Challenges around data

Data is used by organisations of all sectors: insurance, legal, finance, marketing, pharmaceutical. And in all of them it presents similar challenges. The most important of them is the sheer quantity of it in different documents and systems – just think about all contracts, receipts, images, invoices that you deal with in your everyday life!

Processing them is extremely monotonous: it consists of repetitive activities such as entering information in the right place, classifying documents.

Very often employees need to process multiple documents and information from clients at the same time, which can be very error-prone. There are also different types of documents they take care of, and each can have a very different way of being filled.

Another important challenge here is prioritising when it comes to customer service – how to extract clients’ opinions about our organisation and how to understand which information is the most important one?

What is NLP?

An answer to many data-related challenges and problems in business is NLP – Natural Language Processing. We can describe it as a field of artificial intelligence (AI) and computational linguistics that focuses on the interaction between computers and human language.

Its primary goal is to enable computers to understand, interpret and generate human language in a way that is both valuable and meaningful.

NLP is often used in IDP (Intelligent Document Processing) to enhance the automation of document-related tasks. It allows to smartly process documentation, no matter whether it’s a PDF, a Word document, a scan or an audio file. With the right tools, all of those forms can be processed, structured and converted.

Examples of using NLP in business

Automation using NLP and processing of language is being used in business more and more often. Let’s look at when exactly it can be used, and which benefits it may bring to your organisation.

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NLP can be used by organisations:

• for fixing problems
  When something is broken and you want to quickly find a solution, you can create a query around it and, given the huge amount of available content, find a suitable answer.
  
• for extracting information
  When you need certain information, you can enter a query about a specific application and the system finds the necessary information for you.
  
• for simple admin tasks – claiming expenses, scheduling meetings
  Employees who travel for business need to deal with bills and receipts, which can get lost really easily. With NLP it’s enough to take a photo of those documents and immediately transfer them to the system.
  NLP tools can be also used as an assistant that analyses available dates, sends invitations, and later summarises the meetings by analysing the text spoken on the call and extracting key insights or things to do from the transcript.
  An interesting use case of NLP is automatic grouping of files according to their subject or other filters and automatic recognition of content in the document and sending it to the appropriate person/place.
  
• in customer service
  NLP tools can be used in chatbots that handle customer requests quickly, in contrast to hotlines that are known for long waiting times and queues. With NLP your case can be handled automatically by entering it into the chatbot, either as a text or as a voice message.
  An interesting use case of NLP tools is also sorting out reviews and opinions. If there is a lot of them, you can ask about sentiment and quickly read the summary and general conclusions instead of a large number of entries. You can also use such a sentiment search to analyse your own business, for example with customer satisfaction surveys. You can easily find information about things to improve and focus on dealing with them.
  
• for processing information
  IDP system are useful when undertaking social media analysis to quickly alert about disasters and important events thanks to the analysis of people’s posts. As users often post photos and information if they witness something important, a language processing tool can create alarm messages on this basis.
  NLP can also be used to generate questions for large texts, which can be useful when creating an FAQ.
  
• for operating the knowledge base.

An interesting example of NLP use case was given by one of our clients:

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Can NLP be understood as a threat to the work done by humans?

Despite what some people say, NLP is not a threat to people and their work but is intended to support them. The important thing is to have the right approach: analyse what is best to automate/handle using NLP to help humans with easy and repeatable tasks, so that they can be tasked with things that are more difficult and require their attention. Such an approach can significantly reduce costs.

Another benefit is that people do not get bored doing the same, simple things over and over again, but can become more creative and innovative in tasks that really require a human touch. Moreover, when someone does the same repetitive thing all the time, they are more likely to eventually make a mistake because of distraction – the automation of these activities reduces the risk of errors.

Companies need automation of specific business processes that are time-consuming and NLP can solve the problem, but it’s important to know NLP cannot deal with everything – a person is still needed to supervise the operation of the automated system.

Keen to know more about NLP and how to use it to your advantage? Get in touch with us – our experienced team of the best IT experts who can talk to you about the options of automating your work using IDP and NLP. Together, let’s discuss the benefits of this approach and let’s make it a success!

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Data visualisation is a graphical representation of data and information to present complex datasets in a visual format that is easy to understand, interpret, and analyse. It is a powerful tool used in various fields, including business, science, research, and journalism, to communicate insights, patterns, and trends hidden within data.

The role of data visualisation in business strategy

Data visualisation plays a crucial role in business strategy by helping organisations make data-driven decisions, identify opportunities, and gain a competitive advantage.

It contributes to business strategy as it:

• allows business leaders and analysts to explore complex datasets visually, which helps in understanding the current state of the business and identifying areas for improvement,
• helps businesses identify and monitor key performance indicators (KPIs) to track progress toward business goals, identify deviations from targets, and take timely corrective actions,
• allows to monitor real-time data and make data-driven decisions on the fly,
• simplifies complex data and presents it in a visually appealing and understandable format,
• makes it easier to communicate complex data and insights to stakeholders, including executives, investors, and clients,
• aids in market analysis and competitive intelligence by visualising market trends, customer behaviours, and competitor performance,
• helps visualise predictive analytics models and forecasting results which enables businesses to anticipate future trends and plan their strategies proactively,
• assists in visualising customer data, preferences, and behaviours, allowing better understanding of customers,
• helps in monitoring and optimising operational processes,
• aids in resource allocation and risk management by presenting financial data, budgets, and risk indicators.

Shortly put, data visualisation is an integral part of the business strategy, enabling organisations to leverage data effectively, gain valuable insights, and stay agile in an increasingly data-driven business landscape.

Types of data visualisation

Data visualisation offers a wide range of techniques and visual representations to present data in meaningful and insightful ways.

