Ketan Wayfalkar’s Post

📊 Unleashing the Power of Data Analytics: Driving Insights and Decisions! 📊 Embark on a journey into the realm of data analytics and discover how organizations harness the power of data to drive informed decisions and gain valuable insights. Exploring the World of Data Analytics: Data analytics empowers organizations to extract actionable insights from vast datasets, enabling informed decision-making and strategic planning. Let's delve into the core concepts and applications of data analytics in today's digital landscape. #DataAnalytics #BusinessInsights #DecisionMaking Key Components of Data Analytics: 1. Data Collection and Cleaning: The first step in the data analytics process involves gathering relevant data from various sources and ensuring its accuracy and consistency through data cleaning and preprocessing techniques. #DataCollection #DataCleaning #Preprocessing 2. Exploratory Data Analysis (EDA): EDA techniques uncover patterns, trends, and relationships within the data through visualizations and statistical methods, providing initial insights and guiding further analysis. #EDA #DataVisualization #StatisticalAnalysis 3. Statistical Modeling and Machine Learning: Advanced statistical modeling and machine learning algorithms enable predictive analytics, classification, clustering, and other sophisticated analyses to derive actionable insights and drive strategic decision-making. #StatisticalModeling #MachineLearning #PredictiveAnalytics Applications Across Industries: 1. Business Intelligence: Data analytics fuels business intelligence initiatives, providing organizations with a comprehensive view of their operations, customer behavior, market trends, and competitive landscape to drive growth and innovation. #BusinessIntelligence #MarketInsights #CompetitiveAnalysis 2. Healthcare Analytics: In the healthcare sector, data analytics plays a vital role in improving patient outcomes, optimizing resource allocation, identifying disease patterns, and enhancing operational efficiency across healthcare facilities. #HealthcareAnalytics #PatientOutcomes #OperationalEfficiency 3. Financial Analytics: Financial institutions leverage data analytics to mitigate risks, detect fraudulent activities, optimize investment strategies, and enhance customer experience through personalized financial services. #FinancialAnalytics #RiskManagement #FraudDetection Join the Data Revolution: Are you passionate about leveraging data analytics to drive insights and innovation? Share your experiences, insights, or questions in the comments below! Let's connect and exchange ideas to unlock the full potential of data analytics in our organizations. 💡 #DataRevolution #AnalyticsCommunity #DataDrivenInnovation

Excited to share insights on crafting a robust Data Analytics Strategy! In today's data-driven world, businesses are increasingly relying on data analytics to gain valuable insights, make informed decisions, and drive growth. Here are key considerations for developing an effective Data Analytics Strategy: 1️⃣ Define Clear Objectives: Start by aligning data analytics initiatives with your business goals. Whether it's optimizing operations, improving customer experience, or driving innovation, clarity on objectives ensures focused and impactful analytics efforts. 2️⃣ Assess Data Needs and Capabilities: Evaluate your current data landscape—what data sources are available, what data quality issues exist, and what infrastructure is in place? Understanding these factors is crucial for designing an effective data collection and management strategy. 3️⃣ Choose the Right Tools and Technologies: Select analytics tools and platforms that align with your business needs and technical capabilities. Whether it's traditional BI tools, advanced analytics platforms, or cloud-based solutions, ensure they support scalability and integration with existing systems. 4️⃣ Build a Skilled Team: Invest in a team with diverse skills—from data engineers and analysts to data scientists and visualization experts. Collaboration across disciplines enhances the ability to extract meaningful insights and drive actionable outcomes. 5️⃣ Implement Data Governance: Establish clear policies and processes for data governance, including data privacy, security, and compliance. A robust governance framework ensures data integrity, confidentiality, and ethical use. 6️⃣ Iterate and Innovate: Data analytics is iterative. Continuously evaluate and refine your strategies based on insights and feedback. Embrace innovation by exploring new technologies (e.g., AI and machine learning) to uncover deeper insights and opportunities. 7️⃣ Measure Success: Define metrics and KPIs to track the effectiveness of your data analytics initiatives. Regularly assess performance against these benchmarks to demonstrate ROI and inform future strategy adjustments. A well-crafted Data Analytics Strategy not only enhances decision-making but also drives competitive advantage in today's dynamic business environment. Let's harness the power of data to propel growth and innovation! #DataAnalytics #BusinessStrategy #DataDriven #AnalyticsStrategy #BusinessInsights #Innovation #DigitalTransformation

