How do you identify the business objectives of a data mining project?

To effectively identify the business objectives of a data mining project, particularly in defining the problem, adopt a 'Structured Problem Identification Method'. This method involves a thorough analysis of the business environment to pinpoint specific challenges or opportunities that data mining can address. It includes consulting with key stakeholders, reviewing business performance metrics, and analyzing industry trends. By clearly defining the problem, this method ensures that the data mining project is tightly aligned with specific, measurable business objectives, such as increasing efficiency, identifying new market opportunities, or enhancing customer experiences.

Inicia por plantear el problema de negocio que se busca abordar: mejorar audiencias de clientes, optimizar ofertas promocionales, maximizar eficiencia en abastecimiento, disminuir rotación de personal, etc. Para éste objetivo, ?qué información sería ideal tener? ?Con qué información cuentas actualmente? ?Has estimado el valor/retorno/riqueza que significa cumplir el objetivo al 100%? ?Cuánto tiempo y presupuesto tienes disponible? Ante múltiples proyectos es recomendable priorizar por aquellos que presenten el mayor beneficio y menor uso de recursos. Asegura alineación con áreas y líderes; puede se requieran recursos que hoy no tienes, como “expertos en data,” capacitación interna, software o incluso la misma data a recopilar.

I will use a combination of few well know techniques in understanding the business objectives in data mining project. 1- Understanding the current state of business, a problem statement and future state 2- Understanding the data flow across business, its different data sources and SWOT analysis of it. 3-Once we understand the problem the defining end goals which are SMART in nature would be easy. 4- I will use PDCA- Plan Do Check Act, a market research to understand the competitors. Above all can be implemented in defining the objectives of data mining projects. Data mining nothing but making unstructured data into workable and understandable. Drawing meaningful insights from the given raw data

For any requirements or problems, I personally use "The Golden Circle" approach on identifying: Why, What & How. This may look simple but the information we gather through this process will help the team to understand in an effective way. Using the same example quoted in this question, a problem definition could be: "How can we increase customer retention by 10% in the next quarter?" but "Why we need to increase ?", "What is customer retention ?", "How we are going to increase it" will add more insightful aspects to the problem definition itself.

To identify the business objective for data mining is first to define the problem DNA. See where we are and where we want to reach.

Las preguntas a responder, dirán si la operación diaria de tu negocio ya genera la información que requieres (Data que se puede estar registrando… o no), o si es necesario salir a buscar nueva información. Si ya cuentas con Data: 1- asegura provenga de una fuente confiable, que sea certera y vigente. 2- valida se pueden responder las preguntas del proyecto; lo perfecto es enemigo de lo bueno, pero evalúa el costo de mejorar esa Data. 3- ?tienes suficiente como para que sean estadísticamente significativos? Si no cuentas con Data: 4- ?Tienes forma de obtenerla directamente? Si hubiese un 3ro que tenga la data, ?te basta con generar proyectos con data de 3ros (3P, sin manejar directamente esa data como 1P-0P) o requieres ser due?o de ésta?

You need to evaluate the availability, quality, and suitability of the data that you have or need for the project. Method of data collection, You should also explore the data using descriptive statistics, visualizations, and data mining techniques to understand its characteristics, patterns, and relationships. For example, you may want to check the data for missing values, outliers, or inconsistencies, and perform data cleaning and preprocessing if needed.

Assessing the data is a deep dive into it's true potential. We need to think about: Data Quality: assess accuracy, completeness, and consistency. Missing values and inconsistencies can skew results, so identify and address them head-on. Data Relevance: align the data with your potential objectives. Can it actually answer the questions you're asking? Explore data relationships and patterns to uncover hidden gems that might guide your goals. Data Granularity: is the data granular enough to provide actionable insights? Can you segment or aggregate it to reveal hidden trends and nuances? These 3 should be part of your data compass to ensure your business objectives are grounded in reality and you drive real business impact

The most challenging part of a data project is identifying the appropriate data for a project. In my line of work, if this activity is left to the data scientist alone, the project is bound for failure. To select the right data, you must first interview the people who enter the data and those who use the data. If you do not consider the context and the use of the data, it is highly likely that data cleaning and transformation assumptions will be faulty. As a consequence, the project could lead to wrong results and therefore to sub-optimal decision-making.

Assessing the data is perhaps the most important step in this process. There are two part to it, a) knowing what is available and b) evaluating what is available is accurate. A good data governance setup helps to ensure this. Understanding the dimensionality of the data and relationship of various features is the key as well. Associating the right variables with the problem statement then becomes paramount.