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Some common types of data visualisations include:

1. Bar Charts that use rectangular bars of varying lengths or heights to represent data values. They are effective for comparing discrete categories or displaying data over time.
2. Column Charts: similar to bar charts, column charts use vertical bars to represent data values. They are often used to compare categories or display data over time.
3. Line Charts which use lines to connect data points, making them ideal for showing trends and changes over time or continuous data.
4. Area Charts that are similar to line charts but fill the area beneath the line, often used to display cumulative data or compare multiple data series.
5. Pie Charts which divide a circle into segments, with each segment representing a portion of the whole. They are useful for displaying proportions or percentages.
6. Scatter Plots that use individual data points on a two-dimensional plane to visualise the relationship between two variables. They are helpful for identifying correlations or patterns in data.
7. Bubble Charts which are a variation of scatter plots with additional size or color encoding to represent a third variable. They help visualise three dimensions of data simultaneously.
8. Heatmaps which use colour variations to represent data density or distributions on a matrix, making them ideal for visualising patterns and trends in large datasets.
9. Choropleth Maps that use different shades or colours to represent data values for specific geographic regions, such as countries or states.
10. Tree Maps which use nested rectangles to represent hierarchical data structures, with each rectangle’s size proportional to the data it represents.

These are just some examples of data visualisations, and there are many more types and variations that can be used based on the specific data and insights you want to communicate. The choice of a visualisation type depends on the nature of the data, the story you want to tell, and the audience you are addressing.

Best tools for effective data visualisation

There are several excellent tools available for effective data visualisation, catering to different needs and skill levels. Here are some of the best tools widely used for creating impactful data visualisations:

1. Tableau – a popular and powerful data visualisation tool that offers a user-friendly interface, allowing users to create interactive dashboards, charts, and graphs with ease. It supports various data sources and is suitable for both beginners and advanced users.
2. Microsoft Power BI – a comprehensive business intelligence and data visualisation tool by Microsoft. It offers robust features for data exploration, interactive reports, and dashboards. Power BI integrates seamlessly with other Microsoft products and cloud services.
3. Google Data Studio – a free and cloud-based data visualisation tool by Google. It enables users to create interactive reports and dashboards using various data sources, including Google Analytics, Google Sheets, and more.
4. QlikView and Qlik Sense – data discovery and visualisation tools that empower users to explore data, uncover insights, and create visually appealing dashboards.
5. D3.js – a powerful JavaScript library for data visualisation, providing full control over the design and customisation of visualisations. It is suitable for developers who want to create highly customised and interactive visualisations.

How to choose the right data visualisation tool for your business?

The choice of a data visualisation tool depends on factors such as data complexity, desired level of interactivity, budget, and user expertise. Each of these tools brings its unique features and strengths to cater to different data visualisation needs.

When choosing the right tool remember about defining your requirements, consider user skill levels, assess data integration capabilities, think of scalability and performance, as well as of interactivity and customisation. Consider also your budget, integration with your existing systems, cybersecurity and recommendations of users.

The power of storytelling through data visualisation: examples

Data visualisation allows storytelling, which is a powerful way to communicate complex information, engage audiences, and make data-driven narratives more compelling. Here are some examples of data visualisations that effectively convey stories:

The New York Times’ “How Different Groups Spend Their Day”

This interactive data visualisation by The New York Times allows users to explore how various demographic groups in the United States spend their days. The visualisation uses stacked bar charts and line charts to show daily activity patterns, revealing interesting insights about how different groups allocate their time.

The Washington Post’s “How the Coronavirus Infected the World”

This data visualisation provides a timeline of how the COVID-19 pandemic spread across the globe. The interactive map and line charts show the progression of cases in different countries, offering a clear story of the virus’s impact on various regions.

The Guardian’s “The Wind Map”

This captivating data visualisation represents real-time wind patterns in the United States. The animated visualisation uses flowing lines to demonstrate the beauty and complexity of wind movements, turning data into a mesmerising visual story.

NASA’s Earth Observing System Data and Information System (EOSDIS) Worldview

EOSDIS Worldview is a web-based application that provides satellite imagery of Earth from various NASA missions. It allows users to create stories by selecting specific dates, regions, and events to explore environmental changes and natural disasters.

The role of data visualisation in big data and AI

Data visualisation plays a crucial role in the context of Big Data and Artificial Intelligence.

Big Data often involves massive volumes of structured and unstructured data. Data visualisation helps make sense of this complexity by presenting data in a visual format that is easier to understand, analyse, and derive insights from. It also contains hidden patterns, trends, and correlations that can be challenging to identify without the aid of visualisation.

In the realm of Big Data and AI, real-time data processing and analysis are essential. Data visualisation enables real-time monitoring of data streams and the performance of AI models, making it easier to detect anomalies and take timely actions. Those AI models, especially deep learning algorithms, can be complex and difficult to interpret.

Data visualisation can provide insights into how AI models arrive at their decisions, helping users understand the factors that influence the model’s predictions.

Another important role of data visualisation is communicating AI results to non-technical audiences, which bridges the gap between technical experts and non-technical stakeholders, allowing AI insights and results to be communicated effectively to decision-makers, business leaders, and other stakeholders.

The combination of Big Data, AI, and data visualisation creates a powerful framework for data-driven decision-making and innovation across various industries.

How to visualise data: the challenges and solutions

Visualising data effectively comes with its set of challenges, but there are solutions to address them. Here are some common challenges in data visualisation and their corresponding solutions:

Data complexity

The challenge here is that dealing with large and complex datasets can be overwhelming, making it difficult to identify meaningful patterns and insights. To address that issue use data aggregation, filtering, and summary techniques to simplify the data before visualisation. Employ interactive tools that allow users to explore the data dynamically, focusing on specific subsets of interest.

