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Why do I have to clean up data?

2022-07-25 00:01:00 【IDC industry observer】

Data is perhaps the most valuable asset that enterprises can have today . Data defines the market intelligence that large and small enterprises can collect about their customers and their markets . let me put it another way , It can make or break a company .

Data tends to change over time , This fact should not be surprising . People's age and address will change , The phone number will also be updated . With all these things happening , If you can't clean up the data correctly , Your data will become obsolete and useless . Although the data effectively cleaned up is of great value to your business , But unclean data will bring many impacts and troubles .

The challenge of poor data quality

Scarce high-quality data will not only damage the development of an organization , It will also indicate many wrong data insights , Leading to wrong decision making . Data scientists recognize the importance of data cleansing , This is how they almost 80% The reason why we spend so much time cleaning up and collecting new data . Here are some examples of the adverse effects of outdated and poor quality data .

The insight gained from your data analysis will only be as good as the data entered into the machine , No matter what the data is . If the data quality is not good , It does not conform to the actual situation of users , Then your analysis and insight will be flawed , And may eventually lead to wrong decisions . for example , If the data obtained by a marketing company through research is flawed , Then the organization cannot reach their users in the way it wants . If your data analysis system provides wrong data about the geographical location and demographics of the target users , You may waste money targeting audiences who are not involved in your service ( And ignore the participating audience ).

Damage to reputation

In this information age , It is necessary for an organization to create a solid reputation , And then culture . The use of bad data and bad data insights collected through data can lead to widespread reputational damage . An organization that has built a reputation for trust , Especially in banking , Once the response begins , Will regret using uncertain data . Imagine , Tell a potential advertiser that your number of users is a number , As a matter of fact , A large part of these users' email addresses or physical addresses are no longer accurate . Mistakes like this , It's not just your reputation that's being damaged .

Poor growth

Inaccurate data may prevent enterprises from developing specific products , Enter a new market , Or understand the needs of customers . These are the factors that any other competitor with a correct understanding and insight into the data will seize , To expand their business and audience . If they have found and entered this market before you have a chance to catch up , You may be completely unlucky .

revenue deduction

You can imagine , The impact of insufficient data resources and market shrinkage will also be a financial burden . In the U.S. , Poor data quality brings to the country every year 3.1 Trillions of dollars in losses .

The insight you get from the data is the best in the data collected and put into the system . This is to understand how to clean up data correctly for data scientists 、 The reason that is crucial for analysts and the whole enterprise .

Cleaning data 4 A step

Now is the most important part . How do you clean up data ? There are several strategies that can be implemented , To ensure that your data is clean , Suitable for use .

1. A thorough plan

Implementing a thorough data cleansing strategy begins with the data collection phase . Instead of thinking about the final result from the beginning , Try to use better data collection methods , Such as online survey and the use of online traffic to clean up and update data .

By planning, we mean that your data should have a certain degree of accuracy . In addition to planning the tools for entering data , You must also prepare for your expanding workforce . Study your employees' abilities , And plan your data collection methods based on it .

Human factors are necessary to deal with things that your automation cannot deal with , This is why you need to train your team through data analysis methods in your organization to produce high-quality results .. When it comes to data cleaning , You need to plan all the processes accordingly , As part of the system . Make your data analyst a key part of the system , To ensure that they thoroughly clean up the data , For further use .

2. Standardization and Automation

Standardization is where most enterprises make mistakes or deficiencies . You need to standardize the way data is recorded and tracked in the system . In most start-ups and enterprises , Managers know data collection methods and tools , But I don't know the real-time data circulating in many departments .

Once the organization agrees with the need for standardization , There must be a consensus on feasible ways to collect and manage enterprise data . This process may take months , But once consensus is reached , Standardize the process and follow the same approach day after day , Can ensure efficiency , So as to restore the process to normal speed .

Organizations also need to consider regulations governing the use of data within the enterprise . for example ,《 General data protection regulations 》(GDPR) Manage data usage across Europe , For any enterprise with partners and audiences in Europe , It is necessary to abide by this regulation .

3. Add and integrate systems

A single system cannot be responsible for the daily data needs of your enterprise . Every layer in the data cleaning process should be checked , In order to add and integrate any new system . If you currently use Excel To clean up the data , You will find that you need to add another comprehensive method . Once you add a new system to the process , You have to integrate it with other data , And create a unified data stack for the whole organization . then , People in your organization can work on these integrated data cleaning and analysis tools , Bring you the best results .

4. Use different tools

In addition to relying on manpower to clean up data and develop the best strategy , Today's market offers different solutions and tools . In this regard , Microsoft Excel It has always been the first choice of many data scientists , Because it brings a lot of formulas for cleaning up data sets . If Excel Cannot meet your strong data needs , There are many choices today . Some new 、 Automated software tools can provide viable data cleansing , Include .