What types of data should you consider when planning a data mining project?

In my industry (Oil & Gas), It always depends on data provider. (internal, external & 3rd party) Each company, function, system and data type has its standards, platforms and data governance KPIs. Also I'd think about hosting real time data into the model smoothly by AI to extract, standardize, ingest and review data in short time as this is the real nightmare if it's not well managed due to the large sum of data and the critical business decisions that depend on them. I prefer dashboards for validity check and completeness. I believe integrated platforms that can host structured and unstructured would be a good start linked with an updated (auto sync-ed) dashboard.

When planning data mining, look at diverse sources like customer databases, social media, and website logs. Include internal data, like sales records, and external data, such as public datasets. Combine structured data (like tables) and unstructured data (like text) for a fuller picture. Don't forget feedback from customer service or surveys—it's valuable. The more varied your sources, the richer and more useful your mined data will be.

The planning stage is the best moment to gather the team, propose the question "What are we possibly missing?" and write down suggestions. What we tend to neglect will deeply undermine the whole project. For sure the group will regret any hidden aspect here. The initial phase is not about technology but instead has more to do with bias and preconceptions. Also it's necessary to write down the theoretical outcome: What we envision -as a group- to be the result. Avoid chit-chat, don't fool ourselves, stick to the facts. Finally I would suggest to calculate the cost of the project and to compare it with theoretical revs and with the impact of "not doing it". Let's not be naive here.

The diversity of data sources - internal/external, structured/unstructured, historical/real-time - poses unique challenges and challenges. Identifying your methods and tools to each data type is essential for effective data collection and integration. Techniques such as web scraping, using APIs, and working with various databases and file formats are essential skills in today's data-driven economy. The process of cleaning, transforming, and merging data is essential to ensure quality and readiness for analysis.

another things that comes to mind is to analyze the related metadata . metadata can be extremely powerful in understanding behaviours

Data quality is crucial. Make sure your data is accurate, complete, and up-to-date. Check for errors and inconsistencies. Reliable data leads to better insights and decisions. Regularly validate and clean your data to maintain its quality over time. Remember, the success of any analysis or decision-making depends on the quality of the data you use.

Most of the times in any organization, the need to measure and improve data quality is seen as a future step or a kind of reactive step because we are enthusiastic about transforming whatever data we currently have into meaningful insights. This becomes a major issue to solve for later, when we have too much data at disposal but we aren’t sure whether it is of good quality/can be trusted enough. While it is absolutely important to put data to good use, it is equally crucial to follow a proactive approach ensuring the data being put to use is of good quality too.

It is absolutely essential to assess data quality in order to achieve reliable data mining outcomes. Data quality, which encompasses accuracy, completeness, consistency, and relevance, directly impacts the integrity of data mining results. Issues such as missing values, outliers, and duplicates can significantly alter analysis, resulting in erroneous conclusions. It is crucial to employ techniques such as imputation for missing data, normalization, and standardization to address these issues. Feature engineering can also contribute to enhancing data quality by creating more informative data variables.

In my experience most organizations have upwards of thousands on duplicate files, this can be a huge task to undertake though if you can dedicate time and effort to de-duplicating files this can save a lot of money via storage and litigation costs in the future. If organizations do not have internal info governance specialist it would behoove them to hire a consultant to de-duplicate data prior to any type of data mining project.

Ceci doit être réalisé avec le métier car seul lui est en mesure de déterminer la qualité des données au sens métier. Plus la qualité est prise en compte t?t dans le cycle moins il y a de problème de compréhension des résultats par la suite.

Once you've identified the dimensions of your data, the next critical aspect is feature selection and engineering. Here are some considerations for handling different types of data dimensions: 1. Numerical Data. 2. Categorical Data. 3. Ordinal Data. 4. Binary Data. 5. Text Data. 6. Temporal Data. 7. Geospatial Data. Remember that the choice of data encoding and feature engineering techniques depends on your specific data mining task and the algorithms you intend to use. Experiment with different approaches and assess their impact on the performance of your models to determine the most effective strategies for your data dimensions.

