The answer to the question of what is data management is well answered by knowing that well-designed business intelligence processes are perhaps the key to staying competitive in a data-driven industry. Does your organization have the right data management plan in place to stay ahead in the global marketplace?
What is data management?
Data management or data administration refers to the professional creation and maintenance of a framework for the recording, storage, mining, and archiving of all data that is important for modern companies.
Data management is the backbone that connects all elements of the information lifecycle.
Data management goes hand in hand with process management. The goal is to ensure that all teams have clean, up-to-date data on which to base their decisions and actions. Today, this means tracking changes and trends in real-time.
Below we dive deep into this process, its benefits and challenges, and the best practices to help your organization get the most out of its business intelligence solution.
7 types of data management
Data management professionals typically focus on specialties from one or more of the following areas:
- Master Data Management: Through master data management (MDM), organizations can ensure that they are making decisions based on a single version of current, “true” information at all times. If you want to ingest data from all your sources and make it available as a single trusted source across systems, you need the right tools.
- Big data management: Big data is the collective term for the collection, analysis, and use of enormous amounts of digital information for process optimization. Essentially, this is about the recording, integrity, and storage of the countless raw data that are used in the company to optimize processes and improve security and which serve as the basis for BI analyses.
- Data Governance: Data governance sets rules for the data that is used or held in an organization. A data governance framework is like a constitution that defines clear policies for the ingestion, flow, and protection of institutional information. A data governor oversees their network of stewards, quality management professionals, security teams, and other processes to ensure a governance policy that supports master data management.
- Data stewardship: The job of a data steward is not to create data management policies, but to implement and enforce them across the organization. The data steward oversees data collection and data transport policies, ensuring that appropriate practices are implemented and all rules are enforced.
- Data warehousing: Information is the basic building block of modern companies. But the sheer volume of data poses an enormous challenge: What should companies do with this incredible flood of information? Data warehouse management is of great help here: It provides and monitors the necessary physical and/or cloud-based infrastructures to aggregate raw data, analyze it comprehensively and gain valuable insights from it.
- Data Security: One of the most important aspects of data management today is security. Although new practices such as DevSecOps integrate security considerations into every layer of application development and data exchange, security professionals are still responsible for frontline tasks such as managing encryption, preventing unauthorized access, and protecting against accidental movement or deletion of data.
- Data quality management: If you consider the data steward as a kind of digital sheriff, a data quality manager could be his clerk. In quality management, the collected data is checked for possible problems such as duplicate entries, inconsistent versions, etc. Data quality managers support the defined data management system.
Organizations may need to combine some—or all—of these data management approaches to meet their unique needs. Data managers who are already familiar with specific management areas have the background to create personalized solutions for their environments.
Benefits of data management
Through efficient data management, organizations can identify and fix internal problems and ensure a better customer experience.
First, data management allows organizations to measure the amount of data they are dealing with. Every organization has myriad interactions going on behind the scenes—between network infrastructure, software applications, APIs, security protocols, etc.—each of which represents a potential disruptor (or time bomb) to processes should something go wrong. Efficient data management gives managers a comprehensive picture of their organization and supports them in planning ahead.
Once the data is under control, it can be searched for valuable BI insights. This information can be used to implement improvements and make processes more efficient. Here are some examples:
- Intelligent advertising tailored to customers’ individual interests and interactions
- Lower operational costs because the company only pays for the storage and processing power needed for optimal performance
- Machine learning solution that learns the environment better over time and enables automatic and continuous improvements
- Adhere to relevant compliance standards to save time and money
- Holistic security concept to protect critical information
Consumers and buyers also benefit from efficient data management. Companies with an accurate picture of their customer’s preferences and buying habits can offer them faster access to the information they want. In general, customers and prospects benefit from a personalized, simple shopping experience. In addition, you can be sure that personal data and payment information are stored securely.
Leading retailers like Office Depot integrate data management processes into sales cycles that sync shopping, purchase, and delivery in seconds. In this way, efficient data management is instrumental in meeting customer demand in real-time.
Data management challenges
Despite all these benefits, there are also some challenges. Data managers have to deal with a changeable, constantly growing IT landscape that is constantly evolving. This causes headaches even for experienced experts.
In general, there are four main problems:
- Optimal data management can only be achieved with the right data culture. All the processes and systems in the world are useless if employees don’t know how – and perhaps just as important: why – they should use them. By consciously educating their team members about the benefits of data management (as well as the potential downsides of ignoring data management), managers actively involve them in the information process.
- Many organizations keep their data in silos. Different teams – for example in development, in sales, or in the operational area – often work with different data sets. A modern data management solution must be able to access all of this information in order to support innovative business intelligence processes. Real-time data platform services make it easier to stream cleansed information from a single trusted source and make it available to multiple teams.
- The path from unstructured to structured data can be very rocky. Data often arrives in an organization unstructured. Before it can be used for business intelligence purposes, data must be cleaned: data must be organized, deduplicated, and otherwise cleaned. Data managers often enlist third-party support for this process, using tools designed specifically for on-premises, cloud-based, or hybrid environments. (Alternatively, they can also integrate Talend Data Preparation seamlessly with the Talend platform. The big advantage is that data preparation does not take place in isolation, nor does it need to be supported by a third party.)
- Companies are (at least temporarily) overwhelmed with the amount of data. The amount of data that modern companies are confronted with should not be underestimated under any circumstances. When developing systems and processes, you should definitely think big. Specialized third-party services for the integration of big data or the provision of big data as a platform are valuable support.
Companies have to overcome these and other challenges if they want to leave traditional procedures behind and benefit from the immense BI potential of their data. With the right planning, appropriate practices, and the right partners, technologies like accelerated machine learning can address potential pain points and enable deep business insights and a better customer experience.
After answering these questions and more, you should determine where and how you want to provide the data. What used to be repositories is now using software and infrastructure-as-a-service models that are optimized for big data management.
Data Management Software-as-a-Service
Leading data management and integration platforms like Talend enable a unified movement and management of all data and processes – from code creation to cold archive data storage. Data management software accelerates and simplifies complicated processes with user-friendly templates, visualization of complex coding tasks, management of compliance aspects, etc. This gives organizations a complete, centralized view of their entire data estate.
Unlike solutions built entirely in-house, services like Talend’s, with their best-in-class reliability, 24/7 availability, and an easier learning curve, allow greater control at less time and expense.
The first steps in data management
Big data plays a vital role for companies in practically all industries. The greater the importance of big data for real-time decisions – as well as for competitiveness and customer satisfaction – the more important it is to manage this data efficiently.