Database Administration’s Post

Are you working in the data & analytics industry? I've been connecting with some of the top professionals in data across Australia—directors of data & analytics consultancies, heads of data, and data engineering managers, to name a few. What I learnt? The most sought-after skills in the data & analytics sector are🔍 1️⃣ SQL for querying databases  2️⃣ Python for data manipulation and analysis  3️⃣ Data Visualisation with tools like Power BI   4️⃣ Cloud technologies in AWS, Azure, or GCPs Lastly, and often the least spoken about, is communication, problem-solving skills and the growth mindset. Growth mindset can seem corny. But being passionate and able to learn new technologies is of huge value to potential employers. Check out the video for the verbal breakdown 👇 DR Analytics Recruitment - Building data teams | Growing people and businesses.

Available for Micro Projects: Strong Cloud and Data protection Architect Business Problems Solved 1.   Regulatory Alignment: Ensures you're always on the right side of the law. 2.   Bulletproof Security: Builds robust data protection frameworks. 3.   Smooth Transition: Expertly manages your move to the cloud. 4.   Data Strategy: Sets the course for effective data management. 5.   Operational Boost: Streamlines processes for peak efficiency. 6.   Insightful Reporting: Delivers decision-making data on time. 7.   Skill Upgrade: Elevates your team's skillset and job satisfaction. 8.   CX Enhancement: Uses data to level up customer satisfaction. 9.   Cross-Team Harmony: Facilitates seamless multi-departmental collaboration. 10.   Future-Ready: Design systems that scale with your business.   Value Propositions 1.   Compliance Guru : Ensures 100% legal compliance in data protection. 2.   Cost-Saver: Achieves major cost reductions via cloud migration. 3.   Speed Master: Boosts data processing and report generation speeds. 4.   Strategic Leader: Guides teams with a keen eye on long-term success. 5.   Tech Whiz: Masters diverse programming languages and cloud tech. 6.   Data Storyteller: Transforms complex data into actionable insights. 7.   Team Connector: Bridges the gap between tech, legal, and exec teams. 8.   Customer-Centric: Utilizes analytics to elevate customer experience. 9.   Mentor & Trainer: Upskills teams for better productivity and retention. This architect is not just a tech expert but a strategic asset for any U.S.-based company looking to fortify data protection, streamline operations, and drive growth. #DataProtection #CloudMigration #DataCompliance #StrategicLeadership #DataAnalytics #CloudComputing #DataArchitecture #DataSecurity #TeamLeadership #CustomerExperience #RegulatoryCompliance #DataStrategy #BusinessIntelligence #EmployeeTraining #TechnicalProficiency #CostSavings #InterdepartmentalCollaboration #OperationalEfficiency #EmployeeRetention #BusinessGrowth

Introducing data analytics courses in TVET programs can indeed prepare students for careers in data-driven decision-making, particularly in industries like manufacturing and logistics, which are increasingly relying on data analytics for optimization. A case study from IBM, a leader in data analytics and technology, demonstrates the impact of such training programs: 1. Comprehensive Curriculum: IBM offers data analytics training programs that cover a wide range of topics, including data collection, data cleaning, data analysis, and data visualization. This comprehensive curriculum ensures that participants gain a thorough understanding of data analytics principles and techniques. 2. Hands-on Experience: IBM's training programs include hands-on experience with data analytics tools and software. Participants have the opportunity to work on real-world data sets, allowing them to apply their skills in practical scenarios. 3. Industry-Relevant Skills: The skills taught in IBM's data analytics training programs are highly relevant to the manufacturing and logistics industries. Participants learn how to analyze data to identify trends, make predictions, and optimize processes, all of which are essential for success in these industries. 4. Certification: Upon completion of the training programs, participants receive a certification from IBM. This certification is recognized by employers in the industry and can enhance participants' job prospects. 5. Career Support: IBM provides career support to participants, including job placement assistance and networking opportunities. This support can help participants secure employment in the manufacturing and logistics industries. By introducing data analytics courses in TVET programs, institutions can prepare students for careers in data-driven decision-making in industries like manufacturing and logistics. These skills are in high demand, and graduates with expertise in data analytics are well-positioned to succeed in these industries. https://lnkd.in/d4DcbE25

