by Teknita Team | Feb 9, 2023 | Process Automation
Text mining, also known as text data mining, refers to the process of extracting meaningful information and insights from large volumes of unstructured or semi-structured text data. The aim of text mining is to transform raw text into structured or useful data for analysis, such as sentiment analysis, topic modeling, named entity recognition, and summarization.
Text mining techniques include natural language processing (NLP), machine learning algorithms, and information retrieval methods. These techniques help to identify patterns, relationships, and insights within text data, making it easier for organizations to make informed decisions based on the information contained in the text.
Text mining is used in a variety of industries, including business, finance, marketing, healthcare, and government, to analyze customer feedback, news articles, social media posts, product reviews, and other forms of text data.
How to use Text Mining
There are several steps involved in using text mining:
- Data collection: The first step is to collect the text data that you want to analyze. This data can come from a variety of sources, such as customer feedback, social media posts, news articles, and product reviews.
- Data preparation: Once you have collected the text data, the next step is to prepare it for analysis. This involves cleaning the data to remove any irrelevant information, converting the text data into a format that can be processed by text mining tools, and splitting the data into training and test sets for use in machine learning algorithms.
- Text processing: The next step is to process the text data using natural language processing (NLP) techniques, such as tokenization, stemming, and stop word removal, to prepare the text data for analysis.
- Exploratory analysis: The next step is to explore the text data to identify patterns and relationships. This can be done using techniques such as word frequency analysis, word clouds, and association rules.
- Modeling: Once you have explored the text data, the next step is to build a model to extract insights. This can be done using machine learning algorithms, such as sentiment analysis, topic modeling, and named entity recognition, to identify patterns, relationships, and key themes within the text data.
- Validation and evaluation: The final step is to validate and evaluate the results of the text mining analysis. This involves using the test data set to evaluate the accuracy of the model, and making any necessary adjustments to the model to improve its performance.
- Interpretation and reporting: The final step is to interpret the results of the text mining analysis and report the insights to stakeholders. This might involve visualizing the results, creating summary reports, and presenting the insights in a way that is easy to understand and actionable.
Overall, the process of text mining involves several steps, including data collection, data preparation, text processing, exploratory analysis, modeling, validation and evaluation, and interpretation and reporting. The goal of text mining is to turn unstructured text data into structured data that can be used to support data-driven decision-making.
Text Mining – what possibilities does it bring for business?
Text mining can have a significant impact on business by providing valuable insights into customer behavior, market trends, and public opinion. Some of the ways text mining can help in business include:
- Customer feedback analysis: Text mining can be used to analyze customer feedback from sources such as product reviews, social media posts, and survey responses to gain a better understanding of customer sentiment and identify areas for improvement.
- Market research: Text mining can be used to analyze large volumes of news articles, market reports, and social media posts to gain insights into market trends and competitive activity.
- Sentiment analysis: Text mining can be used to analyze customer feedback and social media posts to determine the overall sentiment towards a company, product, or brand. This information can be used to inform marketing strategies and improve customer satisfaction.
- Social media monitoring: Text mining can be used to monitor social media for mentions of a company, product, or brand, and provide insights into customer opinions, preferences, and behavior.
- Risk management: Text mining can be used to analyze news articles and other sources of information to identify potential risks to a company, such as changes in regulations, public opinion, and market trends.
- Content summarization: Text mining can be used to summarize large volumes of text data into a more manageable format, making it easier to identify key insights and patterns.
- Customer segmentation: Text mining can be used to analyze customer feedback and preferences to identify customer segments, and inform targeted marketing strategies.
Text mining can provide businesses with valuable insights into customer behavior, market trends, and public opinion, allowing them to make informed decisions and improve their overall performance.
Data Mining vs Text Mining – Differences
Data mining is a process of discovering patterns and relationships in large datasets, including structured and semi-structured data, such as numerical and categorical data stored in databases. While both data mining and text mining can be used to gain insights and inform decision-making, they use different techniques and algorithms to analyze different types of data. Data mining often uses statistical techniques, such as regression analysis and decision trees, while text mining uses natural language processing (NLP) techniques, such as sentiment analysis and topic modeling.
