Data Analytics’ Post

🌟 Humanizing Data Strategy: The Key to Meaningful Insights and Impactful Decisions🌟 In today's data-driven world, the ability to collect, analyze, and interpret data is more critical than ever. However, amid the rush to leverage big data, we must not lose sight of the human element that drives meaningful insights and impactful decisions. 🔍 Why Humanizing Data Strategy Matters: 1. Empathy-Driven Insights: Understanding the people behind the data allows us to uncover insights that are more relevant and actionable. By focusing on human behaviors, motivations, and experiences, we can craft strategies that truly resonate. 2. Ethical Considerations: Humanizing data strategy means respecting privacy and ensuring ethical use of data. Transparent practices build trust and safeguard the integrity of our analyses and actions. 3. Enhanced Collaboration: Bringing diverse perspectives into the data strategy process fosters creativity and innovation. Cross-functional teams can combine their unique viewpoints to uncover new opportunities and solve complex challenges. 4. Improved Decision-Making: Data alone doesn't tell the whole story. Integrating human context helps us make better-informed decisions that consider the broader impact on individuals and communities. 5. Long-Term Value: A human-centric approach to data strategy ensures sustainability. It helps organizations build lasting relationships with customers, employees, and stakeholders by showing that they are valued beyond mere numbers. 🚀 How to Humanize Your Data Strategy: - Listen and Engage: Regularly gather feedback from your audience to understand their needs and perspectives. - Prioritize Ethics: Implement strong data governance practices to protect privacy and promote transparency. - Embrace Diversity: Include diverse voices in your data strategy discussions to enrich the analysis and outcomes. - Tell Stories: Use data to tell compelling stories that highlight the human impact and drive action. - Continuous Learning: Stay curious and open to evolving your approach as new insights and technologies emerge. By putting people at the heart of our data strategy, we can unlock deeper insights, make more ethical decisions, and drive greater impact. Let's transform data from a mere resource into a powerful tool for positive change. 🌍💡 thank you to Salim Jouili, Ph.D. and Amine Belmabrouk for demonstrating this to us through practice and the corporate culture at elyadata . #DataStrategy #HumanCentered #EthicalData #BigData #Innovation #Leadership #DataDriven

Data goes beyond numbers, allowing us to predict the future and make new discoveries. However, not all data insights are equally valuable. The challenge is figuring out how to obtain truly meaningful and impactful insights from the data we have. ➟Validity Check. Is your data depicting the true reality? Ensure your methodologies are rigorous and your analyses adhere to objective standards. ➟Reliability Review. Consistency is key. Data should yield the same insights under the same conditions across different times and settings. ➟Relevance Reflection. Does the insight serve your current objectives? Insights must be directly connected to your strategic needs to add value. ➟Timeliness Test. Data's relevance fades over time. Frequently update your insights to reflect the latest data, especially in fast-changing fields. ➟Ethical Examination. From data collection to analysis, ensure transparency, fairness, and privacy protection to maintain the integrity of your insights. ➟Impact Inquiry. Beyond accuracy, insights should empower decision-makers to drive meaningful, positive change that reflects societal and business responsibilities. So, when you uncover insights from data, don't just accept them at face value. Ask probing questions to critically evaluate those insights. This process helps turn raw data and information into actionable strategies and decisions that drive real impact. As you analyze your data, consider what meaningful changes or innovations will your data-inspired choices lead to? #DataLiteracy #BusinessIntelligence #EthicalDataUse #StrategicDecisionMaking #ProfessionalGrowth #InsightfulLeadership #DataLiteracyInPractice #TurningDataIntoWisdom https://lnkd.in/en6rpdsR

According to Statista, a German online platform that specializes in data gathering and visualization, the volume of data has grown exponentially 🚀 over the last decade. 1 zettabyte = a one followed by 21 zeros in bytes, that’s 1 billion terabytes! In 2010 there was around 2 zettabytes of data created, captured, consumed, and stored. The amount in 2021 is 40 times larger. As of 2023, 120 zettabytes were generated and are expected to increase by over 150% in 2025, hitting 181 zettabytes. Source: Statista, Bernard Marr & Co. This serves as a testament to the rapid pace of digitalization in the world and highlights the ongoing importance of effective data management and analysis across various industries. Look out for data science learning opportunities and valuable chances to enhance your skills with Innovation Kgotla. #InnovationKgotla #Dcdonates #DataScience

