Effective Data Management: What is it and why does it matter? Nowadays, businesses rely more than ever on having accurate, timely, and convenient information with which to work. From a business’s everyday operations to its strategic choices, vital decisions largely depend on having good data.
However, achieving data excellence does not happen automatically. Effective and active data management is always the result of continuously improving quality standards.
Table of Contents
Importance of effective data management
What are the key pillars of effective data management?
Data management components and systems
Types of data management systems
RDBMS (Rational database management systems)
OODBMS (Object-oriented database management systems)
What is data management?
Managing data includes various procedures and tasks such as collecting, storing, organizing, processing, accessing, and validating data to support decision-making, productivity, and efficiency. Data plays a pivotal role in business today, making it essential for every company to have modern data management systems and strategies. Other elements of data management include:
- Integrating data from different sources (structured as well as unstructured data)
- Ensuring data availability
- Disaster recovery
- Securing and protecting data
- Governing access and use of data by apps and people
- Ensuring data privacy
Importance of effective data management
According to a study by Gartner, poor data quality control costs businesses an average of $14.2 million annually. Moreover, low-quality operational data puts worthwhile organizational goals at risk.
Relying on inconsistent, incomplete, or inaccurate data often results in a series of faulty decisions that can compromise the business’s ability to maintain a competitive edge as market conditions change. Conversely, better decisions follow when an organization supports high-quality data through effective data management. It improves security, agility, productivity, and partner, customer, and supplier relationships.
Moreover, regulatory authorities such as the EU’s GDPR impose legal requirements that organizations maintain a level of data quality adequate to ensure the security and integrity of data that contains personally identifiable information.
What are the key pillars of effective data management?
Data is critical to the operation and functioning of a business. Therefore, companies must make sense of data by finding relevance in the noise created by the wide range of technologies and systems supporting today’s global economies.
Data on its own is useless. Organizations need effective governance, strategies, and effective data management models to utilize all forms of data for efficient and practical use across employee networks, partner and customer ecosystems, supply chains, and much more.
These are the primary aspects of effective data management.
Security
Addressing data security is a critical component of an effective data management strategy. Data security mainly involves creating controls and processes to ensure information safety and prevent data leakage or loss. This process includes protecting databases, essential accounts, and files on the organization’s network primarily by adopting controls that effectively identify the importance and sensitivity of various databases. The company should then secure data by applying techniques based on regulatory requirements.
Businesses should consider the CIA triad (confidentiality Integrity and availability) when dealing with data security. By adhering to this triad, companies prevent unauthorized access to their data. Moreover, a governance strategy that adheres to confidentiality controls ensures that authorized parties can only access data.
Integrity helps ensure reliable and accurate data, and availability guarantees data when needed. Some of the factors you should consider when creating your data security strategy include:
- The location of confidential information: Data cannot be protected unless its location is known.
- People who have access to the company’s data: Check data access regularly and review permissions frequently.
- Real-time security alerts: In addition to detecting suspicious activity, adopting a real-time security alert system ensures data security and compliance with industry regulations.
Architecture
Data architecture is the instructional manual or blueprint for managing your data assets. Your data architecture plan should identify a data strategy and set up designs and requirements to achieve that organizational data strategy.
Data architecture is vital because any data management plan lacking a clear implementation strategy will likely fail. Moreover, the data architecture strategy must consider your business goals and how information and data can help support or hinder your business goals. Lastly, it is essential to establish strategic data management requirements that consider the current as well as plans of your business.
Modeling
Data modeling is a critical aspect that allows companies to create a plan for their data before they make it available for use by authorized personnel. Before offering data to authorized users, companies need to take ample time for data modeling, carry out proper analysis, identify challenges and risks and employ an emergency plan to cater for system crashes and data breaches before making the database accessible. Data modeling allows the organization to clearly understand the rewards and risks of creating a database before making the time and commitment to build it.
After creating the data system, you should apply those same principles to the data management plan. Taking data modeling into consideration in your data management plan helps break down the complexities of data and lets targeted users understand what the data means.
Data modeling helps you standardize across different systems, so the data feels familiar and is understandable for all users. Moreover, data modeling allows the company to identify inconsistencies in the data, compare data to real-life situations, and anticipate future challenges. Data modeling also allows non-technical personnel to understand the information better. Here’s how:
Mobility
Today, data mobility is critical to any data management strategy because it gives users the ability to access data where and when they need it. Since the start of the COVID-19 pandemic, employees have been increasingly working remotely, requiring access to services and files. If personnel cannot access the information when needed, customer satisfaction, supply chain schedules, and company revenue may be negatively affected.
