Data management is the driving force in regulating the decision-making operations of a company. It also contributes greatly to the product development cycle. Therefore, the collection, storage and organization of data in accordance with the nature of the data ensures maximum benefit from customers. In this sense, companies that are able to manage their data effectively are always considered one step ahead in their industry.
Data management is the implementation of policies and procedures that enable organizations to have control over business-critical data. One of the most strategic assets for any organization, regardless of industry, is data, and data management is all about building reliable data to accelerate innovation and decision making.
Having to deal with a large amount of duplicate and conflicting data within the organization and from third-party sources is a common problem. The associated costs can be very high. Also, because the data changes over time, it is easy to get out of sync and become fragmented, incomplete, erroneous, and inconsistent. As data deteriorates, people who use it lose trust in company data.
Data management policies and procedures can help ensure that data is best suited to access, storage, backup, decommissioning, and enterprise applications. This process is important for businesses because data is used to make decisions, improve products and services, understand customers, and carry out marketing activities.
Data management applications are also useful in correcting conflicts between data from new and recurring data sources. Data management is a very good way to ensure that organizations have access to accurate data, without uncertainty or conflict, and thus prevent miscommunication.
Well-managed data is easy to access and search across the organization. Data management also includes the modernization of data warehouses, data lakes, applications, and analytics. The more complete and comprehensive your data management, the faster and more successful your cloud modernization initiatives will be.
It also minimizes the risk of non-compliance with data protection regulations and reduces legal complications with data.
Organizations that want to successfully manage their data as data sources grow need to have the right processes and controls in place to ensure that their data is transformed into a trusted enterprise asset. Processes must also ensure seamless integration of dispersed and fragmented data across any platform and any cloud, including multi-cloud and multi-hybrid environments.
A common approach to data management is to use a master data file called Master Data Management (MDM). MDM creates a master record containing basic information about master data domains (customer, location, product, supplier, etc.) to facilitate a task.
Managed data as an organisational asset also requires the imposition of data governance and data quality rules. Data governance removes ambiguity about what data means and ensures that nuances are documented, understood and data is shared with authorised users. Data quality ensures that the data itself is understood, complete, correctly formatted and fit for purpose.
Data management is very important for companies that want to use data. The data affects the profitability of enterprises, their competitive advantage and the success of the enterprise in general. The main benefits that this process provides for businesses are:
Data management affects the profitability of enterprises, their competitive advantage and the success of the enterprise in general. The use of accurate and up-to-date data allows enterprises to be more effective in their marketing, production, financial and other activities. On the other hand, being able to manage data allows businesses to control, use and share as they wish.
Over the past few years, artificial intelligence and machine learning have been applied to the following three categories of data management solutions, making it possible to realize activities that in the past were too complex to be done on an enterprise scale.
The three categories of data management solutions are:
All users automate analytics in the cloud with AI-powered, cloud-native data management for all data in multi-cloud and multi-hybrid environments.
It enables intelligent discovery and understanding of all data inside and outside the organization, accesses and retrieves any data wherever and whenever it is needed, organizes and prepares the data in a self-service manner, providing a unified, authoritative, 360-degree view of all enterprise data.
Manage and protect data across the organization and beyond with automation to ensure reliable data is at the heart of every decision and reduce exposure to data risk.
While data processing, data storage, data governance, and data security are all part of data management, the success of any of these components depends on a company's data architecture or technology stack. The data infrastructure of a company forms a pipeline for the receipt, processing, storage and access of data, and this line is created by integrating the systems together. Data services and APIs bring together data from legacy systems, data lakes, data warehouses, and applications, providing a holistic view of business performance.
Each of these components in the field of data management is currently undergoing a huge amount of change. For example, currently the transition from on-premises system to cloud platforms is one of the most trending technologies in the industry. Unlike on-premises deployments, cloud storage providers only require payment for specified storage, allowing users to return large clusters as needed.
The move to cloud data platforms is also facilitating the adoption of streaming data processing. Tools like Apache Kafka allow for more real-time data processing, allowing consumers to subscribe to segments to receive data in seconds. Batch processing, however, still has the advantage of being more efficient at processing large volumes of data. While batch processing fits into a specific schedule, such as daily, weekly, or monthly, it's usually ideal for business performance tools that don't require real-time data.
Change continues to accelerate in this area. Recently, “Data Fabrics” has emerged to help with the complexity of managing data systems. Data Fabrics leverages intelligent and automated systems to facilitate end-to-end integration of various data pipelines and cloud environments. As new technologies like this develop, we can expect business leaders to gain a more holistic view of business performance as it will integrate data across functions. Combining data in the areas of human resources, marketing, sales, supply chain, etc., can only give leaders a better understanding of their customers.
Data management can be used in many different industries. Methods that respond to industry-specific needs are utilized to implement data management in each sector. The uses of data management in some sectors can be listed as follows:
Generally speaking, data management can be used in many different industries, and the way it is implemented in each sector varies to meet industry-specific needs. Data management affects businesses' profitability, competitive advantage, and overall business success, allowing businesses to control, use, and share their data.
Data management can be a very difficult task for enterprises, especially when data grows to large sizes, and it can face many processing difficulties. Some of the challenges that businesses face can be:
Whether you are embarking on cloud modernisation or reimagining the customer experience with a 360-degree view of data across your business, intelligent and automated data management is the key to managing your organisation's digital transformation. Thanks to Informatica, of which we are the only partner in Turkey, you can manage your company data professionally and take important steps to increase your productivity.
Data Modelling means data analysis in its simplest form. It is one of the best ways to understand, store and manage data. It is made by data scientists and modelers.
ELT is the initials of the words “extract, load, and transform.” Indicates a data integration process that extracts, uploads, and transforms data from one or more sources into a repository such as a data warehouse or data lake.
Data visualization makes it possible to quickly obtain information by visualizing data regardless of format, type or source. It also allows the presentation of information that is not visible at first glance about what the data means in a clear and clear way.
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