Mar 01

The Benefits of Data Matching

data matchingScrubbing the data on mailing lists, sales lists, etc. is another tool to keep duplicate records from choking your business. The process to accomplish this is called Data Matching. The simple definition is to compare two or more sets of collected data (excel spreadsheets for example) to remove duplicated information and consolidate records used by a business. Removing duplicated data can streamline business practices and save money. By eliminating multiple lists, telephone sales personnel will not waste time on duplicating cold calls to potential clients. This also eliminates sales people who make only cold calls interfering with more senior personnel engaged in follow-up calls.

Data Matching For Efficiency

This same consolidated list can also save thousands of dollars every year in mail-outs. By providing the marketing department with a streamlined list of clients and potential clients, fliers and other advertisements can go out to only one per household instead of three or four. When multiple cards arrive, the person who gathers the mail will read one and then throw away the rest.

It’s not just a matter of saving money however. Data matching of client records is important for companies seeking to provide exceptional customer service. On most occasions, a customer that places a call to an insurance company or a bank will speak to multiple departments and multiple personnel. For example, if the correct address does not match between what the auto insurance department has with what the life insurance department has on file, customer service can be delayed and information can be sent to multiple addresses. This can be even more important to a customer seeking a loan or trying to conduct other banking business over the phone.

Data matching programs can be run on a routine basis to provide this financial and customer support. A simple program can take this data from multiple records with lists maintained by various departments to ensure the most up-to-date data is shared. Not every piece of data can match 100% however. This is where Fuzzy Matching comes into play.

Fuzzy Matching

Fuzzy matching operates on a similar principle as data matching, but does not require an exact match to consolidate data; rather, it works on the probability of a match. For example, a fuzzy matching algorithm used to consolidate data can draw matches for a particular company from outside sources by using recognized abbreviations for the company and even misspellings of the name. The requirements can be adjusted by the user of the algorithm depending on the requirement. Gathering sales data could be a lower match; say a 75 percent probability of a match. Consolidating client data might be higher; beginning at 90% probability.

Data matching is a necessity for a business of any size. Conserving budget dollars and increasing customer support is important for every company from a national call centre to a local hospital. Using both data matching programs and fuzzy matching algorithms ensures that each can provide the best service possible. Download a free demo of our award-winning WinPure Clean & Match software and try out its powerful data and fuzzy matching features today.

Feb 29

What is the Difference Between Data and Information?

difference between data and informationMany people hear the words “data” and “information”, and think the terms can be used interchangeably. In fact, data and information aren’t the same at all, there is a distinct difference between the two words.

In order to correctly recognize and use either one, it’s important to know the difference between data and information.

Data vs Information


Data is a collection of figures and facts, and is raw, unprocessed, and unorganized. The Latin root of the word “data” means “something given”, which is a good way to look at it. Individuals and organizations can’t do much with unprocessed data because it’s so random. Once data is given structure, organized in a cohesive way, and is able to be interpreted or communicated, it becomes information.

Example of Data

J,Smith,123 King St, London, UK, 0202656788


Information isn’t just data that’s been neatly filed away, it has to be ordered in a way that gives meaning and context. This is what allows people to use data for reasoning, calculations, and other processes. With that said, data’s importance lies in the fact that it’s a building block. Without it, information can’t be created.

Example of Information

John Smith
123 King Street
London, United Kingdom
(020) 2656788

To simplify this concept think of it like this:

Data has no meaning until it’s turned into information. In order for people to interpret data or make any use of it, it must be understood. For instance, a company’s sales figure for one month is a piece of data that’s meaningless because it has no context. It tells nothing, and there’s little that anyone can do with it as is. However, if one were to take a business’s sales figures from three months and average that number, we’d be able to derive many bits of information from that data. When one has incomplete data, it’s highly likely that it will be misinterpreted and lead to the development of misinformation. For example, suppose someone saw that his business’s sales were up by 4%, and he drew the conclusion that his current marketing campaign was working well. However, if he found out that a competitor who sold the same products had a sales increase of 16% during the same time period, he’d start to question just how well his campaign really performed and would want to gather more facts (data) to analyze the situation again.

Data Quality

Data quality refers to whether data is useful to make decisions, calculations, or plans. Basically, good quality data is reliable, accurate, relevant, consistent, and appropriate for a given context. Using a good data quality software tool such as WinPure Clean & Match are designed to ensure that business data is as reliable as possible so that it can provide a solid basis for effective decision-making.


Feb 11

Simple Steps For Good Data Quality Management

data quality managementData quality management is very important. Data can often hide in legacy systems and become hard to find. Preparing to ensure data doesn’t get lost is very important for many businesses regardless of small or large amounts of data. An analysis can be performed and will provide organization and reports that can produce detailed statistics. The business can then take the detailed data and use better strategies based on the businesses individual needs.

Data is very strategic. It takes very careful organization. Some data may contain very important, sensitive information such as financial values and personal information. Inaccurate data makes it difficult to produce clear results. Using a virtual data firewall can detect and block inaccurate data as soon as it enters the system. Using a data fire wall will help protect any database from receiving bad data. Bad data is a very serious issue that could be prevented and needs to be prevented from causing potentially dangerous issues.

Even the best systems in the world can not completely protect data. The large amounts of data flowing into a system can cause faults in the data. This often happens in government websites with high volumes of data. High volumes of data increase the risk of bad data, also making it nearly impossible to track every piece of data that is collected. The key is to identify and focus on the amount and types of data. Using business intelligent solutions allow an organization to look closely into the data and figure out how to manage that data. Then, astute data management could be used to sort and collect the data while cleansing and inspecting it. This can be included in a good quality software and do the job for you.

Many companies need to create a system to manage data quality end to end. Usually there is a person selected to look over data related needs. This person usually chooses to work with some type of software, such as WinPure. Whether a group of people are in charge or just one person is in charge, human error is inevitable. A software is necessary to catch errors that people can not catch. A reliable software should be used and this eliminates the risk of human related errors.

Successful data quality starts with a strategy, but relies on a reliable software. Software eliminates errors and catches bad data before entering the system. This is necessary to keep the database clear. Finding the correct software is very important. This prevents the loss of money and time. Accurate software includes many different features. These features will help protect every little part of data.

WinPure provides all these features and more to protect from any data related issues.

Data management can potentially hurt a company. It is best to stay protected from all these issues.

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