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Data management

Written by Ana Canteli on March 29, 2022

Data management is based on collection, custody, and efficient and safe use. Data management aims to help optimize its use based on data management policies and the regulatory framework. We must not forget that there are more and more companies whose core is data management. That is, they are not based on tangible material to create value.

Data management is said to have started around 1890 with the creation of punched cards. But in reality, until 1960, the concept of data management or data warehousing was not discussed as we use it today. The Association of Data Processing Services Organizations (ADPSO) began its advisory activity on master data management and data warehousing, among other issues. A decade later, the data management systems were only operational; they provided reports on operations at a given time - master data extracted from a relational database - data warehouse - stored in rows and columns.

Today, businesses need data management solutions that provide them with a unified way of managing data at multiple levels. Because data management tools are based on data management platforms and can include master databases, data lakes, data warehousing,  big data management systems, data analysis, etc.

A data management platform is the primary system for collecting and providing large volumes of data analytics. Data platforms often include software tools for management and may be developed by the database provider or third parties. These data management solutions help technical teams and database administrators perform tasks such as:

• Identify, diagnose, and resolve failures in the database system or infrastructure.
• Allocate storage resources.
• Update the database design.
• Optimize database query responses for better performance.

As for big data, it is precisely what it sounds like: lots of data, lots of data. But big data comes in a wide variety of formats, and it's also collected at high speed. For example, think of all the data Meta collects from your social networks. The amount, variety, and velocity of that data make it so valuable to businesses, but it also makes it difficult to manage.

As more and more data is collected from sources as disparate as CCTV, social media, audio recordings, and Internet of Things (IoT) devices, big data management systems have emerged.

A data lake is a data storage system in its raw format, and it typically includes essential backups of the data from the source system.

Data is a form of capital, as in today's economy, it can create value for itself. Data are very relevant at a strategic and competitive level. Data is also a defined capital since it is information registered and necessary to produce goods or services. And as we said above, it includes any data captured:

• Organization: transactions, customer records, support reports.
• Mobile: application interaction, device configuration, geopositioning.
• Audio: customer service, voice services, and automated systems.
• Video: satellite images, X-rays, security footage.
• Sensors: temperature, humidity, vibration, acceleration.

Essential elements of a data management process

• Data architecture: it is designed and deployed with a database system and other repositories to house an organization's data.
• Data model: they are created to map the workflows and the relationships between the data so that the information can be organized to meet the company's requirements.
• Data: generated, processed, and stored in a database, file system, cloud service, or other storage systems.
• Transaction system: and other data sources are integrated into a data warehouse or data lake for data analytics results.
• Data quality control: is performed to identify errors and inconsistencies and resolve them through data cleaning tasks.
• Data Governance: Create data definitions and data usage policies.

Challenges in data management

Most of the challenges in data management today stem from the ever-accelerating pace of business and the increasing proliferation of data. The ever-increasing variety, speed, and volume of data available to companies push them to seek more effective management tools to sustain themselves. Some of the main challenges organizations face are:

• Lack of knowledge about data: Many data is collected and stored in quantity and variety, but none of that data is helpful if the organization does not know what data it has, where it is and how to use it. Data management solutions need scale and performance to deliver meaningful information when it's needed.
• Difficulty maintaining performance levels: Organizations capture, store, and use more and more data for longer. To keep response times competitive at the highest level, organizations must continually monitor the type of queries the database answers, and change indexes as questions change without impacting performance.
• Difficulties in meeting changing requirements: Regulatory requirements, such as the General Data Protection Regulation, are complex and multi-jurisdictional, and constantly updated. In these circumstances, companies must be able to quickly review their data and identify any elements that may violate the new regulations. In particular, personally identifiable information (PII) must be detected, tracked, and monitored to comply with increasingly stringent global privacy regulations.
• Need for data processing and conversion: Collecting and identifying the data itself does not provide value; the entity needs to process them. If it takes a lot of time and effort to convert the data into what they need, data analytics,  that won't happen. As a result, the potential value of that data is lost.
• Need for constant adequate storage: Organizations today store data in multiple systems, including data warehouses and unstructured data lakes that store any data in any format in a single repository. Data scientists in an organization need to transform data from its original format to the form, or model they need for a wide range of analyses, following a quick and easy process. As we can imagine, that often looks like squaring the circle.
• Constant optimization of technology performance and costs: With the advent of cloud data management systems, businesses can choose whether to retain and analyze data on-premises, in the cloud, or in a hybrid facility. ICT companies need to assess the level of identity between local environments and the cloud to maintain maximum agility at a technological level while reducing costs, and maintaining or guaranteeing that balance is frankly difficult.

Best practices in data management

A well-designed data governance program is essential in a solid and reliable data management strategy, especially in companies with distributed data environments that include many systems. A strong focus on data quality is also critical to applying best practices. However, in both cases, IT and data management teams can't do it all. Management, employees, and users must be committed to ensuring the proper use of data and maintaining data quality to apply best practices.

In addition, the multitude of databases and other data platforms available to implement requires careful data architecture design, and the appropriate technology must be evaluated and selected. Data managers and data management tools must ensure that the systems they implement fit their purpose and have the data processing capabilities and analytics information necessary for their organization's operations.

The OpenKM document management system has all the functionalities and capabilities to process an organization's data and configure or be part of the data management system that it develops. With OpenKM, your company will be able to get the most out of your data capital.