How do you work with your analytics team?

Some of the very basic checks that I apply as someone overseeing the project are 1. Understand the business requirements 2. Equate the business requirements with team's area of expertise 3. Use Work Breakdown Structure (WBS) - a hierarchical decomposition of a project into smaller, manageable components called work packages and distribute it with the team aligning with their expertise and are of interest (very important). 4. Create JIRA tickets using CPA critical path analysis (management technique used to identify the sequence of activities that directly impact the project's completion date) 5. In a situation where we have to meet the deadlines use the resources with the highest level of expertise to complete the priority items

The first step is to determine the requirements and understand the needs of top managers because after all if you delve deeper into analysis without specific requirements and needs being articulated clearly from the top managers , you will have simply to dismantle all the project and start again so in that sense listening well to the managers during the periodic meetings helps a lot in understanding exactly what they want and expect from you as a data professional because ultimately the objective of data analysis is to deliver value by gleaning the insights that inform future decisions, so it’s incumbent upon you as data professional to inculcate such culture where managers plainly articulate their needs and expectations

In the world of data-driven decision-making, the synergy of minds between analytics teams and co-dependencies is always the compass that guides through the maze of uncertainty. Allowing agile methodologies between data collection experts, data analysts and Business Intelligence experts allows these teams to know what each other's needs are, challenges experienced at work and more so to understand what is required of each one. This forms the basis for a cohesive team and impeccable data companies

In my experience, regardless as role there are three factors that directly impact a data team: Availability, Clarity, and Effectiveness. Availability - Knowing how and where to access and store data. Clarity - Ensuring direction as well as reporting/documentation is provided in clear and plain language with limited use of technical jargon. Effectiveness - Is the process worth the time it takes away from another task? Does this enable the team to move fluidly between tasks or does it foster a start/stop relationship between old and new tasks?

As a data engineer, in my experience it’s definitely important to understand the requirement before gathering any data or building ETL pipelines. By knowing the right set of requirements, it’s easy for data engineers to build a scalable infrastructure and robust architecture to build pipelines for gathering data. Documenting can help but collaboration and effective communication is really important to stay aligned with different teams working with data.

Data warehouse can be established as the single source of truth (SSOT) for the business/ company. A database administrator or a developer is required to manage, maintain and troubleshooting the data warehouse. Some examples of commonly used data warehouse are: 1. Snowflake 2. Amazon Redshift 3. Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) 4. Google BigQuery 5. IBM DB2 Warehouse 6. Firebolt

A data warehouse is very essential for organizations to centralize and integrate data from various sources, improving data quality and storage. It also serves as a foundation for business intelligence, enabling data-driven decision-making. To establish a data warehouse, it's good to start by defining the objectives, selecting an architecture (say cloud or on-premises), and designing a data model.

Establishing a data warehouse involves a comprehensive process. Begin by defining your project's objectives and gathering requirements from stakeholders. Design an effective data model and select the appropriate technology stack, considering scalability and security. Develop ETL processes to extract, transform, and load data from various sources, ensuring data quality and automation. Implement robust data governance and security measures, and document data lineage and metadata. Provide user-friendly access and reporting tools, and offer training and support. Establish backup and disaster recovery procedures, monitor performance, and conduct regular maintenance.

Your team's effectiveness hinges on the quality of the data at their disposal. Make it a priority to ensure that your data is clean, accurate, and complete. Invest in data validation processes, cleansing tools, and governance practices to maintain data integrity. Quality data serves as the bedrock upon which robust analytics can be built.

Data pipeline is essential for data modelling and ETL process. Either data scientist or data engineer is responsible for develop, manage and maintain data pipeline.

It?is?essential?to?import?only?the?data?fields ?that?are?required?while?building?a?. data?pipeline.Adding?the?appropriate data?type?to?the? data?fields?is?also?crucial. Date?fields?should?be?of?the?date?data?type,? while?integer?values?should?have?the?int?data? type. When?we?actually?create?reports?using?the?. data,?this?lessens?the?amount?of?effort? required?to?change?the?field?to?its?data?type.

Ensure to keep your pipelines organised, as it can be much easier to identify leaks/gaps if you know exactly which source is responsible.

The data pipeline is essential as it automate repetitive task. It increases efficiency, Accuracy and scalability. As data continues to grow in complexity and volume, data pipelines future-proof data management strategy

Creating a data pipeline is crucial for efficiently moving and processing data. Start by defining data sources, such as databases, logs, or APIs, and identify the data you need. Next, design the pipeline architecture, including data extraction, transformation, and loading (ETL) processes. Implement data connectors and integration tools to extract data from source systems and apply transformations to make it suitable for analysis. Schedule regular data ingestion to keep the pipeline up-to-date. Consider data validation and quality checks at each stage to maintain data integrity. Utilize storage solutions like data lakes or data warehouses to store processed data, making it accessible for analytics and reporting. Document your pipeline.

