How to maintain data quality, challenges, and leverage opportunities?

Overview

In today’s digital world, data is more pervasive and vast than ever. Businesses use data in more diverse ways to keep up with the expanding digital presence. The international statistics and industry data portal?Statista predicted that?over the next 5 years, the global data creation is predicted to go beyond 180 zettabytes.

Data collecting proves to be advantageous for every organization. Still, to encourage growth and revenue, this data has to be structured; must be of high-quality, secure, and straightforward to utilize internally. Let’s examine the standards for data quality, the procedures for analyzing and managing data, the parties to include, and the most effective technologies.

How can data quality be maintained and improved?

The following five steps will help you continually raise the quality of your data.

1. Set your data quality criteria

The framework for your data quality strategy must be well defined. You will be able to map data requirements of teams and identify pertinent contact points based on your organization’s goals. As this information is beneficial for guiding sales and marketing initiatives. The objective is to rationalize and concentrate your efforts on the crucial information that will aid in managing your online activity.

2. Evaluate your database

This phase includes profiling your data. You must ensure that your databases are complete and free of any irregularities. If you finish this audit completely, you will be able to create an action plan and make recommendations for the rules for creating and maintaining your data.

3. Declutter your databases

Data sets can get “infected” by numerous errors while working with several data sources. All duplicate, incorrect, out-of-date, corrupt, or incomplete information must be eliminated. With the help of this activity, you can work in the future with confidence, prevent changing the outcomes of the analysis, and enhance the enrichment phase. This step can be documented to analyze the causes of mistakes better and keep a track of them.

4. Re-import the dataset, examine and validate it

After completing the data cleaning procedure, you must confirm that your dataset has been effectively cleaned and standardized. To prevent mistakes or failures during the subsequent data transition in your information system, we encourage you to submit a nomenclature import file. Utterly effective cleaning is frequently challenging to do. There is always a chance that a small mistake may sneak through. Therefore, be ready to clean up your dataset again if necessary.

5. Uphold long-term efforts to ensure data quality

Ensure that any employee who regularly creates or handles data, whether when developing a tagging strategy or processing data, is aware of and abides by the standards of cleanliness. Ideally, it helps to establish a data governance board, even a tiny one, to continuously assess your procedures’ effectiveness.

What are the main data quality challenges?

1. Data duplication

Multiple copies of the duplicate records strain storage and computational resources, and if left unchecked, might lead to distorted or false conclusions. Human mistakes, such as accidentally inputting data numerous times, or a flawed algorithm, might be detrimental.

2. Unstructured data

If the data is not accurately entered into the system or in case of file corruption, the remaining data results in missing variables. For instance, the remaining information might not be vital if the address lacks any zip codes because it will be difficult to pinpoint its location. A data integration tool can assist in the transformation of unstructured data into structured data. Move data into a single, standardized form – from diverse formats as well.

3. Outliers

Algorithms for machine learning are sensitive to the distribution and range of attribute values. The training process can be ruined and misled by data outliers, which leads to longer training times, less accurate models, and, eventually, inferior outcomes. Correct outlier management can distinguish between an accurate and a performing model.

4. Hidden information

The most helpful information about client behavior comes from hidden data. Today, there are many ways for customers to communicate with businesses, including in person, over the phone, and online. Although it may be of utmost importance, information on the how, when, and why clients connect with a company is rarely adapted.

5. Data Downtime

To make effective decisions overtime, companies rely on data. Also, there can be duration where the data is not ready due to circumstances such as infrastructure upgrades, or migrations. When such a situation persists, it results in client complaints and subpar analytical outcomes.

Final Words

Disciplined data governance, strict management of incoming data, accurate collection of information, extensive regression testing for change management, and careful design of data pipelines are all necessary for high data quality. Finally, proper data quality can be ensured and perpetuated by adhering to the five procedures outlined in this article.