Data normalization is an essential part of creating accurate and comprehensive Excel dashboards. Normalizing data involves rewriting data values so they fall within a specified range and/or taking categorical data and transforming it into numerical data. This allows organizations to properly compare and analyze data over different timeframes and streams. When done correctly, data normalization can make analysis and reporting on data much more efficient.
There are two main types of data normalization: Numeric Normalization and Categorical Normalization. In this blog post, we'll discuss how organizations can best leverage Excel functionalities to implement these two strategies for their dashboards.
Caching Database Queries
Database query caching is a great way to optimize data normalization strategies for Excel dashboards. Caching reduces the amount of data that needs to be processed, thereby improving the performance of analytics-driven dashboards.
Why Cache Queries?
Query caching is a great way to list and store frequently used queries. It can improve dashboard performance, as the stored queries are accessed quickly and no longer need to go through the overhead of a full query generation process. This can be especially helpful when dealing with complex queries and large datasets.
When to Use Caching
Caching can be helpful for projects where data is frequently accessed. If a query is repeatedly used, it should be stored for later access. This can help improve performance and results accuracy, as the query will not be generated incorrectly.
Caching can be especially beneficial when dealing with large datasets. The overhead of generating complex queries can be eliminated, which can reduce data processing time significantly. The caching of queries can help improve the performance of Excel dashboards and other analytics-driven dashboards.
3. Optimizing Dataset Size
A. Shrinking Datasets
Shrinking datasets is one of the most common data normalization strategies when dealing with Excel dashboards. This involves identifying and removing unnecessary columns, rows and other data elements that are not necessary to the dashboard. To do this, start by reviewing the data stored in the dataset. Remove any columns or rows that are not essential to the analytics and dashboards that are created from the dataset. It is also important to check for duplicates, as having redundant data can unnecessarily expand the size of the dataset. By removing unneeded data, you will be able to significantly reduce the size of the dataset.
Once you have identified the unneeded data, take extra steps to clean the dataset by sorting and reformatting the data. This includes typing the data into specific data types like text, numbers, and dates in order to ensure data accuracy and consistency. Additionally, the sorting process involves applying filters to the data, replacing errors, and properly aligning the data.
B. Reducing Brightness and Contrast
Data normalization also involves reducing the brightness and contrast of graphics within an Excel dashboard. This is important for simpler visuals as well as for graphics-heavy dashboards. By reducing the brightness and contrast of these visuals, you can minimize the amount of data within the dataset. This will help minimize the file size and improve the performance of the dashboard.
To reduce the brightness and contrast of graphics in an Excel dashboard, first select the image that you want to adjust the brightness and contrast levels of. Then, open the brightness and contrast adjuster window and adjust the levels of both parameters according to what is needed. This process can be repeated for all images within the dashboard, resulting in a streamlined and optimized dataset size.
Handling Corrupted Data
Data normalization can be tricky when dealing with corrupted data. This is especially true in Excel dashboards where feedback is expected quickly and accurately. The following focuses on two important strategies that can be employed while normalizing data with Excel Dashboards – identifying corrupted data and isolating/removing corrupted data.
Identifying Corrupted Data
One of the first steps to take in handling corrupted data is identifying it. It's essential to identify this data as soon as possible, or else the results of your dashboard may not accurately reflect the data set. Some ways to recognize corrupted data are:
- Data that does not fit into a normal distribution
- Data that falls outside of expected ranges
- Data that is present in a single column but not spread out across multiple ones
- Data that does not match the format of the rest of the data set
These are just some examples of what corrupted data looks like, and it will be up to the user to determine how to spot it in the data set they're working with.
Isolating and Removing Corrupted Data
Once the corrupted data has been identified, it must be isolated and removed in order to prevent it from affecting the accuracy of the dashboard. This can be done by utilizing filtering tools such as the Autofilter function or Advanced Filter function. By using these functions, the user is able to filter out the corrupted data and only display the valid data. Once this is done, the dashboard can be finalized and the user can trust that their results are accurate.
Data normalization is an important part of creating effective Excel dashboards. Handling corrupted data requires the user to be careful and diligent in identifying it and then isolating and removing it. If this is done correctly, then the results of the dashboard can accurately reflect the data set.
