You want to make data-driven decisions, but don't know where to start. What's the first step?

At the outset, when studying IT and programming you learn that Data is unrefined and unprocessed. Once you have processed and refined Data - it becomes Information. This process is becoming more and more profound as time progresses. We live in a world where there is Data everywhere - what is real, what is fake, what is AI or deep fakes - what data was accurately recorded? I can keep going about this point. The first step to Data (In my language - refined and processed Data) driven decision-making is to ensure that the Data is authentic and if of integrity. Secondly, define what Data is relevant to your respective decision - there is an abundance and overload of Data - You cannot make a decision when too many options are available.

Truly understanding what the data tells you and why it is important is critical. Formulating this question with business leaders can be challenging but it's the only way to start. When you have one specific focus question the collected data tells a story which can then be shared with any level of stakeholder. Data without answering a question is just numbers and can be meaningless to the analyst and the stakeholders. Not understanding if the data source is accurate or what it's telling you is equally worthless. Be diligent with this first critical step or you could be faced with a lot of data that you can't explain.

Begin by identifying the specific challenge or opportunity at hand, framing it as a clear and concise question that demands data-driven insights. Break down your overarching question into smaller, more manageable components, allowing for a focused and systematic analysis of relevant data. Prioritize the most critical aspects of your question, ensuring that your data collection and analysis efforts are aligned with the key drivers of your decision-making process. Leverage creative thinking in designing data collection methods, considering both traditional and unconventional sources to gather comprehensive and actionable insights for informed decision-making.

To make the question you’re solving as precise as possible it’s crucial to implement SMART technique and choose a suitable problem-solving approach. SMART (Specific, Measurable, Achievable, Relevant and Time-Bound) will make your question more up to the point and the objective more tangible. This will afterwards inform a choice of problem-solving approach, should it be hypothesis-led or any other.

Identifying your objectives and the questions you intend to answer is a prerequisite for starting to make data-driven decisions. Knowing the specific problems or opportunities you're trying to tackle as well as the information you'll need to do it effectively are necessary for this. If you are clear about your objectives, you can locate the relevant data sources and metrics that will allow you to monitor your progress toward them. To assist you in making judgments, you may then gather, go over, and assess the data. Ensuring that the data you use is accurate, relevant, and reliable is essential for making informed decisions.

Article is well written, just want to put attention to the cases where even after being well aware of data-driven decision process and steps, objectives are not met: 1. Lack of direction. 2. Collecting/Analyzing irrelavant data. 3. KPIs/Objectives are not measureable. 4. Lack of adaptability: It is an iterative process; objectives cannot be rigidly defined initially and then frozen. Instead, they should be continually reassessed based on insights derived from the data. 5. Vague data visualization: Clear and precise data visualization is crucial for effective decision-making. Poorly presented visuals can distort interpretations, risking inaccurate decisions. 6. Avoid overkilling!

The first step in making a data driven decision should be defining your problem statement. Followed by the same comes steps like yielding a hypothesis and making informed data analytics from relevant data sources. You can't make a relevant outcome statement without defining your critical problem areas. Hence, join the dots, merge the gaps and explain your problem statement in lucid and decipherable language.

Embarking on a journey towards data-driven decision-making is a commendable initiative, and taking the first step can set the foundation for success. To begin, it's crucial to identify your organization's goals and the specific challenges you aim to address. Start by clearly defining the KPIs that align with these objectives. This involves understanding the critical metrics that will measure success in your particular context.

This is very important to ensure you are solving the right problem. Without this step, you may be collecting the wrong data at the wrong place, or worse still, not solving the right problems.

Map Existing Sources: Identify all available data sources within your organization, ranging from internal systems to relevant external data. Assess Quality and Relevance: Evaluate the quality and relevance of each source, prioritizing those that provide accurate information aligned with business objectives. Establish Collection Protocols: Create clear protocols for the regular collection and updating of data, ensuring consistency and reliability in the information used for decision-making.

Look within your organization for internal data sources that can serve as valuable assets for decision-making. Collaborate with relevant departments such as marketing, finance, and operations to access and understand their data. Internal sources often contain insights that are specific to the organization's unique operations and challenges. While internal data is invaluable, supplementing it with external data broadens perspectives and enhances decision-making. Explore external sources such as market research, industry reports, social media data, and government statistics. Integrating external data provides a more comprehensive view of the business landscape, aiding in a holistic decision-making process.

