What Is Semi-Structured Data?

What Is Semi-Structured Data?

Semi-structured is made up of partially unstructured data and partially data structure created by metadata. It is an interesting intersection between the two data types and it can yield transformational insights when analyzed. A good example of semi-structured data is an X-ray. An X-ray consists of a great many pixels. The sheer volume of pixels cannot be searched, queried, and analyzed like structured data. However, X-rays, like most files, contain metadata. This “data about data” is what enables semi-structured and unstructured data to be harnessed.

Every day data is used to shape the direction of businesses, develop new business offerings, and gain a competitive advantage. In a market where businesses pursue innovation and thrive off disruption, harnessing data is integral to success.

Data is everywhere. It comes in different shapes and sizes. There are three different types of data: structured data, unstructured data, and semi-structured data.

Structured data is what has traditionally been thought of as data. It exists in predefined formats and is easily accessible by the average user. Unstructured data is the new frontier of data. It lacks a predefined format akin to endless chaos of data full of powerful insights. The middle between structured data and unstructured data is semi-structured data.

Semi-Structured Data Examples

An X-ray is just one example of unstructured data. Upon further examination, numerous examples of unstructured data can be found in everyday business operations.


Email is a great example of semi-structured data. The popularity of the Internet and the proliferation of social media has created a deluge of new data. This data comes in flexible formats collected from a vast population sample. Digital communications such as emails are a source of semi-structured data. This is because every email has:

– Subject

– “To” line

– “From” line

– Date stamp

– Time stamp

The above fields are sources of structured information. However, the text body of an email is a form of unstructured data. There is no defined format or character limit for the body of an email. Emails collected, searched, and analyzed across an enterprise can represent a powerful source of information and record-keeping. Furthermore, emails can provide data mining opportunities to:

– Analyze customer feedback

– Streamline customer support

– Target marketing initiatives

– Develop social media initiatives

– Shape strategic initiatives

Web Pages

The overwhelming popularity of the Internet has produced a lot of content. Another example of semi-structured data is web pages. Most web pages have an organization with tabs for:

– Home

– About Us

– Blog

– Services

– Contact

These tabs are easy to navigate and search thus representing structured data. However, the web pages are written in HTML containing text and data within each of these pages that have no structure. A wealth of information lies hidden in web pages across the internet. Once a business knows how to leverage digital resources, both internal and external, it will become more successful.

Unstructured Data Vs. Semi-Structured Data

The availability of semi-structured data poses the question: what is the difference between semi-structured and unstructured data? It is a grey area that leaves a lot open to interpretation. All documents, images, and other files have some form of data structure. Therefore, it is hard to distinguish where semi-structured data ends and unstructured data begins. Both semi-structured and unstructured data lack organization and rules that are present in a relational database of structured data.


As more technological advances of data analytics tools evolve, the understanding of semi-structured data and how it relates to unstructured and structured data will deepen. For now, semi-structured data remains a prolific presence on the internet capable of taking businesses forward into the future.

Pros And Cons Of Structured Data Pt:2

Pros And Cons Of Structured Data Pt:2

In today’s competitive market, technological advances are evolving at the speed of business. For a business to be competitive, it needs to innovate and change with the times. One of the most profound, recent developments has been the use of unstructured data.

Data is integral to any business. Historically, data can be of any size and shape classified into structured data and unstructured data. Most businesses have tapped into structured data and its benefits. It has a predefined format and structure that makes it easy to access for business intelligence. Examples of structured data include credit card information and Excel files.

Conversely, unstructured data is more difficult to access and analyze. It can be thought of as being in a chaotic state, an endless alphabet soup of data with hidden insights. Examples of unstructured data include social media posts and emails.

Businesses have been using structured data techniques for some time while unstructured data applications remain to be explored. As more applications are found for unstructured data, data analytics tool will evolve as well. Because unstructured data is still in nascent stages, there are distinct pros and cons to using one type of data over the other.

Pros of Structured Data

1. Ease Of Use

One of the benefits of structured data is how easily it can be used by the average user with a working understanding of the data. It enables self-service of structured data without an in-depth understanding of the complexities of the data. It must be noted that structured data is also easy to use by machine learning. The organized nature of unstructured data lends itself to analysis and manipulation.

2. Convenient Storage

Due to its predefined structure, structured data is conveniently stored in data warehouses. Data warehouses are optimized to save storage space for enterprises and to encourage easy data access. Conversely, unstructured data which is much less defined is stored in data lakes with much greater storage capacity.

3. Access To More Tools

Historically, structured data was the only option for businesses looking to quantify data. Data analytics tools and practices have been developed around structured data. While unstructured data is still in its infancy, data managers have efficient tools at their disposal to process structured data.

Cons Of Structured Data

1. Limitations On Use

Structured data is predefined in its format lending itself to be used for intended purposes. This organizational structure places limitations on its flexibility and use cases. The opposite is the case for unstructured data which has a free form format and can be repurposed for multiple use cases.

