How AI Decision-Making is Improving Enterprise Outcomes
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معاملات دبي العقارية
23 September 2019 
Team Beinex معاملات دبي العقارية في الربع الأول من عام 2018
يظهر سوق دبي العقاري شواهد النمو المستمر لما يملكه من ثقة ونظرة إيجابية شاملة كقطاع حيوي. كشفت دائرة الأراضي والأملاك في دبي عن حجم المعاملات التجارية في القطاع العقاري التي تمت خلال الربع الأول من العام 2018 وتم رصده بما يساوي الـ 58 بليون د.إ. خلال الثلاثة أشهر الأولى من العام كناتج كُلي لـ 13759 عملية تجارية.
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Employee Churn Prediction Model: Experience Certainty in HR Matters
Many businesses make a concerted effort to create work cultures that increase job happiness, encourage people to find meaning and satisfaction in their work, reward and recognize employees for their actions, and foster both personal and professional growth.
While many tactics are adopted for the company’s benefit and to considerably increase retention, leaders cannot rely only on them. They must face the uncomfortable reality that they will eventually lose significant talent if they do not keep an open mind and a realistic outlook on the future. Influential leaders act daily to safeguard themselves, their teams, and their companies from the risk of attrition because wishful retention thinking is not a viable business strategy.
Is it possible to foresee attrition so that it only impacts the business a little less, given that it is impossible to stop employees from leaving? Well, with the help of technology, it is possible. The churn model, among others, can help in this situation. Are you wondering how? Through an employee churn prediction model, we can make it happen. After understanding which employees are on the verge of leaving using the churn model, it is possible to reach out to them and understand their grievances.
Employee Churn Prediction Model
It's a predictive model that calculates the likelihood (or vulnerability) of each employee leaving. It tells us how likely we will lose employees or a specific employee in the future at any given time. It classifies employees into two groups (classes): those who quit and those who don't. It will typically tell us the probability of the employee belonging to which of the groups in addition to placing them in one of the two groups. Thus, a churn model can be used to estimate the chances of resignation.
Explaining the Model
Modern churn models frequently draw their foundation from machine learning, more specifically from binary classification methods. There are several of these algorithms; therefore, it's important to test which one works best in each circumstance. Here we have made use of four machine learning models:
Random Forest
Supervised machine learning algorithms like random forest are frequently employed in classification and regression issues. On various samples, it constructs decision trees and uses their average for classification and majority vote for regression.
The Random Forest Algorithm's ability to handle data sets with both continuous variables, as in regression, and categorical variables, as in classification, is one of its most crucial qualities. In terms of classification issues, it delivers superior outcomes.
KNN
One of the simplest machine learning algorithms, based on the supervised learning method, is K-Nearest Neighbour. The K-NN algorithm assumes that the new case and the existing cases are comparable, and it places the latest instance in the category that is most like the existing categories.
A new data point is classified using the K-NN algorithm based on the similarities after storing all the existing data. This means new data can be quickly and accurately sorted into a suitable category using the K-NN method. Although the K-NN approach is most frequently employed for classification problems, it can also be used for regression.
Decision Tree
The supervised learning algorithms family includes the decision tree algorithm. The decision tree technique, in contrast to other supervised learning methods, can handle classification and regression issues.
By learning straightforward decision rules derived from previous data, a Decision Tree is used to build a training model that may be used to predict the class or value of the target variable (training data).
Support Vector Machine
One of the most well-liked supervised learning algorithms, Support Vector Machine, or SVM, is used to solve Classification and Regression problems. However, it is employed mainly in Machine Learning Classification issues.
The SVM algorithm's objective is to establish the best line or decision boundary that can divide n-dimensional space into classes, allowing us to quickly classify new data points in the future. A hyperplane is a name given to this optimal decision boundary.
SVM selects the extreme vectors and points that aid in creating the hyperplane. Support vectors representing these extreme instances form the basis for the SVM method.
A Step-By-Step View of the Process
Step 1: Loading data to databricks
In the initial stage, CSV data collected are loaded to the churn model. Any type of data sets can be employed here depending on the situation.
Step 2: Transformation: converting to the requisite format
While uploading, objective data sets are transformed into integers.Step 3: Feature selection
There are four feature selection algorithms from which we take the best one to filter out undesired features. The selection of filtering features differs for each type of data based on the algorithm.Step 4: Splitting the data
After the feature selection, the next step is to split the data for training and testing—then divide the data into a 7:3 ratio. In the training set, we train our model with data to understand the attrition patterns and later test it with data in the testing set.Step 5: Standardisation
In this step, data is converted into a standard format that allows for large-scale analytics.Step 6: Model selection
During model selection, datasets are provided to the machine algorithms like Random Forest, KNN, Decision tree and Support vector machine. Each algorithm produces its own sets of accuracy values; from that, the most accurate predictions are selected. Using the same procedure, we can categorize the employees into groups, for example, those who are planning to resign and those who are not.
Step 7: Result generation
The result is built on how each machine learning model performs with the dataset. The accuracy value depends on the performance of each model—the higher the accuracy, the higher the probability of accurately predicting the outcomes for each employee.
Measuring Data Culture Maturity: How to Evaluate and Maximize the Benefits of a Mature Data Culture
What is the Data Culture Maturity Model?
The Data Culture Maturity Model by Alation is a framework designed to guide organizations through various levels of data proficiency. It categorizes data culture maturity into distinct stages, allowing organizations to understand their current position, set achievable goals, and implement strategies to progress further. This model addresses data discovery, data governance, data literacy, and data leadership elements that collectively foster a robust data culture. Each phase in the model encourages organizations to embed data at the core of their operations, transforming it into a valuable resource for decision-making and competitive advantage.