Important differences:
- Data Type: Data mining is focused on the analysis of structured data, such as numerical data stored in databases. Text mining, on the other hand, focuses on the analysis of unstructured data, such as text documents, product reviews, and social media posts.
- Analysis Techniques: Data mining uses statistical techniques, such as regression analysis and decision trees, to analyze data. Text mining, on the other hand, uses natural language processing (NLP) techniques, such as sentiment analysis and topic modeling, to analyze text data.
- Data Volume: Data mining typically deals with large volumes of structured data, whereas text mining often deals with even larger volumes of unstructured data.
- Data Preparation: Data mining typically requires a significant amount of data preparation and cleaning, such as removing outliers and transforming data into a suitable format. Text mining, on the other hand, requires additional steps, such as tokenization and stemming, to prepare text data for analysis.
- Goals: The goals of data mining and text mining can be different. Data mining is often used to make predictions, such as predicting customer behavior or market trends. Text mining, on the other hand, is often used to gain insights into customer sentiment and public opinion.
While data mining and text mining share some similarities, they are different fields that use different techniques to analyze different types of data for different purposes. Understanding the differences between these fields is important for choosing the appropriate tools and techniques for a given data analysis task.
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by Teknita Team | Jan 26, 2023 | Process Automation
Autonomous Database is a cloud-based solution that uses machine learning to automate database optimization, security, backups, updates, and other routine management tasks traditionally performed by database administrators. Unlike a conventional database, an autonomous database performs all these and other tasks without human intervention.
The amount of data available to the enterprise is growing faster and faster. This increases the demand for efficient and secure database management that enhances data security, reduces downtime, improves performance, and is not prone to human error. An autonomous database can help you achieve these goals.
Types of data stored in databases
Information stored in a database management system can be highly structured (e.g., accounting records or customer information) or unstructured (e.g., digital images or spreadsheets). Data can be accessed by customers and employees directly or indirectly through enterprise software, websites or mobile applications. Additionally, many types of software—such as business analytics, customer relationship management, and supply chain applications—use information stored in databases.
Elements of an autonomous database
The standalone database consists of two key elements that are tailored to the types of workloads.
- The data warehouse performs numerous functions related to business analytics and uses data that has been previously prepared for analysis. The data warehouse environment also manages all database lifecycle operations and can scan millions of rows for queries. They can be scaled according to business needs and implemented almost on the spot.
- Transaction processing tools enable timely handling of transactional processes such as real-time data analytics, personalization and fraud detection. Transaction processing typically involves a very small number of records, relies on predefined operations, and allows simple application development and deployment.
How an autonomous database works
The autonomous database uses AI and machine learning to provide full, end-to-end automation for provisioning, security, updates, high availability, performance, change management, and error prevention.
In this regard, an autonomous database has specific characteristics.
- It’s automatic
All database and infrastructure management, monitoring and optimization processes are automated. DBAs can now focus on more important tasks, including data aggregation, modeling, data processing and management strategies, and helping developers take advantage of the features and functions available in the database with minimal changes to the application code.
- Protects itself automatically
Built-in security protects you from both external attacks and malicious internal users. This helps eliminate the fear of cyberattacks on unpatched or unencrypted databases.
- Self-repairs
This can prevent downtime, including unscheduled maintenance. A standalone database may require less than 2.5 minutes of downtime per month, including patching .
The benefits of an autonomous database
An autonomous database provides several benefits:
- Maximum database uptime, performance and security – including automatic patching
- Elimination of manual, error-prone management tasks as a result of automation
- Lower costs and increase productivity by automating routine tasks
An autonomous database also allows an enterprise to redeploy its database management staff to more responsible tasks that deliver greater business value to the enterprise, such as modeling data, helping developers define data architecture, and planning future resource requirements. In some cases, an autonomous database can help a company reduce costs by reducing the number of DBAs needed to manage databases or by adapting them to more strategic tasks.