2. Limited Storage Options

Structured data is stored in data warehouses. Data warehouses have rigid rules about data storage. Any change to structured data is labor intensive requiring a lot of resources and time to update. Some businesses use cloud-based data warehouse to eliminate the need for on-prem equipment and increase scalability.


Traditionally, businesses have used structured data to gain greater insight into operations. Due to its longevity, structured data has better tools, convenient storage and is easy to use. As the predecessor to unstructured data, structured data remains the reliable and preferred source for data analytics.

Pros and Cons of Unstructured Data Pt:1

Pros and Cons of Unstructured Data Pt:1

For businesses committed to innovation, unstructured data presents a lot of opportunities. In an enterprise, data is everywhere. It comes in different shapes and sizes ready to be analyzed for business intelligence. Historically, businesses have relied on structured data for insights. The predefined format and accessibility of structured data lends itself to easy analysis. Yet 80% of enterprise data remains untapped due to its unstructured format. Enterprises did not know how to tap into unstructured data or how to leverage it for opportunity. It was mysterious and out of reach carrying within it transformational insights. As data analytics tools and technologies adapted to using unstructured data, the pros and cons of this type of data emerged.

Pros Of Unstructured Data

In the enterprise, unstructured data has advantages across architecture and business.

Limitless Use

Unstructured data does not have a defined purpose which makes it incredibly versatile. It can be used across different formats. While structured data is trapped in Excel spreadsheets of rows and columns, unstructured data can be generated across social media posts, video, audio, and free form text. This makes unstructured data beneficial for generating a greater number of use cases and applications than structured data.

Greater Insights

The power of unstructured data in delivering transformational insights is unparalleled. Because an enterprise has more unstructured data than structured data, there is more volume of data to work with. Even though unstructured data is more difficult to analyze, once it is processed, it can give a powerful competitive edge to any business.

Cheaper Storage

 Structured data is stored in data lakes which can be costly and time-consuming to access. Conversely, unstructured data is stored in data warehouses which makes it cheap to store and easy to access.

Cons Of Unstructured Data

The new adoption of unstructured data makes it prone to more unknowns and some disadvantages.

Hard To Analyze

Structured data has been used by businesses for years, it has become user-friendly. An average user with data knowledge can access and analyze it. Unstructured data is not that easy to wrangle. It needs trained data scientists and data analysts to take it from raw form and extract value from it.

Data Analytics Tools

For structured data, a user can use Excel to derive insights from it. Unstructured data cannot be managed by traditional business tools. A business looking to derive value from unstructured data needs to invest in the right data analytics tool. All data analytics tools are not created equal. Some tools have Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies that help with data analysis.

Numerous Formats

Unstructured data comes in many different formats. When analyzing diverse formats across medical records, social media posts, and emails, unstructured data may become challenging to analyze and leverage.


For the enterprise looking to succeed in a competitive market, it needs to harness the chaos of unstructured data into insights. The new adoption of unstructured data comes with advantages and disadvantages for the enterprise. While unstructured data can yield powerful insights, it is harder to analyze than structured data due to a lack of predefined format and a number of different formats. Once an enterprise selects the right data analytics tools to harness unstructured data, it will step into the future of possibilities.

What Is Unstructured Data

What Is Unstructured Data

The concept of data has changed from the traditional data of yesterday. These days, 90% of data is defined as unstructured data. Unstructured data is usually text-heavy, not easily stored, or organized in a traditional database. The free-form nature of unstructured data makes it difficult for organizations to analyze and search through it. Unlike structured data, unstructured data has not been leveraged in the marketplace until the rise of data analytics tools. Sophisticated technological tools powered by Artificial Intelligence (AI) and Natural Language Processing (NLP) liberate value from unstructured data to empower businesses and people.

In today’s hyper-connected global economy, a competitive advantage could make or break a business. For a business committed to innovation, new ways to access business intelligence are a commodity. When businesses gain new insights based on data, they did not even know they had, powerful things can happen.

Unstructured Data Examples

Unstructured data is not hard to find in daily business operations. If you think of data that does not have a recognizable structure, you have identified an example of unstructured data.


While some businesses count emails as semi-structured data because they contain: date, sender, and recipient address and subject information. Other businesses consider emails as unstructured data because of the free-form nature of text in the body of the email. Considering 269 billion emails are sent and received daily that is a lot of untapped insights being trapped in email communication.

Social Media

In 2021, social media is a ubiquitous form of communication. It has become part of a lifestyle for billions of people. For most people especially the younger generations, it has become the preferred mode for creating and sharing content. Other than personal use, social media is also used by the government and businesses in industries such as:

– Retail

– Entertainment

– Education


In 2021, there are 2.80 billion active users on Facebook, 192 million active users on Twitter, and 1.074 billion active users on Instagram. Every minute: 243,000 photos are uploaded on Facebook and 350,000 tweets are generated on Twitter.