Choosing the right visualisation type

Selecting the most suitable visualisation type for the data and the story it needs to convey can be challenging. But you can do it right by understanding the characteristics of the data and the insights you want to communicate. Familiarise yourself with various visualisation types and their best use cases.

Experiment with different visualisations and seek feedback to determine which one effectively communicates your message.

Data integrity and quality

Inaccurate or incomplete data can lead to misleading visualisations. To mitigate that, conduct data quality checks and preprocessing to ensure the accuracy and reliability of the data. Handle missing data appropriately and validate the data with domain experts when necessary.

Overloading visuals with information

Including too much information in a single visualisation can overwhelm viewers and hinder understanding. To address that, practice data simplification and visual decluttering. Focus on the most critical data points and use annotations and storytelling techniques to guide the viewer through the visualisation.

Lack of interactivity

Static visualisations might not be sufficient for exploring complex data or answering specific questions. Utilise interactive features that allow users to interact with the data, enabling them to drill down, filter, and zoom in on specific data points of interest.

Choosing the right tools

Selecting the right data visualisation tools that suit your needs and skill level can be overwhelming. To overcome that, evaluate different tools based on their features, ease of use, and compatibility with your data sources. Consider using a combination of tools if one tool alone doesn’t meet all your requirements.

By being aware of these challenges and implementing the corresponding solutions, you can create data visualisations that effectively communicate insights, engage audiences, and support data-driven decision-making.

Always remember that the goal of data visualisation is to simplify complex information and make it more accessible and understandable to your target audience.

Incorporating visual representations of data into your business strategy

Incorporating visual representations of data into your business strategy can significantly enhance decision-making, communication, and overall business performance. To do it effectively:

• determine the primary business objectives and key performance indicators (KPIs) critical to your organisation’s success,
• define data sources and metrics,
• choose visualisation types that best represent your data and facilitate understanding,
• create interactive dashboards that allow users to explore data and customise views based on their needs,
• incorporate data visualisations into reports, presentations, and business reviews to enhance the storytelling process.

Remember also about sharing data insights across departments to encourage cross-functional collaboration, monitoring and tracking performance and staying up-to-dated with new technologies.

By integrating data visualisation into your business strategy, you can unlock the value of your data, drive data-driven decision-making, and communicate insights more effectively. Visualisation enables you to turn complex data into actionable knowledge, empowering your organisation to stay competitive and responsive in a data-rich environment.

Keen to check it out within your organisation? Get in touch with our team of experienced data professionals who will be happy to take you through the process and advise on the best solutions and tools.

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Before we look into Natural Language Processing methods and how they can help you achieve your goals, let’s look at its definition and history.

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The history of NLP dates back to the 1950s. The early years were marked by rule-based systems that relied on handcrafted linguistic rules to process and analyse text.

The early 2000s witnessed a significant breakthrough in NLP with the rise of neural networks and deep learning, which revolutionised NLP by achieving great results in various tasks, including language understanding, machine translation, question answering, and sentiment analysis.

Another key milestone in NLP is the availability of large-scale pre-trained language models, such as OpenAI’s GPT (Generative Pre-trained Transformer) and Google’s BERT. These models are trained on massive amounts of text data, allowing them to learn rich contextual representations of language. Researchers and developers can fine-tune these models for specific tasks, enabling rapid progress in various NLP applications.

Want to know more about NLP? Take a look at the related articles:

• How is Natural Language Processing (NLP) used in business?
• How is NLP transforming finance, FinTech, and banking?
• NLP in the insurance industry: top 5 use cases and benefits

Types of Natural Language Processing methods

Natural Language Processing techniques can greatly enhance the analysis of textual data, which is the key when it comes to doing any kind of business. They can help you streamline your customer service or allow you to make more informed decisions thanks to thorough market analysis.

Some key methods that can improve your NLP analysis include:

Text Summarisation

NLP text summarisation is the process of generating a concise and coherent summary of a given document or text. It aims to capture the most important information and main ideas from the original text, while condensing it into a shorter form.

Tokenisation

Tokenisation is the process of breaking down a text into individual units called tokens, which can be words, phrases, or even characters. It forms the basis for most NLP tasks. Tokenisation helps in counting word frequencies, understanding sentence structure, and preparing input for further analysis.

Parsing

Parsing, also known as syntactic parsing or dependency parsing, is the process of analysing the grammatical structure of a sentence to determine the syntactic relationships between words.

The goal of parsing is to understand the sentence’s structure and how the words relate to each other, which is crucial for various NLP tasks such as information extraction, question answering, machine translation, and sentiment analysis.

Sentiment Analysis

Sentiment analysis determines the emotional tone of a piece of text, typically classifying it as positive, negative, or neutral. It is useful for understanding public opinion, customer feedback analysis, and social media monitoring. Techniques for sentiment analysis include lexicon-based approaches, machine learning models, and deep learning methods.

Lemmatisation and Stemming

Lemmatisation and stemming are techniques used to reduce words to their base or root form. Lemmatisation converts words to their dictionary form (lemma), while stemming reduces words to their base form by removing prefixes or suffixes. This helps in reducing word variations and improving text coherence and analysis accuracy.

Stopwords Removal

Stopwords are common words like “and,” “the,” or “is” that don’t carry much meaning and can be safely ignored. Removing stopwords from text can reduce noise and improve the efficiency of subsequent analyses such as sentiment analysis or topic modeling.

TF-IDF (Term Frequency-Inverse Document Frequency)

TF-IDF (Term Frequency-Inverse Document Frequency) is a commonly used weighting scheme in Natural Language Processing (NLP) that quantifies the importance of a term in a document or a collection of documents.