You can read more about Autonomous Database here.
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by Teknita Team | Jan 18, 2023 | Process Automation
Automation refers to the use of technology to control and operate equipment, machines, and processes without the need for human intervention. It involves the use of automated systems, such as robots, software, and artificial intelligence, to perform tasks that would otherwise be done manually. Automation can be applied to a wide range of industries, including manufacturing, transportation, healthcare, and finance.
AUTOMATION STARTS WITH ENTERPRISE RESOURCE PLANNING.
Automation can begin with the implementation of an Enterprise Resource Planning (ERP) system. An ERP system is a software solution that integrates and automates various business processes, such as accounting, inventory management, customer relationship management, and human resources. It helps to streamline these processes by centralizing data and automating tasks such as data entry, reporting, and analysis. This can improve the efficiency and accuracy of business operations, as well as provide real-time data and insights to support decision-making. By automating these processes, ERP systems can reduce the need for manual labor and increase productivity. An ERP system can also help businesses to better manage their resources, improve their ability to scale, and increase their competitiveness.
RPA CAN TRANSFORM YOUR BUSINESS
Robotic Process Automation (RPA) is a technology that allows businesses to automate repetitive and rule-based tasks, such as data entry, data processing, and data analysis. It can be used to automate tasks across a wide range of industries and business functions, such as finance, accounting, human resources, customer service, and IT.
RPA can revolutionize business by increasing efficiency, reducing labor costs, and improving accuracy and consistency. By automating repetitive and rule-based tasks, businesses can free up human resources to focus on more complex and value-added tasks. This can help businesses to improve productivity and reduce the risk of human error.
RPA can also help businesses to improve decision-making by providing them with more accurate and up-to-date data. This can help businesses to identify new opportunities and respond more quickly to changes in the market. Additionally, RPA can also help businesses to improve customer service by providing faster and more accurate responses to customer inquiries.
RPA can also be integrated with other technologies such as Artificial Intelligence, Machine Learning and Natural Language Processing, this way it can go beyond simple automation and allow for more sophisticated tasks, such as chatbot interactions, sentiment analysis and natural language generation.
In summary, Robotic Process Automation (RPA) is a technology that allows businesses to automate repetitive and rule-based tasks, such as data entry, data processing, and data analysis, which can revolutionize business by increasing efficiency, reducing labor costs, improving accuracy and consistency, and improving decision-making capabilities, as well as improving customer service, and providing more accurate and up-to-date data.
COMPANIES ARE MOVING TOWARD HYPERAUTOMATION
Hyperautomation is a term used to describe the use of advanced technologies, such as artificial intelligence, machine learning, and robotics, to automate business processes. Many businesses are beginning to adopt hyperautomation to increase efficiency, reduce labor costs, and improve accuracy and consistency.
Hyperautomation can help businesses to automate repetitive and dangerous tasks, which can increase productivity and reduce the risk of workplace accidents. It can also help businesses to optimize their production processes and increase their output. Additionally, Hyperautomation can also help businesses to improve their decision-making by providing them with more accurate and up-to-date data.
In summary, Hyperautomation is a trend that is being adopted by many businesses to increase efficiency, reduce labor costs, and improve accuracy and consistency, but its adoption varies depending on the company’s capability and willingness to invest in the technology.
Automation has the potential to revolutionize business by increasing efficiency, reducing labor costs, improving accuracy and consistency, and increasing output. It can also help businesses improve customer service by providing faster and more accurate responses to customer inquiries. Automation can also help businesses to scale their operations, by allowing them to handle larger volumes of work with the same or fewer resources. Additionally, automation can also help businesses to improve their decision making, by providing them with more accurate and up-to-date data. This can help businesses to identify new opportunities and respond more quickly to changes in the market. Automation is also a key enabler for digital transformation, which allows businesses to optimize their processes, improve collaboration and communication, and increase agility.
