Ontotext>Knowledge Hub> What is the DIKW Pyramid?
Knowledge Pyramid, Wisdom Hierarchy and Information Hierarchy are some of the names referring to the popular representation of the relationships between data, information, knowledge and wisdom in the Data, Information, Knowledge, Wisdom (DIKW) Pyramid.
知识金字塔（Knowledge Pyramid）、智慧层级（Wisdom Hierarchy）和信息层级（Information Hierarchy）都是对数据（Data）、信息（Information）、知识（Knowledge）和智慧（Wisdom）之间关系的经典表述的不同叫法。
Like other hierarchy models, the Knowledge Pyramid has rigidly set building blocks – data comes first, information is next, then knowledge follows and finally wisdom is on the top.
与其他层级模型一样，知识金字塔有着严格固定的构建顺序——数据在最底层，信息紧随其后，再往上是知识，而智慧则位于金字塔的顶端。
Each step up the pyramid answers questions about the initial data and adds value to it. The more questions we answer, the higher we move up the pyramid. In other words, the more we enrich our data with meaning and context, the more knowledge and insights we get out of it. At the top of the pyramid, we have turned the knowledge and insights into a learning experience that guides our actions.
每向金字塔上走一层，都在回答关于原始数据的问题，并为其增加价值。我们能回答的问题越多，在金字塔上攀登得就越高。
➊ DIKW模型：数据到信息到知识智慧
➋ 数据是基础：原始事实需要上下文
➌ 层次递进：每层增加价值回问题
How to Scale Data Up the Knowledge Pyramid
如何将数据沿知识金字塔向上扩展
So, let’s have a look at the individual components of the Knowledge Pyramid and how we move from one to the next.
接下来，我们逐一看看知识金字塔的各个组成部分，以及如何从一个层级过渡到下一个层级。
Data
数据（Data）
Data is a collection of facts in a raw or unorganized form such as numbers or characters.
数据是以原始或未组织的形式存在的事实集合，例如数字或字符。
However, without context, data can mean little. For example, 12012012 is just a sequence of numbers without apparent importance. But if we view it in the context of ‘this is a date’, we can easily recognize 12th of January, 2012. By adding context and value to the numbers, they now have more meaning.
然而，缺乏上下文的数据可能意义甚微。例如，12012012 不过是一串没有明显重要性的数字序列。
In this way, we have transformed the raw sequence of numbers into
这样一来，我们就将原始的数字序列转化成了
Information
信息（Information）
Information is the next building block of the DIKW Pyramid. This is data that has been “cleaned” of errors and further processed in a way that makes it easier to measure, visualize and analyze for a specific purpose.
信息（Information）是 DIKW 金字塔的下一个构建块。这是经过「清洗」去除错误，并进一步处理、组织和结构化后的数据，使其能够用于特定目的。
Depending on this purpose, data processing can involve different operations such as combining different sets of data (aggregation), ensuring that the collected data is relevant and accurate (validation), etc. For example, we can organize our data in a way that exposes relationships between various seemingly disparate and disconnected data points. More specifically, we can analyze the Dow Jones index performance by creating a graph of data points for a particular period of time, based on the of data at each day’s closing.
根据不同的目的，数据处理可以涉及不同的操作，例如合并不同的数据集（聚合）、执行数学运算，或处理缺失值。
By asking relevant questions about ‘who’, ‘what’, ‘when’, ‘where’, etc., we can derive valuable information from the data and make it more useful for us.
通过提出关于「谁」、「什么」、「何时」、「何地」等相关问题，我们可以从数据中提炼出有价值的信息。
But when we get to the question of ‘how’, this is what makes the leap from information to
但当我们触及「如何」这个问题时，这正是从信息跃升到
Knowledge
知识（Knowledge）
“How” is the information, derived from the collected data, relevant to our goals? “How” are the pieces of this information connected to other pieces to add more meaning and value? And, maybe most importantly, “how” can we apply the information to achieve our goal?
「如何」让我们理解：从收集的数据中推导出的信息，如何与我们的目标相关？「如何」将这些信息片段连接起来，帮助我们看清全局？
When we don’t just view information as a description of collected facts, but also understand how to apply it to achieve our goals, we turn it into knowledge. This knowledge is often the edge that enterprises have over their competitors. As we uncover relationships that are not explicitly stated as information, we get deeper insights that take us higher up the DIKW pyramid.
当我们不再仅仅将信息视为对收集到的事实的描述，而是理解如何应用它来实现目标时，我们就获得了知识（Knowledge）。
But only when we use the knowledge and insights gained from the information to take proactive decisions, we can say that we have reached the final – ‘wisdom’ – step of the Knowledge Pyramid.
但只有当我们利用从信息中获得的知识和洞察来主动做出决策时，我们才能说已经从信息阶段攀升到了知识阶段。
Wisdom
智慧（Wisdom）
Wisdom is the top of the DIKW hierarchy and to get there, we must answer questions such as ‘why do something’ and ‘what is best’. In other words, wisdom is knowledge applied in action.
智慧（Wisdom）位于 DIKW 层级的顶端，要达到这一层，我们必须回答诸如「为什么要做」和「长期来看什么是最好的选择」等问题。
We can also say that, if data and information are like a look back to the past, knowledge and wisdom are associated with what we do now and what we want to achieve in the future.
我们也可以说，如果说数据和信息是回望过去，那么知识和智慧则与展望未来相关联。
How Enterprises and Organizations Move Up the Knowledge Pyramid
企业如何沿知识金字塔向上迈进
One easy and fast way for enterprises to take the steps from data to information to knowledge and wisdom is to use Semantic Technologies such as Linked Data and Semantic Graph Databases. These technologies can create links between disparate and heterogeneous data and infer new knowledge out of existing facts.
企业从数据到信息再到知识和智慧，一个简单而快速的途径是利用语义技术（Semantic Technology），通过形式化语义模型来连接和描述数据。
Armed with this new knowledge, enterprises can climb up the mountain of wisdomand gain a competitive advantage by supporting their business decisions with data-driven analytics.
掌握了这些新知识后，企业能够攀登智慧之山，通过支持数据驱动决策、提升效率和加强创新来获得竞争优势。
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