The time has come when a data strategy is mandatory for the survival of companies. Data integrity is essential and should be considered as a company value. Making data fit for purpose thanks to data excellence discipline has to become an habit for anyone in the Company. Thank you Tendü Yogurtçu, PhD, Eric Yau, Patrick McCarthy and Emily Washington for your insights. #dataintegrity #dataexcellence #datastrategy

In the world of data, where information reigns supreme, understanding the DIKW (Data, Information, Knowledge, Wisdom) principle isn't just a theoretical concept; it's a fundamental pillar for success. As data professionals, acknowledging and harnessing the power of DIKW can transform the way we approach our work, ultimately leading to more informed decision-making and impactful outcomes. Data: At its core, data is the raw material—the bits and bytes that flood our systems daily. It's the numbers, text, images, and everything in between that represent the reality around us. However, data, in its raw form, lacks context and meaning. It's like scattered pieces of a puzzle waiting to be assembled. Information: As data undergoes processing and organization, it transforms into information. This is where patterns emerge, trends become evident, and insights start to surface. Information provides context to data, answering the 'what' and 'how many' questions. It's the structured data sets, reports, and dashboards that enable us to understand the significance of the numbers. Knowledge: Beyond just understanding the information, knowledge delves into the 'why' and 'how' behind it. It's the deep understanding gained from analyzing patterns, recognizing correlations, and interpreting trends. Knowledge allows us to make predictions, identify opportunities, and mitigate risks. It's the expertise we bring to the table—the culmination of experience, education, and intuition. Wisdom: Finally, wisdom represents the highest level of comprehension. It's the ability to apply knowledge and experience in a way that transcends the immediate context. Wisdom involves making ethical decisions, considering long-term implications, and understanding the broader impact of actions. It's the culmination of a lifetime of learning and reflection. For data professionals, acknowledging the DIKW principle is crucial for several reasons: The DIKW principle emphasizes effective communication tailored to different stakeholders, value creation through focusing on tasks that drive value, continuous learning for skill improvement, and ethical considerations in data roles. Embracing DIKW guides data professionals to unlock data's potential, driving innovation, collaboration, and shaping a brighter future. Let's empower ourselves to make a difference in the world of data.

𝗘𝗺𝗯𝗿𝗮𝗰𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗟𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗮𝗻𝗱 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝗶𝗻 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 A recent experience before the holiday reinforced two crucial lessons in data analytics:  the importance of data literacy the power of alternative solutions. 📊 𝑫𝒂𝒕𝒂 𝑳𝒊𝒕𝒆𝒓𝒂𝒄𝒚: It's not just about what data can tell us, but also its limits. Educating stakeholders on the nuances of data is key. This leads to more viable and impactful data requests, aligning expectations with reality of asking the right questions, i.e. ones that the data can answer. 🔄 𝑨𝒍𝒕𝒆𝒓𝒏𝒂𝒕𝒊𝒗𝒆 𝑺𝒐𝒍𝒖𝒕𝒊𝒐𝒏𝒔: When facing data limitations, it's not the end but a new beginning. Recently, a request for insights proved statistically challenging due to sampling issues. Instead of a dead-end, we broadened the scope to include more data, creating a more robust and meaningful deliverable. This shift not only met the stakeholder's needs but exceeded expectations. It required a change in narrative about the data but it proved to be even more in line with the stakeholder's goal. 🤝 𝑪𝒐𝒍𝒍𝒂𝒃𝒐𝒓𝒂𝒕𝒊𝒐𝒏 𝒂𝒏𝒅 𝑬𝒅𝒖𝒄𝒂𝒕𝒊𝒐𝒏: These instances highlight how vital our role is in guiding and educating our stakeholders. By fostering data literacy and thinking creatively, we can transform challenges into opportunities. In data analytics, our expertise isn't just in numbers; it's in guiding others to see and understand the story those numbers can, and sometimes cannot, tell.

