Smarter ways to manage more data with fewer resources using the data model

Do you have a http://www.aliyun.com/zixun/aggregation/14208.html > data model? Don't you even know exactly what the data model means?

This is an important issue. Today, companies are in a more severe economic situation, forcing them to focus on operational efficiency anytime, anywhere. At the same time, there is a lot of data, and the data is growing exponentially each year. However, if you do not use a schema or business environment to define these data, most of the information resources are still largely inaccessible. In a highly competitive business environment, can you afford to overlook the serious consequences of ignoring valuable resources?

I don't think you could afford it. That's why the data model is rising.

What is the data model?

The data model is a set of logical and physical structures built on a consistent language, designed to help enterprises and IT organizations depict the data environment.

The logical model contains a large number of business entities and their relationships, both defined through a consistent business language and separate from the physical layer. The physical model contains all database implementation objects, including various data vendor constraints and characteristics. It should be able to provide enough information to build dependencies between database objects and can be used for estimating and managing purposes.

For ease of use and reference, we have divided the model into a number of subject areas. These classifications help to show the interdependence of certain elements in each subject area. For example, the following subject areas are derived from the insurance data model:

 Products
 Party
 Claims
 Reinsurance
 Policy
 Finance
 Risk
 Actuarial

In the same industry, approximately 80% of the organization requirements for data modeling are identical. The remaining 20% models must be customized to address company-specific product, geographic, and business priorities.

Today, only a handful of industry-specific models are widely used, for example, the insurance industry uses ACORD, GDV, Teradata, and Oracle models. The telecommunications industry uses ETOM (SID), Teradata, and Oracle Telecom models. In most cases, the vendor model has at least 75% similarity to industry standard models such as ACORD and SID. Physical limitations cause them to not exactly match these models, but they are consistent in principle.

Why is the data model important?

Global data is growing rapidly and IT budgets are not growing at a synchronized pace. This brutal reality means that information leaders must be more intelligent in managing data. Imagine that the CIO is actively engaged in cost-cutting activities while trying to improve data quality, reliability, and processing efficiency. CEOs are also keen to launch new products and quickly penetrate existing and emerging markets. However, it is not easy to ensure that data assets, including massive data generated through social media channels, are responsive and seamlessly integrated to provide support for these efforts. At the same time, it is also important to develop a data environment in a structured way to support streamlined integration. Another approach is to discard the business agility of small amounts of data. Companies must also enable transparent management reporting techniques to meet mandatory regulatory compliance requirements.

Failure to meet all of these requirements could mean loss of income-generating opportunities or, worse, customer dissatisfaction. To take full advantage of the enterprise business Warehouse, you need to use at least a data model. Without models, analytics and business intelligence work would be unworkable.

The more customer base, the more products we provide, the wider the coverage, and the greater the data model will do to your business. Your data is complex and is created in different formats through multiple systems, but they essentially host the same business information. You need to model similar policy data that is saved in different formats in various regions in order to share, transform, and use data in a consistent way across the enterprise.

Data models help build self-confidence and make sure your data is trusted and secure. Why not use the data model?

How do you start using data modeling?

Creating your own data model is a journey. Users can create models by consolidating existing data assets and expertise, but this approach may lack the best business practices. Pre-built industry-specific data models can be launched quickly, but require you to draw all of your existing data environments into the model that you are using. The same is true even if these models have to be trimmed and sorted to suit the environment. Most models are fully designed to meet the needs of businesses of all sizes.

You can add value to your chosen model by consolidating business considerations and nuances to make it a unique asset for your organization. Plan your development direction for your target data model and control expensive application procurement projects that do not meet enterprise standards. Models are necessary for any criteria that you might make.

Whichever approach you choose, you should bring together the most outstanding data modelers, enterprise data Architects, and business data experts to implement the project. Many of the models created in silos cannot be grouped together, thereby losing the fundamental goal of the initial build model. Users can avoid falling into this trap by hiring domain experts.

Taking some time to create your own enterprise model will yield significant rewards for all data-related activities and avoid costly errors that you cannot afford today.

Is it possible to survive without the data model? Maybe you can. But my advice is, don't do it.

Let's start modeling!