In data, dimensions are like categories or aspects that provide context. For example, in sales data, dimensions could be time, product, or location. Dimensions help organize and analyze information. They add depth to your understanding by allowing you to explore data from different angles. Using dimensions effectively can uncover patterns and trends in your data, enhancing your insights and decision-making.

Data dimensions are a crucial aspect of data mining. Recognizing the types of data dimensions - numerical, categorical, ordinal, and binary - is essential. Each type requires specific handling and techniques to be utilized effectively in data mining tasks such as classification, regression, or clustering. In a data scientist's arsenal, techniques such as one-hot encoding for categorical data, label encoding, or binning are essential tools. They help in transforming data into a format that algorithms can efficiently process.

To effectively mine your data, it's crucial to apply appropriate transformations, techniques, or models depending on the types of data dimensions present in your data. For instance, if you have categorical data, you can use one-hot encoding to convert it into numerical data. On the other hand, if you have numerical data, clustering algorithms can help you identify patterns. Therefore, it's essential to preprocess and transform your data correctly before applying data mining techniques to ensure effective data mining.

Attributes or Variables: These are the features that describe your data. For example, in a dataset about customer behavior, attributes might include age, income, spending habits, etc. Types of Data Dimensions: Numerical Data: Represents measurable quantities and can be either discrete or continuous. Examples include age, income, or temperature. Categorical Data: Represents categories and groups. Examples include gender, color, or product type. Ordinal Data: Represents ordered categories, but the intervals between them are not consistent. For example, a Likert scale where responses are "strongly disagree," "disagree," "neutral," "agree," and "strongly agree."

Exploring data distribution is a crucial step in resolving data mining processes. Understanding the distribution - through measures such as mean, median, mode, standard deviation, skewness, and kurtosis - is essential to selecting the right data mining techniques and metrics. It is influenced by how we interpret and validate our models. Adapting data for better distribution, using methods like log transformation, box-cox transformation, or z-score normalization, can significantly improve model accuracy and reliability

Data distribution refers to how data values are spread or arranged. It can be uniform, where values are evenly distributed, or skewed, where they concentrate more on one side. Understanding the distribution helps in statistical analysis and modeling. It's crucial for making accurate predictions and drawing meaningful conclusions from your data. Different distributions impact the performance of algorithms, so knowing the data distribution is key for effective data analysis and decision-making.

In data mining data distribution is important because it helps us determine the most appropirate statical method to use. Understanding the distribution of your data can help you choose the right statistical test, identify outliers, check for normality, and visualize the data.

1. Normality: Normality is often assumed in many statistical analyses. However, real-world data distributions can be asymmetric or follow different patterns. 2. Outlier Identification: Data distribution influences the identification of outliers, which are data points that are unusual in relation to the rest of the distribution. 3. Model Selection: Data distribution can influence the choice of statistical models. For example, some models assume a specific distribution. 4. Transformations: In cases of non-normal distributions, transformations such as logarithmic can be applied to better fit certain models or analyses. 5. Visualization: Plots like histograms and density plots are useful for understanding the distribution of data.

At the insurance company level, data distribution refers to how the data collected is structured and dispersed. It is essential to consider uniformity and balance in the availability of information in key areas such as claims, underwriting and policy management. Effective distribution of demographic, geographic and claims details contributes to more accurate risk assessment. In addition, data management must ensure consistency and accessibility to support strategic analysis and operational decisions within the insurance industry.

Watch out for the lawyers! If your data mining project is being conducted at the request of lawyers, for purposes of producing to adversaries in a litigation, don't assume that the adversary lawyers are going to have proper privacy protections in place. Make sure your lawyers are asking all the right questions and obtaining written guarantees - and ensuring that the data is returned when the litigation is resolved.

Data privacy involves protecting sensitive information. It means controlling who has access to data and how it's used. Safeguarding personal details, like names or addresses, is crucial. Implement encryption, access controls, and follow regulations like GDPR to ensure privacy. Regularly audit and update security measures to adapt to evolving threats. Prioritizing data privacy builds trust and helps prevent unauthorized access or misuse of sensitive information.