In today's data-driven world, the ability to extract insights from data is a game-changer. How can businesses leverage data analytics to make smarter decisions? Let's explore. Data analytics involves examining datasets to draw conclusions about the information they contain. For businesses, it's about turning raw data into actionable insights that drive strategic decision-making. Key types of data analytics: - Descriptive analytics: What happened? - Diagnostic analytics: Why did it happen? - Predictive analytics: What might happen? - Prescriptive analytics: What should we do? Benefits of data analytics in business: - Informed decision making - Improved operational efficiency - Enhanced customer experiences - Risk mitigation - Competitive advantage - New revenue opportunities Challenges in implementing data analytics: - Data quality and integration issues - Privacy and security concerns - Lack of analytical skills - Resistance to data-driven culture - Choosing the right tools and technologies As a Tech Consultant, I guide businesses in harnessing the power of data analytics. Here's a framework for building a robust data analytics capability: - Define clear objectives: What business questions do you want to answer? - Data collection and integration: Gather relevant data from various sources - Data cleaning and preparation: Ensure data quality and consistency - Choose appropriate analytics tools: From spreadsheets to advanced AI models - Perform analysis: Apply statistical methods and machine learning algorithms - Visualize results: Present insights in an easily digestible format - Take action: Use insights to inform business decisions - Iterate and improve: Continuously refine your analytics processes Remember, successful data analytics isn't just about technology—it's about fostering a data-driven culture where decisions are based on evidence rather than intuition. Ready to unlock the power of your data? Let's connect and explore how data analytics can drive your business forward. #DataAnalytics #BusinessIntelligence #DataDrivenDecisions #TechConsulting #BigData #MachineLearning #BusinessStrategy #DigitalTransformation #DataScience #FutureOfBusiness How are you currently using data in your decision-making process? Share your experiences below! 📉👇

