raw, unstructured data.
Let me walk you through how a data scrubbing tool helps and why you should consider investing in one.
Investing in a Data Scrubbing Tool Vs Hiring Data Analysts, Vs, Creating In-House Solutions
Before we talk about the tool itself, it is important to discuss the two other options that companies commonly use to solve data quality problems.
Hiring Data Scientists: This is usually the first solution companies choose. Data scientists, by definition, are experts who study data, derive key insights, and helps organizations capitalize on those insights. Unfortunately, most organizations hire data scientists to clean and fix data instead. These analysts spend almost 80% of their time fixing bad data.
According to a report by InfoWorld,
“Most data scientists spend only 20 percent of their time on actual data analysis and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data, which is an inefficient data strategy.”
And we are all too aware of this fact. Dozens of organizations spend millions of dollars hiring experienced data scientists, only to end up making them do mundane cleaning tasks. The problem of bad data remains. The struggles and frustration remain.
Creating In-House Solutions: When hiring a data scientist is not enough, companies begin hiring development experts in the hope of launching their in-house solutions. Although this may “seem” like an effective strategy (privacy, control, security), in the long run, it becomes an expensive endeavor costing companies at least $250+K per year just in hiring and retaining talent. Even then teams struggle to achieve accuracy in data deduping and data cleansing. Not to mention, it takes months and years to test and try algorithms that work on complex data structures.
Purchasing a Top-in-line Cleansing Solution: Notice I mention top-in-line. There’s a reason. Basic data scrubbing tools only do basic data cleaning. Using simple matching algorithms, these tools just look for duplicates and lets you clean or standardize format issues within Excel files.
Top-in-line or best-in-class data solutions offer a full-fledged data quality management framework. You don’t just clean data – you can also match data, profile it for errors, standardize it, and create a consolidated version of the truth.
Benefits of Using a Top-in-Line Data Scrubbing Solution
There are multiple benefits to purchasing a solution over hiring a data analyst or spending millions in developing a whole data cleaning tool.
Over the years as we’ve worked with 4,500+ clients from across the globe, we’ve seen firsthand the benefits organizations were able to reap when they purchased a solution.
Some of the key benefits include:
1. The Ability to Inspect & Scrub Data Easily & Quickly
Inspection of data via manual methods is a time-consuming activity. When you have millions of rows of data, siloed away in multiple data sources, varying in multiple formats, you’ll have a hard time fixing data. It is therefore imperative that you should be able to inspect data easily to know exactly what you need to fix.
A high-quality data scrubbing tool will let you inspect this data through a data profiling option that will let you get a consolidated view of each column of your data set. It will allow you to see the health of your fields and what are the most common problems affecting the fields.
This mere profiling activity would take employees months to discover. With a software, it takes just a few minutes for each data set.
Once you know exactly what is plaguing your data, the cleaning is a simple process.
2. It Saves Up on Time & Lets You Make Use of Your Data Faster
You do not have to wait for months to get clean data to run a report or get analytical insights. A powerful solution like Data Ladder can clean over a million records in just 45 minutes. Imagine the time your data scientist and the team can save on!
Moreover, the ability to clean up this data using pre-defined business rules makes it an even easier process. You don’t have to spend hours defining business rules such as replacing abbreviations or capitalizing names etc as this is usually built-in.
3. You Can Sort Your Data, Consolidate Lists & Get a 360 Customer View
We have dozens of case studies where companies use our solution to sort their messy data, consolidate records or lists from disparate data sources to get a 360-customer view. While they are scrubbing data, they also get a chance to remove duplicates, merge their data, and get an overview of the quality of their data.
For those aiming to create personalized customer experiences, this is an extremely important opportunity. They are able to integrate multiple data sets from third-party sources, scrub the data, and finally merge it all to create one final master record. This ability to clean, match, dedupe, and consolidate data is what makes a top-in-line solution worth the investment.
4. You Can Implement a Data Governance Framework
If you know the reason, the source, and the types of data errors you’re facing, you’re in a better position to create a data governance framework. For example, you could improve your data collection method, implement a stricter data recording policy across the organization, or even create a data management process.
It’s critical to remember that as complex data is acquired, so is the demand for companies to be responsible with it. Data compliance rules such as the GDPR, the Federal Trade Commission Act enforce stringent penalties on companies that do not take care in protecting consumer data. Most of the time, a careless mistake, such as sending an email to an unsubscribed list of an audience can cause significant damage.
To ensure these issues do not happen, you need clean data and a data governance framework in place.
5. You Can Discover Hidden Opportunities and Increase Your ROI
Dirty, messy data prevents you from seeing or creating opportunities. Take, for example, the case of Maxeda, a retail chain with three international offices. With messy data siloed away, the organization had to first clean its millions of records, dedupe the records & then merge it to get a fair idea of the customer’s journey. Once they did all this, they were able to identify better market opportunities and were able to create a digital experience for their consumers.
It’s hardly a matter of speculation – data impacts revenue in today’s world. With the right data, you can win consumers & beat competitors. Wrong or the lack thereof of quality data and you’re out of the game.
DataMatch Enterprise as Top-of-the-Line Solution that Can Help Your Company Meet its Goals
Data Ladder, a Gartner Certified data quality solution provider is rated amongst the top solutions in line with IBM, SAS, and Oracle. In multiple governments as well as private reports, project tests, and studies, we’ve achieved 98% success rate in terms of data matching that led to the weeding out of deeply nested duplicates & merging of complex data from multiple sources.
The solution offers data cleaning and scrubbing as part of an 8-stage framework that includes data matching, data integration, address validation & standardization, data deduplication as core functions.
Our fundamental goal is to provide you with a one-stop platform that you can use on your premises or cloud server to integrate, match, clean, standardize, verify, consolidate and merge data as you want. You can use the solution as part of a grander data transformation goal or as a necessary tool for your business users and your team of data specialists.
Data scrubbing is only a fraction of the whole data quality framework. If you truly want to be data-driven, your best bet is an Information/Data Manager armed with a solution like DataMatch Enterprise to make the best out of your data.