THE IMPORTANT AND COMPLEX PROCESS OF CLEANING YOUR DATA

Data cleaning is an important and complex process, but it’s one that has to be done.

In data-driven industries like data mining, data analytics, and data science the data you work with can have a significant impact on the success of your project.

To give yourself the best chance at success in these fields it’s imperative to make sure your data is as clean as possible by using some tricks of the trade.

This blog post will seek to discuss how to go about this process aimed at those who are new to data cleansing or just want more information on what they should do when they’re faced with a messy data set.

DATA CLEANSING IS THE PROCESS OF REVIEWING AND CORRECTING DATA TO REMOVE ANY INCONSISTENCIES, DUPLICATES, OR INCORRECT ENTRIES.

an animate graphic of three men working along a folder

Data cleaning is an essential step in the data preparation process that can make or break a project. It’s also one of the most difficult steps to get right because it requires so much attention to detail and often takes more time than other parts of the data preparation process.

Data cleansing is a set of methods for detecting and removing errors in a data set.

Errors generated during data collection are typically removed using simple techniques such as correcting typos, deleting blank fields, or filling in missing values with default values from another source; while errors introduced by subsequent processing require more complex approaches like duplicating records with matching values across multiple columns or filtering out outliers identified through statistical analysis.

The overall goal is to remove any incorrect or irrelevant information so you’re left with only accurate data points.

WHY IS DATA CLEANING NECESSARY?

an animated image of a database and a broom

Data cleaning is one of your most crucial responsibilities to be an informatics expert. Having poor data can affect your processes and analyses.

Poor data can cause an algorithm to fail, but well-structured data might provide impressive results. It is important to understand the various methods that a data cleaner can apply to improve data quality.

Not all data is useful so this is another factor that impacts the data. Data cleansing practices can allow for improved data quality by removing unwanted outliers during the data cleansing process, leaving the data scientists with only quality data, more specifically, clean data.

THE IMPORTANCE OF DATA CLEANING IN BIG DATA

Data purge or scrubbing or adhesion is the procedure for the correction or removal of incorrect or corrupt data. This is an important aspect of data cleaning and removing unwanted outliers during the data cleaning process.

This process is crucial and was emphasized since wrong data has caused business errors in decisions, conclusions, and analysis. This data cleaning process can also be time-consuming depending on the amount of data and the quality of data.

DATA WASHING IS AN IMPORTANT DATA CLEANSING PRACTICE.

Data washers will scrub data by identifying and removing erroneous data that could mess up the analysis of the final data set. When data scientist cleanses their data, they are trying to remove any errors in order for the process or algorithm to progress correctly. They want it as clean as possible so this means no duplicates, correct information with accurate values, and relevant information without irrelevant noise.

Data washing can be a tricky undertaking as it increases the volume and speed of data in many machine learning applications. The real-life dirty data cleaning processes can become expensive, causing businesses to forfeit the data cleansing process. This can prove to be detrimental to decision-making if there are unwanted observations and data set errors plaguing the quality of data.

a floor lined with ones and zeros is being cleaned up by a blue broom

There are businesses that lost huge amounts of money because of poor data. The potential of big data is somewhat elusive, as there is no filter for the raw data that companies are getting. These are the benefits of data cleaning! You save money in the long run when you spend to ensure the business has an excellent data cleansing process so the data analysis can produce sound results that are devoid of unwanted outliers, missing values, dirty data, or duplicate data.

a man sweeping up alphabet letters that have been scattered along the floor

Data purge or scrubbing is another procedure for correcting incorrect or corrupt data. This step is crucial because wrong data can cause business errors in decisions.

REMOVE UNWANTED OBSERVATIONS

When cleaning data, it’s important that you remove unwanted observations first. This means removing duplicate or neologized data. Irrelevant views are those that are not in line with what you intend to solve.

DUPLICATING DATA WILL ADD TO THE QUANTITY OF DATA AND WILL CAUSE LOST TIME. CONCENTRATION ON THIS POINT WILL STOP ANY POSSIBLE TROUBLES.

two animated birds discussing the synonyms for the words duplicating

The majority of duplication in observations occurs in the collection of data. Irrelevant observations are when the observation is not connected with the problem of analyzing. As a result. it improves analysis and minimizes distraction from the main target.  This is another one of the benefits of data cleaning since duplication represents dirty data and must be eradicated in the cleansing process.

