To make sound decisions, businesses need reliable data. However, with the surge in business data volume and the usage of diverse systems to gather information, it can be difficult to manage and analyze figures effectively. Successful data management entails collecting, sorting, securing, and storing information to establish a dependable records resource for organizations to rely on so leadership can make informed business decisions.
A well-structured data management system facilitates businesses in gaining valuable insights into critical aspects of their operations, such as inventory management, sales efficiency, and customer preferences.
What is Data Management
Data management encompasses a range of tasks aimed at collecting, categorizing, safeguarding, and storying a company’s data in a proficient manner. When done well, data management can improve a business’s capacity to analyze information, optimize operational efficiency, fortify security measures, and adhere to regulatory requirements.
Amidst the current trend of digital transformation and process automation, data management has become increasingly critical for businesses. With data being sourced from various platforms such as software as a service (SaaS) applications, mobile apps, application programming interfaces (APIs), and Internet of Things (IoT) devices, it’s vital to integrate all the data sources into a unified and consistent dataset. In order to accomplish this, data management should involve cross-functional teams from both business units and IT departments to gain a thorough comprehension of the company’s information, its usage, and the business requirements.
Data management functions include:
- Comprehending and recording the movement of data among various systems.
- ETL (extract, transform, load) processing involves retrieving data from operational systems such as point-of-sale (POS) applications and converting it into a model specifically optimized for analytical processing.
- Data cleansing, an integral component of ETL processing, entails identifying and removing duplicate data entries, as well as verifying the quality and consistency of data to maintain its integrity.
- Developing data catalogs that contain a comprehensive overview of an organization’s data resources in a single, easily accessible location is crucial. These catalogs provide detailed information such as data set location, content, and security levels, as well as data quality and the date of the last update. By creating data catalogs, organizations can strengthen their security measures while also enabling better utilization of data analytics for maximum business benefits.
- Building data warehouses, data marts, or data lakes that consolidate and store data from various systems is an essential step in modern data management. Conventional on-premises data warehouses are designed to store structured data for analysis purposes. However, contemporary data management systems also accommodate cloud-based data warehouses and data lakes that enable the storage and analysis of both structured and unstructured data, including streaming data.
- Instituting data governance involves implementing established protocols and organizational policies to uphold data security and ensure data integrity for an extended period of time.
- Protecting data includes deploying encryption techniques, enforcing access control measures, and establishing network architecture to prevent cyber-attacks, reduce potential data losses, and determine the extent of data compromise in the event of a security breach.
Why Do Companies Need Better Data Management?
Effective data management enhances data accessibility and instills confidence in the data quality, enabling businesses to respond efficiently to dynamic market conditions. By providing business users with seamless access to accurate and up-to-date information, data management also helps mitigate data silos where different organizational units hold varying versions of the same data, resulting in confusion and reduced operational efficiency.
Effective data management enables your organization to grow and adapt to increasing volumes and velocities of data. It streamlines audit and compliance functions and enables your organization to leverage emerging technologies, like artificial intelligence (AI) tools that can efficiently analyze vast amounts of data to uncover unique and predictive insights.
Benefits of Data Management
Companies generate vast amounts of data from various sources, including customer interactions, operational processes, and external data feeds. The challenge, however, is not just to gather and store data, but also to make it useful for decision-making, analysis, and optimization. In doing so, leaders can transform their raw data into actionable insights, drive operational efficiency, improve customer experiences, and mitigate risks. In this context, it’s essential to understand the benefits of data management and why it’s crucial for organizations to invest in a robust data management strategy.
1. Eliminate Data Redundancy
Eliminating data redundancy is a critical component of effective data management. With many businesses utilizing multiple systems to store their data, redundancy is a common problem that can lead to inconsistencies and errors. For instance, product information may be duplicated across various systems such as point-of-sale, manufacturing, and inventory systems. To address this issue, data management systems aim to create a single, reliable source of data for analysis. This is achieved through ETL processes that can extract data from different systems, resolve inconsistencies, cleanse the data to remove errors, and store it in a data warehouse or data lake.
2. Enhance Data Sharing
In the past, organizations faced challenges in sharing data with their business partners, or even among their own business units. The data sharing was often limited to a subset of reports due to the difficulties in sharing the entire volume of data combined within a single database. However, the importance of sharing the entire data set has increased with the rise of advanced data analysis techniques that are based on data science and AI.
Fortunately, the trend towards scalable, object-based cloud storage has made it easier for organizations to securely share large sets of data with others, provided effective governance and cataloging processes are in place. With data loss prevention tools, organizations can control access to data for a limited period, adding an extra layer of protection against security concerns.
3. Improve Data Privacy and Security
Data security and privacy are critical components of data management. The increasing number of regulations, including GDPR and HIPAA, highlights the potential consequences of failing to secure data. Robust security measures require effective data governance and data catalogs, which enable businesses to identify where sensitive data is stored, whether it needs encryption, and which users or groups should have access. Data catalogs also play a critical role in limiting the impact of data breaches by providing metadata that identifies the compromised data, which can help to reduce potential penalties.
4. Assist with Backups and Recovery
With the increasing frequency and severity of ransomware attacks, having a solid backup and recovery strategy is essential for businesses. Effective data management can play a crucial role in creating such a strategy that aligns with the company’s priorities and data model. By cataloging data assets, organizations can identify which data sets are critical to their operations, allowing them to prioritize backup frequency and plan their recovery processes in the event of a disaster.
In addition to frequency and planning, it’s also important to consider the immutability and air-gapped storage of backups, especially in the case of cloud-based services. Immutable backups prevent attackers from encrypting the files, while air-gapped storage ensures the backups are stored on inaccessible media, keeping them safe from cyber threats.
5. Establish and Maintain Consistent, Real-time Data
When companies adopt data analysis and business intelligence tools, they often seek to incorporate additional data sources to enhance their analyses. Moreover, companies frequently require access to rapidly changing data to make informed decisions.
Traditionally, data warehouses and analysis projects have utilized a batch process, loading new data once every night or every few hours. This is due to the fact that many data management systems have well-established ETL processes that are simple to implement. However, to achieve real-time data, you may need to capture data streams from different sources, which is where data management comes into play. It allows companies to comprehend which data sets require real-time capture to support business decisions. Data catalogs can also provide insights into when each data set is updated, allowing businesses to understand how current the data is.
Better Data Management With NetSuite and MIBAR
As a leading NetSuite partner, we wanted to introduce NetSuite for all of your data management needs and more. NetSuite provides businesses with a comprehensive suite of cloud-based applications built on a single database. NetSuite policies and infrastructure ensure that your data is always replicated, backed up and available from any location, whenever you need it. NetSuite data management also ensures that your users have real-time visibility into the company’s financials, customer data and inventory, allowing them to make informed decisions quickly and easily.
Data management is far from a narrow category. There’s a lot that goes into this topic because a lot also comes out of it. If you’re ready to get started with better data management practices, touch base with our team at MIBAR right away. Otherwise, check out part 2 of our series – Data Management: Types and Best Practices.