↳ 𝑾𝒉𝒚 𝑺𝒕𝒂𝒕𝒊𝒔𝒕𝒊𝒄𝒔 𝒊𝒔 𝒕𝒉𝒆 𝑩𝒆𝒅𝒓𝒐𝒄𝒌 𝒐𝒇 𝑫𝒂𝒕𝒂 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔 (𝒂𝒏𝒅 𝑾𝒉𝒚 𝑻𝒐𝒐𝒍𝒔 𝑨𝒍𝒐𝒏𝒆 𝑨𝒓𝒆𝒏'𝒕 𝑬𝒏𝒐𝒖𝒈𝒉) 𝖮𝗇𝖼𝖾 𝗎𝗉𝗈𝗇 𝖺 𝗍𝗂𝗆𝖾 𝗂𝗇 𝖺 𝗍𝖾𝖼𝗁 𝖼𝗈𝗆𝗉𝖺𝗇𝗒 𝗂𝗇 𝖡𝖺𝗇𝗀𝖺𝗅𝗈𝗋𝖾, 𝗍𝗁𝖾𝗋𝖾 𝗐𝖾𝗋𝖾 𝗍𝗐𝗈 𝖽𝖺𝗍𝖺 𝖺𝗇𝖺𝗅𝗒𝗌𝗍𝗌: 𝖯𝗋𝗂𝗒𝖺 𝖺𝗇𝖽 𝖠𝗋𝗃𝗎𝗇. 𝖯𝗋𝗂𝗒𝖺 𝗐𝖺𝗌 𝖺 𝗆𝖺𝗌𝗍𝖾𝗋 𝗈𝖿 𝗏𝖺𝗋𝗂𝗈𝗎𝗌 𝖽𝖺𝗍𝖺 𝗍𝗈𝗈𝗅𝗌, 𝖿𝗋𝗈𝗆 𝖲𝖰𝖫 𝗍𝗈 𝖯𝗒𝗍𝗁𝗈𝗇, 𝖺𝗇𝖽 𝖼𝗈𝗎𝗅𝖽 𝖼𝗋𝖾𝖺𝗍𝖾 𝗌𝗍𝗎𝗇𝗇𝗂𝗇𝗀 𝗏𝗂𝗌𝗎𝖺𝗅𝗂𝗓𝖺𝗍𝗂𝗈𝗇𝗌. 𝖠𝗋𝗃𝗎𝗇, 𝗈𝗇 𝗍𝗁𝖾 𝗈𝗍𝗁𝖾𝗋 𝗁𝖺𝗇𝖽, 𝗁𝖺𝖽 𝖺 𝖽𝖾𝖾𝗉 𝗎𝗇𝖽𝖾𝗋𝗌𝗍𝖺𝗇𝖽𝗂𝗇𝗀 𝗈𝖿 𝗌𝗍𝖺𝗍𝗂𝗌𝗍𝗂𝖼𝗌. One day, the company faced a problem: customer churn was increasing, and they needed to find out why. Priya quickly created a dashboard showing churn rates across segments. The visualization was impressive but only scratched the surface. Arjun dug deeper, using statistical models to identify key factors contributing to churn, providing actionable strategies. 1. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐎𝐯𝐞𝐫 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Priya’s tools described the current state of churn. Arjun’s statistics explained why it was happening and predicted future trends, crucial for strategic planning. 2. 𝐌𝐚𝐤𝐢𝐧𝐠 𝐈𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 Priya's data tools showcased what was happening. Arjun’s statistical analysis told the story behind the data, helping management make informed decisions. 3. 𝐐𝐮𝐚𝐧𝐭𝐢𝐟𝐲𝐢𝐧𝐠 𝐔𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲 Arjun’s statistical knowledge quantified uncertainty, providing confidence intervals and significance levels that no tool could replace. 4. 𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐓𝐨𝐨𝐥𝐬𝐞𝐭 Tools are essential, but they are just that: tools. Without a solid foundation in statistics, we risk misinterpreting data and making flawed decisions. In the end, both Priya and Arjun were valuable, but Arjun’s statistical insights truly drove the business forward. His ability to interpret data, make predictions, and inform decisions proved that while tools are useful, a deep understanding of statistics is irreplaceable. 𝖲𝗈, 𝖿𝖾𝗅𝗅𝗈𝗐 𝖽𝖺𝗍𝖺 𝖾𝗇𝗍𝗁𝗎𝗌𝗂𝖺𝗌𝗍𝗌, 𝗅𝖾𝗍'𝗌 𝗇𝗈𝗍 𝗃𝗎𝗌𝗍 𝖿𝗈𝖼𝗎𝗌 𝗈𝗇 𝗆𝖺𝗌𝗍𝖾𝗋𝗂𝗇𝗀 𝗍𝗈𝗈𝗅𝗌. 𝖣𝗂𝗏𝖾 𝖽𝖾𝖾𝗉 𝗂𝗇𝗍𝗈 𝗍𝗁𝖾 𝗐𝗈𝗋𝗅𝖽 𝗈𝖿 𝗌𝗍𝖺𝗍𝗂𝗌𝗍𝗂𝖼𝗌. 𝖨𝗍’𝗌 𝗍𝗁𝖾 𝗄𝖾𝗒 𝗍𝗈 𝗎𝗇𝗅𝗈𝖼𝗄𝗂𝗇𝗀 𝗍𝗁𝖾 𝖿𝗎𝗅𝗅 𝗉𝗈𝗍𝖾𝗇𝗍𝗂𝖺𝗅 𝗈𝖿 𝖽𝖺𝗍𝖺 𝖺𝗇𝖺𝗅𝗒𝗌𝗂𝗌. #𝖣𝖺𝗍𝖺𝖠𝗇𝖺𝗅𝗒𝗌𝗂𝗌 #𝖲𝗍𝖺𝗍𝗂𝗌𝗍𝗂𝖼𝗌 #𝖣𝖺𝗍𝖺𝖲𝖼𝗂𝖾𝗇𝖼𝖾 #𝖣𝖾𝖼𝗂𝗌𝗂𝗈𝗇𝖬𝖺𝗄𝗂𝗇𝗀 #𝖯𝗋𝖾𝖽𝗂𝖼𝗍𝗂𝗏𝖾𝖠𝗇𝖺𝗅𝗒𝗍𝗂𝖼𝗌