TF-IDF takes into account both the frequency of a term within a document (term frequency) and its rarity across the entire document collection (inverse document frequency).

Named Entity Recognition (NER)

NER identifies and classifies named entities (e.g., names, locations, organisations) in text. It helps in extracting structured information from unstructured text and is crucial for tasks like information retrieval, question answering, and entity relationship analysis.

Keyword Extraction

NLP keyword extraction, also called keyword detection or keyword analysis, is the process of automatically identifying and extracting the most important or relevant keywords or key phrases from a given document or text.

Keywords are essential terms that represent the main concepts or topics in the text and can help in understanding its content, indexing it for search, or categorising it. As such, keyword extraction is often used to summarise the main message within a certain text.

Word Embeddings

Word embeddings represent words as dense vectors in a high-dimensional space, capturing their semantic relationships.

Popular word embedding models like Word2Vec, GloVe, and fastText have been pre-trained on large text corpora and can be used to obtain vector representations of words. Word embeddings are valuable for tasks such as word similarity, document classification, and information retrieval.

Topic Modelling

Topic modelling is a statistical technique that uncovers latent topics or themes within a collection of documents. It helps in understanding the main subjects or discussions in a text corpus. Popular topic modelling algorithms include Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorisation (NMF).

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Articles you may also be interested in:

• The future of Natural Language Processing
• How do NLP and IDP address business challenges?
• Machine Learning strategy for businesses: a practical guide

Natural Language Processing applications

Natural Language Processing (NLP) has a wide range of applications across various industries and domains.

Some of the most common ones include:

Chatbots and virtual assistants

NLP is essential for building conversational agents like chatbots and virtual assistants. These systems utilise natural language understanding and generation to interact with users, answer questions, provide recommendations, or assist in various tasks.

Sectors that make use of it are numerous, from retail to finance and medical: chatbots at banks or medical centers quickly provide customers with relevant information, without the need of speaking to specialists.

Information retrieval

NLP is used to extract structured information from unstructured text. Named entity recognition, relationship extraction, and event extraction techniques are employed to identify and organise relevant information from documents, articles, or web pages.

A great example of information retrieval in use is the financial sector where there is a need to quickly get real-time information from the stock exchange to stay ahead of competition. 

Question answering systems

NLP powers question answering systems that can understand natural language queries and provide relevant answers. These systems are used in virtual assistants, customer support chatbots, and search engines to deliver accurate and concise responses to user queries.

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Machine translation

NLP enables automatic translation of text from one language to another. Machine translation systems like Google Translate and DeepL use NLP techniques to analyse and understand the source text and generate a translated version in the target language.

Machine translation can be used in document translations, it can also be used by companies that operate on international market and need to communicate with people speaking different languages.

These are just a few examples of NLP applications. The field continues to advance rapidly, with new applications emerging in areas such as document understanding, natural language generation, language generation models, and conversational AI.

How NLP techniques are shaping the future of ML and AI?

NLP techniques are playing a significant role in shaping the future of machine learning (ML) and artificial intelligence (AI). They are like bridges between humans and machines, enabling more natural and intuitive interaction that enhances user experience, enables more seamless communication with machines and allows them to comprehend and interpret text in a way that was not possible before.

What’s more, NLP is facilitating communication and understanding across different languages. Machine translation models, powered by NLP techniques, are improving the accuracy and fluency of translations.

Additionally, techniques like cross-lingual word embeddings and transfer learning are enabling knowledge transfer from one language to another, allowing models trained in one language to generalise to other languages with limited labeled data. These advancements are crucial for bridging language barriers and enabling global collaboration and access to information.

According to Statista, the NLP market is predicted to be almost 14 times larger in 2025 than it was in 2017, increasing from around three billion U.S. dollars in 2017 to over 43 billion in 2025. That’s an impressive fact that cannot be underestimated if you are planning to achieve success with your business.

To speak about possible NLP techniques and applications that can be beneficial to your organisation, do get in touch with us directly. Our dedicated AI/ML consulting services will help you explore data-based opportunities while working with the best specialists in the field.

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Identifying the key components of predictive maintenance

While there have been many proposed architectures for, the main components revolve around several key steps: data collection, data cleaning, feature extraction and selection, fault analysis, and time to failure (TTF) prediction.

Data Collection

Data consists of factual information that conveys valuable insights, such as the knowledge that the temperature stands at 96 degrees. The process of data collection entails gathering information from various sources, ensuring that it is meticulously chosen and aligned with the project’s objectives.

If a company was trying to do weather forecasting, this step would involve gathering data from past weather reports, weather pattern research, and other sources that may help to predict future weather, for example historical air and sea current routes or spatial development of the region.

Data Cleaning

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Data cleaning involves fixing data to make a standardized dataset. Data is often collected from unformatted sources, leading to possibly untrue, incorrectly formatted, or incomplete data being collected. This process goes through and makes sure data is in the right format for feature extraction and selection and that the data is accurate.

For example, if the past weather reports said that a day was 960 degrees Celsius, then the data cleaning process may involve checking other sources to see if it was a typo.

Here’s another example of data unification: bringing together temperature readings from different devices. This task requires converting Celsius and Fahrenheit data into one consistent unit and establishing a standard.

It also involves matching temperature readings with their corresponding dates, standardizing the frequency of minute and hourly recordings, and filling in any gaps caused by missing data.

Feature Extraction and Selection

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A feature is an input, usually a set of attributes, that can be used to train machine learning models to make predictions. Feature extraction extracts information from the original features to create new features, which compresses data while maintaining as much relevant information as possible.

Feature selection is a ML process that finds and chooses the most relevant features from the original data and uses these as inputs for a model.

