How can you define and scope data analytics projects and portfolios to meet client needs?

I have seen that the most underrated (although the most talked about) idea is to truly understand the client’s problem. Why they have called you? What’s troubling them? Remember that sometimes client’s problem can be solved by easiest of the tools (like Excel) and simplest of the models (like Multiple Linear Regression).

I think most of the time we try to fit everything we know about an IT problem or solution in a box so we can try to perfectly define the solution. This never works. I suggest forming an Agile/Value Driven team with a Scrum Master, Product Owner and a stand-alone Dev Team that can deliver everything the customer needs. Eliminate all dependencies and handoffs with external teams and iteratively deliver the solution. This is accomplished by improving the product over a short period of time based on a continuous feedback loop created with the Customer and the Dev Team.

Defining scope of the project is very critical for success or failure of BI projects. - Conduct meetings with Business to understand the business problem they are trying to solve - Engage with analysts and data warehouse teams to understand what is readily available and what is not - Discuss any assumptions you make with the business and get approval

To define and scope data analytics projects effectively, delve into the client's business context. Understand their goals, challenges, resources, and more by asking pertinent questions and actively listening. Identify key stakeholders, discern their roles and expectations. This comprehensive understanding enables alignment of your data analytics approach with the client's overarching vision, mission, and values, fostering a purposeful and tailored project scope.

In terms of management goals and business objectives, sometimes conflict may arise among stakeholders due to differences in priorities, resource constraints, etc. For example, in developing a credit risk model, the growth or sales team would normally aim to increase market share, while on the other hand, risk managers would focus on credit risk reduction. In addition, auditors and compliance officials would aim for regulatory compliance and model risk management. The unique priorities and concerns of various key stakeholders must be carefully considered and integrated into the business requirements gathering phase of any data analytics project.

Scope defination is very important part for any BI project. It will help to identify what all data is required and how we want to source same. It also helps identify transformation, cleaning and enrichment to the data that will be needed, before it is fed into dashboards. Also building models around same and presenting drafts will help confirm user perspective.

If you ask “what’s the business problem?” Then you’re often going to get a filtered and edited response because that is not always clear; people don’t want to look clueless. Instead there needs to be some additional work to refine the problem/outcome scenario Start by asking things that are closer to the person you’re interviewing such as “tell me a little about your role”. People love to talk about themselves so this will help you understand more about this person in the context of the project. It also gives you more insight into the role they can play. Follow up with questions like “what does success look like?” And “how do you think this project will help you?”. These answers will reveal a great deal of the scope etc

In a recent project, one thing I have found useful was identifying the organization's biggest challenges and pain points as well as prioritizing requirements with the stakeholders. This will lead to the development of an agile project plan in accordance with stakeholders requirements and ensures the project plan addresses the most critical or value-adding data analytics needs within the organization.

Problem Statement: A problem statement in data analytics outlines the specific issue or challenge you aim to address through data analysis. It should be concise, clear, and actionable. Clearly identify the problem or challenge that needs to be solved. What are the measurable outcomes or key performance indicators (KPIs) will demonstrate that the problem has been addressed or the question answered? Value Proposition: A value proposition in data analytics communicates the benefits and value that will be derived from solving the problem defined in the problem statement. It explains why investing time and resources in data analytics is worthwhile.

In my experience, Scoping starts with the strategic business goals and then chucking it down to the individual metrics level, which are required to drive those business outcomes. Once that is sorted its time to engage the data, align the insights (through descriptive and predictive). This way we will be able to scope a program of data analytics project and achieve wholistic outcome.

1) Start by understanding the organization's business objectives and goals. 2) Engage with various stakeholders, including business analysts, data analysts, data scientists, and domain experts, to gather insights into their data needs. 3) Identify the specific use cases and scenarios where data engineering is required. 4) Start by cataloging and documenting all existing data sources within the organization. 5) Categorize the data sources by types, such as structured, semi-structured, or unstructured data. 6) Assess the quality and reliability of the data from each source. 7) Define data security and access control requirements. 8) Choose the appropriate data engineering tools and technologies based on the data sources and requirements.

Para identificar fontes de dados e requisitos, come?o determinando onde os dados necessários est?o localizados e como acessá-los. Em seguida, defino as especifica??es dos dados, incluindo tipo, formato, qualidade, quantidade e frequência. Realizo um inventário e uma auditoria dos dados existentes para entender o que está disponível e identificar quaisquer lacunas. Além disso, considero as quest?es de governan?a, ética e seguran?a dos dados para garantir conformidade e prote??o durante a coleta e o uso.

It is important to have general knowledge of what data is available first, before discussing the business problem. If there's no possibility of obtaining the correct data than that conversation needs to happen first before diving to deep into the business problem specifics.

Identifying data sources and requirements is pivotal in data analytics. Thoroughly assess the availability and relevance of data, ensuring it aligns with the project's goals. Establish clear workflows for data handling, including extraction, transformation, and loading processes. It's crucial to document every aspect, from data source descriptions to the methodologies for deriving new KPIs. This documentation not only provides a roadmap for the project team but also ensures transparency and reproducibility, facilitating stakeholder understanding and buy-in.

Data sources might seem like an obvious point to consider, but you'd be surprised how many companies have incomplete or biased data. To ensure the value of your work and avoid the need for redoing your analysis, before commencing the project, ensure that you evaluate whether you have access to all the collected data—not just a portion of it. An example is when a company believes that providing access to only 10 trees from their forest is sufficient for analyzing what's happening in the entire forest. They might grant you access to part of the data while unconsciously withholding the rest of the information. As the analyst, you must be aware of this mistake and ask for all the company's data sources that could be related to your project.

