Data Services In Grand Rapids MI At NW Database Services

Data Cleaning, Data Cleansing, Data Scrubbing, Deduplication, Data Transformation, NCOA, Mail PreSorts, Email Verification, Email Append, & Phone Append Services in Grand Rapids Michigan

Get The Best Database Services In Grand Rapids Michigan

We provide data services to businesses and organizations in Grand Rapids MI and all Michigan cities. With over 3 decades of experience in the database business, you won’t find a company that can solve your specific database needs with higher quality service or better prices than Northwest Database Services. No matter what your specific need is, our team will find a data service solution to suit your situation.


More Cities and States Where We Offer Data Cleaning Services

We Are A Full Service Data Services That Can Help You Run Your Business

Northwest Database Services is a full-spectrum data service that has been performing data migration, data scrubbing, data cleaning, and de-duping data services for databases and mailing lists, for over 34 years. NW Database Services provides data services to all businesses, organizations, and agencies in Grand Rapids MI and surrounding communities.

Get Started


What We Do

Database Services

When you need your data to speak to you regarding your business’s trends, buying patterns or just whether or not your customers are still living.

Data Transformation

We provide data transformation services for Extract, Transform and Load (ETL) operations typically used in data migration or restoration projects.

De-duplication Service

Duplication of data plagues every database and mailing list. Duplication is inevitable, constantly keeps growing and erodes the quality of your data.

Direct Mail - Presorts

It’s true the United States Postal Service throws away approximately thirty five percent of all bulk mail every year! Why so much? Think: “Mailing list cleanup.

Email-Phone Append

With access to more than 500 million email addresses, Northwest Database Services uses one of the most comprehensive and unique data sets in the industry.


Over 40 million Americans change their address annually, which makes us do the work to maintain a high-quality mailing list while you focus on your business.

We Are Here To Help!

6 + 4 =


Woodland, WA 98674


gch [@]
To use email, remove the brackets


Call Us

(360) 841-8168

Information About Data Cleaning And Data Services

Visualizations Can Be Used to Spot Outliers

To be useful, data must be free from any contamination. Data analytics is dependent on accurate data. Data analysis is not possible if the data are corrupt. Visual representations are a great way to display data. To be useful, however, the data must not be corrupt.

Data analysts create data visualizations to present data visually to communicate their findings to relevant stakeholders. These visualizations can show patterns and outliers in large quantities of data by using graphs, charts, and maps. This article will discuss the different types of data visualizations. Data analysts have two options to quickly spot outliers.

Box Plots Can Be Used to Identify Outliers

Visualizing data in a box plot makes it easy to spot outliers. The “box” will be displayed on the box plot. This represents the interquartile range (from the lower to the higher quartiles) and any outliers that are not within the “whiskers”. Each side shows the minimum and maximum data values. If the maximum whisker is greater than the minimum, the prominent outlier will be that value. For boxes with a minimum whisker, the most notable outlier would be the maximum value. With a module such as Plotly, you can create box plots in Excel and Python.

Scatter Plots Can Be Used to Identify Outliers

Scatter plots are scatterplots that display data “scattered along an axis” for 2 variables. Scatter plot visualizations will make it easy to see outliers. These are data points that are the furthest away from the regression line, which is the single line that best fits the data. These visualizations can also be made in Python or Excel, similar to box plots. How to spot outliers using statistical methods Data analysts might use statistical methods to assist in machine learning modeling. This could include the identification, understanding, and sometimes elimination of outliers. Two common methods for identifying outliers will be discussed. There are other options that may prove to be more helpful in your analysis.

Dbscan Can Help You Identify Outliers

DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a method that can be used for machine-learning and data analysis. DBSCAN visually represents the relationships among features, trends, populations, and other data within a dataset. It can also be used for detecting outliers. DBSCAN uses non-parametric algorithms to create density-based clustering algorithms. It is focused on finding neighbors that are close together. Outliers are areas that exist in low-density, isolated areas far from their neighbors. DBSCAN, a data analyst and machine learning specialist, will be familiar to data analysts. This algorithm has been in use since 1996. It won a prestigious ‘test-of time award’ at a major conference in data mining. This makes it a standard in the industry. DBSCAN implementations are available in scikit and R, as well as Python.

Northwest Database Services has 34+ years experience with all types of data services, including mail presorts, NCOA, and data deduplication. If your database systems are not returning poor data, it is definitely time for you to consult with a data services specialist. We have experience with large and small data sets. Often, data requires extensive manipulation to remove corrupt data and restore the database to proper functionality. Call us at (360)841-8168 for a consultation and get the process of data cleaning started as soon as possible.

NW Database Services
404 Insel Rd
Woodland WA 98674

City of Grand Rapids MI Information

Grand Rapids MI

Grand Rapids was U.S. President Gerald Ford’s childhood home. He is buried in Grand Rapids with his wife Betty at the Gerald R. Ford Presidential Museum. He is the name of the Gerald R. Ford International Airport in Detroit and Gerald R. Ford Freeway in Grand Rapids.


In 1845, the first official census recorded a population estimate of 1,510 people and a land area of 4 miles (10 km2). Grand Rapids was established April 2, 1850. Officially, it was established May 2, 1850 when Grand Rapids accepted the proposed charter. At that time, the population was 2,686. Grand Rapids had a total area of 10.5 square miles (27km2) by 1857.


Grand Rapids enjoys a humid continental climate with warm summers and cold winters. It also has short, mild springs.


The 2010 census showed that the city had 188,036 residents, 72,126 households, 41,015 families, and 188,036 total people. The population density was 4,235.1 people per square mile (1.635.2/km2). The average housing unit density was 1,815.7/square mile (701.0/km2). There were 80.619 housing units. The city’s racial makeup included 64.6% White (59.0% non-Hispanic White), 20.9% African American and 0.7% Native American. There were also 1.9% Asian, 0.1% Pacific Islander and 7.7% of other races. 4.2% from more than one race. 15.6% of the population were Hispanics or Latinos of any race.


In 1855, the first road to the city was constructed. This private road, which was toll-planked, ran from Kalamazoo through Wayland. It was used as a main route for passengers and freight until 1868. The Michigan Central Railroad at Kalamazoo connected this road to other areas.

Top Businesses

Grand Rapids has been a manufacturing hub since its roots in furniture production. Grand Rapids is home to many office furniture manufacturers, including American Seating, Steelcase (and its subsidiaries Coalesse, Turnstone), Haworth and Herman Miller.


Grand Rapids MI Map