Data Management Challenges and Best Practices

Data Management Challenges and Best Practices

Did you know the Custom Market Insights (CMI) data shows that the global master data management market is likely to be bigger, which is up to $23.8 billion by 2030?

The reason for its rapid growth is its vitality in personal and corporate lives. In today’s digital age, proper data handling is the need of the hour. Since electronic devices are continuously producing data, it’s challenging to manage that vast amount of data. This is where data management’s best practices come into play.

In this post, you will discover the challenges of managing data and the best practices to overcome them. Let’s get started with the roadblocks in data management, which are a big concern for national and multinational companies in the present scenario.

 

Data management challenges

1. Frequent Flow of Unstructured Data 

The very first concern or risk for data management is related to the ever-growing supply of data from smart devices and applications. The worrisome point is the structure, which is irregular. It means that the generated data is a combination of structured, unstructured, and semi-structured data. To manage this type of record, it becomes tougher if there are no specific database management tools. It generates silos, which are compilations of data that are isolated and not fully useful. Its unstructured pattern makes it difficult to integrate and handle effectively. Also, the analysts find it difficult to assure the accuracy and consistency of those records across all platforms.

2. Dispersed Data

No matter how sophisticated and agile your server and databases are, If the qualitative data is inaccessible, it appears to be a risk. Many companies ignore proper records, files, and database management. It’s difficult to work on the entire data repository, which looks like a mess. This is a big complaint with small- and large-scale enterprises. However, the significance of properly managing catalogs of the available systems, glossaries, and metadata is not hidden. These all-management practices help in effectively maintaining data lineage records. But unfortunately, they remain unattended.

3. Migration-based Issues 

The biggest problem arises when databases need to be shifted. Having data specialists in place can resolve this matter. But dealing with some technical aspects remains a puzzle. Certainly, multiple storage alternatives are available, which can be cloud or server-based, where various organizations secure their databases. But some stringent protocols guard these storages and also make it complicated to transfer data from existing on-premise systems to the cloud. This transfer of records sometimes causes changes in the format, which adversely impacts the processing workloads. This practice puts an additional burden on the budget. Besides, the organization has to closely monitor transferred data to ensure accuracy and smooth processing, which require expensive resources. So, it’s another challenge associated with data management practices and costs.

4. Lack of Data Compliance

This is the fact that data regulations are formed to govern the data management practices of corporate entities. But many of them lack accountability for protecting the accessibility of sensitive corporate records. The companies are said to be accountable if there is any case of breaching the security of personally identifiable information. The concern is that those who manage the entire database are among the employees. They are accountable for their security, but it’s not guaranteed. However, the potential legal liabilities can be minimized if corporate data security is rigorous. Also, its authentication, encryption, and data access authority definition should be properly active in order to meet compliance and industry regulations.

 

Best Data Management Practices to Terminate Challenges 

There are certain best practices that help in managing records effectively. However, the aforementioned challenges hide some clues regarding these practices. Let’s figure out the most valuable practices.

1. Stringent data governance 

The most common of the governance laws for data security is the European Union’s GDPR, which is a data privacy law that has been effective since May 2018. In the very same year, another law was signed, which is called the CCPA. It has been enforced since the beginning of 2020. The last year witnessed the refinement of this law, which transformed into the California Privacy Rights Act. Though the state’s voters approved it in the month of November 2020, it took three years to implement. Eventually, it came into effect on January 1, 2023.

Today, these regulations are active, which can be leveraged to ensure efficient data governance and premium quality. Any governance plan is crucial to regulating data management strategies effectively. The organizations or companies that have scattered data must be concerned that it would be a disastrous and daunting challenge to standardize and handle a diverse set of systems. Once they are converted into a uniform structure, the next thing is to introduce accuracy, consistency, relevancy, and completeness. However, IT and data specialists alone cannot handle and manage these quality concerns. So, business executives and users should be involved to overcome quality issues. It would certainly improve the outcome of data modeling projects, which help in data mining and deriving machine learning algorithms.

2. Introduce smart storage

Deploying data on a DBS or any cloud platform is helpful for effective management. However, the complex structure and diverse platforms should be meticulously understood, and then a strategic approach should be prepared to design a secure and impressive architecture for managing datasets. This can be automated by deploying some automated tools or software. While selecting any tool, it should be a priority to select the one that fits the intended objective. It’s an added advantage if the selected tool is enriched with data processing capabilities and analytics. This is how you can save time and money on resources while impressively handling your databases.

3. Establish futuristic tools. 

The generation of data is a continuous process. It creates opportunities for businesses because the insights underlying it disclose users’ intent. The discovery of intent helps in winning customers’ satisfaction. To add new datasets and achieve business goals, organizations continue to add new data sources, removing obsolete records. Simply put, the database must be adaptive to changing data and requirements.

For this purpose, the data specialists have to work hand-in-hand with end-users’ databases. They should create automated pipelines so that real-time data can be added frequently and the flow of intelligence continues. Typically, a DataOps process is deployed, which helps in developing systems and pipelines. Mainly, it requires experienced data managers, data architects, and users to automate workflows, improve communication, and speed up data deliveries.

4. AI Integration 

Artificial intelligence, or AI, is not a hyperbole but a reality. It is capable of reducing the hard work by integrating automation into various processes, which can include categorization or segmentation, profiling, cleansing, and analysis. Considering the tools, multiple options like IBM Watson, Google Cloud AI, Microsoft Azure AI, etc. are available with the capacities to handle data without much hard work. These tools have machine learning algorithms working in the backend to classify and organize data, filter relational models or patterns, draw insights, and then streamline decision-making. Natural Language Processing (NLP) can also be employed to process data in human language so that computers can understand, interpret, and generate business and data management solutions in a way that sounds meaningful and relevant. Overall, it helps in sentiment analysis, which facilitate data collection and understanding the voice of data.

 

Conclusion

Handling data effectively is not easy. Multiple challenges appear and interfere with its effective use. This is why organizations rely on the best handling practices, which can be related to employing next-level tools, complying with data regulations, or intruding on smart storage.

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