Both of these steps reduce the number of features in the original dataset to reduce error and increase model efficiency. Having too many features can create excessive noise, which can make it difficult to analyze a system’s normal state. Feature extraction and selection aim to narrow down the analysis field for Machine Learning models. This focus on a concise yet comprehensive information channel provides clear insights into the system’s state.

Similarly, humans face challenges when they are flooded with too much information, making it difficult to make well-informed decisions. To overcome this, people define indicators that provide understandable and easily interpretable cues for decision-making.

For example, in a scenario where a conversation involves a large group of people and results in chaos and noise, selecting a representative to carry on the discussion ensures a more organized and productive outcome.

Fault Analysis

Fault prediction is the process of trying to predict deviations in normal operations, often using machine learning models. In the case of predictive maintenance, fault prediction could try to predict when a machine’s part would break from wear and tear, thus causing deviation from everyday operations.

Time to Failure Prediction

Time to Failure (TTF) prediction is the process of predicting the remaining time before a system fails using data science techniques. This allows companies to wait until the system is about to break before replacing it, allowing them to save on replacement costs while still being able to do maintenance before failure occurs.

Developing a predictive maintenance strategy

Why Predictive Maintenance?

Reactive maintenance strategy has been used in the past to fix equipment and systems after they have already broken, which has had its pros and cons.

Although this strategy requires less staffing and maintenance training fees, it disrupts the efficiency of the system it is integrated into and does not account for unexpected machine failure. With the age of digital transformation, however, reactive maintenance gave way to preventive maintenance, in which equipment and assets were routinely checked to prevent any unexpected failures.

Although this greatly improved asset life and efficiency, the cost of hiring maintenance staff and frequently testing assets can add up quickly. The best way to reduce these drawbacks while keeping the advantages of the maintenance systems is to use predictive maintenance.

While extensive staff training is still required, time and money no longer have to be wasted on unnecessary system checks like they did under preventive maintenance.

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The use of predictive maintenance can decrease the amount of unscheduled downtime and resulting downtime losses that manufacturers experience, as well as decrease the mean time it takes to make repairs and increase the mean amount of time between system failures.

All things considered, predictive maintenance allows equipment to last longer without wear and tear, and repairs can be made as quickly as possible.

A study by FMX, a provider of maintenance management solutions, reveals that, on average, plants that implemented predictive maintenance experience a 30% increase in mean time between equipment failures. The study also shows that in the 500 surveyed plants that implemented predictive maintenance, there was a 30% increase in equipment availability.

As the use of predictive maintenance cut down costs in spare parts, unplanned repairs, and breakdown of equipment, companies saved over 50% of their operating costs. It can be concluded from these results that a successful predictive maintenance program can make equipment more reliable and accessible.

Internet of Things

Predictive maintenance systems rely on the Internet of Things, in which multiple devices and machines with sensors communicate with each other by transferring data to each other. As they continue to share data, the machines set parameters and standards of performance, so when one of them fails to meet the set standards, they alert the appropriate personnel to fix the issue immediately.

Therefore, the most important part of developing a predictive maintenance strategy is ensuring that all the systems work in tandem with each other.

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Drawbacks of Predictive Maintenance

Although there are many advantages to implementing a predictive maintenance solution, there are some drawbacks to consider before doing so. While operating costs are cut to a fraction of the original cost, upfront costs to implementing predictive maintenance can be expensive, as it includes integrating new technology and hiring employees to interpret data from the new technology.

Furthermore, there would be a learning curve for employees to learn how to use and interpret data from the new technology, so not too much time will be saved at the beginning.

Best practices for predictive maintenance implementation

Predictive maintenance is crucial for equipment reliability and operational efficiency. However, its success and sustainability rely on following best practices, such as prioritizing data quality. With accurate and complete data, organizations can gain deeper insights into equipment health, detect potential issues before they become problems, and minimize downtime.

On the other hand, poor data quality can lead to false alarms, reactive maintenance, and decreased confidence in the predictive maintenance system. By focusing on data quality and implementing data quality metrics, regular data audits, and data validation checks, organizations can ensure that their predictive maintenance program is successful and sustainable over the long term.

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Factors Impacting Data Quality in Predictive Maintenance Programs

There are various factors that can impact data quality in the context of predictive maintenance.

Incomplete or missing data is one of the most common issues that can affect the accuracy of predictive models. If data is not collected regularly or is missing key information, it can be challenging to make accurate predictions and identify potential issues before they become problems.

Inconsistent data is another challenge that organizations may face. If data is collected using different methods or with different instruments, it can be challenging to compare and analyze. Inconsistent data collection can also lead to discrepancies in the results obtained from predictive models, which can make it difficult to identify issues and determine the most effective maintenance strategies.

Incorrect data is another issue that can impact data quality. If data is entered incorrectly or is otherwise inaccurate, it can lead to incorrect predictions or maintenance decisions. This can result in unnecessary maintenance or repairs, which can increase costs and reduce the overall effectiveness of the predictive maintenance program.

Finally, corrupt data can be a significant challenge in predictive maintenance. When data is impacted by errors or system malfunctions, it can pose a challenge for the predictive maintenance program to differentiate between false data caused by measuring device failures during a normal system state and invalid data resulting from an abnormal system state. Corrupt data can lead to false alarms or missed issues, which can lead to breakdowns and disruptions.

Best Practices to Handle Data Quality

To address these issues, organizations must prioritize data quality in their predictive maintenance programs. This involves implementing best practices for data collection, such as establishing regular processes to monitor, validate, and clean data to ensure that it meets the required quality standards.

This process involves establishing data quality metrics to track and measure the accuracy, completeness, consistency, and integrity of the data.

