Here's how you can drive informed decision-making within your organization using data analytics.

Depending on the business and its use cases, data sources can vary, but two key principles should be kept in mind: 1. Internal Data Sources: - Collect and store data from your own website, apps, or surveys in a clean and organized manner as you are its sole owner. - Make this data readily available for different business units in a centralized location. This prevents the need for creating new data pipelines or conducting additional surveys for different use cases. 2. Open Data Sharing: - Promote open data sharing by creating a framework for safely sending and receiving PII data. - Utilize third-party tools and resources to manage data handling, so it does not become your primary focus.

To make informed decisions, the first step is pinpointing and accessing the right data sources. It's important to identify which data is relevant to your business goals and determine where to find it. This might involve leveraging internal systems like CRM or ERP software, or looking at external sources such as market research and social media analytics. Ensuring data quality and accuracy is critical, as this data forms the foundation of your analysis.

In an environment where lack of awareness and strong opinions can sometimes cloud judgment, the power of data analytics shines through as the beacon of clarity. Start by conducting AB tests—one with metrics and one without—to demonstrate the undeniable impact of data on decision-making. This approach not only proves the concept but also paves the way for informed discussions and strategic pivots based on tangible results. Embracing this iterative process, we can collectively navigate towards smarter, more impactful decisions.

In my opinion, Building on Data: The Foundation for Informed Decisions Data is the fuel for smart choices! Here's how to get started: Identify the Right Data: Not all data is created equal. Find data relevant to your business goals. Source it Wisely: Internal systems (CRM, ERP) or external sources (market research) can be goldmines. Ensure Quality: Accurate data is crucial! Double-check your sources to avoid building on a shaky foundation. By focusing on the right data, you lay the groundwork for data-driven decision-making that propels your organization forward! Contribution: (RM Tehseen, Certified Data Analyst)

1. *Define Clear Goals*: Identify specific business objectives and questions to answer. 2. *Collect Relevant Data*: Gather pertinent data from various sources, ensuring quality and integrity. 3. *Analyze and Visualize*: Apply statistical techniques and data visualization tools to uncover insights. 4. *Interpret and Communicate*: Translate findings into actionable recommendations, presenting clear and concise results. 5. *Implement and Monitor*: Put data-driven decisions into action, tracking progress and refining approaches. 6. *Repeat and Refine*: Continuously cycle through this process, iterating and improving decision-making.

As paramount of data available across wide range of systems, often times it's challenge to access the data from diverse data sources, derive insights. Starburst, Trino helps to connect data sources of wide variety with using central catalogs like Nessie, Unity Catalog. Use traditional tools Tableau/Qlik/Tableau or ThoughtSpot with AI enabled helps to ask the questions in natural language and get insights quickly. Build the custom libraries to domain specific join the datasets and available them to both business and ML users who often times struggling to access the data with better access patterns. Leverage the tools with AI enabled capability which improves the speed and provide optimized way to get insights.

In my view, Building on Data: Equipping Your Team for Success Data is the fuel, but you need the right engine! Here's how to empower your team: Tools for Transformation: With the right data, choose the right analysis tools. Options range from spreadsheets to powerful Business Intelligence (BI) systems. Skill Level Matters: Select tools that match your team's skill level. User-friendliness is key for efficient data exploration and insights. Remember: We want actionable insights, not technical headaches! Bonus Tip: Explore free or open-source options to get started and scale up with advanced needs. By equipping your team with the right tools, you transform data analysis from a chore into a strategic advantage!

To drive informed decision-making, leveraging robust analytics tools is essential. Utilize platforms like Tableau or Power BI for intuitive data visualization, enabling stakeholders to grasp complex insights quickly. Employ SQL for efficient database querying, ensuring accurate data retrieval. Integrate Python or R for advanced statistical analysis and predictive modeling, enhancing the precision of your forecasts. Additionally, use Google Analytics for web data, providing valuable insights into user behavior and engagement. These tools collectively empower your organization to transform raw data into actionable strategies.

In an agile project to improve our data analytics capabilities, selecting the right tools was critical. We evaluated various platforms, from spreadsheets to advanced BI systems, to find the best fit for our data complexity and team skills. For instance, adopting Tableau enabled us to visualize complex datasets and derive actionable insights quickly. This tool's user-friendly interface helped team members at all skill levels contribute effectively. By choosing the right analytics tools, we streamlined our data processing and enhanced decision-making, demonstrating the importance of tool selection in agile projects.

Today’s data space is highly fragmented and packed with tools – it's easy to get lost in the hype. Picking the right data stack and tools basis your team's skills and goals can help you unlock actionable insights that move the needle – not just vanity metrics.

