Data Services In Chicago IL At NW Database Services

Data Cleaning And Date Transformation

Data cleaning and data transformation are two critical steps in the process of data preparation and they work hand in hand to ensure that data is suitable for analysis.

Data cleaning, also known as data cleansing, involves identifying and correcting (or removing) errors in datasets. This may include dealing with missing or incomplete data, inaccurate entries, duplicate records, inconsistent formatting, or irrelevant data. Cleaning data ensures the quality and reliability of the data set, which is crucial for generating accurate and trustworthy analysis results.

Data transformation, on the other hand, is the process of converting data from one format or structure into another. This could involve scaling numeric data, encoding categorical data into numerical values, or converting date/time data into a consistent format. The goal is to make the data suitable for specific analysis needs or machine learning algorithms.

How Does Data Cleaning Improve Data Transformation

The link between data cleaning and data transformation is quite strong. Here’s how data cleaning helps with data transformation:

Improves Accuracy: Cleaning data before transformation ensures that the transformed data is accurate and meaningful. If the data is not cleaned, errors may be propagated through the transformation process, leading to inaccurate results.

Enhances Consistency: Cleaning data can help ensure consistency, which is important when transforming data. For example, if categorical data is inconsistently labeled, it could lead to issues during transformation.

Reduces Complexity: By cleaning data, we can reduce the complexity involved in the transformation process. For instance, by removing irrelevant or redundant data, we can streamline the transformation process.

Optimizes Performance: Cleaning data reduces the size of the dataset, which can make the transformation process more efficient. It can also help in optimizing the performance of machine learning models, which often benefit from clean and well-prepared data.

Overall, data cleaning enhances the effectiveness and efficiency of data transformation, contributing to better quality insights and outcomes from data analysis or predictive modeling. Data cleaning and data transformation are integral steps in data pre-processing, a vital process in data science and analytics that prepares raw data for analysis or machine learning models.

Data Cleaning – More Information

Data cleaning aims to improve the quality and reliability of the dataset. Its tasks include:

• Missing data handling: Missing data can skew the results of data analysis or cause machine learning models to perform poorly. Techniques for handling missing data include removing records with missing values, imputing missing values with statistical measures (mean, median, mode), or using predictive models to estimate missing values.
• Outlier detection and treatment: Outliers are data points significantly different from others. They can be legitimate variations or errors. Outliers can distort statistical analyses and can be detrimental to machine learning models. Cleaning can involve removing outliers or transforming them to fall within an acceptable range.
• Duplicate removal: Duplicate records can bias data analysis and increase computational load for machine learning models. Data cleaning typically involves identifying and removing these duplicates.
• Inconsistent data correction: Inconsistencies can occur in many ways, including discrepancies in data representation, spelling errors, or varying units of measure. Cleaning aims to ensure consistency across the dataset.

More on Data Transformation

Data transformation involves converting data from its raw state into another format to make it more suitable for analysis or predictive modeling. Its tasks include:

• Normalization and Standardization: These techniques adjust the scales of numeric features to a standard range. Standardization transforms data to have a mean of 0 and standard deviation of 1. Normalization typically scales data to a range of 0-1. These techniques can be necessary when different features have significantly different scales or units.
• Categorical Encoding: Machine learning algorithms require numeric inputs. Categorical encoding techniques convert categorical data into numeric form. Common techniques include one-hot encoding, ordinal encoding, and binary encoding.
• Feature Engineering: This involves creating new features from existing ones to better represent underlying patterns in the data. It can involve operations like combining features, creating polynomial features, or applying mathematical functions (log, square root, etc.) to features.
• Discretization and Binning: This process involves converting continuous variables into discrete counterparts. This can be useful for certain types of analysis or for handling outliers.

Both data cleaning and transformation are iterative processes. As you clean and transform your data, you might uncover additional issues or opportunities for transformation. Good data preparation practices can significantly impact the results of data analysis and the performance of predictive models, ultimately leading to more reliable insights and decisions.

Data Transformation Challenges

Data transformation can present many challenges. Big data transformation can be resource-intensive and costly. It requires a lot processing power and computations to transform billions upon billions of records. Data transformation requires both domain expertise and knowledge of the technologies that underlie the ETL/ELT pipelines.

Requires Intensive Computation

It takes a lot of resources to transform big data. Without the right hardware to handle the data transformation pipeline, systems may run out of memory and be inefficient enough to keep up with the volume of data. One example: I was performing a data conversion on millions of records and joining data from different tables.
My server didn’t have enough RAM, so I kept getting Out Of Memory errors. It takes time and effort to correct these errors and retry data transformations.

