Data stands as the lifeline of every successful business. But while leaders push for data-driven initiatives, they are unaware of data quality challenges – meaning, dirty, duplicated, and disconnected data that becomes a bottleneck for all business initiatives. In fact, about 60% of businesses remain clueless about the impact bad data has on their operational efficiency and financial bottom line. Data management challenges remain a matter of when not if.
In 2024, dirty data simply isn’t a problem businesses can afford to ignore or live with. It impacts everything – from sales to marketing, brand value, and more. Is there a workable solution? We think so.
Read on to know more.
Addressing key Data Quality Challenges
Before we talk solutions, it’s imperative to address some common data management challenges that some businesses might not even know exist. In fact, many of our customers were not aware their data scored poorly in terms of data completeness and accuracy. Many more struggle with data duplication and disconnected records that prevent them from making use of their data.
To round up, some of the key data quality challenges are:
- Missing or incomplete records compromise the accuracy of analyses.
- Inadequate data-capturing processes result in information gaps.
- Varied formats and standards across datasets impede uniform analysis.
- Changes in data structure over time create inconsistencies.
- Errors in data entry and processing contribute to inaccurate information.
- Outdated or obsolete data can mislead decision-makers.
- The absence of clear data ownership and stewardship leads to accountability issues.
- Inadequate policies and procedures for data management.
Data Integration Challenges
- Difficulties in integrating diverse data sources into a unified system.
- Ensuring consistency when merging data from various platforms.
Data Quality Monitoring
- Lack of real-time monitoring tools for identifying and rectifying data issues.
- Insufficient proactive measures to maintain ongoing data quality.
- Difficulty in maintaining data quality as the volume of data grows.
- Scalability challenges with existing data quality management processes.
Given the complex and evolving nature of data, how can companies satisfy their need for ensuring reliable, high-quality data?
They have two options to solve their data quality management (DQM) issues:
- Option 1: Hire a team of data scientists and talent that potentially takes hours to solve data matching issues.
- Option 2: Use an all-in-one data management suite to get runaway data under control.
Let’s explore the two options in detail.
Option 1: Hire Data Scientists to Clean Up Your Data
When companies discover they have bad data, they either hire an in-house data engineer OR outsource the project to data cleaning consultants. In both cases, it takes months before any visible results take place. The data engineer will have to do a deep dive into data context and ownership, while also building and testing new algorithms and processes to resolve the data quality challenges described above. On the other hand, the outsourced data consultant will simply use spreadsheets and third-party systems to clean up the data.
These two approaches demand time, back-and-forth communications, and a hefty budget to manage processes. Even then, there is no guarantee to data accuracy or improved quality.
This is why, it’s imperative to use a more modern approach that cuts back on the time spent on menial efforts.
That’s where we recommend option 2.
Option 2: Use a No-Code Data Management Suite to Fix Data Issues
Instead of having a data professional spend 80% of their time cleaning data, it’s better to use a no-code solution that allows for easy identification of data quality issues. These solutions come equipped with a complete data quality framework that enables faster and easier data preparation as well as standardization. With the right platform, your team can handle complex activities like data matching, data merge, and purge with relative ease and flexibility.
If you prefer to explore option two, then an all-in-one solution like WinPure can be used to empower data scientists, IT managers, and business users to prepare and treat the data without having to worry about infrastructure or resources.
Why Use an All-in-One Enterprise Solution for Data Cleansing?
For faster turnaround times and greater accuracy, companies are discovering the benefits of choosing all-in-one no-code data management tools over traditional data-cleaning human resources.
Often, these tools complement the work being done by seasoned data professionals. Data scientists, engineers, and analysts are in demand. They take care of data handling work by collecting, processing, and utilizing all data across various sources in a manner that aligns with organizational standards, industry regulations, and best practices.
A data scientist’s job is to be strategic about the whole data cleaning process. They need to do bigger things, but unfortunately, they are forced into becoming data janitors in the absence of cost-effective data matching tools.
WinPure’s all-in-one data quality management suite empowers business users as well as IT managers to take charge of the company’s data. You don’t need multi-million dollar software or a burgeoning team of data professionals to overcome your data quality challenges!
How Does Winpure solve data quality management challenges?
One of the biggest roadblocks to solving DQM challenges is the manual work involved in fixing the data. It takes weeks if not months to build algorithms and processes just to clean and standardize data. Moreover, small enterprises simply do not have the budget or capabilities to hire data engineers to do the job and end up relying on IT teams to solve the problem. With limited experience in programming and algorithm building, IT teams resort to implementing expensive platform integrations with the hope of automating the process.
Unfortunately, this is a flawed approach that increases the challenges instead of solving them! There’s no ownership of data, processes are murky and software integrations fail flat.
WinPure addresses these challenges by simplifying the data cleaning and matching process through a no-code approach. Now anyone, be it an IT manager or a sales manager can use the software to clean, de-duplicate, merge and purge records effortlessly.
Consider this – you’re transitioning from one database to another, with information about the same entity scattered across different databases or systems. If you’re not careful, data migration can potentially increase database size and complexity.