The sheer volume of data that is being generated by social media makes it a good resource for unstructured data. It is global, instantaneous, responsive, and round-the-clock data in the form of text, images, videos, and geolocations.

Social media can be broken down into structured and unstructured data. The text in social media posts is unstructured data. Conversely, data about friendships, followers, and groups is structured. Using unstructured data for text analytics to conduct sentiment analysis can yield powerful insights into social media:

– Behavior

– Trends

– Influencers

– News

Internet Data 

The Internet is a deep source of unstructured data that is continuously updated and curated by users. Using robust data analytics tools can generate powerful insights for businesses into customer behavior and collective trends that influence consumer buying behavior and loyalty. Having these insights enables businesses to shape marketing strategies and drive revenues with actionable data rather than guesswork.

Some examples of unstructured data gathered on the internet include:

· Text files

· Photos

· Video files

· Audio files

· Webpages and blog posts

· Presentations

· Survey responses


A lot of unstructured data can be found in agreements/contracts that are used in various industries. For example, the legal industry can parse a contract and identify key legal terms that were previously inaccessible with structured data analysis. Likewise, the real estate industry benefits from instant parsing and analysis of lengthy real estate contracts.

Medical Documents 

In today’s economy, the healthcare industry is booming and making cutting-edge advances in technology and service delivery. It is an industry that generates a tremendous volume of machine and human-generated unstructured data.

Examples of machine-generated unstructured data include:

– Data collected by imaging devices

– Wearable health monitoring devices

– Biosignal data

Human-generated data includes:

– Transcripts of patient/provider conversations

– Audio of patient/provider conversations

Unstructured data in the healthcare industry presents a tremendous untapped potential for mining and analysis. The benefits of unstructured data could revolutionize patient treatment. By using AI to improve diagnostics and patient care, public health management, and medical as well as pharmaceutical research, the healthcare industry moves forward.

Business Documents

The business world is flooded with unstructured data such as:

– Emails

– Presentations

– Text

– Images

– Videos

These formats represent sources of important information repositories within the organization. However, most businesses are not using unstructured data to drive decisions. The industries that do use insights from unstructured data are market leaders. For example, financial services in particular banking might use emails to understand a consumer’s credit rating. The legal industry can utilize insights to validate a legal contract. Human resources can apply insights from unstructured data to automate the resume process and improve the applicant selection process.

For business documents to be mined successfully, natural language processing (NLP) and machine learning (ML) techniques are used to understand large volumes of documents. When unstructured data is processed automatically, it minimizes the human error of manual processing.

Structured Vs. Unstructured Data

For businesses to effectively leverage unstructured data, there needs to be a distinction made between unstructured data and structured data.


Organizational Structure

Structured data is easy to search and organize. It is stored in a defined formats while unstructured data is usually stored in an unorganized format. Structured data is organized in rows and columns so it can be mapped into predefined fields. Conversely, unstructured data does not have predefined fields and can be presented in a variety of formats.


Structured data yield quantitative insights using numbers and statistical analysis. Unstructured data yields qualitative insights from sources such as social media, customer surveys, and interviews. These sources are not in a numerical format and require more advanced analytics techniques like data mining to extract insights.


Data warehouses are used to store structured data. Data lakes store unstructured data. The difference is that a data warehouse is an endpoint for data whereas a data lake is an almost endless repository for data. Data lakes store unstructured data in its original format or after the initial cleaning process.

Ease Of Use

Structured data is easy to understand. Unstructured data requires more effort to analyze and understand. This is one of the most significant differences between structured and unstructured data. For humans and algorithms alike, structured data is easy to understand. It lends itself to analysis. On the other hand, unstructured data lacks a predefined data model, and it is challenging to deconstruct. The best practices for unstructured data are still in infancy making it challenging to analyze customer reviews, social media data, and customer communication.


Structured data comes in predefined formats. Unstructured data comes in a variety of formats. Because unstructured data is housed in a data lake that does not require transformation, it comes in a variety of shapes and sizes. Unstructured data can be in any medium such as video, audio, images, and text.

Industries That Have Unstructured Data

The nascent stages of unstructured data techniques have opened opportunities for businesses across industries. The more customer touch-points an industry has the greater the opportunity to capitalize on unstructured data insights. Businesses have leveraged Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) practices with unstructured data techniques to transform and innovate business practices.

Types Of Industries

Some of the industries that have benefitted from unstructured data include retail and financial services. The retail industry was at the forefront of using customer data to guide operations. It used customer emails, voice, images, and store records to segment marketing and influence consumer behavior. Other industries followed the example of retail to transform operations. This included real estate, legal, healthcare, marketing, and hospitality.

Financial Services

For financial services, investing in unstructured data has paid off in time savings. In finance, time is money, and the ability to make decisions faster than competitors is a competitive advantage. For example, NLP can be used to parse unstructured data in financial documents to pull out key numbers for earnings reports. Leveraging unstructured data can also help increase compliance in finance.