Data processing involves collecting, cleaning, transforming, and storing data to ensure its accuracy and usability. Start by gathering relevant data from various sources such as databases, APIs, and external files. Clean the data to remove inconsistencies and errors, then transform it into a usable format through normalization, aggregation, and integration. Store the processed data in a secure and scalable data warehouse or cloud storage. Use ETL (Extract, Transform, Load) tools to automate these processes, ensuring that data is consistently updated and ready for analysis. This systematic approach enhances data quality and supports reliable decision-making.

Data processing is very important task and a pre-requisite for almost any Data Analysis project. It's always best practice to bring only what you need. I can think of it as a hike. e.g if you carry unnecessary items with you on a short walk you it most probably wont effect you so much, but if you carry unnecessary weight on a long hike it will make the hike very difficult for you and you need to utilise more recourses or energy to the project or hike. So its always best practice to bring only what you need for a better optimized data model.

Talk to your business teams to understand their goals, challenges, and data needs – What insights will help them achieve their goals? Most importantly, make data a habit: Get your stakeholders using data from day 1. Whether it's a simple daily dashboard or a weekly insights report, foster a culture where data informs every decision, big or small.

To drive informed decision-making within your organization using data analytics, focus on extracting actionable insights from your data. Begin by identifying key performance indicators (KPIs) relevant to your business objectives. Utilize descriptive analytics to understand historical trends and patterns. Apply diagnostic analytics to uncover the causes behind these trends. Leverage predictive analytics to forecast future outcomes and identify potential opportunities or risks. Finally, implement prescriptive analytics to recommend data-driven actions. By continuously analyzing data, you can make strategic decisions that enhance efficiency, improve performance, and drive growth.

I think Insight generation is also an exploratory task where you can ask questions of your data and figure walk arounds for getting insights that don't present themselves at first glance. In my perspective the number characteristic a required for insight generation is curosity.

Data reporting never lives in isolation in any organization. If you want to drive insights, you need reliable methods to reporting data that is of interest to your business stakeholders. Like many things in a business, this is reliant on your ability to communicate. First, learn about what Metrics and Key Performance Indicators (KPIs) are of importance to the company. Next, figure out simple ways to relay this information. Do your stakeholders prefer Dashboards or Slide Decks? What high level information is important to highlight/drill-down on? Try to get at the root of what your stakeholders want and need.

Instead of adding all metrics, build a dashboard focused on North Star or core metrics to increase its adoption. Dashboards should be accessible – Some of your teams are on the move, so make sure data & insights are accessible on all platforms – whether it's automated email snapshots or a mobile-friendly dashboard. Once they are ready, conduct a series of fun & gamified workshops/webinars to onboard business teams to use the dashboards.

To drive informed decision-making using data analytics, create reports that are concise, focused, and actionable. Follow these steps: Identify Key Metrics: Focus on metrics that align with organizational goals. Data Visualization: Use charts and graphs to make data easily interpretable. Insight Summary: Highlight critical insights and trends. Recommendations: Provide clear, actionable recommendations based on data analysis. Follow-Up: Include steps for tracking the impact of actions taken.

creating actionable reports are not the hardest part of data analysis. - Insights alone don't make your report standout, but meaningful insights that can force decision making will definitely make your report better. - Your report should be as simple as possible, it should highlight the most important metrics only on a high level view. - Detailed tooltips and drill down and drill through options are best for getting to the finer details of your data.

Continuous learning is the cornerstone of adaptability and innovation in data analytics. Embrace ongoing learning as a means to stay ahead of industry trends and technological advancements. Encourage a culture where team members feel empowered to experiment with new methodologies and tools. Leverage insights gained from continuous learning to not only refine current strategies but also to explore new avenues for growth and efficiency. By making continuous learning a priority, organizations can foster a dynamic environment where data-driven decisions are not just reactive but proactive, driving sustained success and competitive advantage.

Continuous Learning Initiatives in My Organization My organization promotes continuous learning in multiple ways: 1. Learning Days: Every Tuesday is designated as a Learning Day. It is a no-meeting day, allowing you to focus on your personal learning. 2. Free Resources: We provide free memberships to Udemy, Coursera, and Pluralsight. 3. Competitions: We host hackathons and ideathons to encourage learning and innovation. 4. Patent Culture: Promoting a patent culture is one of the best ways to foster continuous learning. It not only helps in enhancing existing skills but also encourages the creation of new ideas.

Some points to keep in consideration: - Data Privacy: Ensure compliance with data privacy regulations (eg. GDPR, CCPA) - Data fairness: Integrate data from multiple reliable sources for a holistic view, to ensure fairness. - Change Management: Address resistance to change by educating stakeholders on the benefits of 'data-driven' decision-making.