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by Teknita Team | Dec 6, 2022 | Process Automation
Data entry is a necessary, albeit time-consuming and frustrating aspect of both large and small businesses alike.
With data entry automation and business process automation (BPA), you can streamline repetitive, manual tasks allowing your employees to greatly reduce the time spent on these activities, save money, reduce human error, and improve the reliability of data.
By creating automated processes, you can simplify a number of your daily business processes and data entry tasks and eliminate the manual data entry tasks that complicate and slow down your team. This results in a number of significant advantages for your business including:
- Reduced spending on labor
- Reduced typos, data loss, and other administration errors
- Enhanced approval processes
- Greater accuracy in auditing
- Improved visibility and decision making
Automate Your Data Entry Processes with Microsoft Power Apps
With Microsoft Power Apps, you can quickly build and share applications that streamline your workflows and automate repetitive functions with little to no code.
With Power Automate, specifically, you can automate your workflows, enable business logic to simplify app building, and model your processes across connected data sources and services. Let’s explore these benefits more closely.
Design Business Logic
Use Power Automate to design logic for your Power Apps. Instead of having to hire a developer to write endless amounts of code, you can use Power Automate’s simple point-and-click flow designer to build out your business logic. These flows can fire-and-forget, or return data back to your app to display information to the user eliminating the need for manual data processing.
Ensure Data Consistency
Ensure consistency and keep users on track regardless of what stage of the business process the data is entered.
Connect All Your Data
Additionally, you can connect all of your data and create automated workflows that empower your team to collaborate productively. This includes deep integration with SharePoint, OneDrive for Business, and Dynamics 365 provides automation right in-context of the applications you use every day.
Extend Your Functionality
Using an excel-like expression language, you can connect more systems and have greater control via native extensibility for professional developers.
Combining Power Automate with Power Apps can help you to boost productivity by building these time-saving workflows into every aspect of the data entry process and seamlessly integrating with all your data sources, like SharePoint.
Furthermore, with robotic process automation (RPA) and optical character recognition (OCR), you can build secure workflows, with little to no code, and automate all of your mundane, repetitive data entry tasks.
This frees your team members up to focus on tasks that directly contribute value to your business and your customers.
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by Teknita Team | Aug 22, 2022 | Process Automation
OLAP (online analytical processing) is software for performing multidimensional analysis at high speeds on large volumes of data from a data warehouse, data mart, or some other unified, centralized data store. High-speed analysis can be accomplished by extracting the relational data into a multidimensional format called an OLAP cube; by loading the data to be analyzed into memory; by storing the data in columnar order; and/or by using many CPUs in parallel (i.e., massively parallel processing, or MPP) to perform the analysis.
OLAP CUBE
The core of most OLAP systems, the OLAP cube is an array-based multidimensional database that makes it possible to process and analyze multiple data dimensions much more quickly and efficiently than a traditional relational database. Analysis can be performed quickly, without a lot of SQL JOINs and UNIONS. OLAP cubes revolutionized business intelligence (BI) systems. Before OLAP cubes, business analysts would submit queries at the end of the day and then go home, hoping to have answers the next day. After OLAP cubes, the data engineers would run the jobs to create cubes overnight, so that the analysts could run interactive queries against them in the morning.
The OLAP cube extends the single table with additional layers, each adding additional dimensions—usually the next level in the “concept hierarchy” of the dimension. For example, the top layer of the cube might organize sales by region; additional layers could be country, state/province, city and even specific store.
In theory, a cube can contain an infinite number of layers. (An OLAP cube representing more than three dimensions is sometimes called a hypercube.) And smaller cubes can exist within layers—for example, each store layer could contain cubes arranging sales by salesperson and product. In practice, data analysts will create OLAP cubes containing just the layers they need, for optimal analysis and performance.
