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Power BI Reporting For Better Data

When it comes to company growth, every strategy your business implements will rely on the data you’re collecting. Even then, not every nugget of data you unearth is going to be helpful. In some cases, duplications and errors in your datasets can lead you toward misleading or incorrect conclusions. That’s why every business needs to prioritize data cleaning and data transformation methods.

Clean business datasets form the foundations of accurate reports. If you plug in flawed raw data, the reports and analytics you get will also be flawed. The goal is to use the most precise datasets you have available. And extracting meaningful insights from reliable intel will guide your business decision-making toward bigger and better results. As you use Power BI Reporting for your data reports, take additional steps to produce clean data. Learn more about cleaning and scrubbing your company data to ensure every report is actionable and useful in a way that boosts your bottom line. When clean data goes in, precise and actionable data reports come out, resulting in the best possible insights.

What Is Clean Data?

Clean data is a term used to describe datasets that have undergone thorough cleaning. Data cleaning involves implementing a series of processes designed to fix mistakes, remove duplicates, and complete any incomplete metrics. It’s a quality control process you can perform on your data before assimilating it into any of your reporting and action items. It’s not uncommon for these data-related errors or duplications to occur when collecting and assembling your data. But it’s critical to clean these metrics before you further evaluate the reports and insights. Otherwise, you could be making vital business decisions based on imperfect data, steering your company ship in the wrong direction.

Data cleaning follows a simple process:

  1. Remove irrelevant observations and duplicate entries.
  2. Fix structural errors within your datasets.
  3. Filter out any unwanted outliers presenting in your metrics.
  4. Contend with instances of missing data.

What Is Data Transformation?

In addition to data cleaning, you might also conduct data transformation. This concept is different from cleaning. With data transformation, you’re converting captured data from one particular format to another. You might have industry colleagues who call this “data wrangling.” Essentially, you’ll repurpose raw numbers or information into discernable insights your teams can actually use. While data transformation processes will vary based on the data and your business model, it’s a critical step to take immediately after you’ve gone through a proper data cleaning process.

What Does Clean Data Look Like?

Implementing basic data cleaning efforts is a great step forward, but how can you be sure the finalized data you’re working with is truly clean? Depending on the types of datasets and the volume of metrics available, there are different variables to consider. If your scrubbed data is lacking any of the attributes listed below, it’s very possible there are still errors or anomalies within your datasets. Clean data always has the following characteristics:

  • Metrics that make sense.
  • Data that follows your established field rules.
  • Datasets that prove or disprove your theories.
  • Data that outlines visible trends.

Accuracy and Reliability

Once you have clean data, you’ll be able to extract trends and insights that are free from inconsistencies, inaccuracies, and data-related errors. Clean data translates to more accurate and reliable reports and, thus, accurate and reliable business decisions.

Consistent Formatting and Labeling

Clean data is easily formatted and labeled. And when you have consistency in your categorization and labeling, you can enjoy enhanced readability. Structuring clean data in a universal format will also make it easier to compare different elements. With clean data, organized labeling, and simplified comparisons, you can expect more coherent and comprehensive results and insights.

Reduction in Bias and Noise

Don’t make critical business decisions based on datasets with missing values, random outliers, or biased entries. The output won’t be helpful to you and might even provide incorrect insights. Data cleaning should remove the anomalies that often lead to distorted analysis. Reduce the noise and eliminate the bias so you can proceed with more accurate and relevant metrics. Clean data, without these instances of noise, will also improve your ability to spot real-world trends and patterns.

Fine Tunes Data Relationships

Clean data can help you better understand valuable data relationships. Data relationships allow for the reorganization of metrics and the storage and formation of unique tables you can use and share among your teams.

  • Clean datasets will establish meaningful and relatable relationships between data points. For example, you could connect customers’ geographic regions with the types of products they buy, linking demographics and sales.
  • Clear and well-defined data leads to robust reporting, especially with Power BI Reporting, which can generate reports that highlight complex interactions and dependencies.
  • Clean data and fine-tuned data relationships will allow key decision-makers to understand and visualize how different factors contribute to and impact each other.

Cleaner, More Uniform Data Scales Better

As your business grows, you’ll accumulate more data. More sales, client engagements, industry trends, challenges, and opportunities; all of it will now be on your radar, whether you’re ready for it or not. With clean data processing, you can be sure your data efforts will scale with your business as it grows. Put an effective data cleaning process in place now so that you can be ready to build on it and accommodate the influx of data that will inevitably come as your business scales. Doing so means tapping into other advantages, like:

  • Less backtracking and re-evaluating after the fact.
  • Standardization and templates to help you simplify results.
  • Proven processes that are easier to model and scale.

Power BI Reporting for Clean, Usable Data Reports

As a growing company, you will need to streamline how you collect and analyze your data. Raw data should never be reviewed without first going through some kind of data cleaning process. Scrub your metrics and then evaluate the results using reliable, clean, consistent, and actionable data analysis software, such as Power BI Reporting. With such intuitive software at your fingertips, all the reports you’ll need are just a click away. Just remember, the cleaner the data going in, the more useful the reports will be coming out.