Some common data analytics tools and platforms include: Python: Widely used for data analysis, with libraries like pandas, NumPy, scikit-learn, and more. R: A language and environment for statistical computing and graphics. SQL: Essential for querying and analyzing structured data in databases. Apache Hadoop: For processing and analyzing big data using distributed computing. Apache Spark: A powerful data processing framework for big data analytics. Jupyter Notebook: A popular interactive environment for data analysis and visualization. SAS: Offers a suite of analytics tools for various data-related tasks. Alteryx: A platform for data blending, preparation, and advanced analytics.

There is general trend to chose the latest and most talked about tool for any new project. e.g. In many of projects we implement, during final presentation to user, we get requirement to run it and send summary on email in some format. If this feature is not given out of box by the tool, we keep searching for workarounds and that becomes a pain to build and maintain.

Selecting the right data analytics methods and tools involves balancing expertise, ease of use, stakeholder familiarity, and cost. Opt for tools and methodologies that the team is proficient in to ensure efficiency and accuracy. Choose user-friendly tools to facilitate smoother project execution and stakeholder engagement. Consider what your stakeholders are accustomed to; familiarity can aid in better understanding and acceptance of results. Lastly, factor in the cost to ensure the selected tools and methods deliver value without exceeding budget constraints. This strategic selection is key to the project's success.

It should be an Agile approach. Evaluate the current technology stack, including data storage, data processing, and analytics tools. On basis of this we can choose any like KNIME,Python, Alteryx , R and R shiny. For data visualisation we can use Tableau , R shiny ,qlik, power bi if it is small projects we can project it in excel as well

This is one of the major step when you handle projects.. we should always maintain transparency and open communication to provide milestones of the projects. We need set clear project boundaries**: Clearly outline what is within scope and what is not. This includes specifying data sources, analysis methods, expected deliverables, and timelines.

.. It's crucial to foster open communication channels with the client throughout the project. Regular check-ins and feedback loops ensure that your data analytics strategy stays aligned with evolving business needs. Flexibility and adaptability are key to success in this dynamic field. #DataAnalytics #BusinessContext

Defining deliverables and creating a communication plan are essential steps in successfully managing a project or any organized effort. These elements help clarify expectations, set the course for the project, and ensure effective collaboration among team members and stakeholders. Begin by understanding the overall scope of the project. What are the objectives and goals? What is the problem you aim to solve or the value you intend to deliver?

Clearly define project deliverables like reports, dashboards, or presentations. Review these with the team, highlight key findings, and schedule a feedback session for comprehensive stakeholder engagement and iterative refinement. Ensure deliverables are well-defined, encompassing reports, dashboards, or presentations tailored to stakeholder needs. Conduct internal reviews to refine these outputs, emphasizing main insights. Document critical takeaways for clear communication. Proactively schedule feedback sessions post-delivery to allow stakeholder questions and facilitate further discussions, ensuring the project aligns closely with expectations and allows for iterative improvements.

The best way to save yourself a big hassle is to define the deliverables clearly. Have a written statement that your client has acknowledged, so when the project concludes, they are not surprised by what they are receiving. Furthermore, go the extra mile and clarify how communication and project delivery will occur. They might have different expectations than what is considered the norm and may not be aware of best practices.

Start by identifying all the resources needed for the project. This includes human resources, materials, equipment, and software tools. Break down the project into phases or milestones. Each phase should have specific objectives and deliverables. Identify the tasks required to complete each phase. List them in detail, and consider dependencies between tasks.

To successfully estimate resources and timelines for data analytics projects, ensure you conduct thorough research on market trends and technology advancements. This will help in creating a forward-looking estimate that accommodates potential disruptions and emerging opportunities. - Do remember to add in "buffer" - Additionally, maintain open communication channels with your team and stakeholders throughout the project to adapt resource allocation and timelines as needed, promoting flexibility and agility in project execution.

It always takes longer than you think. There will always be unforeseen consequences which you can not anticipate in project planning. Be flexible and lay out the potential barriers early on to set expectations.

A common pitfall in data analytics projects is the "work in a cave" approach, where teams isolate themselves and derive solutions based on their own understanding. This often leads to failure due to a lack of business context and stakeholder input. Recommended Approach Weekly Touch Points: Regular check-ins with stakeholders to keep them informed and engaged. Continuous Education: Keep stakeholders updated on findings throughout the project. Alignment Checks: Ensure data findings and business understandings are in sync. Inclusive Process: Involve business teams throughout the journey. Presenting just a final deliverable is a red flag and often leads to failure. Consistent communication and collaboration are crucial.

Defining and scoping data analytics projects begins by understanding the client’s goals. Engage in clear dialogue to identify their challenges and objectives. Next, prioritize tasks based on impact and feasibility. Determine data sources and ensure their integrity. Choose appropriate analytics tools and techniques in alignment with project objectives. Set clear milestones with measurable outcomes. Lastly, always factor in a feedback loop, ensuring adaptability and alignment with the client’s evolving needs. Remember, a successful data project blends client objectives with robust analytical approaches.

In addition to the above considerations, it is also important to identify the key stakeholders (especially business process SMEs) that you can actively collaborate with throughout the project dev’t process. Define the roles and responsibilities that you are expecting them to perform and the engagement protocols. The SMEs are crucial particularly for helping you to understand the key data elements, clarifying business rules, and validating initial results to ensure they are complete and accurate as well as fit for purpose.

Know when to stop. If the project is not running smoothly let people know. Get help. Stop the work, reset, reorient. It's always better to admit to mistakes early and often than building a bigger and more complicated problem.