Documentation is definitely one of the key aspects to ensure effective knowledge is available to help folks across the team leverage the agenda and tasks. Whereas, data engineering must collaborate and communicate effectively by having some brainstorming sessions to explore the scope and precise requirements. This can help data engineers and analysts to stay in line with the kind of data required along with precise details such as data types, schemas, variety, storage details, transformation to be applied, output patterns, frequency and a lot more.

As an analyst I relay heavily on documentation. Good documentation can save me hours of work chasing after people, defining and agreeing upon terms and documenting what was decided. I love being able to get a request and start working on it based on well documented data!

The most crucial reason why you need to document every step of your data process and definitions is for the ability to go back and refine your process, whether you have long-tenured staff or are working from scratch after a complete turnover of personnel. Being able to go back to answer why you chose to describe your data a certain way allows for continuous improvement and deeper engagement from stakeholders.

In addition, data documentation is essential for the following purposes: 1. Guidance to data engineer and scientists for process flow, coding and lineage 2. Itemization to database administrator for data dictionary 3. Reference to security engineer for data integrity and stress test 4. Support to governance team for audit and assurance exercise

Comprehensive data documentation is essential for efficient data management and analysis. It involves creating a metadata catalog that details data sources, types, and lineage. A data dictionary clarifies data element meanings and business rules. Documented access controls and permissions enhance data security and compliance. Tracking data lineage helps in understanding data flow and transformations, aiding troubleshooting and auditing. Implementing version control and change logs ensures data integrity and keeps a record of schema changes and updates. This documentation fosters collaboration, data reliability, and informed decision-making.

In addition, the following elements should be considered for effective communication between team members: 1. Guideline - it can be terms of reference (TOR), unspoken rules or do's & don'ts 2. Platform - it can be physical, virtual or hybrid 3. Purpose - it can be two-way (meeting/ discussion, sharing/ briefing, deliberation/ presentation, review) or one-way (notification/ instruction) 4. Documents - it can be minutes, notes, logs, decks or media 5. Frequency and duration of the communication - it can be one-time, scheduled, recurring or ad-hoc

Regular communication is crucial for maintaining strong relationships and effective collaboration. To communicate regularly, establish a consistent schedule for meetings or check-ins with your team or stakeholders. Share updates, progress, and important information during these interactions. Use various communication channels, such as email, video conferences, or messaging platforms, to cater to different needs and preferences. Encourage open and transparent communication to foster trust and understanding. Actively listen to others' feedback and concerns and address them promptly. Regular communication helps keep everyone informed, aligned, and engaged, ultimately contributing to the success of projects and initiatives.

Schedule periodic meetings to discuss your analytics needs and project priorities. This ongoing dialogue helps maintain alignment and ensures the analytics team focuses on critical tasks.

Maintain an open feedback loop. Regularly communicate about progress, challenges, and insights.Define project goals and requirements. Ensure everyone understands the objectives and desired outcomes.

In addition, learning new skills during collaboration is definitely a bonus for both sides: 1. Develop trust and relationships with others 2. Rapport for future reference 3. Improve self-esteem and confidence 4. Stepping stone for both personal and professional development

Expect your team to provide valuable insights and recommendations regarding your data management practices. Be open to their feedback and ready to implement changes where necessary. A culture of continuous improvement ensures that data management aligns with evolving analytical requirements, fostering a more dynamic and responsive collaboration.

Trust the expertise of your analytics team. They are the experts in data analysis. Additionally,encourage skill development. Analytics is an evolving field, so ongoing learning is crucial.

A data management team can provide valuable knowledge and expertise in effectively handling and leveraging data within an organization, which is increasingly important in today's data-driven world

Working with analytical teams transcends mere data provision. Elevate your collaboration by productionalizing data into "data products." Think of these as tailored, operational data assets: well-documented, easily consumable, and routinely maintained. Just like software products, data products follow a lifecycle—from inception to retirement. This approach ensures data's relevance, accuracy, and utility. By framing data as a product, you set clear expectations, streamline workflows, and bolster the efficiency of analytical endeavours. Data products bridge the gap between raw data and actionable insights, setting the stage for transformative decision-making.

The best analytics are the ones that are accepted, understood for their strengths and challenges and most importantly change with business understanding. Why do I say this? because they are not the creation of one person or team, but through true collaboration and bringing together diverse views, skills and understanding on an ongoing basis