🔍 "Navigating Success: The Data Analysis Process Unveiled 📊" Embarking on the journey of data analysis is akin to setting sail on a voyage of discovery. Each step in the process—evaluate, clean, summarize, and predictive—charts a course toward unlocking valuable insights and driving informed decision-making. 🔍 Evaluate: The first step in the data analysis process involves assessing the quality and relevance of the data. By understanding the source and structure of the data, analysts lay the foundation for meaningful analysis. 🧹 Clean: Like polishing a gemstone, data cleaning is essential for removing inconsistencies, errors, and outliers that could skew results. Through meticulous data cleaning, analysts ensure the integrity and accuracy of their analysis. 📊 Summarize: With the data refined and pristine, analysts transition to summarizing key findings and trends. Visualization techniques such as charts, graphs, and dashboards bring the data to life, making complex insights accessible to stakeholders. 🔮 Predictive: Armed with a thorough understanding of the past and present, analysts turn their attention to the future through predictive analysis. By leveraging statistical models and machine learning algorithms, analysts forecast trends and anticipate outcomes, empowering businesses to proactively shape their strategies. Join me on this exhilarating journey through the data analysis process. Together, let's harness the power of data to drive innovation, optimize performance, and unlock a world of possibilities. #DataAnalysis #Evaluate #CleanData #Summarize #PredictiveAnalytics #DataCleaning #DataVisualization #Insights #DecisionMaking #DataScience #MachineLearning #BusinessIntelligence #Strategy #Innovation

𝗗𝗮𝘆 18: 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 🚀 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝗰𝗵𝗼𝗼𝗹: 𝗛𝗮𝗿𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗣𝗖𝗔 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 🚀 In data analysis, more isn’t always better—large datasets with many variables can obscure patterns and increase processing time. That’s where dimensionality reduction techniques like Principal Component Analysis (PCA) come into play. In the Data Mining and Machine Learning for Business course, I explored how PCA transforms complex datasets into simpler, actionable representations without losing critical information. 𝗞𝗲𝘆 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗶𝗻 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻: • 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗣𝗖𝗔) : PCA identifies the most significant features in a dataset, projecting them onto new axes to minimize redundancy. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Reducing noise in high-dimensional datasets, improving the accuracy of clustering and classification tasks. Simplifying visualizations for multi-dimensional data, enabling more intuitive insights. • 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗣𝗖𝗔 : Understanding eigenvalues and eigenvectors helped me grasp how PCA retains variance while discarding irrelevant dimensions. By focusing on principal components, I could distill complex relationships into a manageable format for analysis. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 : Customer Segmentation: Streamlined analysis by reducing hundreds of behavioral variables to a handful of meaningful components. Supply Chain Optimization: Simplified operational data to identify key drivers of inefficiency and cost reduction. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: Dimensionality reduction is a cornerstone of efficient data analysis. It ensures that decision-makers focus on the most relevant insights, reducing noise and improving interpretability. PCA, in particular, empowers analysts to tackle high-dimensional data challenges with precision and clarity. In the next post, I’ll bring together the course’s key techniques—regression, clustering, association rules, and dimensionality reduction—to highlight how they collectively solve real-world business problems. 𝗙𝗲𝗹𝗹𝗼𝘄 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀: 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝗴𝗼-𝘁𝗼 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗳𝗼𝗿 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀? #BusinessAnalytics #DataMining #MachineLearning #PCA #DimensionalityReduction #DAmoreMcKim #CareerJourney

**"Exploring Key Trends in Data Analytics for 2024"** As data continues to be the driving force behind strategic decisions, I’ve noticed a few trends in the field of data analytics that are shaping how organizations approach their data. Here are some key trends I’m excited about: 1. **The Rise of Augmented Analytics**     With advancements in AI, augmented analytics is allowing analysts to go beyond traditional dashboards. These tools use machine learning to generate insights automatically, making data exploration faster and more accessible to non-technical teams. I see this as a game-changer for democratizing data. 2. **Increased Focus on Data Quality and Governance**     With high-value decisions relying on data, clean and reliable data is crucial. Many companies are investing heavily in data governance frameworks, as even the best models are only as good as the data feeding them. Implementing robust data validation processes in my past projects has always delivered more actionable insights and greater stakeholder trust. 3. **Real-Time Analytics for Faster Decision-Making**     As businesses demand quicker responses, real-time analytics is becoming essential. Platforms that integrate streaming data are providing immediate insights, allowing companies to make decisions as events unfold. For me, this has meant building solutions that prioritize both speed and accuracy – a challenging but rewarding balance! 4. **The Shift Towards Predictive and Prescriptive Analytics**     Moving beyond descriptive analytics, businesses now want to know what will happen and what actions they should take. Predictive models, coupled with prescriptive insights, are helping companies plan proactively. I’ve recently worked on a project in this area and witnessed firsthand how forecasting can be a powerful tool for strategic decision-making. Data analytics is evolving rapidly, and staying on top of these trends is key to delivering value. What trends are you seeing in your industry? #DataAnalytics #DataScience #AnalyticsTrends #DataQuality #RealTimeAnalytics #PredictiveAnalytics