The provided example illustrates how a structured question can be used to address a specific objective, such as improving customer satisfaction, by targeting key areas like product features, service quality, and pricing. Therefore it is important to have a clear understanding about the question that you have to gather the necessary and correct information.

Data-driven decisions can only be made when you have a clear idea about your data sources, both internal and external. While, the internal sources are those that you are working on your own, such as developing records or focusing on website analytics; the external sources are those that comes from your external surroundings. In my opinion, it is the external sources that play a major role in helping one to make better decisions regarding their business, as it not only helps in improving knowledge, but also helps with trying out new ways of doing business.

To start, before defining the questions, ask stakeholders what they need to know. If you still have doubts, continue gathering information. It's important to have clarity on the problem domain to understand the specific areas of analysis required.

Take into account the timeliness of your data. Utilizing outdated information can result in misguided decisions. It's essential to consistently update your datasets and institute clear protocols for maintaining data currency. This involves implementing regular reviews, establishing automated processes for data refresh, and staying vigilant to changes in your data sources. By prioritizing data currency, you ensure that your analyses and decisions are based on the most recent and relevant information available. This proactive approach not only enhances the accuracy of your insights but also empowers your organization to respond promptly to dynamic circumstances, fostering a more agile and adaptive decision-making process.

From my experience there are 3 very useful metrics to assess data quality: (a) Data Time-to-Value (TTV): It measures the amount of time it takes for the Firm to get the value from the respective data/information. (b) Ratio of Data to Errors: it determines what percentage of your data is correct. It is calculated by dividing the total number of errors in a dataset by the total number of items in the dataset. (c) Amounts of Dark Data: data that is not amenable to computer processing mostly because it is in formats that are difficult to process, categorize, identify, and analyze e.g. scanned page-images.

Some data also have "shelf-life". Once it goes past the shelf life, the data value to the decision making process is significantly diminished. Keep that in mind!

Outline your data sources - where did they come from, what background info are they operating on, what biases do they bring, and how can best help you address the problem? In my experience, approaching the data as individual puzzle pieces allowed our team to better analyze the data. Which pieces are crucial? What data should be used immediately versus put to the side to be looked at again later? As the data begins to help answer your problem, the picture becomes clearer and more data falls into place!

Mantenha sempre em vista a atualização dos seus dados, e se a comparação entre os dados faz sentido ou e comparável. Fontes de dados confiáveis são cruciais para que as informações seja de alta qualidade.

Tools like PowerBI may allow you to ingest the data intelligently and look at it the right way to get the right level of information. There is tremendous value in having the data presented in a good way that allows for the insights to shine through!

Define Clear Objectives: Identify specific goals you aim to achieve through data analysis, focusing on key areas of your business. Select Appropriate Tools: Choose data analysis tools that meet your needs, considering ease of use and the ability to extract relevant insights. Start with Essential Data: Initiate the analysis by concentrating on the most crucial data for your objectives, ensuring an efficient and targeted approach.

-Make sure data analysis based upon how metric works to get desired result or not. -The level of skill you need will vary according to what you need to analyze but Sometimes simply analyzing work effectively to desired result.

Ensure effective communication of your findings, translating technical results into actionable insights for decision-makers. Regularly revisit and refine your interpretation process as you gain new data or insights, fostering a continuous improvement cycle in your data-driven decision-making journey.

Turning insights into action, Example : if churn rate increase, product team must analyze the reasoning behind it Like as user interface, user experience, content & technical glitches etc. & use feedback as problem analysis; work on it properly.

Identify Strategic Objectives: Clearly define the strategic objectives that will guide the collection and analysis of data, aligned with the organization's goals. Assess Data Availability: Conduct a survey of available internal and external data sources, identifying those relevant to support your decisions. Build Analytical Competencies: Foster the development of analytical skills within the team and invest in tools that facilitate effective data interpretation, empowering informed decision-making.

Eventually the making of decision i lies with individual deep down after doing analysis and interpretation. The intent or interest to do it irrespective of gain or loss. People who think not think of monetary benefit can learn and grow more than one who is thinking on monetary benefit, his mind would be deep around the monetary benefit that he would not give chance to the other personality traits.