Definido el problema, las preguntas correctas, entendiendo los campos de Data requeridos vs Data disponible, se puede tener un estimado del costo y tiempo para poder tener las primeras respuestas, descubrimientos y aprendizajes. Es muy fácil ponerse ambicioso y querer saberlo todo, por lo que procura dar foco al problema priorizado y ser consistente con las métricas que definan el éxito. El objetivo debe ser S.M.A.R.T., donde la métrica a mejorar debe tener mediciones actuales o al menos recientes, cosa de contrastar con su valor al final del periodo que dure el proyecto. Un ejemplo: Para promedio mensual 2024, aumentar en 15% la cantidad de clientes que compran los productos “X” e “Y” simultáneamente en su compra mensual vs promedio 2023.

Increase customer loyalty by finding reasons for customers leaving and creating strategies to keep them. The goal is a 15% improvement in retention within the next fiscal year. This involves using data mining to identify crucial factors impacting customer churn. The aim is to implement targeted measures for retaining customers. This straightforward objective focuses on understanding and addressing the reasons behind customer attrition for sustainable business growth.

An important objective from the Clinical Trials perspective will be to identify reasons of screen failure from different study designs and leverage that Intel to develop a protocol (which can also address the regulatory and scientific need)

Step three involves defining the goals of the data mining initiative by leveraging the problem statement and data evaluation. These objectives must outline the project's intended outcomes, measurement metrics, and criteria for assessing results. It's essential that the objectives adhere to the SMART criteria—Specific, Measurable, Achievable, Relevant, and Time-bound. For instance, a goal might be: "Identify pivotal factors impacting customer retention and construct a predictive model categorizing customers into distinct retention segments by month-end."

In my opinion, customer loyalty is a never-ending process that needs constant analysis and adaptation. Data mining can provide valuable insights to enhance customer loyalty, but it's crucial to question the results and incorporate other data sources and insights.

Hay veces que un objetivo se puede lograr de múltiples formas: se puede aumentar la base actual de clientes a) aumentando el volumen de captación de nuevos clientes b) re-capturando clientes antiguos que hoy no están comprando c) disminuyendo la tasa de deserción de los clientes actuales Las distintas alternativas suelen tener distinto tama?o de oportunidad y diferentes niveles de complejidad, por lo que nuevamente hay que priorizar según costo-beneficio de cada alternativa. Ten en consideración las restricciones de tiempo y recursos, para ir por la forma que mejor se adecúe a solucionar el problema del proyecto.

Prioritize the objective by assessing its impact on overall business goals. Consider the potential benefits of increased customer retention, such as enhanced revenue and customer satisfaction. Evaluate the feasibility and resources required for successful implementation. Additionally, align the objective with the current business environment and market trends. By weighing these factors, you can prioritize the objective based on its significance and potential contribution to the organization's success.

In prioritizing data management objectives, ensuring data security stands as a paramount concern. Safeguarding sensitive information against breaches or unauthorized access is critical. Following this, maintaining data accuracy and quality is essential to derive reliable insights and support informed decision-making. Accessibility, enabling swift and efficient retrieval of data, is another key objective, ensuring data is available for analysis and use. Additionally, optimizing storage infrastructure for scalability and cost-efficiency, complying with regulations, and fostering a data-driven culture through proper training and awareness are also significant priorities in effective data management strategies.

Align project goals with organizational strategy-is this about boosting revenue, reducing churn, or optimizing operations? Next, scrutinize data availability and quality-is the data readily accessible, consistent, and trustworthy? Can you clean and pre-process it to meet analytical needs? Prioritize objectives that extract maximum value from available resources. Then there's technical feasibility-consider limitations in computing power, software, and team expertise. Prioritize objectives that leverage existing capabilities and don't require a leap. Finally, weigh potential returns against effort. Estimate the resources and time required for each objective. Prioritize those with the highest projected impact relative to investment.

This step is very important, specially if you are working with a lean team. Assessing value of the objective will be crucial, so it would be important to understand short term and long term value. Again aligning with company OKR or important KPIs help in prioritization.

Step five involves effectively conveying the goals of the data mining initiative to all relevant parties, such as the project team, management, clients, and end-users. This communication must be transparent, brief, and uniform, outlining the project's reasoning, advantages, and anticipated outcomes. Additionally, it should seek feedback, input, and endorsement from stakeholders while addressing their inquiries, apprehensions, or objections. Clearly defining the roles, duties, and expectations of each stakeholder is also a crucial aspect of this communication process.

Communication is the key. It would be much easier to pursue a data mining project if we can buy in from key stakeholders. Before the data team assigns the value it is important to solicit feedback and also set a deadline for the project. Most of the times data projects can easily drag on as we discover more insights but the data team should be able to draw a line and share with stakeholders on an ongoing basis.