MAKE SURE DATA IS OBVIOUSLY IRRELEVANT AND THEY AREN’T NEEDED FURTHER DOWN TIME.

An important aspect of data collection is ensuring that the target is right, that the data science is applicable to the issue at hand. Quality data is relevant data, and this is representative of clean data.

WHEN DEALING WITH BIG DATA AND MACHINE LEARNING YOU MUST ENSURE THAT THERE ARE NO MISSING VALUES, DUPLICATE DATA OR IRRELEVANT INFORMATION, OR ELSE!

the word essentials bolded, capitalized and in red

IS DATA CLEANING ESSENTIAL?

As the amount of data used becomes more widely the more a problem can arise. Example: A company could call clients in a predictive dialing service. Any business whose data is not clean may have some errors made in communicating with clients. As a professional in the IT industry, it is your job to keep it flowing smoothly. That also means a great part of the data cleaning is required – so if the wrong name appears beside the wrong number, this can cause huge problems including some disgruntled customers.

Data scientists’ jobs are to ensure that data remains clean, but data cleaning is only one step of the ladder, they also need to be able to leverage and visualize that clean data.

Whatever the case, Incus Services can help. 

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IF YOU’RE A DATA NOVICE OR LOOKING TO GET THE MOST OUT OF YOUR EXISTING DATA MANAGEMENT, GET INTO CONTACT WITH THEM ABOUT THEIR WORKSHOP OR SPECIFIC SERVICES THAT ARE TAILOR-MADE FOR YOUR ORGANIZATION. 

But the workshop is just the beginning. Consulting with Incus Services as part of your data improvement drive can make the difference between being a leading organization or falling behind the competition. 

Incus Services can work closely with your organization to help your data talk to you and offer key insights. It is our objective to provide businesses with the machine learning and artificial intelligence strategies that they need to succeed. 

Aren’t you ready to take your business to the next level? Why wait another moment to lead and explore your sector through technology and digital transformation? 

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YOU’VE GOT THE DATA AND INCUS SERVICES HAS THE EXPERTISE TO HELP YOU REMAIN LONG-TERM LEADERS IN YOUR FIELD.



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Marsha Rudon
Marsha Rudon
2021.11.19.
I thoroughly enjoyed this course. The material presented is invaluable. I can see how Tableau can help our organization to ask and answer, the really difficult questions and help us inevitably to increase our profitability. Thank you to all the presenters who have helped peak my interest in BI.
Samantha Singh
Samantha Singh
2021.11.18.
Very interactive workshop, awesome coaches and definitely value for your money, I highly recommend this workshop and Incus Services !!! You will not regret it
Nicole Orosco
Nicole Orosco
2021.11.18.
The workshop was very hands on, this team was very knowledgeable about the process. I fully endorse 100%. They left me wanting to know more about data Analytics and to develop this skill. Time is life, Team Incus it is!!!
I Beneebee
I Beneebee
2021.11.18.
Wasn't expecting so much for the first day of a workshop. But by the end of the session I was able to create my 1st Tableau Dashboard. Learned tips for better visual presentations, Learned how to incorporate color, publish and present the dashboard. The onus is on me to recreate this now in my own environment. Pressure TALL! Great Job Incus Services!!
Mark Eting
Mark Eting
2021.11.18.
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Edward Alexander
2021.11.18.
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James Padji
James Padji
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I was not sure what to expect because the class was taught remotely, but I was quite impressed with the entire process. The facility was very professional and there were no problems with connectivity. The instructor's were experts and very clear in there method of teaching. The things I learned I can and will surely use in my daily work.
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Thank you for the opportunity. It was an amazing journey in Data Analytics using Tableau. From the coaches I have learned how to Aggregate, Sort and Filter data putting it into a dashboard on how to solve problems using this software. As a group (2) we had to do it by ourselfs and present the data by telling our story about the data and give our findings and recommendation. It was fantastic. Thanks to everyone and especially the Coaches.
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