Here's how you can utilize big data analytics to inform your decisions.

It's impossible to be successful if you aren't defining what that success looks like! We live in a world with a finite amount of data, but an infinite amount of metrics that can be created to measure performance over time. Carefully choosing how you'll know what success looks like is the pivotal first step to be taken prior to any real work taking place, otherwise you'll never know if you're moving in the right direction!

I wish the title of this was Define Goals and Objectives. Too often the Goals are well defined but the objectives are underdeveloped. Understanding the distinction between goals and objectives is crucial for effective planning and execution, ensuring that you have a clear vision (goals) and actionable steps (objectives) to get there. The subsequent steps are going to be difficult if you have a well developed goal but the objectives are too vague.

Begin by examining the overarching goals and objectives of an organization and then works downward to pinpoint the specific insights that will best help achieve those objectives. Setting Clear Goals and Success Metrics: Starting with the organization's overall objectives, this approach helps identify the insights that will be most effective in achieving those goals. This clarity of purpose focuses team efforts and ensures alignment toward shared objectives. Identifying Key Stakeholders: The top-down approach helps identify key stakeholders who play a crucial role in the decision-making process. Understanding Extended Team Impact: It also helps identify extended teams who may be affected by the insights.

In the vast ocean of big data, clarity is your compass. Start by defining your goals. What truths do you seek? Which data will guide you, and how will you recognize success? Clear objectives transform data chaos into meaningful insights, steering you toward your strategic horizon. Success is an elusive muse without definition. Goals paint the dream, but precise objectives carve the path. Distinguish between them to sculpt a roadmap of vision and action. This harmony between ambition and practicality ensures your data journey aligns with your grand design, illuminating the path to achievement.

In my experience, big data analytics can be utilized for disease forecasting, which involves collecting data from sources like electronic health records and public health databases and integrating it for accuracy. Advanced techniques such as machine learning and geospatial analysis are used to predict trends and outbreaks. Visualization tools like interactive dashboards and real-time monitoring systems display these predictions, aiding timely, informed decisions. Ensuring data privacy and addressing biases are crucial, enabling effective public health interventions and resource allocation. This approach enhances the ability to foresee and manage disease spread efficiently.

In big data analytics, after setting your goals, the next step is to gather relevant data. This can come from internal sources like organizational databases or external sources like social media, sensors, and public databases. High-quality data is crucial. It must be cleaned to remove inaccuracies and inconsistencies, as these can distort your analysis and lead to incorrect conclusions. Ensuring data quality is essential for accurate and actionable insights.

Gathering data can be one of the hardest parts of gaining valuable insights. Making sure that the data you bring in is accurate, updated, and governed correctly ensures that decisions being made downstream are made under the right assumptions. This gives you a strong basis onto which you can make decisions, gain insights, and give recommendations to stakeholders. Without this foundation, it may be next to impossible to know if the decisions being made are the right ones.

Collect relevant data from multiple sources, ensuring it is accurate, complete, and relevant to your defined goals. This could include internal data, such as sales figures and operational metrics, as well as external data, such as market trends and competitor analysis. Example: For improving product delivery, gather data from warehouse management systems, transportation logs, customer service records, and industry benchmarks.

Identifying the right data sources, whether from your own databases or external sources like social media, sensors, or public databases, can be tough. However, it's crucial to make sure this data is high-quality and cleaned of any errors or inconsistencies. Doing so will help ensure your analysis is accurate and reliable

Once the business requirements are clearly defined and KPI's are identified, the next step is to identify the data sources and owners. In most of the scenarios the data will be available within the organization. In certain sceneries (Eg. Survey details ) data will be coming from 3rd party vendors. The crucial step is to identify the correct data/layer. Eg. Same customer data can be available in the raw layer and silver layer. Here silver is preferred as it's more clean. This will be based on the alignment with business needs too.

To properly analyze the data, the standards and definitions need to be understood in order to look at the data with the same eyes, the goals set at the beginning area a good start, but more often than none we will be asking more questions and may need to review the firsts steps based on the pre-assumptions made.

In today's day and age, there's a myriad of tools at our disposal to begin analyzing data. From platforms like PowerBi or Tableau to more technical stacks like machine learning libraries in languages like Python or R, the possibilities are next to endless. The goal of these efforts should be to analyze the data in a way that actually garners insights and upon which real decisions can be made. This should feel akin to research, as the main deliverables to come out of this effort should be new insights that can be shared with stakeholders

Quick note: As your pipeline matures and you continue iterating for further insight; break your data up into "eras" as capture and analysis evolves over time you will want to maintain your volume insights but also account for the data-driven decisions and their affect and effectiveness.