"Why should I care about Data unless I work in Data?"   Guess what? We all work in Data.   Every facet of every industry is reliant on Data. From manufacturing to finance; customer service to engineering; marketing to technology- all of our jobs require data. Do we need to order more parts? What is the rate of return on this investment? What's the average wait time for inbound calls? The list goes on and on.   All employees need to understand data to perform their day to day tasks. Leaders also need to understand data in a way that helps them diagnose problems, identify opportunities, make wise long-term plans. We don't need to be experts in all aspects of data- we can (and should!) hire people to do that. But we must be able to ask questions using the right vocabulary if we want to get the most value out of our data.   Yet in a recent survey by Qlik.com, only 24% of business decision makers said they felt confident in their ability to use data. That's scary. So how do we change it? Improving our Data Literacy is a critical step.   Launching a formal Data Literacy program offers a pathway for employees and leaders to learn some of key foundational terminology and principles of Data and can help us all speak a common language. If data experts can speak in terms that resonate with business leaders and business leaders can become conversant in data terminology, we can elevate our discussions to allow us to truly harness the power of data and propel our organizations forward.   There are also things we can do individually to improve our Data acumen. This article from Forbes offers some helpful suggestions on how to Enhance your Data Literacy skills. Ring the bell (🔔) on my profile to see more daily content about continuous learning, leadership, mentoring, data, data visualization, and technology. #DataLiteracy #Data #DataAnalytics https://lnkd.in/gPiih54x

🔐💾 Data Management Hacks for a Smoother Workflow 💾🔐 In today's data-driven world, managing your data efficiently is crucial for personal and professional success. Here are some data management hacks that can help you stay organized and make the most of your valuable information: Cloud Storage Convenience: Embrace cloud storage solutions like Google Drive, Dropbox, or OneDrive. Access your data from anywhere, collaborate seamlessly, and ensure automatic backups for added security. Version Control Mastery: For collaborative projects, use version control systems like Git. Easily track changes, revert to previous versions, and work with your team more effectively. Folder Structure & Tags: Organize data with a logical folder structure and use tags to categorize and retrieve information effortlessly. Automate Backups: Set up automated backups to ensure you never lose essential data due to hardware failures or accidental deletions. Eliminate Duplication: Regularly scan for and remove duplicate data. Not only does this save space, but it also improves data quality. Create a Data Dictionary: Maintain a data dictionary that outlines the meaning, format, and relationships of your data elements. This ensures everyone understands the data's context and usage. Fortify Data Security: Implement encryption and access controls to protect sensitive information from unauthorized access. Data Compression: Compress data files to optimize storage usage and improve data transfer times. Regular Data Cleansing: Keep your datasets clean by removing outdated or irrelevant information. This practice maintains data quality and relevance. Automate Data Entry: Use automation tools for data entry to reduce errors and save time on repetitive tasks. Choose the Right Tools: Leverage data management tools such as databases, visualization software, and integration platforms to streamline your data operations. Access Control Management: Ensure authorized personnel have appropriate data access permissions to maintain data security and compliance. Regular Auditing: Conduct periodic data audits to identify issues and address data integrity concerns proactively. Data Synchronization: Automate data synchronization processes if you use multiple systems. This ensures consistency and accuracy across all platforms. Empower Your Team: Train your staff on data management best practices, security protocols, and data handling procedures to create a consistent approach throughout your organization. 💼📊 Data management is the backbone of your success in the digital era. By adopting these hacks, you'll gain better control of your data, enhance productivity, and safeguard critical information. Happy data managing! 🚀💪 #DataManagement #ProductivityHacks #DataSecurity

"The PESTEL analysis technique is a key tool for a company’s management team during enterprise strategic planning. When correctly done, this analysis technique can help anticipate future challenges and opportunities. This technique is especially helpful when formulating a strategic business plan."--IdeaScale The decision to choose a Knowledge Graph or Vector Database Should never be made in the absence of the business objective to be achieved "Knowledge Graphs have frequently been referred to as similar to how the human brain works. Graphs enable explicit relationships to be stored and queried against — reducing hallucinations and increasing accuracy through context injection."--Neo4j Technology breds more technology leading to implementations Of technology for technology's sake "Vector databases have gained significant importance in various fields due to their unique ability to efficiently store, index, and search high-dimensional data points, often referred to as vectors. The vectors can represent a wide range of information, such as numerical features, embeddings from text or images, and even complex data like molecular structures."--Medium In addition to Porter's Five Forces Competitive Rivalry Threats of Substitutes Threats of New Entrants Power of Buyers Power of Suppliers Business objectives and strategic plans must be connected to the PESTEL influences of the industry and macroeconomic ecosystem in which the business is concentrated. Political - domestic legislative changes impacting the pace of innovation in AI Economic - pandemic era disruptions in supply chain and inflation Social - consumer shift from mission-driven to purpose-drive organizations Technological - introduction of widely available LLM capabilities Environmental - rising C02 levels, rapidly decreasing availability of power for servers Legal - Europe expands GDPR leading to compliance requirements for international US companies. The ability of the business to navigate these challenges is based largely on the SWOT of the company. Strengths - what we do better than others Weaknesses - what we could be doing better Opportunities - what we could be capitalizing on Threats - what we better keep our eyes on When you factor those things into resource allocation models and capital constraints the strategic use of Knowledge Graphs and Vector Databases becomes clear. The LLM of the company should be connected to RAG systems Retrieval Augmented Generation (think Perplexity and Microsoft Bing Co-Pilot) To ensure a cross-referencing of the most up-to-date and contextually relevant information when answering the user's input or query But each query in and of itself requires one or both technologies to fully satisfy the requirement that the collective knowledge of the company makes it to the decision-makers in a consumable and actionable format. "Run an analysis of our current operations costs and identify areas of cost optimization" = Knowledge Graph and Vector Database

