Discover the 8 most common misconceptions about enterprise data management in 2023 and learn how to overcome them for successful data governance and decision making.
Organizations today receive and retain vast amounts of data, constantly expanding in volume and evolving in variety.
IDC predicts that by 2025, the amount of information managed by enterprises worldwide will have increased by 61%, reaching 175 zettabytes. Businesses that wait to develop an EDM strategy risk being non-compliant, losing their edge in the marketplace, and seeing a drop in income.
Enterprise Data Management (EDM) Misconceptions: Importance of enterprise data management in today's business world
Enterprise Data Management (EDM) is how a company manages the information it collects and uses for internal operations, customer interactions, and software applications. Enterprise Data Management's primary objective is to build and sustain trust in all stored data.
Successful businesses need access to relevant, reliable data to make educated decisions, plan strategically for the future, and achieve their goals.
Efficient data management (EDM) is critical because it establishes a standardized method for companies to maintain, audit, store, protect, access, and exchange their data. This method simplifies the process of finding, analyzing, and using a company's data so that it can make timely, informed decisions.
If information is not adequately cataloged or filed, it might lead to the following issues:
- Operations inaccuracies and inefficiencies
- Inconsistent findings and falling confidence in investments
- Disparities and discrepancies in the news coverage
- Misuse of resources trying to harmonize disparate data stores and workflows.
Managing data is a difficult task for some businesses. This can make it challenging to identify, catalog, and store possessions.
However, this task may be completed with the help of a dependable technology partner with the necessary knowledge, methods, and talent to design business data management solutions.
Business intelligence (BI) and advanced analytics have been promising a future where data is easily accessible and transformed into knowledge and insights for making quick, reliable choices for decades. Each department, from the executive suite to the front lines, depends mainly on technology teams to help them decipher data and extract meaning from dashboards and other reports.
Several misconceptions that have gained widespread acceptance as "facts" directly result from these restrictions. These misunderstandings have hampered the efforts of many companies to implement self-service analytics, reducing their access to and trust in the data they need to make pivotal decisions.
Misconception 1: Data governance is not necessary for effective data management.
Data inconsistencies across an organization's systems must be addressed with data governance. For instance, sales, logistics, and customer support databases may all have separate records for the same consumer. That could make it more challenging to integrate data and lead to data integrity problems, which could compromise the reliability of business intelligence (BI), corporate reporting, and analytics tools.
The reliability of business intelligence (BI) and analytics may also be compromised if data inaccuracies are not detected and corrected. In order for EDM to work, good data governance is required.
Goals of Data Governance
- To eliminate data silos.
- To ensure data is used correctly, both to prevent data errors from entering systems and to prevent the misuse of customers' personal data and other sensitive data.
- Benefits data scientists, other analysts, and business users can expect to reap from these efforts, among others, are higher quality data, decreased data management expenses, and easier access to relevant data.
The importance of data governance for businesses
- To prevent data from being stored in silos across teams and divisions.
- A unified view of data requires that its components be defined consistently.
- Find and correct data set flaws to increase data quality.
- Improve the precision of analytics for sound decision-making.
- To enforce policies that reduce the likelihood of data mistakes and abuse
- To facilitate adherence to applicable privacy laws and other requirements about data.
Data governance examples that show how to make data management better
Problem: Finding a happy medium between data security and ease of use is essential when formulating a data governance policy. Picture this: your company provides medical services. Protecting health records is essential since they contain some of the most private information a person may have.
Better solution: Your data governance team should include defining who needs access to which data and implementing those restrictions.
It is crucial to simplify and standardize data access and interpretation. Data organization that is simple and logical should be the norm, but far too many systems today still need to provide this.
Problem: Make sure you're taking the advice of your team members into account. Suppose you'd rather have your sales team use a centralized customer relationship management (CRM) application rather than a hardcopy notebook. In that case, it's essential to find out why they're still using the notebook.
Better solution: Some team members would benefit from further guidance and training, especially regarding data and database management.
3. Protection and Archiving
Poor quality data at scale leads to subpar business decisions. Data quality needs to keep up with the increasing speed and sophistication with which data may be summarised, manipulated, and analysed.
Problem: Some data within any given firm will require constant employee access, while others will be needed far less frequently. Some data may become so infrequently used that it could be safely discarded or preserved after some time.
