Data Mining Vs. Process Mining

Data Mining Vs. Process Mining

Data mining is a part of Business Intelligence (BI) that seeks to understand relationships and patterns in large datasets found in big data. Big data is a term referring to massive databases of both structured and unstructured static information that can be exploited for business intelligence needs. This data can have the potential for improving business operations. The data is mined from such sources as emails, data storage, phones, applications, and databases. This data is mined, processed, and analyzed. Companies gain insights from these harvested data.

Process mining seeks to understand real-time procedure steps to detect inefficiencies or make improvements in the accomplishing of a business task. Process mining is the analyzing and monitoring of business processes. Data is gathered through, or mined from, corporate information systems which displays the actual process. It does this by capturing a time-stamp and an event log of each of the process steps. The process mining is accomplished by using strong algorithms combined with advanced data transformation enabling the discovery and improvement of the business processes.

Similarities

There are similarities between data mining and process mining. Both are a subset of business intelligence (BI) and both access large volumes of data to achieve information for action. Both use algorithms to obtain hidden patterns and relationships within the data.

Differences

Data Mining Finds Static Data

Data mining is static and used by corporations to analyze big datasets to predict business patterns. The data analyzed is harvested from static datasets such as databases, which are available records. It looks for things like what group of consumers will buy what product, or where does a marketing effort have the greatest impact. Data mining has no concern with business processes.

Process Mining Finds Dynamic Data

Process mining is dynamic and gathers needed information from created actions. It can be from real-time events provided through a live feed. It looks for steps that are inefficient or time-consuming to control and improve those steps. It reveals a true, end-to-end process.

Data Mining Looks At Arbitrary Data

Data mining obtains information from what happens to be available. Data mining arbitrarily gains information from large databases without targeting a specific inquiry.

Process Mining Looks At Real-Time Data

Process mining targets a specific question about a process. Process mining gets current activity.

Data Mining Looks At Results

Data mining can only look at the results of available data. It cannot answer how those data came to be.

Process Mining Looks At Causes

Process mining can see the cause of actions.

Data Mining Analyzes Patterns

Mainstream patterns are analyzed by data mining. Exceptions to those mainstream patterns are not considered for analysis.

Process Mining Sees Exceptions

But exceptions and irregularities can be very useful for the process mining technique. They could provide clues to what is not working well and what needs improvement.

Conclusion

Both data mining and process mining serve important purposes in the realm of business intelligence (BI). They are necessary for successful, efficient business operations. Data mining provides the source of market knowledge for companies to make smart decisions. The analyzed results are applied in various industries such as retail, journalism, and scientific research. But process mining provides the knowledge of operations that help companies improve and function smartly.

Disadvantages Of Unstructured Data

Disadvantages Of Unstructured Data

Difficult To Analyze

Since there is a lot more unstructured data than structured data, there are also challenges associated with this type of data. First, unstructured data is difficult to analyze. An average person with a working knowledge of Excel cannot mine unstructured data. This is more realistic for using structured data sets with business intelligence. Processing unstructured data is reserved for data scientists and data analysts with proper training and tools. Unstructured data in raw format is difficult to wrangle and interpret.

Requires Specialized Tools

Second, unstructured data requires specialized tool. Most businesses invest in a specific data management tool to analyze data. For example, the text analytics platform from Scion Analytics, the CAP liberates value from unstructured data with AI and NLP powered capabilities. With minimal training, a business can use the CAP to analyze and process data they did not even know they had. These new insights could amount to data drive an actionable decisions on business intelligence that was previously based on guesswork.

Storage

Other challenges of unstructured data include the storage aspect. Structured data has a predefined format, and it is easy to store and organize. Due to a lack of schema and structure, unstructured data is expensive and difficult to store. Having to manage the storage aspect of unstructured data is just one facet that differentiates it from structured data.

Indexing Difficulties

Since structured data has been traditionally used for a long time, approaches to unstructured data are still being developed. Indexing unstructured data is difficult and prone to error due to free form structure and a lack of pre-defined attributes. The difficulty of accessing and analyzing unstructured data makes search results not very accurate. Finally, the complexity of unstructured data makes security a challenge. Whereas security methods for structured data are available, they are still being developed around the security of unstructured data.

Conclusion

The buzz about unstructured data has gripped businesses across industries. Many businesses from healthcare to technology have been revolutionized by access to previously hidden insights that reside in unstructured data. Executives across the world embraced unstructured data with its boundless opportunity.

A new crop of technological advances grew up around unstructured data. Text analytics platforms, statistical models, and AI powered tools have been developed to harness unstructured data. This innovation has not gone unnoticed by the business sector. Benefits of unstructured data are far reaching in implications. For a business if 80-90% of data is unstructured, the insights gained from analyzing this data set are unlimited. These insights create new revenue streams, scale businesses, and push innovation forward into the future.

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

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.

Conclusion

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.

Conclusion

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.

Conclusion

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.

Emails

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

Politics

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

Agreements/Contracts

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.

Differences

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.

Insights

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.

Storage

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.

Format

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.

Insights

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.

Conclusion

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.