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Diagnostic Analytics

    Diagnostic Analytics: In this analysis, we generally use historical data over other data to answer any question or for the solution of any problem. We try to find any dependency and pattern in the historical data of the particular problem.
    For example, companies go for this analysis because it gives a great insight into a problem, and they also keep detailed information about there disposal otherwise data collection may turn out individual for every problem and it will be very time-consuming.
    Common techniques used for Diagnostic Analytics are:

    • Data discovery
    • Data mining
    • Correlations

    Data Discovery

     “We are drowning in information but starved for knowledge” according to best selling author, John Naisbitt. Today’s businesses can collect piles of information on everything from customer buying patterns and feedback to supplier lead times and marketing efforts. Yet it is nearly impossible to draw value and truth from the massive amount of data your business collects without a data discovery system in place.

    Data discovery is a term related to business intelligence technology. It is the process of collecting data from your various databases and silos, and consolidating it into a single source that can be easily and instantly evaluated. Once your raw data is converted, you can follow your train of thought by drilling down into the data with just few clicks. Once a trend is identified, the software empowers you to unearth the contributing factors.

    For instance, BI enables you to explore the data by region, different employees, product type, and more. In a matter of seconds, you have access to actionable insights to make rapid, fact-based decisions in response to your discoveries. Without BI, discovering a trend is usually a case of  coincidence.

    With data discovery, the user searches for specific items or patterns in a data set. Visual tools make the process fun, easy-to-use, swift, and intuitive. Visualization of data now goes beyond traditional static reports. BI visualizations have expanded to include geographical maps, pivot-tables, heat maps, and more, giving you the ability to create high-fidelity presentations of your discoveries.

    Discover trends you did not know where there

    With data discovery, executives are often shocked to discover trends they didn’t know were there. Michael Smith of the Johnston Corporation had this to say after implementing Phocas:

    “Five minutes into the demo, I had found items that didn’t have the margin I was expecting, customers that didn’t have the profitability I was expecting and vendors that weren’t performing the way I expected. I realised that we were onto something that would be very impactful to our business.”

    These discoveries allow companies to discover unfavourable trends before they become a problem and take action to avoid losses.

    Data Mining

    Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends.

    In data mining, association rules are created by analyzing data for frequent if/then patterns, then using the support and confidence criteria to locate the most important relationships within the data. Support is how frequently the items appear in the database, while confidence is the number of times if/then statements are accurate.

    Other data mining parameters include Sequence or Path Analysis, Classification, Clustering and Forecasting. Sequence or Path Analysis parameters look for patterns where one event leads to another later event. A Sequence is an ordered list of sets of items, and it is a common type of data structure found in many databases. A Classification parameter looks for new patterns, and might result in a change in the way the data is organized. Classification algorithms predict variables based on other factors within the database.

    Clustering parameters find and visually document groups of facts that were previously unknown. Clustering groups a set of objects and aggregates them based on how similar they are to each other.

    There are different ways a user can implement the cluster, which differentiate between each clustering model. Fostering parameters within data mining can discover patterns in data that can lead to reasonable predictions about the future, also known as predictive analysis.

    Data mining tools and techniques

    Data mining techniques are used in many research areas, including mathematics, cybernetics, genetics and marketing. While data mining techniques are a means to drive efficiencies and predict customer behavior, if used correctly, a business can set itself apart from its competition through the use of predictive analysis.

    Web mining, a type of data mining used in customer relationship management, integrates information gathered by traditional data mining methods and techniques over the web. Web mining aims to understand customer behavior and to evaluate how effective a particular website is.

    Other data mining techniques include network approaches based on multitask learning for classifying patterns, ensuring parallel and scalable execution of data mining algorithms, the mining of large databases, the handling of relational and complex data types, and machine learning. Machine learning is a type of data mining tool that designs specific algorithms from which to learn and predict.

    Benefits of data mining

    In general, the benefits of data mining come from the ability to uncover hidden patterns and relationships in data that can be used to make predictions that impact businesses.

    Specific data mining benefits vary depending on the goal and the industry. Sales and marketing departments can mine customer data to improve lead conversion rates or to create one-to-one marketing campaigns. Data mining information on historical sales patterns and customer behaviors can be used to build prediction models for future sales, new products and services.

    Companies in the financial industry use data mining tools to build risk models and detect fraud. The manufacturing industry uses data mining tools to improve product safety, identify quality issues, manage the supply chain and improve operations.

    Text Mining

    Text mining (text analytics) is the process of exploring and analyzing large amounts of unstructured text data aided by software that can identify concepts, patterns, topics, keywords and other attributes in the data. It’s also known as text analytics, although some people draw a distinction between the two terms; in that view, text analytics refers to the application that uses text mining techniques to sort through data sets.

    Text mining has become more practical for data scientists and other users due to the development of big data platforms and deep learning algorithms that can analyze massive sets of unstructured data.

    Mining and analyzing text helps organizations find potentially valuable business insights in corporate documents, customer emails, call center logs, verbatim survey comments, social network posts, medical records and other sources of text-based data. Increasingly, text mining capabilities are also being incorporated into AI chatbots and virtual agents that companies deploy to provide automated responses to customers as part of their marketing, sales and customer service operations.