Better solution: An effective data governance strategy will identify these data types and provide explicit rules for working with them. Data safety must be guaranteed, and at the same time, it must be simple to locate when required.
Misconception 2: Data quality is not a priority for enterprise data management
Currently, data is a major talking point in the corporate world. This data is among the most important assets at the disposal of modern businesses. However, good data is essential for making informed decisions. Erroneous information is, at best, irrelevant. If things get bad enough, it can cause businesses to make mistakes that end up costing a lot of money. IBM calculates that the annual loss to the U.S. economy due to inaccurate data is $3.1 trillion. Employees lose time fixing mistakes and incorrect information, which leads to unhappy clients, and so increases costs.
Consequently, the distributed master data will be inaccurate in the absence of rules or inspections for data quality. When a company lacks the ability to interpret its data, it cannot make sound strategic decisions. Customers may get dissatisfied and operations may be slowed.
Organizations may improve validity, consistency, and correctness across the data lifecycle with the use of data quality guidelines. Since data is ever-evolving and businesses may have several users updating master data at once, data quality rules are essential for minimizing the risk of human error and maximizing company-wide consistency.
Quality data is among the best methods for getting to know your customers. Data quality is always of the highest priority in EDM. Data quality can benefit in many ways:
- With high-quality data at your disposal, you can provide superior service to your clientele. Customers that have a positive interaction with your business are more likely to buy again and spread the word about it.
- Your company's adaptability depends on accurate data. You can anticipate shifts in the market and seize opportunities or meet obstacles head-on before the competition does.
- Good data quality can be maintained with consistent management, but only if you do so. The good news is that cutting-edge data solutions and platforms can streamline and automate your routine data validation and administration tasks.
Prioritize data quality. Data quality is an enterprise-wide issue, Data quality affects everyone, hence a cross-functional strategy is needed. Emphasize simple ways employees can obtain and comprehend data.
Misconception 3: Enterprise data management is only relevant to large organizations
Whether you're a large, medium, or small business, data governance is essential if your data is flowing via fragmented systems and you lack the tools to oversee and govern your master data. The importance of an Enterprise data management strategy cannot be overstated when several business processes depend on the same data.
Further, data quality checks should be maintained to ensure compliance with industry standards on the sharing of data with business trading partners. Even though they face the same difficulties as larger companies who don't have an EDM strategy, most small businesses view this as an issue solely for larger ones. Do you intend to wait until things get out of hand before implementing an EDM strategy as your company expands and the amount of information and the variety of applications you use both grow? No, we certainly don't want it to happen!
Data management is useful for any business that has enterprise data that needs to be shared between different departments or platforms. In the case of client information, even the smallest businesses likely make use of a customer relationship management system, finance department records, and information gathered by the company's training, support, and service departments. Consider the possibilities opened up by a complete picture of a customer's interactions and experiences with your business.
EDM applies to both small and mid-market organizations. It improves processes and reduces human errors.
Why do small businesses need data management?
Responsiveness: Small firms can quickly adapt to client behavior and data. Proper data management enables small firms to have real-time market intelligence, enabling them to adapt to market developments.
Small firms can benefit from appropriate data management since larger companies have far more infrastructure and complications, which take much longer to adjust, even with good data management.
Cost: Due to their limited resources, smaller enterprises must be cost-effective. Data management helps small firms minimize resource waste and natural resources more effectively.
Cyber security: Small firms are vulnerable to cyber-attacks because they assume their data is less valuable than larger companies. Cyber-attacks can ruin smaller firms. Data may remain operational and compatible with data governance rules with effective management information strategies and solutions.
Examples of a few Organizations that have begun to implement enterprise data management
Uber uses business intelligence to make crucial decisions about the organization. They are using surge pricing as an illustration. When demand rises while traffic conditions vary, rates adapt in real-time thanks to algorithms that track traffic conditions, travel times, driver availability, and consumer demand. Airlines and hotels use real-time dynamic pricing to modify prices based on demand.
Miniclip uses EDM to improve user experience. It emphasizes client retention to make games extra profitable and assist business growth due to its nature and sector.
EDM reporting, analysis, experimentation, and advanced analytics data products allow the organization to measure and incorporate successful product features in future initiatives while deleting or enhancing problematic features.