In my experience a number of tools exist from S Studios to R Studios, all which are aimed at strengthening data analytics. I have come to appreciate and acknowledge the undervalue that has been placed on Microsoft Excel. While i love and use packages such as R studio and STATA, over the years i have fallen in love with Microsoft excel and its flexibility to conduct any logical computations, coupled with beautiful visuals. I have used Microsoft Excel as a data analysis software and a data visualisation workbook, dealing with big data from multiple data sources.

If possible, using a platform that enables the end user to customize the analysis is a great option. The ability to export the data is also something that many people will demand.

This is where the rubber meets the road! It's easy to get lost in the data and the analysis, but the real value comes from putting those insights into action. Sometimes, the hardest part is knowing which insights are the most important to focus on. It's helpful to start with small, manageable changes and then track their impact. This way, you can build momentum and confidence in your data-driven decisions.

An Insgits is nothing more than a number if it is not helping the stakeholders in decision-making. The application of data-driven insights is the key to the success of any data analytics. The trends and patterns that emerge from the data analysis process should confirm with the real-life business scenario and must have a positive impact on the business. One good way to apply insights is to run a pilot project that can span from a few weeks to a few months. Businesses can easily measure the success of a Data analysis process with the impact that it creates after successful implementation.

Here is where the Objectives that you defined in the first step come into play. If you just defined a goal without clear objectives it is may be very difficult to make the insights actionable. It is rare that the data can drive actionable insights if the goal definition did not include the specific, measurable steps or actions that you need to take to achieve a goal.

When you came up with an "insight", ask yourself, is it something that can be created with 1~2 steps of simple excel formula? If it is, don't call it insight, it an observation. True analytics requires model and sample data

There are three things one needs to take heed of when conducting big data analytics to provide insights on decision making namely, description, prediction, and prescription. These three themes are essential elements to which an analyst needs to be cognisant. Descriptive analysis looks at making sense out of the big data, this looks at measures of central tendency, correlational analysis and all other forms of manipulations that make sense of the raw data. Predictive analysis looks at the ability forecast from data or tells how things will be like should there be no change in the status quo. Prescriptive analysis looks at the set of logical recommendations an analyst provides guided by the descriptive and predictive analysis.

Once modeling has been created, there's two ways to look at it - maintenance and ingenuity. A big part of data modeling is to ensure that what's been created stands the test of time by introducing monitoring and governance. The other, arguably more important, avenue is to continually iterate on what's been built to deliver more value than what was scoped at the project's inception. As new features continue to roll out, an essential part of this time is to ensure that this new data can be connected to existing insights which should drive further product development!

Review of results should be a continuous process that is done at specific intervals and not wait for the end of the project or intervention. These should form part of continuous learning and adaptation, as this helps to examine the variability of actual performance from the forecast and looks at the risk factors limiting progress.

Continuously monitor and evaluate the outcomes of your decisions to ensure they align with your goals. This step helps in understanding the impact of the changes made and in identifying areas for further improvement. Example: Track delivery times post-implementation to see if the changes have reduced delays and improved customer satisfaction.

In today's competitive landscape, integrating data analytics into strategy formulation enhances competitiveness and adaptability in a rapidly evolving marketplace. Organizations that harness the power of data analytics gain a significant edge, driving growth, profitability, and sustainable success in the digital age.

Take privacy and security seriously, very very seriously. As data is captured and crafted into valuable datasets it also becomes more vulnerable to capture or leveraged attack. Companies and organizations typically run on sheer luck when it comes to data security. Just because something on your network isn't explicitly consumer facing doesn't mean there can't be eyes on it.

Customer behavior analysis: Eg: Retail scenario: A retail chain has to tailors it's marketing campaign to promote eco-friendly product in that region leading to increase in some % sales. Operational efficiency optimization: Eg: Manufacturing scenario: Implementing a predictive maintenance of car schedule based on insights, reduces unexpected downtime by some %, saving significant costs and improving overall production efficiency.

Definindo as visualiza??es dos teus dados com base na resposta que busca para seu problema, poderá agregar muito às análises e FUP's com a organiza??o.