Automate Your Data Cleaning with Intuitive Repair Process (IRP)

Your business intelligence is only as good as the underlying data it’s built on. Yet, real-world data is often messy. Rogue data, unmatched fields, missing information. It all has to be cleaned to be useful.

But ask just about any IT team about data cleaning and you’ll likely hear them groan.

That’s no surprise. Data cleaning is crucial to ensure accuracy, but it’s a tedious and time-intensive task. Traditional methods require IT teams to build data-cleaning factories to identify rogue, missing, or incomplete data. In fact, research shows that IT teams and data scientists often spend as much as 60% of their time cleaning and organizing data. It’s not fun and it often leads to mistakes and burnout among IT teams.

At the same time, the cleaning process means delaying your use of the data. You might not be able to include the most current data into your decision-making process and by the time you get it, it might be out of date.

IDA’s Intuitive Repair Process (IRP)

IDA’s Intuitive Repair Process (IRP) changes that proposition, cleaning rogue data during use. Our IRP algorithm eliminates the need for IT to build data-cleaning factories and enables you to use your data right away.

With natural use, you can intuitively fix, repair, and improve data quality over time. Not only are you cleansing your data, but IRP is training your BI algorithm to detect and repair fresh data as it’s added. So, data quality continues to improve, further reducing concerns about data quality.

Through use and training, IDA simplifies the task of data cleaning. As you or IDA uncovers incomplete or rogue data, you can trigger the pattern-hunting algorithms to autofill or repair. With minimal effort, you can:

Uncover patterns and trends within data sets to automatically fill in missing data and repair wrong data
Recognize the prevalence of dirty data in any data set
Use algorithms and human intuition to recognize rogue data patterns that don’t make sense
Validate data accuracy against outside reference sources automatically
Automate the training of algorithms to detect and prevent the introduction of rogue or incomplete data

Through use, data gets cleaner and cleaner. Data quality quickly and dramatically improves — without having to tie up your IT team. As new data is introduced, IDA will automatically review and cleanse it.

Automated Pattern Hunting

One example would be addresses. You can validate addresses automatically against postal service databases to ensure you have the right address and auto-fill missing data. The IRP algorithm hunts for discrepancies to find empty fields, partially missing data, corrupt or wrong addresses, and automatically identifies and repairs them. It can be as simple as matching city names with zip codes to determine the right state or as complex as evaluating multiple combinations of missing data.

Did you know there are 28 cities in the U.S. named Franklin? What if some entries in your data set lists Franklin as the city but do not include the state or zip code? IRP can analyze the street names in the postal service database across each of these cities to find the right match — something even your data-cleaning factory might not catch.

As you work with data sets, even what’s considered clean data sets, you might also notice discrepancies. Maybe you’re searching for sales data by state and several records show up as empty or attribute information to the wrong state. You can instantly launch an investigation queue to resolve the issue.

IRP is built into the IDA platform, eliminating a time-consuming step in the data analysis process. Instead of a separate effort. Data cleaning becomes a natural byproduct of using your BI tool. The more you use it, the smarter it gets and the cleaner your data becomes.

To learn more about the Intuitive Repair Process and IDA, contact the business intelligence experts at Intuitive Data Analytics for a free demo.

Recommended for you

September 17, 2024

The Power of Automated Anomaly Detection: Unlocking Insights From Big Data

September 17, 2024

What-If Models and Sensitivity Analysis: Tools to Forecast Revenue, Expenses, and Profitability

September 10, 2024