5 Data Management Challenges every Business needs to Address
Today, businesses are increasingly making use of data in their daily operations. While data management helps find, communicate, and automate information, it creates an increasingly complex data environment, which can make present several data management challenges for all businesses.
Managing data across an organization becomes challenging, mainly when dispersed across multiple locations and platforms.
Below, we explore some of the most common data management challenges faced by businesses and how they can be addressed.
What is Data Management?
Data management is collecting, organizing, securing, and keeping organizational data to evaluate it for business decision-making purposes.
As businesses produce and consume data at exceptional rates, data management solutions must make sense of the massive amounts of data.
Types of Data Management
Data management performs multiple organizational roles, facilitating and expediting key data-related activities.
These data management methods consist of the following:
- Data Architecture: Offers a structured process for developing and controlling data flow
- Data Catalogs: Provides an overview of the data’s locations, changes, and quality while making it simple to locate
- Data Governance: Helps maintain data integrity and security through defined standards, procedures, and policies
- Data Modelling: Records the data flow inside an application or organization
- Data Pipelines: Facilitates automatic data transmission between systems
- Data Preparation: Used to cleanse and convert raw data to the appropriate format for analysis, including adjustments and the merging of datasets
- Data Warehouses: Systems used to aggregate diverse data sources, manage the many data types firms retain, and give a clear path for data analysis
- Data Security: Secures data against unauthorized access and corruption
- ETLs (Extract, Transform, Load): Takes data from the source system, converts it, and feeds it into the enterprise data warehouse
Why Data Management is important
Data management is a prerequisite for implementing successful large-scale data analysis, which yields valuable insights that add value to your consumers and increase profitability.
Employees can locate and obtain reliable data for their queries with excellent data management.
Some advantages of an efficient data management system include the following:
- Reliability: Data management decreases errors by setting usage practices and rules and building trust in decision-making data. With reliable, up-to-date data, businesses may adjust to market fluctuations and customer expectations.
- Scalability: Data management allows businesses to grow data and employ repeatable methods to update data and metadata. When procedures are easy to repeat, your company may reduce duplicate costs, such as workers conducting the same research or rerunning costly queries.
- Security: Data management protects your company and employees against data loss, theft, and breaches. Strong data security retrieves vital company data if the primary source is unavailable. Security is critical if your data involves personally identifiable information (PII) that must be protected for consumer protection.
- Visibility: Data management may increase the visibility of your organization’s data assets, making it easier to find data for analysis. Data visibility makes your organization more organized and efficient, allowing employees to find the needed data quickly.
5 Data Management Challenges
Globally, businesses increasingly depend on data to conduct day-to-day operations and make business choices.
Data management has facilitated communication, automation, and the discovery of information. However, this has also complicated the data environment.
The difficulty of managing such vast people throughout a business is increasing. This is particularly true when the information is dispersed across several corporate platforms and locations.
Next, let’s take a look at the most pressing challenges in data management:
Duplicate/ Incomplete Data
Organizational silos are typical in any business; based on the complexity of the organization, it faces the challenge of data duplication.
Multiple processes and software in the company can collect data, resulting in duplicate entries.
Another major challenge is associated with inaccurate and incomplete data. Analysis and decision can be as accurate as the underlying data. When data is captured manually, incomplete records are common.
The best response to this challenge is to improve data processes. It is important to define roles and responsibilities within the organization so that it is easy to spot, avoid, and correct data errors.
Using the right data verification processes and de-duplication solutions will help eliminate repeated records. This makes it easy to organize corporate information for efficient processing.
Multiple suppliers can provide the same data differently, so it is essential that the de-duplication tool you use can identify similarities and flag such records.
Data is a valuable asset collected by spending efforts and resources for any organization. A lot of sensitive information can hurt the organization and the employees, suppliers, and customers in several ways if not protected well.
Based on the methods of storing and handling data, a business can face many challenges associated with data security. This is one of the biggest hurdles for organizations compared to other data-related challenges.
An effective way to address this challenge is safeguarding data using the latest technology. Organizations can avoid security breaches by controlling data access and preventing unauthorized use.
You should also comply with the regulations applying to your corporate data depending on the type of data you manage.
Moreover, you should have a solid data backup and recovery plan to ensure your business faces minimum loss in case data is compromised.
Most organizations today confront dealing with the problem of data quality. While businesses utilize different databases to collect and update information from various sources, maintaining data quality becomes challenging when recording or processing it further.
To ensure that data-driven systems return accurate results, retaining the highest quality data with maximum accuracy while eliminating unuseful data is essential.
Businesses must consider the fact that not all data is equal. Like any other resource, data stored in the systems can be incorrect, outdated, or malfunctioning.
Making decisions based on such data can result in a lot of loss yearly. Therefore, companies must have proper monitoring standards for data quality to ensure that decisions are based on reliable, high-quality data.
Massive and multiple Data Storage
One of the most prominent issues organizations face today is the increasing amount of data they handle daily. Medium to large-scale businesses may use several solutions for their operations, each with its repository.
When data is collected and stored in multiple siloed systems, it gets difficult to locate and integrate it on a central platform for quick data-driven decisions.
This is why a company must focus on creating a single source of data by eliminating silos and combining data from customers, suppliers, and employees.
Keeping different systems synced across the organization is often a problem. As business solutions relying on data are only as accurate as the data they get, it is vital that you enter data in a timely and consistent manner.
A good solution to this problem is to implement real-time data streaming, which means data is pulled into the systems immediately rather than at regular intervals, like once a day or a week. Most data management systems often incorporate it as a standard, automated process.
Lack of adequate Skills
As data gets increasingly massive and complex, organizations need skilled data management professionals to ensure smooth operations.
However, businesses face a severe shortage of talented and experienced specialists. Experts in the field often get attractive packages because they are vital in any company that requires maintaining strict control and management of their data.
Most businesses face this challenge because they cannot afford to hire trained specialists for data management.
Moreover, training freshers can also be expensive for companies looking to adopt newer technology. These organizations must try and retain these employees after they acquire the required skill set. Many businesses are also turning to automation for data-driven insights.
When done well, data management can provide the business with actionable insights that help it grow and succeed.
However, making the most of the available data and being able to make informed decisions can be a complicated task.
Organizations must focus on avoiding common data management challenges discussed here by adopting the right technology and methods to unlock the power of data and the resulting opportunities.