Insights into Master Data Management (MDM)
Traditionally, most businesses did not actively pursue extensive data collection or consolidation. Instead, employees were responsible for manually recording their task completion, transactions, billing, and other clerical work. This information management was used strictly to keep organizations afloat and operational.
This was until companies realized the value of data and how it could improve their strategies and competitive edge.
Modern data management takes full advantage of information collected from each internal process, from sales to inventory, to optimize business strategies and performance. Today, many companies have left behind traditional record-keeping methods and transitioned to digital data management systems to streamline data collection, exchange, and analysis.
What is Data Management?
Data management is the clerical process of finding, gathering, validating, storing, organizing, and maintaining information to ensure that data is reliable and accessible to users. This data can be collected from a company's internal processes to gain insights on productivity and efficiency levels.
Businesses can also gather big data, which contains both internal and external information to keep companies aware of customer behavior, market trends, and competitors.
With the large volumes of data that modern companies are exposed to, it is impossible to use manual collection methods. Effective data management requires proactive IT systems, such as management solutions, that orchestrate business applications and collect flowing information. This enables organizations to efficiently process and store real-time information without being overwhelmed.
Inefficient data management can result in siloed datasets that make it difficult to retrieve and aggregate information, often leading to duplicates, inconsistencies, and inaccuracies.
While the software employs numerous data management functions, its aim is to ensure all information meets the company's quality standards, as well as government regulations. Proper management systems maintain compliance with data privacy and protection laws, including the General Data Protection Regulation (GDPR).
Access to accurate and timely data gives organizations the insight to make data-based decisions and improve performance. Companies can improve their-
- Marketing strategies
- Budgeting strategies
- Operational costs
- Generated revenue
- Profit margins
- Employee management
- Customer experience
What is Involved in Data Management?
There are several practices involved in proper data management. The primary components include-
- Data Governance is the general planning of all data management practices that ensure information is available, useable, consistent, and secure.
- Data Architecture is the overall structure of a company's data.
- Data Modeling and Design involves data analytics and planning, creating, testing, and maintaining analytical systems.
- Data Storage and Operations considers the software and hardware needed to store and maintain data.
- Data Security includes all of the safety precautions necessary to keep information safe, such as only granting software access to verified users.
- Data Integration and Interoperability transforms unstructured data into structured information and places the datasets in organized databases.
- Documents and Content include every form of unstructured data and the necessary steps to integrate it with structured databases, so employees have access to the information.
- Reference and Master Data standardizes data values to avoid redundant and inaccurate information.
- Data Warehousing and Business Intelligence include management solutions and business applications to enhance data analytics and decision-making.
- Metadata involves creating, gathering, organizing, and maintaining metadata, which references other datasets.
- Data Quality involves multiple practices used to monitor data sources to ensure all misinformation is filtered out, so the final datasets are accurate and reliable.
These elements are included in every data management model to ensure high-quality information. Even if one process is forgotten, the data's quality can significantly decrease.
For example, it is almost impossible to consolidate figures from various sources into one database without data integration and interoperability. This makes it challenging to organize and manage information efficiently.
6 Types of Businesses That Use Data Management
As data is among the most valuable asset an enterprise can have, there are many types of businesses that practice data management, including-
1. Small and Midsize Businesses
Startups and midsize businesses need access to insightful data in order to promote expansion and compete with larger companies. By working towards a digital transformation, small companies can utilize data management solutions to enhance decision-making and identify strengths and weaknesses.
2. Government Institutions
Local and national government sectors handle a broad range of services and programs for citizens, making it crucial to practice proper data management.
With management software, government institutions can prevent fraudulent activity, orchestrate payments, and streamline data exchange. This enables the government to oversee the population's safety, economic health, and city initiatives.
3. Health Care Providers
Health care providers use enterprise data management solutions to integrate information from every site and keep patient details secure. This ensures that providers remain compliant with regulations, protect sensitive data, and keep formatting consistent at every health care location.
Likewise, banks use data management software to secure clients' information and abide by compliance regulations. Banks need to assure account holders that their personal and financial information is safe to gain trust and maintain loyal clients.
Manufacturing companies need to ensure their data is high quality and accurate to adequately fulfill clients' orders.
With proper data management, manufacturers can maintain healthy stock levels, integrate enterprise information, and detect demand trends. Managers can also monitor production levels to determine how to improve performance and drive sales.
Retailers need access to data to understand consumers' needs and perspectives to improve their customer service. From marketing campaigns to sales, data shows how retailers can promote profitability and customer loyalty. Besides customer experience, data management also enables stores to connect to other sites and distribution centers to locate items.
Best Data Management Practices
As enterprise solutions evolve, data management practices improve, providing more accurate, insightful, and trustworthy data. Having reliable information gives companies the power to innovate their internal processes to enhance their competitive edge.