How Can I Find Value In Unstructured Data

As unstructured data is gaining momentum in industries and advancing in technology, many users pose the question: how can I find value in unstructured data?

To maximize the benefits of unstructured data, the user needs to choose the right data analytics tool and techniques. For example, the Content Analytics Platform (CAP) from Scion Analytics empowers users and businesses to liberate value from unstructured data. If you think of unstructured data as chaotic with free form structure, CAP takes it and transforms it into structured data so you can use the data and convert it into usable knowledge.

Data Discovery Process

In the data discovery process, CAP uses “indexing”, a process of indexing words within the tool. Another data discovery process is common concepts for one or more documents. It is when the platform analyzes a collection of documents to see what they have in common. After the data discovery process, data technologies are used to process the data.

Data Technologies

When it comes to unstructured data technologies, CAP does a parsing (breaking it apart) of unstructured data documents, normalizes the data, and then identifies key concepts, keyword phrases, and dictionary terms to find the elements that help you identify the area of the data that is important to you. Once the data is transformed into digestible content, it allows you to make decisions and perform actions with the content.

The CAP uses AI to help organize and transform the data and NLP technologies to understand textual content. NLP technology helps with the readability capability to understand the purpose of the content.


A user can customize the capabilities of the CAP to maximize the insights gleaned from unstructured data for a particular business. Previously untapped data now becomes available, previous questions now have answers and the user is empowered to make data-driven decisions. For businesses, the transformation from guesswork to actionable insights demonstrates the power of unstructured data.


Unstructured data is just beginning to dictate the future of businesses across industries as it carries more inherent value and opportunity than structured data. It is through the analysis of social media, emails, and legal contracts that businesses learn how to predict the behavior and influence buying patterns of customers. From retail to financial, businesses are adapting to change and embracing unstructured data with the right tools like CAP. This is just the beginning of what unstructured data can do.

AI In ECommerce

AI In ECommerce

Artificial Intelligence (AI) has shaped, changed, and accelerated the world forward into the future. While it has many sweeping and generalized effects on society, it is interesting how AI has transformed the industry of eCommerce. A relatively new industry that is quick to adopt new technologies and innovate to inspire customer loyalty. Since 2018, there has been a 186% increase in AI adoption across industries. More findings indicate that 79% of businesses attribute revenue increases to marketing and sales AI. A powerful predictor that AI technologies are going to revolutionize a diverse array of industry sectors.

Benefits Of AI In ECommerce

The adoption of AI has touched all corners of the eCommerce industry from customer support to manufacturing having a profound effect on efficiency and productivity. Also, eCommerce has capitalized on AI in its capacity for data analysis. For businesses, the capability to analyze data especially on a granular level increases personalization in sales and marketing. Personalization is a powerful tool for revenue generation. Businesses can segment their audience, a tactic that increases response rates and engagement with advertising. 72% of customers report that they only engage with personalized messaging, one that is more likely to ensure their loyalty to a business.

ECommerce And Sentiment Analysis

Sentiment analysis or opinion mining is an interesting application of a subset of AI called natural language processing (NLP). Businesses use this practice to analyze large sets of consumer text data to understand the emotional sentiment behind conversations. A powerful example of sentiment analysis in action is an eCommerce business listening to social media comments or customer reviews to determine how a target audience feels about a specific product. By analyzing thousands of consumer reviews at once, a brand can understand on a deep level how consumers feel about their product offering.

This enables brands to better position themselves in the market by being responsive to the needs and desires of their customers. Also, sentiment analysis can help brands understand how to improve products, pricing, marketing, and sales campaigns as well as customer service. In eCommerce, an industry that is extremely customer-driven sentiment analysis can be a valuable tool for data-driven insights into customer behavior.

Customer Service Chatbots

Excellent customer service and service delivery are essential in the eCommerce industry. Research has found that 46% of eCommerce businesses are using AI-driven chatbots to deliver consistent quality customer service. A chatbot can ask and answer questions from customers. It can act as a personalized virtual assistant that improves the customer journey. Personalized attention from chatbots goes a long way as far as improving profits. For example, the luxury brand Burberry uses a Facebook Messenger chatbot to answer questions 24/7 and check for product availability which improves customer retention and satisfaction.


In the world of eCommerce, using AI to improve customer service via sentiment analysis and customer service chatbots can serve as a competitive advantage. In an industry that is highly sensitive to customer demands and preferences, the capabilities of AI ensure that an eCommerce business can make data-driven actionable decisions.

Digital Transformation In Financial Services

Digital Transformation In Financial Services

In today’s hyper-connected, global economy, digital transformation has been embraced by businesses across industries. This trend especially became relevant in the wake of the 2020 COVID-19 pandemic when customers migrated online, and businesses had to cut costs to operate.

One industry that has adapted well to digitization is financial services.

