It's important to address some fundamental questions about what you want to achieve with cloud data management. Dive in here to start.
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Data is one of the most valuable business assets in today’s digital world, and data management is a crucial part of any business strategy. With data mining and analytics, businesses can leverage their data to compete and thrive.
Cloud data management is the process of ingesting, structuring, transforming, tagging, and securing large volumes of data and making it accessible in a format that is ideal for processing, analysis, reporting, and data sharing. Any data your company is using or storing should be considered by your cloud data management plan and, in many cases, there are legal and compliance ramifications as well.
There are many kinds of data within an organization, some of it is structured in relational databases or spreadsheets, some unstructured in free-form text documents.
Cloud data management focuses on the process and compliance components that enable organizations to have useful, effective, safe, and compliant data practices.
It’s very important to have data accessible to the right people at the right time. For example, consider payroll information. There are lots of valuable insights to be gleaned by management through analysis of payroll data, but this information should not be available to everyone in the organization. For this reason, it is critical that data be properly labeled, and that there are rules and processes in place for each type of data to ensure only the appropriate people in the organization have access.
Data management is also extremely important for legal and compliance reasons. Many organizations are required to keep transaction records and communications such as emails and chat message history for a particular period of time. If your organization is in a regulated industry, you may also be required to process information in a particular way. A good example of this is businesses in the Healthcare Industry, which need to follow HIPAA rules and regulations.
Adoption of a data management strategy begins with assessing the requirements of the organization. Beginning with the size and type of organization, these will differ greatly. A public company will have legal requirements around data retention, frequently having to keep records for a period of seven or more years. Companies may also have industry-specific compliance requirements for data.
Once the requirements have been defined, the strategy typically begins by establishing specific data categories (or labels) to which different policies need to be applied. Often these policies will be based on the intended use of the data (or data persona).
Using Data Personas
The objective of data personas is to create idealized user types to serve as a basis for all data analysis, reports, and other decisions. Product Owner, Accountant, Salesperson, and HR Leader personas will have very different data needs, specific to the organization. All personas should have associated policies that limit or restrict how they share the enterprise’s proprietary and personal information.
Implementing Data Governance
All good data management strategies should include robust data governance policies. One of the main objectives of Data Governance is to ensure that all stakeholders are following business rules. Data governance allows organizations to set rules and processes for accessing and using structured and unstructured data within their enterprise. This reduces repetitive requests and ensures that sensitive information remains secure in accordance with privacy regulations.
Managing Cost
Return on investment (ROI) for data assets must consider the denominator of cost as well as potential value. In all aspects of data management, design tradeoffs will be available. Faster processing at a higher cost may be the right choice for time-sensitive or competitive business processes, while slower access is usually an acceptable tradeoff for long-term compliance-related storage at much lower cost.
Many organizations prefer using a centralized database that allows secure access to all users around the world. This centralization helps to streamline the overall data management process and ensure compliance with company policies.
There are four fundamental aspects of data management that can be addressed by the proper selection and application of technology. Where the data is stored is a critical component, as well as how that data is ingested into the storage location, and how that data will be processed. Lastly, once the data is processed there are tools that will allow you to analyze or visualize the data in a useful way.
Data Storage
There are many ways to store large volumes of data, and the best choice will be related to what kind of data is being stored. For large volumes of historical structured data, a data warehouse like Amazon Redshift is a good choice.
When considering unstructured data, this will typically be stored in a Data Lake which can be created as a series of Amazon S3 Buckets using Amazon Lake Formation and catalogued by AWS Glue.
Specific types of data may require a specialized storage format. One example of specialized data is time-series data streaming from IoT devices. This kind of data fits best in a time-series database like Amazon Timestream.
Data Ingestion
Once a data storage solution has been selected, it is important to consider how you will get the large volumes of data into the storage solution in the first place.
For structured data such as what goes into a data warehouse, ingestion is typically done with ETL (Extract, Transform and Load) processes using a tool like Amazon Glue.
For unstructured and time-series data, the best solution is often a streaming tool such as Amazon Kinesis or Amazon Managed Kafka.
Data Processing
Processing large volumes of data is very time-consuming and difficult using traditional methods. Most traditional forms of data processing and analysis require loading data, running the process, and then writing out a result. When the volume of data to be processed is extremely large, loading all of that data into memory can be time and cost-prohibitive.
To get around these limitations, data processing at scale is frequently done using a MapReduce process. MapReduce tools define a specific kind of data processing pattern, where the computational process can be brought to where the data resides rather than the other way around. The same Map process can be run simultaneously on lots of distributed blocks of data, and the end result from each of these data blocks is then Reduced to provide a single resulting data set as output.
To facilitate this kind of data processing Amazon provides an Elastic MapReduce (EMR) service, and an associated development environment called EMR Studio. Using EMR Studio, you can quickly define MapReduce processes that can then be run against very large distributed data sets.
A critical, but sometimes overlooked part of successfully managing your data lies in having an effective backup policy in place for when things go wrong. Having reliable information available at all times is essential for succeeding in today’s fast-paced business environment.
Data Visualization
AWS offers a complete package to store, process, analyze, and present data when it comes to cloud data management tools. AWS provides Amazon Redshift for data warehousing, Amazon Athena for SQL-based Analytics in Data Lakes, Amazon Quicksight for dashboard construction and data visualization, and Amazon Glacier for long-term backup and storage.
Always develop a data management process with the end goal of making your organization more efficient. A strategic plan will accomplish this by helping you to stay organized and ensure that everyone involved is aware of their responsibilities.
When it’s time to get serious about capitalizing on data assets, one of the first things to do is to develop such a data management plan. Setting up governance policies from square one will:
In order to successfully manage your data, it’s significant to set up and follow a defined process.
Research: Gather as much relevant information as you can so that you can make informed decisions about your data management strategy.
Implementation: Implement the plan so that it becomes a reality. If necessary, adjust the plan based on feedback from others throughout this process.
Evaluation: Determine what worked and didn’t work during this process so that you can learn from these experiences for future projects. This phase will allow you to continuously improve upon your current data management process.
Data management strategies, tools, and processes vary from enterprise to enterprise because of their different goals. However, these are eleven best practices that every enterprise can follow to get the most out of their data management plans.
Juan Ignacio Giro