Every company’s data management strategy should ensure that data is available to users whenever they need it. Nowadays, most remote employees employ VPN or cloud services to distribute and centralize data. Besides enabling efficient data mobility, these services also encourage productivity and ensure timely file synchronization and sharing.
Quality
This measure of data integrity is based on a wide range of factors, including consistency, completeness, timeliness, and accuracy. Ensuring and regularly measuring data quality ought to be one of the integral parts of your organization’s data management strategy. Adopting data quality measurement procedures allows companies to quickly recognize and resolve a wide range of data errors to ensure continuity of operations.
If not managed properly, data can be highly overwhelming and useless. However, with the right strategies and tools, you can use data to empower organizations with more profound insights and more accurate forecasts. It can help companies better understand what customers want and what companies can do to deliver outstanding customer experiences based on the insights data provides.
Data-Driven Models
Effective data management can also drive data-driven models that wouldn’t be obvious or evident without interpreting and analyzing big data.
- Data security, compliance, and governance
Governance mainly incorporates the responsibilities and rules for ensuring data quality, availability, security, and compliance across the company. Data governance establishes the infrastructure and identifies positions or individuals responsible and authority for safeguarding and handling specific types of data. Thus, it’s a crucial part of data compliance.
What is big data?
Big data are enormous data sets characterized by the volume, variety, velocity, veracity, and value collected. Data-driven businesses have a significant competitive advantage. By employing advanced tools, companies can handle more data from a wide range of sources.
Moreover, they can leverage various data types in real-time, including IoT (Internet of Things) device data, social media comments, internet clean stream data, and video and audio files. This opens up more chances of monetizing data and using it as a business asset.
The importance of effective data management has only increased since the start of the COVID-19 pandemic. Many businesses instantly felt the pressure to put their data to better use and use it to pivot rapidly, adapt, and build resilience into business plans and models.
For instance, machine learning requires vast and varied data sets to identify complex problems, learn, solve problems, and ensure algorithms and models are running efficiently and up to date. On the other hand, advanced analytics depend on large quantities of data to generate actionable and relevant insights that companies can adopt with confidence.
Data privacy laws
Data is the key to digital transformation. Before any business can take advantage of emerging technologies, become intelligent organizations, and transform processes, they need an effective data management strategy. Studies show that continued survival depends upon an effective data management structure that responds to the dynamic world.
Effective data management is essential for compliance with industry-specific security and privacy guidelines and international and national data privacy laws. Having an effective data management strategy in place is extremely important when such protections are proven or audited.
Data management components and systems
Data management systems consist of data management platforms and various processes and components that generate value from data. In most cases, these include analytics, data integration tools, data lakes and warehouses, and database management systems.
There are many types of database management systems, the most common ones being OODBMS (object-oriented database management systems), columnar databases, in-memory databases, and RDBMS (relational database management systems).
Types of data management systems
These are the most data management system types:
RDBMS (Rational database management systems)
Rational database management systems mainly contain data definitions so that retrieval systems and programs can reference items by name instead of describing the location and structure of the data items each time. The system also maintains relationships items that avoid duplication and enhance access. For instance, each data item’s characteristics and basic definition are stored once and directly linked to pricing tables and order detail lines.
OODBMS (Object-oriented database management systems)
Object-oriented database management systems provide a unique approach to defining and storing data stored as self-described and self-contained entities or objects.
In-memory database (IMDB)
In-memory databases store data in the random access memory rather than on the hard drive. An in-memory database is ideal for any application requiring rapid response times because retrieval is much faster than recovery from hard disk-based systems.
Columnar database
These databases store related data for rapid access. Companies utilize it for standalone data warehouses and in-memory business apps where retrieval speed is critical.
Data lakes and warehouses
Data warehouses are central repositories of data collected from various sources for the core purpose of analysis and reporting. On the other hand, data lakes are large pools of data stored in their natural or raw format. A data lake mainly holds big data (structured, semi-structured, and unstructured).
Data quality management
Quality management is a set of standards aimed at high-quality housing data. Data quality management programs use different quality tools and procedures to ensure information is accurate, reliable, and meets a set of quality standards.
Organizations acquire real-time data that parties can use to create business intelligence. Quality control managers oversee big data to identify quality issues and eliminate errors. Then the executives, as well as employees, use organizational data to improve problem-solving and enhance decision-making.
However, inaccurate information usually leads to poor decisions. When executives and employees extract useful information from big data, they can quickly eliminate bottlenecks and pinpoint inefficiencies.
Conclusion
From managing the supply chain to customer relationships, effective data analytics and management can improve any company’s overall operational effectiveness.
Data yields information, and information is power. Therefore, effective data management could be your organization’s “holy grail.” As such, effective data management practices have evolved to become unmatched change agents in leveraging new trends, driving cloud adoption, and delivering significant strategic value to businesses.
