Data Cleaning: Why It’s Necessary and How to Get Started

Challenges with Poor Data Quality

Sparse quality data can not only harm the growth of an organization but can also signal many false data insights, leading to poor decision-making. Data scientists recognize the importance of data cleansing, which is why almost 80 percent of their time is spent trying to clean and collect new data. Here are some examples of the adverse effects of outdated and poor-quality data:

Faulty Decision-making

The insights garnered from your data analytics will only be as good as the data that is fed into the machines, whatever those may be. If the data is of bad quality and doesn’t match the reality of your users, then your analytics and insights will be flawed, and may eventually lead to faulty decision-making. For example, if the data garnered through research for a marketing company is flawed, the organization wouldn’t be able to reach out to their users in the way that it wants. If your data analysis system is giving the wrong data regarding the geographical location and demographics of your target users, you could be wasting money by targeting an audience that isn’t engaged with your service (and ignoring an audience that is).

Damaged Reputation

In this age of information, it is necessary that an organization create a solid reputation and then foster it. The use of poor data and the poor data insights gathered through the data can lead to extensive reputation damage. An organization that has built a reputation of trust, especially in the banking sector, would rue the use of inconclusive data once the repercussions start coming in. Imagine telling a potential advertiser that your number of subscribers is one figure, when, in fact, a large percentage of the email addresses or physical addresses for those subscribers are no longer accurate. A slip like that can damage more than your reputation.

Poor Growth

Inaccurate data could potentially prevent a business from developing a particular product, going into a new market, or understanding customer needs. These are all factors that any other competitor with the right understanding and insights of data would jump on, expanding their business as well as their audience. And if they’ve identified and penetrated that market before you have the chance to catch up, you may be entirely out of luck.

Decrease in Revenue

As you can imagine, the impact of inadequate data resources and a shrinking market would be a financial burden as well. Poor data quality in the U.S. costs the country $3.1 trillion every year.

The insights you get from your data are only as good as the data that is being gathered and put into the system. That’s why understanding how to properly cleanse data is crucial to data scientists, analysts, and the business as a whole.

4 Steps for Cleaning Data

Now for the most important part: How do you clean data? There are several strategies that you can implement to ensure that your data is clean and appropriate for use.

1. Plan Thoroughly

Performing a thorough data cleaning strategy starts with the data collection stage. Rather than thinking about the end game from the beginning, try to incorporate better data collection methods such as online surveys and harnessing online traffic to achieve clean and up-to-date data.

What we mean by planning is that your data should have a certain degree of precision to it. In addition to planning for the machines the data will be fed into, you also have to prepare for your augmented workforce. Study the capabilities of your workforce and plan your data collection methods based on it.

The human element will be necessary for handling whatever your automation can’t, which is why you need to train your team to produce quality results through data analysis methods you have in place within your organization. When it comes to data cleaning, you need to plan accordingly for all processes and facets to be incorporated as part of the system. Make your data analysts a crucial part of the system to ensure that they clean data thoroughly for further use.

2. Standardize and Automate

Standardization is where most businesses are at fault or fall short. There is an imperative need for you to standardize how you record and track data within your system. In most start-ups and enterprises, managers are aware of the data collection methods and tools but are not aware of the live data being circulated across numerous departments.

Once the organization has agreed upon the need for standardization, it must reach a consensus over the methods that are feasible for gathering and managing data for the business. This process will likely take several months, but once there is consensus, standardizing the process and following the same methods day in and out ensures efficiency, which can bring the process back up to speed.

The organization also needs to take into account regulations that govern the use of data within the business. General Data Protection Regulation (GDPR), for example, govern the use of data within Europe, and compliance with the regulations is necessary for any business with partners and audiences in Europe.

3. Add and Integrate Systems

One single system can’t be responsible for your business’s everyday data needs. Each layer of the data cleansing process should be examined in a bid to add and integrate any new systems. If you’re currently working with Excel for cleaning your data, you will find the need to add another integrated method to the mix. Once you add a new system within the process, you must integrate it with the rest of the data and create a data stack that is uniform across the organization. The human workforce in your organization can then work on these integrated data cleaning and analysis tools to give you the best results.

4. Utilize Different Tools

In addition to depending on human efforts to clean data and strategize the best ways to do so, today’s market offers different solutions and tools for this purpose. Microsoft Excel has been the go-to option for many data scientists in this regard, as it brings forth a plethora of formulas that can clean data sets. If Excel isn’t able to meet your robust data needs, there are lots of options out there today. Some new, automated software tools that provide feasible data cleaning include:

• IBM Watson Data Studio
• Talend
• Winpure
• Data Ladder

Choose the Right Data Science Program

To assist you in making an informed decision and propelling your data science career forward, we have prepared a comprehensive course comparison that provides detailed insights. This comparison will help you select the most suitable program from our offerings, enabling you to gain a competitive edge and excel in the field of data science.

Program Name	Data Scientist Master's Program	Post Graduate Program In Data Science	Post Graduate Program In Data Science
Geo	All Geos	All Geos	Not Applicable in US
University	Simplilearn	Purdue	Caltech
Course Duration	11 Months	11 Months	11 Months
Coding Experience Required	Basic	Basic	No
Skills You Will Learn	10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more	8+ skills including Exploratory Data Analysis, Descriptive Statistics, Inferential Statistics, and more	8+ skills including Supervised & Unsupervised Learning Deep Learning Data Visualization, and more
Additional Benefits	Applied Learning via Capstone and 25+ Data Science Projects	Purdue Alumni Association Membership Free IIMJobs Pro-Membership of 6 months Resume Building Assistance	Upto 14 CEU Credits Caltech CTME Circle Membership
Cost	$$	$$$$	$$$$
	Explore Program	Explore Program	Explore Program

Conclusion

All these tools simplify the process of data cleaning and give users the option to clean their data without much of a hassle. For a deeper understanding of the repercussions of messy data, and how to use the appropriate tools to clean data and create standardized data collection plans, consider a course like Data Science with SAS, Python, or R. Prefer to master them all? Simplilearn offers a Data Scientist Course that covers all of the above, plus Excel training, Hadoop and Spark, Machine Learning, and more.