    How text mining works

    Text mining is similar in nature to data mining, but with a focus on text instead of more structured forms of data. However, one of the first steps in the text mining process is to organize and structure the data in some fashion so it can be subjected to both qualitative and quantitative analysis.

    Doing so typically involves the use of natural language processing (NLP) technology, which applies computational linguistics principles to parse and interpret data sets.

    The upfront work includes categorizing, clustering and tagging text; summarizing data sets; creating taxonomies; and extracting information about things like word frequencies and relationships between data entities. Analytical models are then run to generate findings that can help drive business strategies and operational actions.

    In the past, NLP algorithms were primarily based on statistical or rules-based models that provided direction on what to look for in data sets. In the mid-2010s, though, deep learning models that work in a less supervised way emerged as an alternative approach for text analysis and other advanced analytics applications involving large data sets. Deep learning uses neural networks to analyze data using an iterative method that’s more flexible and intuitive than what conventional machine learning supports.

    As a result, text mining tools are now better equipped to uncover underlying similarities and associations in text data, even if data scientists don’t have a good understanding of what they’re likely to find at the start of a project. For example, an unsupervised model could organize data from text documents or emails into a group of topics without any guidance from an analyst.

    Applications of text mining

    Sentiment analysis is a widely used text mining application that can track customer sentiment about a company. Also known as opinion mining, sentiment analysis mines text from online reviews, social networks, emails, call center interactions and other data sources to identify common threads that point to positive or negative feelings on the part of customers. Such information can be used to fix product issues, improve customer service and plan new marketing campaigns, among other things.

    Other common text mining uses include screening job candidates based on the wording in their resumes, blocking spam emails, classifying website content, flagging insurance claims that may be fraudulent, analyzing descriptions of medical symptoms to aid in diagnoses, and examining corporate documents as part of electronic discovery processes. Text mining software also offers information retrieval capabilities akin to what search engines and enterprise search platforms provide, but that’s usually just an element of higher level text mining applications, and not a use in and of itself.

    Chatbots answer questions about products and handle basic customer service tasks; they do so by using natural language understanding (NLU) technology, a subcategory of NLP that helps the bots understand human speech and written text so they can respond appropriately.

    Natural language generation (NLG) is another related technology that mines documents, images and other data, and then creates text on its own. For example, NLG algorithms are used to write descriptions of neighborhoods for real estate listings and explanations of key performance indicators tracked by business intelligence systems.

    Benefits of text mining

    Using text mining and analytics to gain insight into customer sentiment can help companies detect product and business problems and then address them before they become big issues that affect sales. Mining the text in customer reviews and communications can also identify desired new features to help strengthen product offerings. In each case, the technology provides an opportunity to improve the overall customer experience, which will hopefully result in increased revenue and profits.

    Text mining can also help predict customer churn, enabling companies to take action to head off potential defections to business rivals as part of their marketing and customer relationship management programs. Fraud detection, risk management, online advertising and web content management are other functions that can benefit from the use of text mining tools.

    In healthcare, the technology may be able to help diagnose illnesses and medical conditions in patients based on the symptoms they report.

    Text mining challenges and issues

    Text mining can be challenging because the data is often vague, inconsistent and contradictory. Efforts to analyze it are further complicated by ambiguities that result from differences in syntax and semantics, as well as the use of slang, sarcasm, regional dialects and technical language specific to individual vertical industries. As a result, text mining algorithms must be trained to parse such ambiguities and inconsistencies when they categorize, tag and summarize sets of text data.

    In addition, the deep learning models used in many text mining applications require large amounts of training data and processing power, which can make them expensive to run. Inherent bias in data sets is another issue that can lead deep learning tools to produce flawed results if data scientists don’t recognize the biases during the model development process.

    There’s also a lot of text mining software to choose from. Dozens of commercial and open source technologies are available, including tools from major software vendors, including IBM, Oracle, SAS, SAP and Tibco.

    Web Mining

    In customer relationship management (CRM), Web mining is the integration of information gathered by traditional data mining methodologies and techniques with information gathered over the World Wide Web. (Mining means extracting something useful or valuable from a baser substance, such as mining gold from the earth.) Web mining is used to understand customer behavior, evaluate the effectiveness of a particular Web site, and help quantify the success of a marketing campaign.

    Web mining allows you to look for patterns in data through content mining, structure mining, and usage mining. Content mining is used to examine data collected by search engines and Web spiders. Structure mining is used to examine data related to the structure of a particular Web site and usage mining is used to examine data related to a particular user’s browser as well as data gathered by forms the user may have submitted during Web transactions.

    The information gathered through Web mining is evaluated (sometimes with the aid of software graphing applications) by using traditional data mining parameters such as clustering and classification, association, and examination of sequential patterns.

    In summary: Both descriptive analytics and diagnostic analytics look to the past to explain what happened and why it happened. Predictive analytics and prescriptive analytics use historical data to forecast what will happen in the future and what actions you can take to affect those outcomes. Forward-thinking organizations use a variety of analytics together to make smart decisions that help your business.