The Financial Services Industry Overview

This is because the financial services ecosystem lends itself to digital initiatives. It is a highly regulated and complex industry reliant on the customer experience to drive revenue. Digital transformation provides the financial services industry an opportunity to differentiate customer experience and transform it into a digital experience. It enables banks, insurers, or fintech to provide what customers want, when customers want it and how customers want it.

Such instant service delivery makes businesses more competitive in a sector where innovation is hot. The financial services industry was once comprised of a few well-known institutions that had significant resources to innovate. Now, new businesses of different sizes and services are entering the market.

Barriers In Financial Services To Innovation

This creates an interesting dynamic. Smaller startups are more agile and able to quickly pivot in response to market trends. But the middle-sized companies neither have the resources of the larger counterparts nor the agility of the smaller companies to be responsive. A barrier to innovation forces the industry to become more creative in finding digital transformation solutions.

The Adoption of Digital Transformation In Financial Services

As the market landscape shifts, financial services companies are in the process of developing or implementing a digital transformation strategy. Research has found that nearly 97% of financial services firms are actively pursuing digital transformation. In the big picture, 68% of financial services firms are committed to digital transformation v 63% of overall businesses across industries.

A closer look reveals that only 23% of financial services firms view themselves as “advanced” in digital transformation compared to 35% of all organizations. This indicates that the financial industry is hyper aware of the challenges and benefits of digital transformation on the road to adoption. Middle-sized financial services firms stand to gain the most benefits from digital transformation as they can scale up to the big players.

3 Benefits Of Digital Transformation For Financial Services

1. Better Customer Experience

During the pandemic, customer trends, and buying trends shifted online and businesses had to respond. Now, financial service companies are competing with other brands for more than just customer service, but an overall customer experience. The customer journey from start to finish is critical in driving revenue and building brand equity. It can be compared to the way customers had a personal relationship with a bank in the past. The same level of care and personalization needs to be expressed online as part of a successful digital transformation.

2. Improved Employee Experience

As much as customer experience is a driver for financial services firms, part of successful digital transformation is an improved employee experience. Robotic Process Automation (RPA) can be a part of the digital transformation. Banks and other customer-facing services are embracing automation such as virtual assistants and bots for service delivery. This enables employees to focus on higher-value tasks and to be relieved from mundane and repetitive tasks. The increase in employee satisfaction and the buy-in of employees is what financial services firms need to execute digital transformation initiatives.

3. Increase In ROI 

For financial services firms, a better customer experience and more operational efficiency led to an increase in ROI. This industry can see even more profitability than other sectors. Some middle-market financial services companies can anticipate a revenue increase of 10% in the first few years of adoption.


In the financial services industry, the road to digital transformation is complicated by changing market demands, customer preferences, and technology. By being responsive and focusing on the customer experience, financial service firms can digitize, increase revenues, and drive innovation.

What Is Content Analytics?

What Is Content Analytics?

Content analytics is the practice of applying business analytics and business intelligence to digital content.

Since the internet embraced content as king, the content transformation has been top-of-mind for enterprises globally. The goal for the digitization of content is to make it more digestible, valuable, and engaging for readers. For content transformation to be successful, an enterprise needs to include content analytics to assess the performance of digital content.

It includes analyzing:

– content patterns

– content trends

– key performance indicators (KPI)

– valuable insights

Research has shown that 75% of enterprises believe that content analytics is correlated with powerful business insights. Insights that can truly take a business inside the mindset of customers and what they are most interested in reading about.

Businesses engage with different types of content such as:

– Blog posts

– White papers

– Social media

– Customer support (text and audio)

– News sites

These can indicate behavioral and consumer trends that drive revenue and organizational growth.

Content Analytics Benefits

Reader Engagement Tracking

Because content analytics tools are more precise and reliable in measuring engagement than web analytics tools. These tools measure when readers are actively engaged with content, with or without subsequent action. It can get as granular as being able to track when a reader opens a page v. engages with a page. Having such useful metrics enables the enterprise to truly understand the impact of their content and how to shape the content strategy to be the most effective.

Actionable Metrics and KPIs

The benefit of the content analytics tool is access to insights about how content performs across multiple platforms or how long readers are actively consuming content. There is a wide range of metrics that are more precise than web analytics tools in giving marketers an in-depth look at digital content. This can inform the overarching content transformation because it enables marketers to identify and focus on top-performing content.

Real-Time Decision Making

The advantages of content analytics for an enterprise are actionable insights and real-time decision-making. Having a large data set is effective for making strategic decisions about which content to create, promote and edit. It gives the enterprise a real-time pulse on content to focus on the most ROI per page.

Content Analytics Structure

1. Choose Content To Analyze

Select the content to analyze based on:

– Medium and genre

– Specific criteria (ex website for a specific type of product)

– Dates and location of content

2. Define Parameters Of Analysis

Once the content for analysis has been chosen, it is time to define the parameters such as:

Units of meaning

Repetition of words and phrases, themes and concepts, people, and images

Set of categories

Objective or conceptual characteristics for analysis.

