Bernard Akondoh’s Post

Mining executives spend a significant 27% of their time dealing with data. Why isn't there more discussion about the often-overlooked challenges of mining data management? 33% lack essential information for critical decisions. Six out of ten mining companies operate without a robust data management framework, putting their operational efficiency at risk. The bright side? More than half of them are getting ready to establish solid frameworks within the next three years. Taking a closer look at data management, the problem of old data becomes a major concern. 30% have unorganized historical data that could cloud future insights. Just imagine the untapped potential hidden within this old data. Then there's the 26% who rely heavily on key experts. Is their backup plan robust enough? The challenge of finding data exposes a technological gap. 24% face difficulties. Only 27% prioritize data security. A single breach could cause chaos. Are you prepared? Lastly, 21% struggle with data workflows. Efficient processes can make a world of difference, preventing loss of time and opportunity. Let's be honest: the mining industry needs to sharpen its data skills.

Managing Data Creep in Process Mining Storage can be a tricky problem. Especially after going live on a process mining project when the live data feed drives the number up. Continuous data pulls can be massively important but can also be a massive pain when they start to creep larger and larger. Each new idea seems to take far more data than initially expected and you can quickly be looking down the barrel of a large expansion fee. Data creep in process mining doesn't have to happen to you -   Doculabs 5 pronged approach to begin a continuous cycle of data awareness: -> Document a list of tables outside of the tool and maintain its accuracy -> Survey your data usage from extraction to dashboard -> Be mindful of creating new tables -> Do the math -> Don't be afraid to remove data Want these in your inbox? Let us know in the comments and we'll add you to the list.

Data mining is the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information. Companies use data mining software to learn more about their customers. It can help them to develop more effective marketing strategies, increase sales, and decrease costs.

Extract valuable insights and patterns from large data sets with Yalantis data mining services! Our data experts use cutting-edge technologies and techniques in machine learning and statistical analysis to deliver high-performance data mining solutions that enable seamless data analysis. Interested in learning more about how our data mining services can benefit your business? 📎 Visit our new page or reach out to schedule a consultation: https://lnkd.in/eFmUR9ej #datamining #datainsights #dataanalytics #digitaltransformation

MINING ENGINEERING: Collecting, processing, and analyzing drilling data is crucial for mining engineering, but it can be a challenging task. To ensure accuracy and make informed decisions, there are several tips and techniques you can use. Standardized protocols, data validation and verification tools, data visualization and analysis techniques, and data feedback and learning mechanisms are some of the ways to optimize your drilling operations and outcomes. By implementing these practices, you can improve the quality, reliability, and accuracy of your data while supporting your decision-making with evidence and logic. What methods or tools do you use to guarantee drilling data accuracy and enhance decision-making in mining engineering? https://lnkd.in/gpidT_zR

Considerations for Installing new Software tool to your Data mining System Health check and Backup Your System When installing new software tool to your data mining system, there are some considerations about integration and Consistency those like Backup your whole system and make health check to the system before installation of new tool to ensure availablity of your system in case of any Faliure or Breakdown to the system or other performance potential issues. You can also keep two images of your system running, one with new tool and the other isolated without the tool and let them working together in Parallel this will help in monitoring performance of the system and measure performance before publish your system online.

Process mining ⛏️ is finally unlocking a new age of data driven process improvements! 👍🏽 We know we can’t take the output of technology as granted, without ensuring a good input. 🛒 I actually recognize myself 🥸 in many projects, reading the process mining challenges by IBM. 💻 Data quality: Finding, merging, and cleaning data is usually required to enable process mining. Data might be distributed over various data sources. It can also be incomplete or contain different labels or levels of granularity. Accounting for these differences will be important to the information that a process model yields. Concept drift: Sometimes processes change as they are being analyzed, resulting in concept drift. TELL ME ABOUT IT 🤯 https://lnkd.in/dYXGwBuT #processmining #datagovernance #myrthapools

Process Mining vs. Business Intelligence: The Power of Combining Insights for Process Excellence Business Intelligence (BI) and Process Mining are both valuable tools, but they serve different purposes. BI is designed to give you a top-down view of your organization's performance, while Process Mining works from the ground up, showing how work is actually done. By using them together, you can not only spot high-level trends but also drill down into the exact flow of processes to uncover inefficiencies. Key Differences between the two tools: Business Intelligence (BI) -> Focuses on **what** happened. -> Analyzes historical data to identify patterns and trends. -> Helps businesses make strategic decisions based on metrics like sales performance, customer retention, etc. Process Mining: -> Reveals **how** processes are executed by analyzing event logs. -> Maps out real-time workflows, identifying hidden bottlenecks, inefficiencies, and deviations from standard processes. -> Offers transparency into business operations and helps in root-cause analysis. Why Combining Them Matters: BI gives you a high-level understanding of your performance, while Process Mining shows you the operational flow, down to the smallest details. Together, they:   -> Improve Decision-Making: Combining insights from both tools gives leadership a more complete view, allowing for smarter, more informed decisions.  -> Identify Bottlenecks: BI shows performance gaps, while process mining uncovers the specific workflow inefficiencies causing them.  -> Enhance Efficiency: BI helps set key performance indicators (KPIs), and process mining ensures that processes align with those KPIs by pinpointing areas for improvement. Practical Use Cases: -> Customer Service: BI shows declining customer satisfaction scores, while process mining reveals slow response times at specific touchpoints in the customer journey. -> Supply Chain Management: BI shows overall delivery delays, while process mining highlights the exact process steps where delays occur—helping you streamline operations. -> Compliance Audits: BI reports financial discrepancies, and process mining maps out how unauthorized steps in workflows lead to the issue, ensuring compliance. By blending high-level BI insights with the granular operational visibility of process mining, organizations can optimize processes, reduce costs, and drive continuous improvement. #ProcessMining #BusinessIntelligence #DataAnalytics #OperationalExcellence #ProcessOptimization #ContinuousImprovement #BusinessTransformation"

Is process mining really always about creating Process Transparency? Of course it is! But only if you do it right. But then there are also many other added values that arise from Process Mining. 📌 However, the key to success is not so much the selected process mining tool, but rather the all-important Value Engineering. 🛠️ When building Process Mining Applications, an iterative cycle of engineering work packages is necessary. These start with Use Case Evaluation and Requirements Engineering, have Data Engineering and Analysis at their core for Process Reconstruction and end with Process Validation and gaining deep insights into the process networks. Nowadays, this process is also supplemented by Data Science / AI elements, for example to detect anomalies in processes or set up early warning systems. 💲 And now Value Engineering comes into play, which has its own cycle. To this end, value engineers accompany the data engineering process and ensure that added value is generated. Ideally, this begins (as a sidequest) with the identification of weaknesses in the company's target definition and in terms of data quality. Process Mining also creates Data Transparency on the way to process transparency! The value is thus already visible during the Process Mining activities and not just at the end. The value grows and becomes greater and greater with each new work stage, up to 100% Data and Process Transparency. #ProcessMining #DataAnalytics #AI #DataScience #DataEngineering #ValueEngineering