IT professionals have long understood the importance of data. Now, CEOs and executives are beginning to understand that it is an asset and must be properly managed and protected. Companies are investing time and resources in the tools, skills, and infrastructure needed to store and manage data properly.
- Successful data management framework provides an integrated approach to IT infrastructure, support services, and metadata.
- Teamwork and collaboration between departments enable data management infrastructure that allows for available, validated and usable information.
Data management requires collaboration among all departments to share, extract insights and create actionable plans. Companies need to consider rules and regulations on data storage, customer interactions and how data is used across all departments. Data management now needs to be viewed as holistic, involving the entire organization. Companies are still planning and investing in creating a strong data management framework. Given how important data is to all operations of most corporations, the question must be asked: Is your data management infrastructure stable? To answer that, we need to look at several factors including data integration, data lifecycle, quality, and controls. How can you find strengths and opportunities to help prioritize decisions to develop a solid infrastructure that will offer a competitive advantage in the marketplace?
Data integration is the use of processes to combine data from different sources into meaningful and valuable information. A successful data integration solution delivers data from a variety of sources. Data integration will give a unified view of data assets. Several different areas are covered under data integration including data warehousing, migration, application and information integration and data management. Understanding that data is an asset will allow for consistent and successful integration.
When a company first develops its integration process there are several important questions to ask such as:
- Which systems will the data be sourced from?
- Is the data available to fulfill all requirements?
- What rules and regulations need to be followed?
- What is the support model?
- Why is the data integration being done, what are the goals?
Once you have answered the questions about why and what you can begin to figure out the how. This starts with analyzing the source systems. What options do you have for extracting the data? Are the required data fields populated properly and consistently? You also need to consider the number of users and carry out security and backup policies. Answers to these questions and resulting policies must be well documented and agreed upon by all departments involved. Companies that are just developing their data warehousing need to decide what tools they need to start the solution. Companies that have already established some data warehousing or integration systems may have tools in place. Existing knowledge can be used to implement an efficient system. As always, testing is crucial. Proper testing will make sure that data is correct and complete. IT and other departments involved need to take part in testing for good results.
Options for Integration
- Manual Integration, which allows users to get access to all the source systems, but no unified view of the data exists.
- Application Based Integration uses applications to handle all the integration but is limited by the low number of applications needed to make it manageable.
- Middleware Data Integration uses a middleware layer to transfer integration logic from applications.
- Virtual Integration leaves data in its source system and provides access to the unified view. This method has very little latency and current data. It does not allow users to view the data’s history. It can also only combine similar data sources, like using the same type of database.
- Physical Data Integration usually involves creating a new system that keeps a copy of the data and manages it separately from the source system. This is basically a data warehouse. It allows you to combine data from different sources. However, you will need a separate system to physically integrate the data.
Data Life Cycle Management
Properly managing your data’s life cycle is necessary for any company that is storing and utilizing data and any company that works with consumer data. Improper management will lead to lost and unusable data. Not accounting for data throughout its lifecycle increases the chances that data can be stolen. All of these outcomes cost the company time and money. Data must be stored in compliance will all rules and regulations and be destroyed completely when it is no longer needed. Proper data lifecycle management helps your company mitigate the risks of data loss, corruption, and breaches. It allows all departments run smoothly and makes network maintenance easier. Data lifecycle management steps include:
- Defining your data types: Various types of data require different management. Are you handling customer data, accounting information or parts information? Is the data used with multiple applications? How long does the data need to be kept? Are there specific rules about the destruction of the data? Identifying all the types of data used in the starting point for outlining the life cycle management policy.
- Create a file naming process: Files should be labeled in a simple and thorough structure that will allow for easy access to information. This is an easy way to prevent data loss due to inability to search for or find the data.
- Implement a strong data backup plan: Any data, file or application that is stored on a physical storage device or computer is vulnerable to loss. Data is immediately vulnerable to physical damage, natural disaster, virus or malware damage and even human error. Investing in a strong backup solution is critical. Many companies are now relying on cloud-based backup solutions. There should also be scheduled backups and tests of the backup system.
Life Cycle of Data Phases
Using these phases it is easy to define and plan for data quality and controls.
- Creating Data: In this phase, while you create new data you will plan data management including formats and storage, determine consent and sharing, find existing data and capture and create metadata.
- Processing Data: In this step, you will check, validate and translate data as needed. This is also the phase to manage and store data.
- Preserving Data: Now you can migrate data to the best format and medium, store and back-up the data and create metadata.
- Giving Access to Data: In this phase, you can set up access protocols and share and distribute data.
- Destroy Data: Finally, you will need to properly destroy any data that is no longer needed.
Stable data management and IT infrastructure will support the entire organization; hardware, software and other facilities which underpin data related activities. Support services should be available including toolkits, information, and training. Metadata will be available for both internal and external purposes. Roles and responsibilities should be clearly defined, standards will be outlined, and adequate resources are provided. Teamwork and collaboration between employees, IT specialists, technical support staff and management is key to designing strong infrastructure. Companies need to emphasize a data-driven culture that supports data protection. These practices will build trust, decrease downtime and increase your bottom line. For more information about the best practices for stable data management infrastructure contact us today.