3. Figure Out Rules For Coding

For analysis to be effective, content needs to be coded consistently. This involves organizing units of meaning into defined categories, what will and will not be included.

4. Code The Text Based On The Rules

After coding rules are in place, marketers must go through the research sample and code it.

5. Look At Results And Actionable Insights

After coding is done, marketers can examine data to find patterns and draw conclusions. An experienced marketer can use statistics to find correlations and trends in the content.


As part of content transformation, content analytics can be an effective tool in analyzing and measuring content that performs well over time. By using content analytics tools and a structure, marketers can glean actionable insights and data from digital content that moves the business forward.

Digital Transformation Guide

Digital Transformation Guide

As the world becomes more connected, businesses are facing a new era of digitization. Digital transformation is ubiquitous, a process that uses digital technologies to change or update business processes, systems, and customer experiences to keep up with changing market conditions.

To understand digital transformation is to take a big picture snapshot of a business. It is a process that transcends typical organizational roles like sales, marketing, and customer service to reimagine and redefine the business. It is the evolution from analog to digital, from paper to spreadsheets to apps to disrupt how business is done.

Steps In A Digital Transformation

1. Define The Problem And Be Clear About Priorities

In pursuit of digital transformation, businesses have an opportunity to examine current and legacy processes and reinvent them with digital tools. By spending time with employees from different departments, a business can get a holistic idea about the challenges and problems of existing processes. While personalization of customer experience leads to more customer loyalty and retention that is just the tip of the iceberg of digital transformation.

In preparation for digitization, businesses need to consider employee experience and harness the power of Artificial Intelligence (AI) and automation for true digital transformation. As those priorities are set, the business can outline the metrics of what successful digital transformation entails.

2. Outline Metrics Of Successful Digital Transformation

Once the business has identified which problems to tackle with digital transformation, it must set the metrics of what a successful digital transformation will look like for the organization. This depends on the goals of an organization and the scope of the digital transformation.

For some businesses, digital transformation will be limited to:

– New business unit

– Department

– Region

– Product line

For other businesses, digital transformation becomes an enterprise-wide initiative that delivers positive results over time. Often, digital transformation is a lengthy process that happens over years with cumulative results. Therefore, any digital transformation initiatives needs to show progress metrics over time.

3. Designate A Leader, Promote Company-Wide Buy-In

The foundation for success in digital transformation is picking the right people in the business to share the “big picture” vision for the initiative. The business needs to pick a leader, ideally from the C-suite that will oversee the initiative. It is also imperative that individuals across the organization are educated in the benefits of digital transformation. For some business departments, immediate gains of digital transformation may not be tangible. Therefore, the leader needs to instill confidence and a timeline across the organization for digital transformation.

4. Embrace Flexibility

For most businesses, the road to digital transformation is filled with uncertainty. That is why it is important to embrace flexibility in the process.

External factors:

– shifting market dynamics

– new trends in customer behaviors

– disruptive competitors

– advancements in AI technology

By encouraging employees to share about difficulties and project fluctuations, the business ensures its survival and thriving in the face of change.

5. Carefully Choose Digital Transformation Tools

Optimal digital transformation happens when the right mix of people, processes, and technologies comes together to innovate a business. A business must choose the right digital transformation tools or software. As part of the set-up, a business needs to consider whether to rely on a single vendor or multiple vendors for a solution. Other considerations include utilizing a digital transformation software that is new on the market or a tried-and-true solution. These decisions become integral in executing a digital transformation that is right for the business.

What Technologies Are Used In A Digital Transformation

Fundamentally, digital transformation is driven by advancements in technologies that disrupt business practices. These technologies may include 

Content Transformation Example

Likewise, content transformation is at the core of a successful digital transformation. This is because content plays a critical role in the digital customer experience. A business cannot achieve digital transformation without looking at the content. The goal of content transformation is to make relevant and engaging content accessible to customers anywhere at any time. When a business chooses the right solution to transform legacy content into digital content it enhances business operations. Optimized content serves as a sales and marketing tool for attracting customers and positioning the business in the marketplace.

Digital Transformation Benefits

Once a business has taken the steps to digital transformation it can experience the benefits. Some benefits will be more immediate than others. However, in modern business digital transformation has become more of a requirement than an option for being competitive.

Digital transformation benefits include:

Optimized For Mobile

These days customers spend a lot of time online but the way that they hang out online has changed. Increasingly, customers are tied to smartphones. Businesses need to reflect these trends with increased mobility. A focus on the end-user experience with mobile platforms is the way to:

o Enhance operational efficiencies

o Improve satisfaction rates

o Retain customers

o Learn about customer behavior

o Attract new customers

Data Security

An effective digital transformation strategy is committed to data security. Businesses need to protect user-information collected from connected devices and big data. These large quantities of sensitive information may be subject to privacy laws which need to be considered in the transformation. A business that has secure data policies in place gives confidence and consideration to customers who share sensitive information online.

