Mastering data management principles is the foundation for delivering trustworthy data to the right people at the right time. A data lake allows you to store and process all your data, including big data, from a wide variety of data** without having to pre-structure it. You can populate your data lake with all types of data, whether structured, unstructured, or multi-structured, so your business leaders and analysts can derive more innovative insights from more data.
When planning a data lake, there are nine design principles that companies should adhere to in order to maximize the value of the data lake environment.
Everyone talks about agile development, and one of the biggest flaws is that they don't recognize the importance of cross-functional teams. There are many benefits to using cross-functional teams during a data lake project. A data lake project requires knowledge of data engineering implementation, the ability of a data steward to analyze the business environment, and the analytical skills of a data scientist and analyst. Having multiple perspectives allows you to get accurate and consistent business insights in a timely manner to effectively meet business needs.
Self-service data has become increasingly important in recent years. Self-service data preparation enables knowledgeable business analysts to combine, transform, and cleanse relevant data before analysis, making it more trustworthy and reliable. State-of-the-art tools enable users to publish their prepared datasets to a collaboration area so that multiple business stakeholders can access and prepare data together. In addition, machine learning techniques in the tool can provide guidance for business analysts as they explore and find data in a data lake.
As data becomes more and more loosely distributed across the enterprise, implementing a self-service approach used by data users requires an enhanced traditional IT-driven approach to data management. Crowdsource data management. In a self-service environment, every user has the ability to put their subject skills to work to improve the quality and structure of their data. Through a collaborative approach, business analysts can help each other achieve a common business goal of providing trusted data assets. Machine learning is also a way to automate data domain discovery through classification algorithms.
The manual ingestion and transformation of data is a complex, multi-step process. Successful enterprises leverage pre-built connectors and high-speed data ingestion platforms to load and transform datasets into data lakes. This allows the data lake to quickly accommodate new types of data and scale up to accommodate the growing volume of ingested data. Automation also increases the high-speed iteration and flexibility required for agility because automated processes allow the system to make changes quickly and eliminate the risk of errors.
By adding rule-based data validation to your data lake and applying AI technology, you can automatically detect and correct incomplete, inaccurate, or inconsistent data. Detecting and fixing these anomalies early can significantly improve the accuracy and consistency of business insights. As you collect and transform data into a data lake, you can use data lake rules to configure and filter the data. Data quality scoring boards and dashboards help improve visibility and help team members understand where to focus their efforts.
Using AI to unearth data structures in unstructured data, and then automatically loading other similar, unstructured data, can ultimately dramatically improve the efficiency of an otherwise time-consuming task. A machine learning-based approach can proactively monitor and detect all data across the enterprise to ensure maximum data protection and compliance. In addition, by having a comprehensive view of data assets, it is possible to generate an intelligent catalog of all data assets and infer the relationships between them. Data consumers, such as business analysts, can then use the catalog to discover new data assets that may be of interest, and in fact, some catalogs can even recommend data assets based on machine learning techniques.
Following the principle of co-location is the key to maximizing the benefits of a data lake. Enterprises need to build a limited number of large-scale data lake environments and organize them around key business areas. In addition, leveraging data sharing, data labeling, and project workspaces in data lake management can drive the necessary collaboration between data scientists and analysts. Data consumers should treat each other as team members in their analysis work, and when one analyst completes their work in the data lake, they can publish it and share it with another analyst for use.
As demand increases, the lack of standardization continues to undermine the utility of a data lake, as such an environment is not suitable for scaling. Standardization and consistency are key to the long-term expansion of your data lake. Having standard processes and a consistent architecture in place allows data scientists and business analysts to focus on innovation and analytics, not data management.
By developing a standard process, taxonomy, and glossary, you can ensure that everyone on the project team is following the same standards. Establish simple procedures early in the process to determine what your critical data assets are. How you manage and apply these data assets can save your team from frustration and frustration. Having standard taxonomies and policies in place can radically simplify auditing and derivative tracking in compliance, so you always know where your data is coming from and proactively protect sensitive data.