There’s a growing misconception that only mid-sized companies have data management problems. Not true. Even the most agile enterprise organizations struggle. They have lots of data and lots of refined processes. But they’re not connected together. Sometimes, updating a spreadsheet with the data their team needs can take weeks to get the job done. I’ve seen it firsthand. We’re not talking about mid-sized companies using Excel as a database and sorting through paper invoices to process payments. Enterprise organizations. With beautiful dashboards. Pulling data from numerous sources, loads of information, and incredible views… All wokring great until you want to add a level of view. (Or adjust the insights.) Then, the team has to send a request to IT. And wait 3 - 4 weeks. Collaboration slows down. Employees get frustrated. They download spreadsheets. Convert them from Excel to Sheets or Sheets to Excel. Or CSV. Manually align data rows. Present that data. And hope for the best… It limits collaboration. Worse, there’s a greater chance for human error, security risks, and data manipulation (among other risks.) So, before you think, “This can’t happen here.” Remember: Data management challenges impact every organization. No matter the size. — #lowcode #automation #datamanagement #data #digitaltransformation #lcnc #developers #softwaredevelopment #enterprise #analtyics

One Simple Shift for Massive Database Performance Gains There is a small change in approach can lead to huge improvements in database performance. Many treat databases like a person methodically loading a dishwasher—one item at a time. But databases thrive on set-based operations. Imagine loading all your spoons at once into the dishwasher. That's how databases prefer to work—handling sets of data together, not individually. Shifting from row-by-row processing to a set-based approach can unlock massive efficiency gains. This mindset shift is a powerful tool for anyone looking to optimize database performance.

📊 Navigating Data Pitfalls in Operations: Learn, Adapt, Excel! 📉(My Experience) In the dynamic landscape of operations, data plays a pivotal role in driving decisions and optimizing processes. 🚀 However, there are common mistakes that can trip us up along the way. Let's explore some of these pitfalls and how we can clear them of: 1. Ignoring Data Quality: Garbage in, garbage out. Relying on poor-quality data can lead to flawed insights and misguided actions. Prioritize data cleansing and validation to ensure your analyses are based on accurate information. 2. Chasing Shiny Metrics: Flashy charts and numbers might grab attention, but not all metrics are created equal. Focus on the metrics that truly align with your operational goals, rather than chasing trends that don't contribute to meaningful improvements. 3. Neglecting Context: Data without context is like a puzzle missing pieces. Always consider the broader operational context when interpreting data. External factors can significantly impact the insights you draw. 4. Overlooking Data Security: Data breaches can be catastrophic. Don't compromise security for convenience. Implement robust data protection measures to safeguard sensitive information from unauthorized access. 5. Lack of Cross-Functional Collaboration: Operations involve various departments working in harmony. Failing to collaborate and share insights across teams can lead to inefficiencies and missed opportunities for synergy. 6. Not Embracing Change: Operations are in a constant state of flux. Using historical data alone might not cut it. Embrace predictive analytics and data-driven forecasting to stay ahead of operational shifts. 7. Data Hoarding Syndrome: Accumulating data without a clear purpose can lead to clutter and confusion. Define what data is essential for your operations and discard the rest to maintain a streamlined approach. 8. Forgetting Human Insights: Data complements human expertise; it doesn't replace it. Leverage the experience of your team members to contextualize and enrich data-driven decisions. 9. Incomplete Data Training: Equip your team with the skills needed to understand and interpret data. A lack of data literacy can lead to misinterpretations and poor decision-making. 10. Rigidity in Analysis: Being overly reliant on a single analysis methodology can limit your insights. Explore different analytical approaches to gain a more comprehensive understanding of your operations. Remember, mistakes are stepping stones to growth. By acknowledging and addressing these common data pitfalls, you're positioning yourself to become a more adept and insightful operations professional! 🌟 #DataInOperations #OperationalExcellence #DataDrivenDecisions #ContinuousImprovement

To be successful with AI and machine learning, you need to know how to collect and clean data, and how to interpret the statistics that come out of it. Even though chatty AIs are getting a lot of attention, if you can learn to work with data, you will be in a good position to face the future business challenges of applying AI in a practical way.