Better User Experience

Part of successful digital transformation includes consideration for user experience as well as customer experience. As more work tasks are being automated, users are becoming more discerning about technology. The business needs to focus on simple interfaces and intuitive interactions to make work easier for employees. In return, employees are more productive and engaged in the workplace.

Digitization Of Customer Experience

At the core of digital transformation is a technology and the purpose of this initiative is an enhanced customer experience. In an increasingly competitive marketplace, the leverage of digital technology is the key to:

– better decision-making

– smarter efficiencies

– paradigm shift in customer experience

– Efficient processes

– Greater productivity

– Better customer and employee experience

– Manage business risks

– Control and reduce costs

New Insights To Drive Business

Digital transformation disrupts business processes and creates new ways of doing business. Part of the interaction of the right processes, people, and technology is responsible for new insights for business leaders and employees. Business leaders can shape the direction and the trajectory of the business as it enters new markets and develops new product lines. Employees can be trained and up-skilled on incorporating new technologies into their tasks. Digital transformation creates a win-win for the business.

The Future Of Digital Transformation

The rapid advancement of technology has changed the way business is done. The days of analog and “pushing paper” are gone as new technologies such as RPA and content transformation emerge to drive business processes forward.

A business that embraces digital transformation is undertaking a project that will create a paradigm shift in operations. For some businesses, it is the insight into the customer experience and the opportunity for personalization that leads them to take the steps toward digital transformation. Other businesses, seek out the positive effects of digital transformation to gain a competitive advantage in the marketplace which has become increasingly hostile to obsolete business practices.

The businesses that remain flexible and vigilant about internal processes are the ones that see the most transformational results. These businesses gain new insights that breed innovation and birth new ideas and solutions. These are the businesses that push the marketplace forward, gain competitive advantage, and shape the future.

As digital transformation continues to evolve with technology, it will be exciting to see how businesses transform into the future.

What Is A Content Transformation

What Is A Content Transformation

As businesses evolve globally, content transformation is used to give access to dynamic and relevant content to customers anywhere at any time. Content transformation is the seamless transfer between physical and digital media of content, often legacy stored in business repositories.

Technological innovation has changed the way people access and interact with information daily. The days of paper trails are in the past and today’s audience expects content that is:

– Personalized

– Omnichannel delivery

– Available in any format/language

– Instantaneous

The demands of the audience have placed user experience at the core of digital transformation and therefore content transformation efforts. For businesses, it is not enough to transform into digital formats for the mere understanding of “content”.

True content transformation happens at a “context” level for businesses. Technology tools like content analytics platforms are used in content transformation to adjust, change, and dramatically improve content.

Content Transformation Benefits

Given the importance of user experience in content transformation, it is important to understand how unstructured data provides unprecedented insight into a user’s mindsets.

Converting Unstructured Data To Knowledge 

Intrinsically, data has no value until you understand it. When you understand it, it becomes knowledge that can be used in action. Content analytics takes information and converts it to knowledge. For example, a content analytics platform can take a large paper document and convert it to knowledge by identifying keywords or dictionary terms.

Prioritizing Information

By adding weight to dictionary terms, it tells you how important the content is in the document. Once the document is broken down into manageable pieces, the reader can gain more insight into the text. It reduces the complexity of the content which improves the ROI of the content.

Content that performs well is ready for content transformation. The ultimate form of content transformation uses a combination of machine conversion and human expertise.

Content Risk Analysis

Another use case for the benefit of content analytics platform in content transformation is risk analysis:

– A user can create a dictionary of risk terms and weigh them to compare multiple agreements for which one has the lowest risk.

– By assessing risk, a business can mitigate difficult risks that pose a financial or legal threat.

– The benefit of having weighted risk is that it reduces the level of subjectivity in the assessment.

Decision Making

When a business needs the content to do more but not cost more it invests in content transformation.

For example, for knowledge insights, you can use a compliance matrix to divide the knowledge up and give it to people to address those areas.

Decision Making Example:

– Take an RFP for building a ship, there are different components of a ship that need to be put together to make a complete vessel.

– The scenario can be used to have more decision-making with the shipbuilding process.

– What are the risks with these requirements as far as legal and engineering for tolerances and backup systems?

– Using content analytics helps you make decisions in your approach and avoid making costly mistakes.

The Content Transformation Process


Content transformation capabilities are distinct in the two areas of knowledge and decision-making. There are differences when it comes to the players in the game. Decision-making is typically relegated to the middle to upper management and knowledge is made at the director level. In small companies, upper management does both knowledge mining and decision-making.


Once the right person for content transformation is identified, the right tools are chosen for services. For example, in the Content Analytics Platform (CAP), the QC Readability capability is helpful in content transformation. It helps you identify an area of the document that is poorly written and guides how to rewrite them for clarity.