Data matching is crucial for creating a unified and accurate view of information, eliminating duplicates, and improving data quality.
WinPure has a comprehensive data matching module that allows for faster and more efficient data matching with just a few clicks. It uses an intelligent combination of data-matching criteria to create a consolidated and accurate representation of your customer data.
WinPure Clean and Match Enterprise helps you with:
- Fuzzy data matching: Match names and text strings more efficiently and accurately with WinPure’s powerful fuzzy match capabilities. Unlike other traditional methods, you don’t need to manually build or test fuzzy match algorithms for matching. With WinPure, you just need to select the columns you want to match and it’s done. No more coding or algorithm testing.
- Exact data matching: Want to match exact fields? You don’t need to learn Excel formulas. Simply plug in the data into WinPure and choose an exact match option to match phone numbers or zip-codes.
- Data merge/purge: Want to keep or remove duplicate records? WinPure lets you merge and purge data and create master records by selecting bulk records. No more having to create multiple copies of your master records.
Imagine having two separate records, Pam Smith and Pamela Smith with the same address and contact information. One of these entries contains an email address. The other contains their birth date. Now imagine this thing happening to thousands of customers in your database. You need a solution that can detect non-exact duplicates without relying on programming expertise.
Data deduplication is a common practice in database management, particularly when dealing with large datasets or merging data from various sources to maintain a clean and reliable dataset.
Using WinPure’s data deduplication feature helps you create a single and true source of data faster than ever. Identifying and removing duplicate or redundant records within a dataset with improved data quality doesn’t get easier than this.
WinPure’s data deduplication software helps you with:
- Marketing Lists
- Customer Relationship Management (CRM) systems
As new data continues to be added over time, the challenges of maintaining accurate data for meaningful analysis, reporting, and decision-making grow in tandem. Be it validating data, standardizing formats, removing duplicates, or resolving inconsistencies between data sources, data cleansing remains a complex process.
Data cleansing, also known as data cleaning or data scrubbing, is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets.
With a no-code data cleansing software, there’s no need to remember long formulas to clean your data. WinPure’s easy-to-use interface can help you make sense of your data in no time. Correcting data errors doesn’t have to be hard.
Here’s how WinPure handles your data cleansing efforts:
- Handling missing data
- Using standardized formats for consistency
- Removes duplicate data using matching criteria
- Takes care of typos and inconsistencies with tried and tested algorithms
- Data validation with predefined rules
- Identifying data points that deviate significantly from the norm
- Resolving inconsistencies between multiple data sources
Imagine being a global ecommerce brand, with a database of addresses that correspond to different countries, formats, and standards. With customers from all around the world, inaccurate and non-standardized addresses mean facing untold losses in shipping, logistics, and service delivery. Fixing those addresses manually can take ages.
Address verification is important for businesses reliant on precise location data, such as shipping companies, e-commerce platforms, financial institutions, and marketing teams.
Alternatively, you can use WinPure’s powerful Global Parsing Engine (GPE) to parse, standardize, verify, cleanse, and format address data intelligently for you.
So you’re faced with records that are similar but not necessarily identical. Say like having multiple data entries for one customer, each entry replete with minor errors, typos, variations, or inconsistencies. It’s like finding a needle in a data stack!
Fuzzy data matching, also known as fuzzy matching or approximate matching, is a technique used in data cleaning and deduplication processes to identify and match records that are similar but not necessarily identical.
Enter WinPure’s intelligent data matching engine that helps you find the truest matches with the least false matches. This feature also utilizes the in-built knowledge base library system with fuzzy match and proprietary match algorithms to consolidate similar data.
Making a choice between in-house talent vs using winpure
Believe it or not, data scientists spend 50% to 80% of their work time on cleaning and organizing data, leaving little time for actual data analysis.
If you’re still assessing between options 1 and 2, here’s a quick breakdown to help you in the decision-making.
|Typically more cost-effective.
|Requires significant financial investment (salaries, benefits, training, infrastructure).
|Designed for quick implementation with user-friendly interfaces.
|Building a team takes time from recruitment to training, potentially causing delays in implementation.
|Most tools are scalable and can handle growing datasets seamlessly.
|Scaling may involve hiring, resource acquisition, and internal process adaptation, potentially less flexible.
|Expertise and Maintenance
|User-friendly for varying technical expertise; software providers handle maintenance.
|Recruiting and retaining skilled professionals is challenging; ongoing training and technology updates are required.
|Focus on Core Competencies
|Allows organizations to focus on core competencies and strategic objectives.
|Managing an in-house team may divert attention and resources from core business functions.
To conclude – combine tool & talent to overcome dqm challenges
Given that 30% of company data becomes outdated every year, using a data deduplication and matching software suite can be one of the best investments you can make for your business. In fact, it helps empower data scientists who otherwise spend 50-80% of their time collecting and preparing unruly data.
Embracing a data matching and cleansing tool like WinPure isn’t just about tidying up your data; it’s a strategic move that turbocharges your business.
Want to get your data under control and in shape? Contact us today.