Automated checks can be implemented to detect and correct errors in the data. These checks should be established for both incoming and historical data. By doing so, organizations can quickly identify and rectify any discrepancies in the data.

Manual checks should also be conducted to validate the data. These checks should involve a team of experts who can evaluate the data against known standards and ensure that it is accurate and reliable.

To optimize data quality, organizations should also implement data governance policies. These policies help ensure that data is managed consistently across the organization and adhere to industry-specific regulations. Data governance policies should outline data ownership, data access, and data privacy guidelines.

Establishing data quality metrics and implementing automated and manual data checks can help organizations maintain accurate data for their predictive maintenance programs.

These practices will ensure that data is reliable and can support effective decision-making for predictive maintenance tasks. By implementing data governance policies, organizations can also ensure that data is managed consistently, which will help build trust in the predictive maintenance program among stakeholders.

Overall, following these best practices for data quality management in predictive maintenance will help organizations maximize the benefits of predictive maintenance, reduce downtime, and improve their maintenance strategy.

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Best Practices for Staff Training and Upskilling in Predictive Maintenance

Implementing predictive maintenance is an effective way for organizations to improve equipment reliability, reduce maintenance costs, and increase overall efficiency. However, it requires investing in staff training and upskilling, as well as effective communication to gain buy-in from stakeholders.

Here are some best practices for implementing successful predictive maintenance programs:

• Identify necessary skills: Determine the specific skills required for successful implementation of predictive maintenance, including data analysis, machine learning, statistical modeling, and data visualization.
• Develop a comprehensive training plan: Create a detailed training plan that addresses the needs of different staff members through online courses, classroom training, on-the-job training, and mentoring.
• Provide ongoing professional development: Offer ongoing professional development opportunities such as attending conferences, webinars, or online training courses or participating in professional networks.
• Encourage a culture of continuous learning: Create a culture of continuous learning within the organization to engage staff with training and upskilling. Provide incentives for staff who participate in training, recognize and reward those who demonstrate new skills, and encourage knowledge sharing among colleagues.

Communicating the benefits of predictive maintenance to all stakeholders is also crucial for the success and sustainability of the program. It involves gaining buy-in from leadership and motivating maintenance staff to support the implementation. Effective communication can help stakeholders understand the value of predictive maintenance and their role in its success.

By following these best practices, organizations can successfully implement predictive maintenance programs, adapt and evolve their maintenance strategies over time, and achieve improved equipment reliability, reduced maintenance costs, and increased overall efficiency.

Conclusion

Overall, predictive maintenance is a proactive maintenance strategy that relies on data analysis to predict what maintenance needs to be done before the system fails. Through multiple steps, including data collection, data cleaning, feature extraction and selection, fault analysis, and TTF prediction companies can reduce downtime, time to repair, and save on operating costs.

While there may be higher upfront costs in investing in new technology like a computerized maintenance management system, the benefits are well worth it to maintain the critical assets of machinery.

Additionally, with advancements and integrations of tools like the Internet of Things to integrate sensors and condition monitoring equipment, predictive maintenance is getting more and more accurate.

What is worth highlighting here is the fact that predictive maintenance is not about replacing humans with machines, but about supporting the work of specialists by offering accurate insights into the actual state of systems, and in particular, by enhancing the unique knowledge and skills of human experts.

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Artificial Intelligence for everyone

Although AI solutions do require some investments, the costs are actually getting lower year by year. According to the Artificial Intelligence Index Report 2021 published by Stanford University, training costs of a contemporary image-recognition system have gone down by around 150 times in the past 4 years.

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Everybody knows that technology gets cheaper with time. This is also connected with the American engineer Gordon Moore’s observation that the number of transistors in a dense integrated circuit double about every two years. Moore’s law means in fact that we can expect the efficiency of an application to double every two years, with the same cost of components. However, in the case of AI, Moore’s law is falling way behind.

Since 2012, AI performance has been doubling every 3.4 months. This growth makes it possible not only to develop increasingly advanced models but also to produce the currently applied models in a simpler and cheaper manner. Nowadays, every company using data in some way – e.g. by analysing the number of visits to a website, product and service sales, financial results, or CCTV coverage – can enhance its performance without an exorbitant budget. We’re heading towards the development of smart solutions at affordable prices.

Saved by the cost reduction in business maintenance

Recent years have shown that business operating costs are getting more and more burdensome. General and administrative expenses are growing much quicker than revenues (15.4% to 6.0%). Businesses are forced to unlock serious cost reductions, but some approaches may be too extreme and they may significantly lower the company’s performance and the comfort of work. This problem can be solved by using smart solutions in business. According to 63% of the organisations surveyed, the need to reduce costs will require them to use AI.

There is huge potential in business process automation and business logic calibration. The use of AI saves time and money through automation of complex processes and replacing humans in simple and monotonous tasks, which can prevent losses generated by human error. Automation can take place in a number of areas: replacing manual data input by OCR systems, product and service recommendations, production skills relocation as well as expenditure and collection effectiveness analyses. According to McKinsey & Company, the use of cutting-edge technologies, including AI, can reduce indirect costs by as much as 15 to 20% in 12 to 18 months.

Compete with the best

To survive on the market, you need to constantly improve the attractiveness of your business. Nowadays, the pressure related to customer acquisition and retention is so high that competition between companies across the market is getting more aggressive and exhausting. So far, you would be able to gain a competitive advantage by building brand awareness and increasing the accessibility of your offer, for instance by means of a website.

Currently, most businesses have implemented these approaches and their work is focused on reaching as many users as possible. To achieve this, they can make use of AI solutions, which provide a totally different perspective on the market. A study run by BCG shows that as many as 84% of the managers surveyed believe that AI will allow their companies to obtain or sustain a competitive advantage.

