数据类型常见问题

Most businesses have a solid understanding of structured data, which usually has a row-column format and very explicit metadata elements, such as month/day/year. Largely numeric, structured data comes from transactional systems, databases and back-office applications (for example, ERP systems). While businesses have an overwhelming amount of structured data, they generally know how to manage, analyze and apply it because of how well it’s defined.

Finally, semi-structured data represents a hybrid of these two data types. This group could include Excel spreadsheets that contain important financial information, but the data itself is hard to extract. These data objects may have structure within them, but they lack the external structure needed for standard data management processes. Like unstructured data, these objects contain important insights that can be hard to extract and apply without an intelligent data governance strategy.

Semi-structured data refers to any information that uses a self-describing schema, such as XML or JSON. These types of data have an open-ended schema that enables application data flexibility. Sometimes, this type of data is combined with structured data to record additional properties for specific types of records within a structured data store.

The open-ended schema means that semi-structured data does not rely on the application that created it to define the embedded structure. For example, an Oracle database would be considered a structured data type. The rules governing the database are bound and applied by the application that creates the file, or, in this case, the database.

With semi-structured datasets, the definitions and constraints are embedded within the file, regardless of the application that created them. For example, XML files and cascading style sheets for web pages are both forms of semi-structured data. They can be created by almost any kind of application — such as Notepad, a website builder app or an Office app like Word — so there is no way for the application to apply structure or rules to these data types.

Semi-structured data is challenging for organizations to manage because it does not necessarily have the same level of organization and predictability as structured data. It does not reside in fixed fields or records. At the same time, it does have more rigidity than unstructured data, because it does contain elements that can separate the data into various hierarchies (think of comma delimited files or tab delimited files).

Unlike structured data — which represents data as a flat table —semi-structured data can contain n-level hierarchies of nested information. This means that it can be easy to apply standard data management processes to semi-structured data, and it can be easy to extract insights from. The real issue is making sure your business has the tools and technology necessary to load the data into structured or unstructured data models, which can be managed via data governance.

In simplest terms, intelligent data governance means bringing data under control, keeping it protected and enabling access to it, to carry out the top-level business strategy. But data governance also means knowing where data originated, where it is currently located, who can access it, what it contains, and how long it should be retained. Intelligent data governance also implies that trivial data is distinguished from strategically important information.

Once data is centralized and thoughtfully managed, its true strategic potential can be unleashed. Businesses can easily identify customer needs, anticipate emerging issues, explore new business opportunities and respond to regulatory inquiries. They can optimize the costs of storing and administering these information assets, while still allowing key stakeholders in the business to leverage data for improved decision-making.

When it comes to data governance, striking an appropriate balance is key. Data of all types must be closely managed, but the organization still needs to make it accessible, supporting the high degrees of flexibility and speed that are essential in today’s fast-moving world.

The good news is that there are innovative, automated solutions that can help streamline and accelerate the process of data governance, saving your organization valuable time and costs.

Intelligent data governance provides a range of strategic benefits for the typical company, including:

• Improved decision-making. Well-governed data is easier to access and apply, which means that stakeholders across the business can make decisions based on facts, not intuition or guesswork.
• Operational efficiencies. Critical data, including performance metrics, can be used to identify and address bottlenecks and inefficiencies in the way the company works every day. Having access to accurate, current data is essential to accomplishing this.
• Improved data understanding and lineage. Understanding the “data trail” and all accountabilities for data translates into timely responses to audits, more effective early case assessment activities, and a more proactive approach to preventing data corruption and breaches.
• Regulatory compliance. Increasingly, companies need to comply with complex privacy and security regulations related to the data they manage and store. Data governance is a critical aspect of ensuring and proving organizational alignment with the rules set forth in any applicable regulatory requirements.
• Increased revenues. Confidently armed with accurate, cleansed, real-time data, businesses can make better, faster decisions that positively impact sales and operating margins.

An emerging concept, DataOps — or data operations — is enterprise-level data management for the artificial intelligence era. By implementing an overarching DataOps strategy, you can seamlessly connect your data consumers and creators, to rapidly find and use all the value in your data.

Data operations is not a product, service or solution. It’s a methodology, a technological and cultural change aimed at improving your organization’s use of data through better data quality, shorter cycle time and superior data management.

Since DataOps spans the entire cycle of gathering and applying information, it’s absolutely essential that your organization manages every type of data efficiently. By having data cleansed, well managed and immediately accessible, your DataOps initiative can be supported with the right information you need to make strategic decisions based on facts, not guesswork.

Because Hitachi Vantara has proven expertise in both DataOps and data governance, across every type of data, Hitachi is a natural partner. By instilling a data-driven culture and mindset, Hitachi can help make data a focus for your business every day.