A Data Manager develops and governs data-oriented systems designed to meet the needs of an organization or research team. Data Management includes accessing, validating, and storing data that is needed for research and day-to-day business operations. Currently, a wide array of organizations are using Big Data to gain insights into customer behavior and to provide Business Intelligence, making a Data Manager a necessity. Data Managers are needed in a variety of industries, including financial, medical, and educational organizations.
Typically, a Data Manager comes with a bachelor’s degree in a computer-related field (information technology, computer science, etc.) and one to four years of experience. Some positions may require an MBA. A Data Manager must be familiar with mainframe computers and hard disk arrays and have a logical, analytical mind with good problem-solving skills.
Examples of a Data Manager’s responsibilities include:
- Manage incoming data files on clients and staff
- Provide individual accounts with access to appropriate data
- Maintain databases and perform routine maintenance as necessary
- Streamline data collection and analysis procedures to ensure fast access to metrics
- Generate and review documentation for all database changes or refinements
- Review data for inconsistencies or anomalies that could skew analytical results
- Make recommendations for software, hardware, and data storage upgrades
- Communicate with managers and staff about data changes or requirements
With the new General Data Protection Regulation (GDPR) guidelines that dictate how data should be managed across all of Europe, Data Management becomes both a form of protection and a necessity. Data Managers implements the policies a business uses to provide guidelines on goals and behavior. Data Management also refers to how data moves through an organization and its lifecycle.
Challenges in Data Management
Some organizations are very good at collecting data, but not terribly good at managing it well or understanding it. The simple collection of data is not enough. Business leaders and managers must understand that Data Management – and the data analytics staff – can only be successful if the organization’s culture changes to support the use of Data Analytics. Only when there is a cultural shift can the value of data become maximized. Cristian Rennella, CEO of oMelhortrato, said:
“On many occasions, a Data Management team ends up working in a separate area of the organization. However, for the company to be successful, this area must be integrated within the company, and for this to succeed it is necessary first to define the policies and culture of a business.”
Another challenge develops when businesses gather data and organize it without considering the questions they will be using when processing the data. Each step in the data collection process should lead toward gathering “useful” data and analyzing it with the goal of developing actionable Business Intelligence.
The sheer volume of incoming data could easily overwhelm a novice Data Manager. It has been estimated that 2.5 quintillion bytes of data are created each day. Data Managers must face the challenge of collecting, managing, and finding value in the data. (If on-site storage is not available, a contract with a Cloud service provider is a reasonable option. Do your research – Clouds come with other tools that might be useful.)
Taking a reactive approach rather than a proactive approach to problems that arise is considered one of the most serious problems facing Data Managers. Many don’t realize there is a problem with the data, until after the damage has been done. A solid, proactive maintenance program can save significant amounts of money and prevent the staff from suffering downtime.
Best Practices in Data Management
Gaining the insights needed to make data-driven decisions begins with asking a business question and then collecting the data needed to answer the question. This requires collecting vast amounts of data from different sources and then using best practices while storing and organizing it, cleaning and mining it, and then analyzing and presenting it in the best way to make business decisions. The use of best practices produces better analytics. As much as 40 percent of all strategic processes break down as a result of poor data. A few useful best practices Data Managers should use include:
- Determine what data can best predict an outcome with the understanding more data is better than less. Using data templates can ensure only relevant, usable data will be collected.
- Scrub data, which requires profiling, adding missing data values, correcting data, and finding duplicate data.
- Introduce Data Governance practices to help ensure data is high quality. A well-designed Data Governance program includes clearly defined procedures, long term-planning, and a governing council.
- Documented data should describe its content, context, parameters, and identify staff members who can use the data. The documentation process should also include creating broad-ranging metadata tags to promote the discovery and use of data.
Data Management Platforms
The top Data Management platforms provide businesses with the ability to leverage Big Data from multiple sources in real-time. Using a good platform allows a Data Manager to be more effective with both staff and customers. The top Data Management platforms provide organizations and businesses with a broad view of their customers and critical insights into their behavior. Data Management platforms have helped organizations collect, sort, and store their information, allowing them to repackage it in ways that are useful to managers and sales staff. Data Management platforms, combined with Data Analytics, allow businesses to:
- Personalize the customer’s experience
- Improve customer engagement
- Identify the causes of marketing failures in real-time
- Increase customer loyalty
- Acquire revenues linked with data-driven marketing
A state-of-the-art data platform should be able to transparently automate and organize the lifecycle of data. By optimizing hardware utilization and the data’s lifecycle, costs can be reduced through the coordination of security, tiering, and redundancy. A data platform acts as a software layer to control underlying storage resources.
How is Data Managed?
The use of Master Data files is a popular method for managing data. This is called Master Data Management (MDM). MDM files define assets and properties with the intention of removing vague or conflicting data policies and give an organization near-total control over its data. Effective Data Management can reduce errors by using the MDM as the accurate master copy for the organization’s most important information. This helps ensure any applications built using master data are accurate and effective.
However, managing data efficiently requires more than MDM. The organization of data needs to line up with the organization’s business strategy and what data the company needs to move forward. The challenge most Data Managers face is how to best use Analytics and how to integrate Analytics with business processes. Integrating Analytics with Data Management will assure a higher degree of success in Analytics projects. When archiving data, a business should use a storage system capable of supporting data discovery, access, and distribution, and when data archiving, regulations and policies must be considered.
Data is also subject to quality control, which might involve double-checking manually-entered data through the use of quality level flags designed to indicate potential problems and check format consistency. Additionally, data should be documented, defining its context, content, and parameters.
Data Management is an essential step toward controlling the massive amounts of structured and unstructured data deluging organizations every day. The use of best practices helps an organization maximize the value of their data and find business insights. An additional benefit is improved compliance – the result of organizations striving for greater transparency in their business processes. Data Management can also enhance customer relationships and loyalty by tailoring services to customers and personalizing their interactions.
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