Methods for Safeguarding Data Privacy: Encryption: Protect sensitive data by converting it into a coded format that can only be deciphered with the right encryption key. Anonymization: Remove personally identifiable information (PII) from the dataset, making it difficult or impossible to identify individuals. Pseudonymization: Replace identifying information with artificial identifiers, allowing for data analysis while protecting individual identities. Consent Management: Obtain explicit consent from individuals before collecting and using their data. Ensure that individuals are aware of how their data will be used.

??It starts with consent! If you are collecting sensitive data, do you have the data owner’s consent to collect, use, store, transmit, share, what is the expiry/ destruction process… and how will it be secured across the entire lifecycle. Are terms of data use clear?

La vision éthique est nécessaire pour éviter l'inéquité dans les traitements d'IA. Il faut prendre en compte au niveau des jeux de données d'entra?nement un équilibre pour éviter les biais liés aux données qui engendrent un algorithme qui n'est pas équitable. Il faut faire attention à la combinatoire des champs servant dans votre algorithme pour éviter toutes dérivés. Pour cela, il existe des moyens de tester ces données et faire en sorte que celles-ci soient représentatives et equitables

While privacy and ethical considerations are essential when organizations use data, often ethical considerations are not a priority to administrators who are solely focused on achieving their goals. This can lead to potential misuse or mishandling of data, putting individuals' privacy at risk. It is crucial for organizations to prioritize ethical considerations and implement strict guidelines to ensure responsible data usage and protect the rights of individuals. Ignoring ethics in data management is costly and can lead to severe consequences for the organization.

In my experience data value is driven by the relevance it provides to the outcomes and visualisation aspect adds different perspectives. For example, in CPG industry according to market data, requires for a store within 2 miles however, putting that visually might show the impact of having additional store might be greater in comparison to economically driven decision.

In all data mining projects in health care industry, it's important to ensure compliance with privacy regulations (such as HIPAA) to protect patient confidentiality and to obtain proper consent for data usage. Then consider diverse healthcare data types: EHRs for patient info, claims data for services, clinical trials for treatments, health surveys for lifestyle data, genomic data for personalized medicine, and wearable data for real-time monitoring.

Insights backed by data is valuable if you can position /present it well … then you just need to find out who has the most to gain from these insights …. as it could mean saving a considerable amount of time, money, resources, unlock growth opportunities, or assist with legal discovery ??

La base para monetizar datos es simple: cuente con una repositorio con datos "saludables y nobles". Siendo que saludables nos indica el índice de calidad de los datos claves para ser usados en el modelo analítico que se desarrolla. Desconocer sus niveles de calidad nos lleva al 'espejo de la pirámide DIKW', y en vez de conocimiento terminamos en dudas. Mientras que datos nobles hace referencia a que son datos conocidos, con sus descripciones, su taxonomía, identificado su origen, conocido su custodio responsable; se sabrá con certeza cuáles datos son los que se deben utilizar como entrada en el caso de uso analítico en desarrollo. Partiendo de esta simple e importante premisa se establece el valor que entrega el caso de uso analítico.

When embarking on a data mining project, strategic success hinges on the judicious selection of data types. Consider customer transactional data for behavior patterns, historical supply chain data for trend analysis, and real-time inventory data for agile decision-making. Each dataset serves as a critical puzzle piece in unlocking actionable insights.

In addition to prioritizing temporal data, it's crucial to consider aspects such as data quality, feature selection, preprocessing, model selection, validation, and ethical and legal considerations ...

It is also necessary to take into account the diversity of the data. Depending on the type of research, structured and unstructured data may be needed, which creates a challenge for data management by using different techniques and methods for processing and subsequent linking.

I think before actually working on the data, one needs to affirm the problem statement first and determine what exactly is the data we're looking for and what is available. This ensures effort efficiency as even extraction takes skilled effort and time as well.

When you are undergone in data mining. firstly you should approaches towards source of data. In my industry (Pharmaceutical), I prefer to identifies the source of data then validate and assess the risk associate with the data. According the risk assessment status, I usually prefer to establish the risk minimization activities.