Data Services In Kenosha WI At NW Database Services
Data Cleaning, Data Cleansing, Data Scrubbing, Deduplication, Data Transformation, NCOA, Mail PreSorts, Email Verification, Email Append, & Phone Append Services in Kenosha Wisconsin
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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 Kenosha WI and surrounding communities.
What We Do
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.
We provide data transformation services for Extract, Transform and Load (ETL) operations typically used in data migration or restoration projects.
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.
We Are Here To Help!
Woodland, WA 98674
To use email, remove the brackets
Information About Data Cleaning And Data Services
Why Is Data Cleaning Important For Big Data Accuracy?
Data cleaning has been a key part of both data management and data analytics throughout history and continues to rapidly develop. Data cleaning, also referred to as data cleansing, in Big Data can be difficult due to the increasing variety, volume, and velocity of data across multiple applications.
Real-life data can be costly because it is messy. This highlights the importance of data quality management for businesses. Data cleansing, sometimes called data scrubbing or appending, is the process of correcting and removing corrupt data or inaccurate data. The data cleaning process is a critical step that must be completed because bad data can lead to poor business decisions and conclusions. Some businesses have suffered huge financial losses due to bad Big Data.
What Are the Various Types of Big Data?
Big data can be very diverse, as they come from many different sources and take different formats. It is crucial to determine the structure of data as it will affect how data will be processed, stored, analyzed, and retrieved. This is essential for making the raw data useful.
There are three types of big data:
- Structured data
- Unstructured data
- Semi-structured Data
Let’s take a closer look at each one of these big data types.
Structured data is data that has been stored in databases. These data can be stored, processed and retrieved in a predetermined format. They are also the most accessible type of big data because they don’t require any preparation. Structured data can come from two sources: either they are generated automatically by machines or manually entered by humans (e.g., when a customer signs up for a new account, their name, address, and age). Structured data can be thought of as the rows and columns that you would see in an Excel spreadsheet. Structured data is only 20% of the available big data.
Unstructured data, as the name implies, is the opposite of structured data. It’s completely unorganized and has no defined format. Unstructured data can be described as data that isn’t meaningful if it’s not contextualized. A tweet on Twitter, for example, is just a collection of words. It has no meaning or sentiment (before analysis). This is true for an image that you share, or a phone call that you make. These are examples of unstructured data and need to be put in a real-world context to make them meaningful. Unstructured data can be more labor intensive, as it involves complex algorithms like those used in machine-learning, AI and natural language processing. Unstructured data makes up around 80% of global big data.
Semi-structured information is unstructured data that has some organizational properties. This makes it easier to process than unstructured data. Semi-structured data may have metadata attached (data that describes or gives information about another piece). If you take a selfie with your smartphone, it may attach a timestamp and log the device ID. These additional details give context to the image, even though it is not structured data. Similar to the above, if you email a friend, even though the content is unstructured, there are some “clues”, such as the IP address and email address from which it came.
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 Kenosha WI Information
Kenosha, a city in Wisconsin is the seat of Kenosha County. According to the 2020 census, it had a population of 99,986, making it the fourth largest city in Wisconsin. Kenosha, located on the southwestern shores of Lake Michigan is part of the greater Chicago metropolitan region (Chicagoland), as defined by U.S. Census Bureau. It has also been connected to the Racine, Milwaukee and other areas to the north for many years. Interstate 94 links Kenosha with the Chicago and Milwaukee metropolitan areas. Kenosha is located about halfway between these two cities.
The area was originally called Kenozia by the Potawatomi (also transcribed ginoozhe and kinoje), “place of the pike”, while Menominee referred the place to as Kenusiw which means “Northern Pike”. Masu-kinoja (trout or pike) came all at once is the Ojibwa early name. These are the names of the annual spawning of trout in which thousands of fish enter the rivers from Lake Michigan to provide food for the following months.
Kenosha’s humid continental climate (Koppen Dfa) is bordering on Dfb and has warm summers as well as cold winters. In July 2012, the record high was 105 degrees F (41 degrees Celsius). The record low is -31 degrees F (-35 degrees Celsius), which was set in January 1985.
The population was 99,986 at the time of the 2020 census. 3,529.6 people per square mile (1.362.8/km2) was the population density. At an average density 1,470.0/square mile (567.6/km2), there were 41 641 housing units. The ethnicity of the population was 19.7% Hispanic/Latino of any race. The city’s Hispanic and nonHispanic population was 67.9% white, 10.8% Black or African American and 1.9% Asian. 0.1% Pacific Islander and 7.3% from other race. 11.5% of the city’s residents were from two or more races.
According to Walk Score Kenosha is a heavily “car dependent” community with a total walk score of 45/100. It also has minimal biking infrastructure with a score of 49/100. However, its downtown business district scores much higher, at 84/100 & 72/100, respectively.
Kenosha used to be a manufacturing hub. It is now a bedroom community due to its easy access to the Chicago-Milwaukee corridor. According to county statistics, 49% commute outside Kenosha County for their jobs. Many commute northward to Milwaukee and south into Chicago. A 2016 study found that Kenosha’s “out-commuters most likely work for positions in healthcare, manufacturing, professional/scientific and technical services. The majority of occupations included management, business/financial, and office/administrative support position”, and 73 percent of out-commuters have a bachelor’s degree or a higher level of education. In the 2010s and 2020s Kenosha and Wisconsin saw large influxes in Illinois residents who moved to Kenosha due to lower housing costs and lower taxes.