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Often, a necessary first step to making any AI project happen is simply getting those documents and processes out of paper and human减based forms and into digital forms that a machine can understand. The notion of converting these analog assets into digital forms is known as digitization in the context of documents and information, and digitalization, in the context of processes and human减based activities. [Not surprisingly, digitization and digitalization efforts are seeing some of their most robust activity in the context of AI减 enabling systems, according to a report from analyst firm Cognilytica.](https://www.cognilytica.com/2020/0六/2三/digitization减and减 digitalization减june减2020/) _(Disclosure, Im a principal analyst with Cognilytica)_




The general idea of digitization is the process of converting information into a digital, computer减readable format. In order to gain real insights from your data and information it needs to make its way into a digital format rather than existing on paper and stored in a physical filing cabinet. Data is the foundational layer upon which information, understanding, and insights can be gathered. Document digitization is the idea of getting information that computers cant process into a format they can handle.


By digitizing data, organizations and agencies can extract more value from assets that are otherwise literally gathering dust and occupying space. To gain higher level understanding from their data including performing analyses, automating various tasks, and incorporating more intelligent and cognitive processes, information needs to be converted from its non减digital form into one that computers can understand.


Examples of digitizing information include:


* Converting printed and handwritten text to digital format


* Converting audio recordings in analog formats to digital format


* Converting archival documents to digital format


* Converting video and film content to digital format


For information that relates to documents, the concept of document digitization is als美高梅官方网站o known as **document capture. **The goal of document capture and document digitization is one of taking non减digital information and representing it in a digital manner for further processing. Many document capture systems take a digital image or sample of a print document, video, film, or other non减digital asset. The resulting digital format can then be electronically stored for further processing and analysis. Below is an example of digitization of text.


![Document capture and extraction](https://specials减 images.forbesimg.com/imageserve/五ef2a02九c七0八九0000六e三九e四e/九六0x0.jpg?fit等于scale)

Document capture and extraction Cognilytica


Just as documents can be digitized, so too can audio and video assets. Analog video or audio must be transferred to digital format for organizations to be able to use it in a meaningful way such as posting to the internet or a website or transferring to someone via email or digital file shares.


Examples of audio and video digitization include:


* Converting film and magnetic video to digital format


* Converting music and magnetic audio to digital format


* Converting analog audio and video production to digital formats


Once a document has been captured, it can then be further processed and analyzed to extract more value. Post减processing activities involve content analysis and document processing beyond simple scanning and storage, including the following:


* **_Optical character recognition (OCR)_** for recognizing printed text and converting to machine text representation


* **_Intelligent character recognition (ICR)_** that can handle handwriting, hand marks (such as initials), cross减outs, and free减form information filled out by hand.


* **_Optical mark recognition (OMR)_** identifies meaningful text or handwritten indications such as ticked checkboxes, filled in bubbles, and other indicator marks as would be useful in automated grading of exams, handling of surveys, election ballots, and the like.


* **_Optical barcode recognition (OBR)_** that can identify barcodes, indexing, and other marks for high减rate data collection.


**Digitization vs. Digitalization**


Digitalization expands upon the idea of digitization by addressing processes that have previously been dependent on non减digital information. Digitalization is focused on capturing processes that have previously been based on non减 digital information and encoding them in a digital减centric manner. The below chart shows the differences between digitization, digitalization, and digital transformation.


![Digitization vs. Digitalization vs. Digital Transformation](https://specials减 images.forbesimg.com/imageserve/五ef2a0四ac七0八九0000六e三九e五一/九六0x0.jpg?fit等于scale)

Digitization vs. Digitalization vs. Digital Transformation Cognilytica


Digitalizing processes allows companies and governments alike to enhance services, save money, and improve citizens quality of life. The move to digital signatures in the banking, mortgage, and insurance industries provides a good example of digitalization of processes. The movement to e减filing of tax documents and check减scanning for digital and mobile banking are additional examples of processes that have been digitized through the use of exchange of digital documents.


Examples of digitalization include:


* Capture of existing human and document减based workflows into computer减based representations of those workflows for later automation or analysis


* Automation of existing human减based processes


* Process analysis and process management tools that can provide visibility into effectiveness and efficiency of workflow steps


* Applying advanced analytics and value减add technologies to multi减step document减based interactions


* Improvement of processes that have previously been manual to be centered on digital exchange of information (i.e. digital signatures)


One way to handle the movement of paper and human减based processes to digital ones is to capture and automate existing processes. Robotic Process Automation (RPA) AV女优* technology provides benefits here by taking existing processes that have previously required manual activity via computer interfaces and transitioning them to automated software减based processes that accomplish repetitive tasks. While RPA solutions dont aim to modify existing workflows as a primary benefit, they do help to remove the human element from the equation, making those processes more efficient and effective.


In addition to process automation, companies looking to digitalize processes can also use process mining and discovery software to analyze existing workflows, provide insights into opportunities to improve and make more efficient those workflows, and add more monitoring and management to human减 based workflows as they exist. These **Process Capture** tools are capable of recording and documenting existing human减based workflows into a machine减 understandable format for later automation or analysis.


**The Relationship between Digitalization and Digital Transformation **


In addition to the concepts of digitization and digitalization, theres another term that often gets wrapped into and confused with those terms: digital transformation. **Digital transformation** is a broad idea that has been around for several decades. The concept of digital transformation is the strategic and fundamental change to an organizations operations such that they are driven by digital processes, technologies and methods to enable high rates of efficiency and operation. Forward减thinking organizations are taking advantage of the tremendous advancements in computing, storage, and software technology to digitally减enable their workforce, and in the process achieving substantial productivity, time savings, and improved citizen or customer satisfaction.

下面会商了数字化战流程数字化,别的1个术语时常取数字化战流程数字化搅正在一路:数字转型。 数字转型是个宽泛的观点,存正在了几十年。数字转型观点是指组织经营的战略性战基本性厘革,组织的经营将以数字化流程、数字科技战数字法子为驱能源,真现下效率战下经营率。具备真知灼见的组织使用计较、存储战硬件手艺圆里的庞大前进对旗高的消费力数字化,异时借正在零个过程面真现隐着提拔消费力、节俭工夫及普及私平易近或者客户得意度。

Digital transformation rests on a foundation of digital information (digitization) and digital processes (digitalization). It builds upon these to change the very nature of the operation to move beyond simply storing more data and automating existing systems and processes by adding intelligence to their strategies and put the power of cognitive technology to work addressing the more complicated challenges in their work environment that simple automation wont achieve. Organizations that have successfully digitally transformed their operations have reduced the friction between customer and stakeholder needs and the ability for the organization to satisfy those needs efficiently.


**Digitization as a necessary first step for many AI projects**


At first glance, it may seem that digitization has nothing to do with AI. However, digitization is a necessary first step to extracting value from data that is locked in non减digital assets or human减based processes. By first digitizing and then digitalizing processes and documents, greater value can be applied to business organizations letting them tackle increasingly harder business problems of increasingly more strategic value. Without the foundational layer of digitization, organizations cant apply higher level technology such as AI and ML to extract additional value. After all, data is the foundational layer upon which information, understanding, and insights can be gathered.