While communicating objectives, it is also important to clearly communicate the expectations that align with the objectives; which will later play a key role when it comes to change management. Leaving the comfort zone for many people is not an easy task for most people but by clearly articulating what is expected during the early stages is a contributing factor to a successful implementation

identify the objectives of a data mining project, engage in open communication with key people in the business. Discuss their challenges, goals, and questions they have about improving processes or making better decisions. This communication helps clarify the specific objectives the business aims to achieve through data mining.

I agree that stakeholder management is also a very important part of deliverables. I always make sure that my deliverables are explained in non-technical terms and relate it to business operations so they could easily understand how does the solutions helps in business operations in the future.

In the last year, my team conducted a review the barriers preventing staff from requesting accessibility accommodations for reported disabilities. Along the way, not only did we discover multiple new barriers but also found some source data was federally protected information and would not be accessible to us. We had to find new data sources from which we could extrapolate relevant information. In this case, data access was a limiting factor and changed the direction of the data mining project to ensure that we would continue to have reliable and available data.

The objectives should be encompassed by the business needs. What work yesterday, does not necessarily mean it would work today. What methodology would suffice to ensure that the objectives identified is supportive to Business needs. Most instances of Data Mining are, to strengthen the organizational data core, streamlining and optimizing existing data.

Identifying the business objectives of a data mining project involves understanding the specific goals and outcomes that the business aims" 1. Problem Definition:Clearly defining the business problem or opportunity that the data mining project aims to address. 2. Stakeholder Engagement: Engaging stakeholders to determine their needs, expectations, and desired outcomes. 3. Defining Success Metrics: Establishing measurable key performance indicators (KPIs) that align with the business goals. 4. Resource Allocation: Understanding the resources needed (financial, technological, human) to execute the project successfully. 5. Risk Assessment: Identifying potential risks that could impact the achievement of business objectives.

Especially in data projects, there is often still the idea that a project setup must last for years. “We discussed it differently”, “That wasn’t what you asked for”, “You don’t know what you want” are common reactions. Similar to step 5, the challenge lies in a massive change in the requirements for data managers in recent years. Changing conditions, rapid changes in company or project strategy and an understanding of the background of data projects are critical to success in project teams. In reality, most companies check the course of their data strategies too rarely rather than too often.

Embrace flexibility and maintain a clear goal-setting approach to easily assess project outcomes. The key here is elasticity. As outlined in the final step, regularly review the objectives of the data mining project, adapting them as necessary. Monitor progress, performance, and outcomes, comparing them with the initial objectives. Evaluate the validity, reliability, and relevance of data, methods, and results, identifying changes, challenges, or opportunities that may impact the project. Engage stakeholders in the review process and communicate findings, insights, and recommendations. This adaptability ensures the project remains aligned with its goals in an ever-evolving landscape.

- Align with organizational strategy: ensure that the objectives of the data mining project are in alignment with the overall strategy and long-term goals of the organization. - Legal and Ethical Compliance: consider legal and ethical aspects, especially in terms of data privacy and usage. The objectives should comply with all relevant laws and ethical guidelines.

ITERATE Iteration stands as a foundational concept, embodying the repetitive execution of a series of steps or actions aimed at attaining optimal results. In data mining projects, the iterative approach is frequently harnessed to enhance and fine-tune models. Initial models are built leveraging available data. Subsequent iterations involve adjusting model parameters to augment accuracy and reliability. Several iterative processes characterize data mining endeavours, encompassing tasks such as data preprocessing, feature selection, engineering, and parameter tuning. These iterative cycles contribute to the continuous refinement and improvement of models, ensuring their alignment with the desired outcomes.

Dans les projets de datamining, je crois beaucoup à une approche laboratoire/ industrialisation. Implémenter une boucle apprenante en élargissant progressivement les scopes m'a semblé le plus efficace pour embarquer de plus en plus de collaborateurs engagés et surtout permettre en terme de gouvernance d'affiner et de confirmer les objectifs métiers.

In data mining projects a crucial part is to understand that the mining process is not a one time thing. It's a continuous improvement and refining process, where output should be evaluated and executed n times until all results are coherent, valid, reproducible and not possible to improve in a significant way. This is a concept is known as iteration.

Data mining alligned with business inteligence are two incredible tools to achieve every corporative goals. This can contribute organizing better teams and complete companies. Once you know what the company needs, team objectives are clear. Steps that have worked for me are 1. Collecting all important data 2. Speaking with decission makers within the organization 3. Define goals (economic and cultural within the organization) 4. Define performance metrics. 5. Storytelling collected data to team leaders. 6. Measure progress. 7. Evaluate results and repeat cycles. This leads to continuous improvement to achieve goals.