AI makes it easier to understand the needs of every customer thoroughly, especially when it comes to their unique characteristics. Thanks to this, companies can offer them the right products in the right place at the right time.

The recommendations show not only what product is suitable for a particular customer but also how and when to offer the suggestion. What’s more, by using, AI you can automate the contact with the customers (e.g. with the help of smart chatbots) and thus ensure 24-hour customer support. This way, you’re going to enter the world of modern AI-based marketing. The biggest tech corporations have made use of AI for a long time now and they still see its potential – this is visible in their growing financial outlays on AI.

By employing AI, you can provide your customers with unique experience that is unattainable among your competition. Besides, the process of AI popularisation in business is only at an initial stage. This means you have a chance to become the so-called first mover – a business that gains a competitive advantage by being the first to use smart solution in a given sector.

Supporting Business Analytics

Knowledge has always been the key to success: it helps understand the present and prepare for what the future may bring.

The current situation requires businesses to analyse huge sets of data to obtain crucial information about the market. However, an analysis of mass data without any specialist tools may lead to overlooking some relevant or even essential facts. Such omissions don’t have to translate directly into financial losses but they are tantamount to missing out on a chance to gain a competitive edge.

AI plays a key role in data analytics. It makes it possible to analyse data in an adequate and comprehensive way, which leads to practical conclusions. Humans have limitations that make analysing big and unstructured data difficult. AI, on the contrary, can do this very well, detecting patterns and relations hidden in data. This is why you should seriously consider introducing AI into your business so that you don’t miss out on a great opportunity.

Endnote

No matter if you’re a tech tycoon or a small start-up, smart solutions are within your reach. Progress makes technology affordable. Thanks to its huge processing capacity and access to large datasets accompanied by new approaches to machine learning, it is already possible to implement automation at an advanced level. Based on this state of the art, every serious business is likely to integrate AI to some extent in the nearest future.

For some, it will be a means to increase the profit and sales, while for others – a method of cutting their losses. AI can also be used to establish relationships with the audience. So, think about how you can benefit from artificial intelligence, so that you don’t lag behind but rather outdistance your competition.

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What are recommendation systems?

The key to successful sales of products and services lies in close scrutiny of customers, especially in relation to their reactions to particular trade offers. Being able to get insight into customers’ tastes enables businesses to adjust their offer to satisfy their audience. Recommendation systems respond to this need, as their goal is to predict how a given person might rate a specific product. By analysing the visits to your online shop using a recommendation engine, you can adapt the way its content is viewed to users so that it draws their attention and, as a consequence, increases the value of the shopping cart.

What are recommendations good for?

Recommender systems have a number of applications, but their primary purpose is to address every user as a unique individual. A person is the focus of recommendations and the task of the system is to get to know this person, especially when it comes to the individual characteristics that differentiate them from other people. Based on the acquired information, the system browses the shop offer to find the products which are best suited to the particular customer’s needs. As a result, the offer presented to the customer is carefully adjusted to their individual preferences. This mechanism can be used in various industries including healthcare.

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These are the main goals of recommendation systems:

• Increasing sales volumes – one of the principal methods of increasing the sales of regular offers is marketing. Marketing activities involve addressing the whole population or a selected target group and directing special offers and promotional material to them. As far as recommendation systems are concerned, sales offers and promotional activities are directed to specific users. This approach increases the likelihood of selling the promoted products.
  

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• Customer retention – one of the greatest problems of modern companies, in particular those based on the subscription model, is the churn rate. Retaining customers for a long time is necessary to get a return on the investment made to acquire them. Popular methods of customer retention, such as discounts and coupons, are often rather costly and with short-term effects. This problem can be overcome by adopting an individual approach to customers. Recommendation systems help in understanding the needs and objectives of customers and, eventually, in selecting the correct activities to maintain the relationship with them.

• Increasing customer satisfaction – the use of recommendation systems helps increase customer satisfaction by enhancing the functionality of the sales service. Spotify, the popular music streaming service, offers 70 million tracks – trying to find the music that you like in such a crowd may be a cumbersome task. Here’s where recommendation systems come in handy: they shorten the distance between the customer’s need and satisfaction. Not only do they help find the search product; they also discover the needs that customers are not even aware they have.

How do recommendation systems work?

Recommendation engines are based on state-of-the-art functions of machine learning.

ML algorithms build mathematical models to make predictions or decisions without being explicitly programmed to do so by humans. They are used in a variety of applications where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. The training data for algorithms includes, for example, user behaviour (ratings, clicks, purchase history), user demographics (age, education, income, location), or product attributes (book genre, film cast, food origin).

How to present the recommendations

Preparing the presentation

Since lists are the most popular method of presenting the recommended items, a proper ranking model is necessary to assign rating to every item and rank them from the most to the least attractive ones.

The basic ranking function which increases consumption is the popularity of the item. However, popularity is the opposite of personalisation. If a list is based on popularity, every user gets the same order. This is why it is essential to personalise the experience.

Personalisation can be achieved by completing the system with ratings given by the user to other items. And yet, this is not enough either, because in this approach, the prioritised items might be too niche and the most interesting suggestions might get omitted. It’s easy to picture a situation where a user gives a low rating to a film with a terrible plot even though they love the particular film genre. To make up for this, instead of focusing on popularity or predicted rating only, the ranking should balance these factors.

Learning to rank

There are numerous ways of constructing the ranking function, from simple rating methods to pair preferences and ranking optimisation. One of the available options is combining the popularity factor with the user rating predictions.

You must bear in mind, though, that in the case of ranking recommendations, personalisation is essential: the goal is not to create a universal concept of accuracy but to find ways of optimising customised models.