Expensive

Data transformation can be expensive as it requires a lot storage and expertise. ETL/ELT pipelines must store the transformed data in order to allow for analysis. This means that an organization must have a data warehouse, in addition to the databases that store raw data.
In-demand, well-paid jobs that pay well, other than the storage costs, include data analysts, data engineers, and data scientists. Many organizations might not have the funds to purchase many, leaving only a few people responsible for managing large data operations.

Domain Knowledge Is Required

As a product analyst who has been working in education technology for 10 years, I am well-versed in the challenges involved with transforming educational data. There are many calculations that can be used to combine attendance data, generate GPAs or score standardized exams.
Data transformation without domain knowledge can lead to inconsistencies and errors that lead to incorrect analysis and predictions. To be able to effectively transform data, it can take time and effort.

Summary

Data-driven decision making is becoming more important as organizations collect more data from more sources. Effectively transforming data in an ETL/ELT pipeline is essential. Data transformation refers to the process of transforming raw data into useful information for downstream processes. It is performed in four steps: planning, reviewing, planning, performing and reviewing.

Transforming data has many benefits, including improving data quality, data modeling and analytics, as well as improving data governance. Data transformation can improve an organization’s ability make data-driven business decisions. However, it can be difficult to transform large amounts of data. Big data requires a lot of storage space and expert-level domain knowledge. Despite the challenges, data conversion remains an important part of data management and helps organizations get the best out of their data.

Northwest Database Services has 34+ years experience with all types of data services, including data cleaning and data transformation, 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
(360)841-8168

Chicago is the largest city in Illinois, and third in the United States after New York City or Los Angeles. It is the Midwest’s most populous city, with a population of 2,746,388 according to the 2020 census. The city is also the seat of Cook County, the second-most populous U.S. county. It is also the heart of Chicago’s metropolitan area.

History

Chicago, located on the shores of Lake Michigan was established as a city in 1837. It is situated near the portage between the Great Lakes watershed and the Mississippi River. Chicago grew quickly in the middle of the 19th century. By 1860, Chicago had surpassed 100,000 people. Chicago’s population grew to 503,000 in 1880, and then more than a million by the end of the decade. In the decades that followed, Chicago was the fifth largest city in the world. It reached this rank less than 30 years later. Chicago was a notable contributor to urban planning and zoning standards. This included new construction styles such as Chicago Schoolarchitecture and the City Beautiful Movement.

Climate

The climate of the city is hot-summer humid continental. It experiences four seasons. The summers are hot and humid with frequent heat waves. July’s average temperature is 74.9 degrees Fahrenheit (24.4 degrees Celsius), with afternoon temperatures reaching 85.0 degrees (29.4 degrees Celsius). Normal summer temperatures can reach 90 degrees F (32 degrees C) for as many as 23 days. Lakefront locations are cooler when the wind blows off the lake.

Demographics

Chicago was the world’s fastest growing city for 100 years. In 1833 Chicago was founded. There were only 200 residents living on the American frontier. Seven years after its founding, more than 4,000 people lived in the city. The city’s population grew by a little more than 30,000 in the 40 years between 1850 and 1890. Chicago was the fifth largest city in the world at the close of the 19th century. It was also the largest of all the new cities. After the Great Chicago Fire of1871, Chicago’s population grew from 300,000. to more than 3 million. It reached its highest recorded population in 1950, at 3.6 million.

Transportation

Chicago is an important transportation hub in the United States. It is a key component of global distribution as it is the third largest intermodal port in the world, after Hong Kong and Singapore.
Chicago has a higher percentage than the average of households that don’t own a car. Chicago had 26.5 percent of households without a car in 2015 and this number rose slightly to 27.5 percent by 2016. In 2016, the national average was 8.7 %. In 2016, Chicago had an average of 1.12 cars per household, compared with a national average average of 1.8.

Top Businesses

Chicago is an international financial hub. Chicago Board of Trade is where the first standardized futures contract was created. It generates 20% of all commodities and financial futures volume and is today the world’s largest and most diverse derivatives marketplace. According to Airports Council International’s tracked data, O’Hare International Airport consistently ranks among the top six busiest international airports. It also boasts the most federal highways in the country and is the nation’s railway hub. Chicago has the largest gross domestic product (GDP), with $689 billion generated in 2018. Chicago’s economy is diverse. No single industry employs more than 14%. There are many Fortune 500 companies in Chicago, such as Conagra Brands Midland, Conagra Brands Midland, JLL and Kraft Heinz.

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