OLAP cubes enable four basic types of multidimensional data analysis:
Drill-down
The drill-down operation converts less-detailed data into more-detailed data through one of two methods—moving down in the concept hierarchy or adding a new dimension to the cube. For example, if you view sales data for an organization’s calendar or fiscal quarter, you can drill-down to see sales for each month, moving down in the concept hierarchy of the “time” dimension.
Roll up
Roll up is the opposite of the drill-down function—it aggregates data on an OLAP cube by moving up in the concept hierarchy or by reducing the number of dimensions. For example, you could move up in the concept hierarchy of the “location” dimension by viewing each country’s data, rather than each city.
Slice and dice
The slice operation creates a sub-cube by selecting a single dimension from the main OLAP cube. For example, you can perform a slice by highlighting all data for the organization’s first fiscal or calendar quarter (time dimension).
The dice operation isolates a sub-cube by selecting several dimensions within the main OLAP cube. For example, you could perform a dice operation by highlighting all data by an organization’s calendar or fiscal quarters (time dimension) and within the U.S. and Canada (location dimension).
Pivot
The pivot function rotates the current cube view to display a new representation of the data—enabling dynamic multidimensional views of data. The OLAP pivot function is comparable to the pivot table feature in spreadsheet software, such as Microsoft Excel, but while pivot tables in Excel can be challenging, OLAP pivots are relatively easier to use (less expertise is required) and have a faster response time and query performance.
You can read more about OLAP here.
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by Teknita Team | Jun 2, 2022 | Process Automation
Automation, if executed poorly, can have negative impacts on data usage, processes, employee morale and customer satisfaction.
10 Automation Mistakes to avoid:
1. Focusing on a single technology
Once an organization has purchased and implemented a specific process automation tool, such as robotic process automation (RPA), successfully, it’s natural that colleagues want to adopt it more widely.
2. Believing that business can automate without IT
More and more business users believe that the adoption of RPA and low-code/no-code applications don’t require the assistance of IT. But business users may lack knowledge of how customer and data records work, and there’s a risk of mishandling the information.
3. Thinking automation is always the solution
Automation may be the best long-term option for business and IT processes, but leaders cannot simply use it to cover gaps in a poorly designed process. Automation is not meant to make up for failures in systems or defer system replacement.
4. Not engaging all stakeholders
Automation, by nature, has a broad impact on the enterprise, which means you should engage stakeholders from across the organization for decision making and sign off. For example, if adoption of new automation processes changes the nature of people’s roles, involve HR; changes to access rights and IDs, or server requirements must involve security or IT.
5. Failing to devote enough time to testing
Automation technologies only work when the algorithms and rules are exactly correct. The technologies may seem easy to use, but they are unforgiving when programmed incorrectly. They can very quickly wreck business data and fail to deliver the desired business outcome.
6. Wasting effort on overly complicated processes
At times, organizations find themselves in a quagmire when automating a process. That most often happens when processes are not well-documented or understood, if the workflow is not consistent or if there are too many variants in the decision-making process.
7. Treating automation as simple task replication
Using automation tools to copy exactly what is being done manually misses a critical benefit of automation — improving the end-to-end process to create a better customer and employee experience. If process redesign is not part of the automation process, you may use the wrong automation tool and lose the business outcome you hope to achieve.
8. Failing to monitor in postproduction
Just like any system implementation, automation projects will require extensive “hands-on” IT involvement after implementation. For example, for RPA rollouts, establish continuous assessment, monitoring and regular quality checks to ensure that robots have been scripted correctly and are continuing to work as expected. This avoids huge data cleanup tasks.
9. Using the wrong metrics to measure success
It’s typical to measure technology applications and tools to ensure that they are working as designed. However, this doesn’t reflect whether or not the project is successful. Measuring the impact on processes and the enterprise as a whole is key to the success of automation.
10. Ignoring the culture and employee impact
While it’s critical to focus on how to adopt and scale automation, it is equally important to consider the impact on employees, especially if roles are eliminated or reimagined.
You can read more about Automation Mistakes here.
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