Document differences, another capability of the CAP compares the differences between two pieces of content. A useful capability when you are dealing with legacy documents and multiple versions or drafts of documents as part of a larger digital transformation initiative.

Content Transformation Vs. Digital Transformation

Digital transformation vs. content transformation is to think of digital transformation as unlocking the potential of a business and content transformation as unlocking the potential of content. At the core of a successful digital transformation is an effective content transformation practice.

Digital Transformation

Digital transformation is a process that happens over several years in a business. It happens by using the right tools and targeting the right content that is useful and relevant to the customers. A business leverages content transformation as a part of the overall digital transformation strategy.

Digital Transformation Benefits

As a result of a successful digital transformation, a business can have:

– Better sales

– Better services

– Improved customer experience

– Better personalization

What Industries Utilize Content Transformation

Content transformation can be used by any industry that has a collection of unstructured data and normalizes it into a structure that provides either knowledge or decision making. Thousands and thousands of business areas can innovate using content transformation to take legacy content and make it more digestible and relevant to the customer. By targeting the right audience, these industries from marketing to tech can sustain long-term growth, become more user-centric and encourage innovation.

The Future of Content Analytics

In today’s competitive business landscape, content transformation is a necessary component of an effective digital transformation. Digital transformation is the bridge between legacy content and the future of doing business.

As the future of content analytics continually grows in terms of insights into knowledge so will the power of content transformation. In the future, decision-making will be partly automated and partly reliant on human beings.

It will be a part of a different workflow process for businesses that enhance human capabilities and cognition with technology. As a result of content transformation, in the future, the content will become more customized and responsive to the audience. It will anticipate their needs with data-driven insights and hyper-personalize the message to enhance the relationship between the business and its customers.

The CAP and Customization

At Scion Analytics, the CAP is at the forefront of the content analytics industry with customization capabilities. For example, a user can customize the output of the CAP when an artifact is being generated with tasks or applications.

This capability is unique to the CAP in the industry and has many benefits for business insights. Rather than a static output, the CAP gives the user the ability to define what they want to do. A single user with the ability to customize content analytics output contributes to building uniformity and consistency in teams. Having uniform teams contributes to the enterprise-wide success in improving systems and processes needed to scale.

Will Artificial Intelligence Replace Creativity?

Will Artificial Intelligence Replace Creativity?

The new age of technology has swept across offices all over the world. Words that were once foreign to our collective human dialect became familiar: Artificial Intelligence (AI), machine learning (ML), unstructured data, text analytics, and big data. These words carried power within them to change the pace of humanity, one computer at a time. The human mind has prevailed with advancements in technology and innovation that light up and transform industries. But can AI replace genuine creativity? Where is the line between human ingenuity and the programming of a machine?

What Is AI?

It was not long ago that AI was a concept in science fiction novels. These days AI especially capabilities like machine learning power automation. It takes vast amounts of data and processing power to generate analysis that would take humans much longer to achieve. AI has proliferated into various domains of human existence, from Spotify playlists to scientific research on cancer and COVID 19. For example, traditionally scientists have been mapping complex proteins involved in cancer research. It is a time-consuming and costly process for individual proteins out of a constellation of 200 million proteins. Scientists assigned the task of unfolding proteins to an AI program and what took humans decades to accomplish was instantly done by machines. This is the height that AI influence on scientific research that moves society forward.

What Is Creativity?

The newness of AI is juxtaposed with the centuries of history shaped by creativity. Creativity is an interesting concept shaped by the elusive nature of the human imagination. Creativity is the ability to mix original and unoriginal ideas in an inventive way. The mind remains on a physical plane but do human ideas? Leading neuroscientists have proven that the mind is part of the physical world and can be modeled in the virtual world. Technically, there is no aspect of human cognition that cannot be replicated by a machine. This raises the question of ethical AI and how much of human cognition should be gifted to machines?

How is creativity shaped by AI? AI can enable humans to discover and explore new possibilities. Analysis of large data sets, especially unstructured data can yield valuable insights into consumer behavior. Insights that influence how a product or service is marketed or sold at large, shape economic trends.

Artificial Intelligence Vs. Creativity

If machines can essentially replicate the human mind and alter the course of society, can AI influence creativity? Are sculptures, novels, paintings, and advertising enhanced by the application of AI? One argument is that the presence of AI frees up the human mind for greater pursuits. While machines automate mundane and repetitive processes, humans are left to create works of art.

Another and perhaps more compelling argument is the intangible nature of creativity. While AI can replicate human cognition, can it infuse the passion and imagination needed for the creative process? Creativity is part whimsy and part judgment. The human judgment that is collected over a lifetime of understanding one’s uniqueness and how it combines with the uniqueness of others.


The human experience, flawed and ephemeral, is reflected in the creative process. While advancements in AI are beneficial for economics and business, they are not nuanced enough for the human spirit. It remains mysterious to technology and replication.