To finetune their business intelligence strategies, organizations should consider using the best data management practices, including-
1. Outline Business Goals
Before delving straight into a data management project, businesses need to sit down with their stakeholders and department representatives to define top-priority goals. The objectives need to be something the company can achieve through data management.
By outlining business goals, project managers can identify which processes, departments, and data sources they need to collect information from. This ensures that only relevant information is gathered so the management software does not get overcrowded.
Many businesses make the mistake of gathering and storing large volumes of irrelevant data, which lags internal systems and makes it challenging to maintain databases. By consolidating information, companies can maintain an organizational system that makes data exchange easy and efficient.
While there are countless ways companies can use their data, common business goals include-
- Improve automation
- Improve marketing strategies
- Extend customer reach
- Define customer behaviors
- Promote inventory turnover and sales
- Develop buyer personas
2. Prioritize Data Security
Companies must prioritize data security to ensure sensitive business information is not compromised in the event of breaches and hacks. When organizations detect suspicious activity, both the company's and its customers' data is at risk. This can break clients' trust, requiring the business to rebuild customer relationships.
By implementing advanced data management software, businesses adhere to GDPR restrictions, which are in place to protect enterprise data. The data management team also needs to be properly trained on the systems to ensure they do not expose information to external threats.
Only authorized users who have undergone multiple clearance procedures should be allowed to access databases and orchestrate exchanges. Managers also need to design a plan of action in the event the company experiences a data breach.
3. Enhance Data Quality
Refining data collection to only relevant information significantly improves the dataset's overall quality and provides companies with the necessary insights to meet their goals.
To improve data quality, businesses need to perform regular audits to ensure accuracy and filter out outdated information. When irrelevant or obsolete data accumulates in management systems, they can negatively impact algorithms, forecasts, analytics, and other internal processes.
For companies that do not utilize system integrators, which automatically exchange data between established software, employees must be trained to input information accurately. If data fields are left blank or filled out inaccurately, all sequential processes will yield erroneous results.
Before running data through analytical or reporting tools, it must be cleansed to ensure it is in the correct format and structure. Otherwise, unstructured data could cause system malfunctions and generate inaccurate reports.
4. Eliminate Duplicate Datasets
There are many ways businesses can receive duplicate data, from multiple data entries to departments replicating the same tasks. While duplicate data doesn't affect the information's integrity, it can waste valuable space in data warehouses and lag management systems.
To avoid duplicate datasets, companies need to program filters that cross-examine data entries with the databases to ensure the information doesn't already exist. The software should also automatically update inputs when data is changed or deleted.
5. Improve Data Accessibility
It is essential to balance data security and accessibility to ensure that external parties cannot enter management systems, but internal employees can access information. While employees should be screened and authorized before being able to access data, they should not have to go to great lengths to perform their daily routines.
Managers should consider setting up unique logins and passwords for each authorized employee to establish clearance levels and enable the software to track workers' activity. This measure promotes employee accountability and ensures that each worker can only access information related to their department.
6. Establish a Data Recovery Strategy
Unexpected events are bound to happen, making it critical to have established data recovery plans. Without a recovery strategy, businesses can completely lose valuable customer, sales, inventory, and financial information. Lost data can stem from-
- Accidentally deleting data entries
- Shutting down accounts
- Software malfunctions
- Technical issues
- Data breaches
There are several ways companies can ensure their data is secure when facing these issues, including-
- Regularly backing up data
- Utilizing cloud storage
- Making copies of datasets on a separate hard drive
Managers must ensure that their software is programmed to approve these actions without perceiving them as security threats. With an adequate recovery plan, organizations can restore their data after resecuring their management systems.
7. Implement Robust Data Management Software
Depending on the industry, some businesses collect and filter overwhelming amounts of data, which requires an advanced management system. Data management software should have a user-friendly interface so employees can navigate functions but orchestrate complex management processes.
Data management platforms should also have the ability to grow as the business expands, or else the IT department will be tasked with transferring data to a more robust software.
While these solutions require an initial investment, they generate insights that enable companies to improve their internal systems. Over time, the software yields a high return on investment (ROI).
Managing Data Stored in Point-of-Sale Systems
One of the most valuable data management systems is a point-of-sale (POS) solution. Commonly used by restaurants and retailers, POS software complete and record transactions.
Aside from sales, there is much information that POS solutions can collect from purchases, including-
- Inventory Data
POS solutions also generate reports to show the turnover rate for each product line, high-profit items, and goods that are hurting the bottom line.
- Forecasting Data
For example, if a retail store experiences a sudden drive in sales one weekend due to a nearby concert, they can correlate their ordering schedule with future external events to boost revenue.
- Customer Data
- Marketing Data
- Employee Data
Data management is a valuable tool that every business can benefit from. Understanding the elements of internal processes enables companies to improve their performance and stance within the industry.