Extracting Data from Images
Data extraction from images is becoming increasingly popular for a variety of use cases, from retrieving data from receipts and invoices to locating and collecting text from screenshots and scanned documents. As data extraction from images requires manual interpretation and extraction, it should be combined with data normalization strategies for optimal usefulness.
Common Applications of Image Extraction
Image extraction is able to help with a variety of tasks, such as:
- Retrieving text values from scanned and photographed documents and receipts
- Analysing images to detect objects and areas of interest
- Extracting text from images and screenshots
- Locating, tracking and extracting tables from images
Techniques Used in Image Extraction
Image extraction involves multiple sophisticated techniques and technologies, depending on the requirements. Some of these include:
- Optical Character Recognition (OCR), which is used to identify and extract text from images
- Template Matching, which is used to detect objects of interest in images
- Object Recognition, which is used to recognize areas and objects within the image
- Object Tracking, which is used to analyse motion within the image and identify objects of interest
- Feature Detection, which is used to locate and extract tables from images
Image extraction can be both time-consuming and challenging, but with the right techniques and technologies it can be a powerful tool for generating new insights and unlocking data from images.
Benefits of Data Normalization
Making sure your data is streamlined and organized is essential for any type of analytics project. Normalizing the data is a technique that enables your business to benefit from access to accurate and high-quality data. It ensures data consistency, hence eliminating the need for manual data work and saves resources in the long run. In this article, we will discuss the benefits of data normalization as it relates to Excel dashboards specifically.
Data normalization ensures that vital data is stored in more than one place. This makes it easier to identify and execute queries on the data. It helps to streamline the data across multiple tables and ensure that all parts of the data are actively and accurately linked together for Analysis. With data normalization, it becomes easier for the users to manage and access it. This not only reduces processing time but also facilitates the fetching of data. It also eliminates unnecessary complexities related to data modification.
Improved Data Quality
Data normalization techniques can help you improve the quality of data entered into spreadsheets. This is because data normalization standards set certain restrictions for data entry, ensuring that all data is entered in the same format. This helps identify and eliminate any incorrect information, thus ensuring that only valid and accurate data is accessed. In addition, data normalization greatly reduces the risk of introducing errors into the system, as it ensures that all data is consistently stored in the right place, with the right values.
Reduced Processing Time
By normalizing the data, you can eliminate the need for redundant data entry as you can create shortcuts with the normalized data. This means that the same data doesn’t have to be entered multiple times. This not only decreases the time taken to enter the data but also reduces the amount of data stored in the spreadsheet. Additionally, data normalization helps make the data easier to recognize, as all records will be consistent and will not contain any unnecessary information.
Overall, data normalization helps to streamline data entry and retrieval on Excel dashboards and ensures the accuracy of the data. It is an efficient tool for eliminating redundant data entry and improving data quality. Data normalization is a key strategy for success when dealing with spreadsheets, and its benefits can be further enhanced when combined with other data analysis techniques.
Data normalization is an important component of creating effective Excel dashboards. By recognizing areas of potential inconsistencies or redundancies, data normalization allows the dashboard to become functional and actionable, providing the user with deeper insights into their business data. This article explored a few of the most common data normalization strategies, as well as the main benefits that they provide when incorporated into an Excel dashboard.
Summary of Data Normalization Strategies
In brief, data normalization can be achieved by restructuring the layout of the data by creating separate tables for related data points, using a single source of truth for data values, establishing a consistent naming convention for fields within the data set, and auditing for accuracy. By implementing these core strategies, the nature of the data can truly be understood by the user, providing the ability to quickly make decisions.
Benefits of Data Normalization for Excel Dashboards
Incorporating data normalization into an Excel dashboard builds trust in the data and fosters more effective decision making. It increases the accuracy and reliability of the dashboard, allowing users to confidently rely on the insights they’re exploring. Furthermore, data normalization reduces complexity in the dashboard by eliminating the need for manual calculations or changes in formatting, freeing up valuable time.
In conclusion, data normalization is a key component of any successful Excel dashboard. By restructuring the data, normalizing values, and creating a singular source of truth, the user is able to gain greater insight into their data. This makes data normalization an essential tool for businesses looking to make the most out of their Excel dashboards.
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