Blog 60 Demystifying Data: The Power of Analytics and Business Intelligence Businesses in today's data-driven world are awash in information. But how can you turn this information into practical insights? Enter data analytics and business intelligence (BI), two strong tools that help firms make educated decisions and achieve their strategic objectives. Data Analytics: Unveiling the Story Data analytics is a wide phrase that encompasses the different tools and methodologies used to discover hidden patterns and trends in data sets. It's like digging through a pile of sand for rare jewels. Analysts use statistical analysis, modeling, and data visualization to transform raw data into a clear and engaging narrative. Here are some important functions of data analytics: • Cleaning and preparing data is necessary before beginning analysis to remove inconsistencies and inaccuracies. This validates the validity of the derived insights. • Descriptive analysis involves summarizing data using averages, medians, and frequencies to gain a fundamental grasp of its features. • Predictive analytics: Using past data and statistical models, analysts can forecast future patterns and outcomes. This enables proactive decision-making. • Prescriptive analytics suggests specific measures to enhance outcomes based on data, rather than just making predictions. Business Intelligence: From Insight to Action. Data analytics serves as the foundation for business intelligence. It is the process of collecting, integrating, and analyzing data so that business users may easily obtain the insights they require. BI technologies such as dashboards and reports convert complex data into user-friendly formats, enabling real-time monitoring and informed decision-making at all levels of the business. Think of BI as the bridge between data and action. It takes the insights gleaned from data analytics and presents them in a way that empowers employees across departments to: • Identify and solve problems: BI can help pinpoint operational inefficiencies or customer service issues, allowing for timely interventions. • Optimize marketing campaigns: Data on customer behavior and preferences can be used to target marketing efforts more effectively. • Improve resource allocation: BI helps understand where resources are being spent and how efficient processes are, enabling smarter allocation. • Gain a competitive edge: By understanding market trends and customer needs, businesses can develop data-driven strategies for differentiation. Here is an analogy: Imagine you're a detective investigating a crime scene. Data analytics is the detective who methodically collects fingerprints and examines evidence. The BI is a clear and succinct report submitted to the police chief that summarizes the results and recommends a course of action. #DataAnalytics #BusinessIntelligence #AI

Data Analysis Myth : "Tools Do Everything" In today's data-driven world, the misconception that tools can do everything in data analysis is pervasive. While it's true that powerful software and algorithms have revolutionized our ability to process and analyze vast amounts of data, relying solely on them is a mistake. Here’s why: 1. Critical Thinking is Key: Tools can automate processes, but they can't think critically. The insights they generate need to be interpreted by someone who understands the context, nuances, and potential implications of the data. 2. Domain Knowledge Matters: A tool can highlight trends, but without domain expertise, those trends might be misinterpreted or missed entirely. It's the analyst's knowledge of the industry and specific business challenges that turns raw data into actionable insights. 3. Tools Have Limits: Even the most advanced tools are limited by the quality of the input data and the assumptions they’re based on. Human judgment is crucial in recognizing these limitations and adjusting the analysis accordingly. 4. Creativity and Intuition: Data analysis isn’t just a science, it’s an art. Creativity in framing the right questions, combined with intuition built from experience, often leads to breakthroughs that tools alone could never achieve. 5. Communication Skills are Essential: Even the most sophisticated analysis is useless if it can’t be effectively communicated. Translating complex data into clear, compelling narratives is a uniquely human skill that no tool can replicate. In summary, while tools are indispensable in modern data analysis, they’re just that, tools. The real magic happens when these tools are displayed by skilled analysts who bring critical thinking, domain knowledge, creativity, and communication to the table. Let’s remember: It’s not the tools that drive data-driven success, it’s the people using them. #Dataanalysis #criticalthinking #Analytics

Quite informative. Strategy starts with foundations. As data grows and matures into insights, it passes through different hands. The only way to realize value from data is to have a well-organized strategy. This goes just beyond skillset selection to infrastructure but more so to the and also to overall organizational goal. The strategy needs to be in a position to champion the firm's goals, vision, and mission. In today's information value chain, every organ is as important as the other, whether it be data engineering, Data Engineering, Business Intelligence, Data Science or even AI and Machine Learning. Few things to consider 1 Data Framework 2 Data Strategy 3 Data Leadership. 4 Rightful hire. Understanding this, in my view pivots the company and data initiatives closer to attaining maturity. Thanks for sharing !!