"Data quality comes first. Everything else, after!" I read this in a post, here on LinkedIn, and I totally agree! Creating "data technologies" (products and systems) using models and insights, where you don't have sure enough about the data quality, is not just wrong, but it's a very dangerous thing. And it is not just dangerous for the clients that are buying a solution and their business, it's dangerous for the company that is selling too. Because early or late, the truth comes, and the numbers doesn't lie. And maybe, you will create a false expectation from a supposed success that someday could be ruined. So I think that it's a sense of ethics. A sense of responsibility! We can't create anything without being sure about the data quality! And you? What do you think about? #datascience #business #dataquality #datasolution #modeling #digitalproducts

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬: - Clean data ensures that your analysis is accurate and the insights derived are reliable. Garbage in, garbage out is a fundamental principle in data analytics. - High-quality, clean data leads to better decision-making. When data is free from errors, inconsistencies, and duplicates, the results are more trustworthy. - Spending time on data cleaning upfront can save significant time later. - Clean data simplifies analysis, reduces errors, and minimizes the need for rework. - Clean data allows for deeper and more meaningful insights. It enables the application of advanced analytics techniques, leading to more innovative and actionable outcomes. 𝐊𝐞𝐲 𝐬𝐭𝐞𝐩𝐬 𝐢𝐧 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: 1. 𝐑𝐞𝐦𝐨𝐯𝐢𝐧𝐠 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬: Ensuring each data point is unique to avoid skewed results. 2. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬: Addressing gaps in data to maintain the integrity of the dataset. 3. 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐢𝐧𝐠 𝐅𝐨𝐫𝐦𝐚𝐭𝐬: Ensuring consistency in data entry formats (e.g., dates, currencies). 4. 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐧𝐠 𝐄𝐫𝐫𝐨𝐫𝐬: Identifying and rectifying inaccuracies and inconsistencies in the data. As data professionals, we know that data cleaning is not the glamorous part of the job, yet it is undeniably one of the most important. Let’s acknowledge the critical role data cleaning plays in our data-driven world! 🌟 Follow Alisha Surabhi for more content of Data & AI !! #DataCleaning #DataAnalytics #DataQuality #BigData #DataScience #CleanData #Analytics #DataIntegrity #DataDriven #TechInsights

Using the Power of Data Responsibly: Ethical Considerations for Data Visualization Data visualizations can be incredibly persuasive. They inform decisions, shape opinions, and even influence policies. Here’s what you should consider when using the power of data responsibly: ✅Context: Data doesn't exist in a vacuum. Explain outliers and the source of the data. ✅Present All Sides: Data can often have several perspectives. Presenting data from multiple viewpoints helps provide a balanced understanding. ✅Beware of Bias: Check for any inherent biases in your data sources and be transparent about them. ✅Avoid Misleading Data: Bad data = misleading visualizations. This can lead to unwanted interpretations and presentations. What other ethical considerations do you make when dealing with data? Share in the comments! #dataethics #dataanlysis #Accessibility

When it comes to data strategy, don’t be like Alice in Wonderland. You don’t need to know the exact way, but you should know where you want to go. As executives, your data initiatives should align with your business strategy, not the other way around. Here are some thoughts to consider 👇 : • Don’t rely on data to tell you what to do. That’s not being data-driven, that’s being lost. • Define your business goals first. Data is a tool, not a roadmap. • Reframe the question: Instead of "What should data do?" ask, "What business problems can data help solve?" Some ideas from the latest reading list: ↳ Progress with data and AI stalled? Maybe it’s not the technology. Focus on culture change, business partnerships, and starting small (HBR). ↳ Incident management for data teams isn’t one-size-fits-all, but a 5-step approach might help: issue detection, response, root cause analysis, resolution, and learnings (Synq). ↳ Psychological targeting on social media is more effective than we realize. Are ads based on personality traits influencing real-world actions? Yes, they are (Behavioral Scientists). ↳ What’s a good ratio of data staff to engineers? Anywhere from 1% to 5% depending on the company size (Inside Data by Mikkel Dengsøe). Reshare ♻ if you agree! What’s your business’s North Star for data?