With large sets of data available, in terms of both the amount and the type, it is vital to adopt an in-depth approach to model selection, training, and testing. This is why all-encompassing approaches to ML algorithms are usually adopted: from unsupervised learning methods, such as cluster analysis, to a series of supervised classification methods, which have given optimum results in various contexts.

Supervised learning happens when the data set supplied for training includes the expected output: for instance, a collection of emails including the information which of them is spam and which isn’t. In this case, a new email should end up in the inbox or in the spam, in accordance with its content. Unsupervised learning, on the other hand, makes it possible to process untagged data to identify previously undetected patterns, e.g. collecting press articles on similar subjects.

These are some of the most important algorithms:

• Linear regression
  
• Logistic regression
  
• Elastic nets
  
• Singular value decomposition
  
• Restricted Boltzmann machine
  
• Markov chains
  
• Latent Dirichlet allocation
  
• Association rules
  
• Gradient-boosted decision trees
  
• Random forests
  
• K-means
  
• Affinity propagation
  
• Matrix factorization
  
• Support vector machines
  
• Neural networks

In the last couple of years, there have been many new algorithms specifically designed to learn to rank, such as RankSVM and RankBoost.

There is no universal method of choosing the best model for a given ranking task. The simpler the feature set is, the simpler the model. It is easy to fall into a trap where a new attribute shows no value because the model is unable to learn it. Or another trap: deciding that a more powerful model is not useful only because there are no attributes that use its advantages.

A/B testing process

Although ratings prove very useful in deciding whether a given model deals well with training data, you cannot be sure that the results will translate into actual improvement of user experience. This is why it is necessary to implement A/B testing to test new algorithms. Tests are normally made on thousands of users and 2 to 20 elements which are variants of the basic idea. With A/B tests, you can check out brave ideas or test multiple projects at once. All in all, their key benefit is that they help you make data-based decisions.

To measure the model performance, a number of factors can be taken into account: from ranking measures, such as NDCG (Normalised Discounted Cumulative Gain), mean mutual rank, and fraction of concordant pairs, to classification indexes, such as accuracy, precision, recall, and F-score.

The analysis shows to what extent these factors correlate with tangible results in A/B tests. However, since mapping is not perfect, performance is used only as a guideline to making informed decisions concerning further tests. If model tests confirm the hypothesis, A/B tests are designed and run to prove that the new functionality is relevant from the user’s point of view.

Application

One of the most popular recommender systems has been created by Netflix. Netflix is based on the subscription business model, which means they need to not only acquire new users but also to retain the current ones. For this reason, it is very important for them to retain the users’ attention and to encourage them to extend their subscription.

The current video base of Netflix includes over 5500 TV shows and films and it is regularly updated. The wide range of content allows Netflix to satisfy their customers’ needs but there is also a risk involved. If users need to take a lot of time looking for a film they want to watch, they might become uninterested and cancel the subscription as a result.

To prevent this, Netflix uses a recommendation engine that helps users find new films. The effectiveness of this approach is confirmed by the fact that most shows watched by Netflix users get through to them by means of the recommendation system. The system enables Netflix to retain their regular customers, which means they also retain profit and, additionally, acquire new users. If recommendations are effective, they attract new users, who want to watch shows they find interesting without spending a lot of time looking for them.

Other online services also use effective AI-based recommendation engines. One of them is Spotify. Spotify puts together a unique list called “Discover Weekly” for every user. The list contains 30 songs selected based on the user’s personal preferences.

As far as machine learning is concerned, Spotify uses a model based on the multi-armed bandit problem that balances between exploitation and exploration. In this case, exploitation refers to providing recommendations in the app, based on the previous music and podcast choices.

Exploration is the opposite of exploitation. It is based on users’ uncertain engagement and it serves as a research tool that provides information on how people interact with the suggested content. Exploration makes it possible to discover new interesting items for users, something they haven’t heard before. Thanks to this balanced approach, shelves and cards are personalised for new and current users.

Unlike traditional department stores, online sellers such as Zalando need to use digital representations of their offer to attract clients. This normally means that there is a database gathering information about the properties, prices, and images of particular items. And yet, the relationship between these attributes and the features that customers actually look for (e.g. how much a given object fits their style) is much more complex.

The recommendation engine is able to provide accurate suggestions concerning a given product or recommend an accessory to match the outfit. One of such networks is Style2Vec, which allows saving an image of a piece of clothing so that its qualities can be easily modified: for instance, you can change the colour, the length, or the cut. Browsing the base with Style2Vec consists in adding and subtracting traits of the clothes.

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The results can be used to generate entire outfits and collections based on users’ preferences. This allows:

• recommending similar or matching products,
  
• following the trends and, consequently, launching new products that meet the customers’ needs,
  
• shortening the shopping time.

Another popular platform using an impressive recommendation engine is YouTube. The system consists of two neural networks responsible for different sorts of data filtering.

The first one filters user data to find correlations between users’ preferences, whereas the other sorts videos based on their characteristics and other data, including their ratings, number of views, comments, and descriptions. YouTube recommendations are displayed both on home screen and in the “Skip to next” section, where suggestions show up as you watch a video.

Summary

Comprehensive use of data and practical application of artificial intelligence determine the success of the entertainment and e-commerce tycoons. Thanks to access to unlimited sets of data, they are able to offer extremely accurate recommendations.

The benefits of ML-based solutions outweigh the effort that must be put in their implementation. The fact that this area is still developing is also beneficial. The future may bring brand-new and incredibly effective AI-based methods of interacting with users.

Are you wondering how recommendation systems can help your company? Contact us so that we can help you find the best solution for your needs. You can also learn more about data science.

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