Why is Data Culture Maturity Important?
Data culture maturity is crucial for leaders who recognize that a data-driven approach can be a differentiator in today's competitive market. For CDOs, CIOs, BI professionals, and business leaders, fostering a mature data culture means establishing a strong foundation for data-enabled innovation and agile decision-making. As data culture evolves, organizations can explore the benefits of data self-service, increase trust in data, and leverage data literacy to make decisions backed by concrete insights.
Empowering a Data Culture: Key Tenets
The Alation Data Culture Maturity Model comprises four core tenets that organizations should focus on to elevate their data culture: Data Search & Discovery, Data Governance, Data Literacy, and Data Leadership. Let’s explore each tenet and its role in building a mature data culture.
1. Data Search & Discovery
Data Search & Discovery is the foundation of any data culture. It focuses on enabling users to quickly and easily find, understand, and trust the data they need. Organizations with mature data search capabilities invest in technologies like data catalogs, which streamline data search through features like intuitive search, contextual data, and cross-platform integration. These tools reduce the time users spend searching for data, empowering analysts to focus on value-added tasks instead of answering repetitive data queries. Alation pioneered the data catalog concept, which has evolved into a comprehensive data intelligence platform. The modern data catalog supports not only data search and discovery but also functions like data governance and cloud migration. These capabilities create a data culture that encourages self-service and fosters a deeper understanding of the data available to all employees. Measuring Value: Data search maturity can be measured by the time saved on data searches, the frequency of data queries, and the volume of self-service analytics. Organizations can leverage these metrics to assess their return on investment (ROI) and the efficiency of their data catalog.2. Data Governance
Data Governance establishes the rules and policies that ensure data is managed responsibly and is readily accessible and secure. In a mature data culture, governance extends beyond compliance, enhancing data search and data literacy. Organizations with strong governance frameworks reduce the risk of regulatory fines, establish data trustworthiness, and improve data quality. Defining Data Governance: Data governance can be seen as the “authority and control” over data assets. This entails organizing policies, procedures, roles, and responsibilities to align with the company’s data goals. Alation emphasizes that governance must go beyond traditional definitions to include active governance, which fosters collaboration, defines common data language, and establishes shared processes. Measuring Value: Effective governance can be measured by the percentage of data assets that meet governance standards, the number of governance-related issues resolved, and regulatory compliance rates. This not only assures data quality but builds trust in data for decision-making.3. Data Literacy
Data Literacy is about ensuring that individuals at all levels can read, work with, analyze, and argue with data. This element focuses on equipping employees with the skills to understand and utilize data effectively, bridging the gap between raw data and actionable insights. Building data literacy involves training, creating a framework for collaboration, and promoting data-driven thinking. Building Data Literacy: Successful data literacy programs generally follow a step-by-step approach, starting with assessments, followed by targeted training, and promoting an internal culture of data use. Organizations can embed literacy initiatives in data catalogs, where employees can access learning resources, engage in discussions, and collaborate with subject-matter experts. Measuring Value: Data literacy maturity can be assessed by monitoring the percentage of catalog contributions from a broad base of users, showing a shift from “gut-based” to data-driven decision-making. Additionally, organizations can track the frequency of cross-departmental data collaborations as an indicator of a well-integrated data culture.4. Data Leadership
Data Leadership is the most vital element, acting as the catalyst that drives data culture maturity forward. Effective data leaders champion data initiatives, implement change management programs, and consistently highlight the connection between data and business outcomes. They focus on aligning data objectives with strategic goals, ensuring that data initiatives generate tangible business value. The Role of Data Leadership: Mature data leaders embed data in strategic planning, empower departments to utilize data in decision-making, and foster a data-driven mindset throughout the organization. They work to make data initiatives visible, promoting metrics and KPIs that reflect the value added by data maturity. Measuring Value: Organizations can measure data leadership through the number of data stewards and subject matter experts identified, the impact of data on key business outcomes, and the frequency of data-driven initiatives across departments. When leadership drives data culture, the organization benefits from enhanced innovation, agility, and competitive advantage.Articulating Business Value Through Data Maturity
One of the primary objectives of the Data Culture Maturity Model is to showcase how advanced data culture drives business outcomes. To demonstrate this, data leaders can tie maturity metrics to specific business cases, such as self-service analytics, regulatory compliance, and data democratization.
Self-Service Analytics
In organizations with high data culture maturity, self-service analytics is a practical application. With accessible data catalogs and robust data literacy programs, employees can independently search, analyze, and interpret data. This capability speeds up decision-making and fosters a sense of ownership in data-driven outcomes. Measuring Success: Key metrics include time saved in data discovery, the reuse of existing data reports, and improved analytics turnaround. Organizations with a mature self-service model also report a higher degree of cross-departmental data sharing, indicating a well-established data culture.Active Data Governance
Active data governance ensures that data is handled in a structured and compliant manner. This framework allows organizations to confidently share data, meet regulatory standards, and promote accountability. Cataloging data assets facilitates governance, giving leaders insight into who accesses data, where it’s used, and how it complies with policies. Measuring Success: Metrics such as compliance rates, the reduction of data-related risks, and the number of governance-compliant assets serve as valuable indicators. Strong governance fosters trust in data, enhancing organizational agility and data-driven decision-making.Cloud Data Migration
Cloud data migration initiatives also benefit from a mature data culture. When data is cataloged and governed effectively, migrating to the cloud becomes a streamlined process. Migrating to cloud-based platforms not only reduces infrastructure costs but enables scalable data access and faster analytics. Measuring Success: Metrics to gauge the success of cloud migration include the speed of migration, the reduction in storage costs, and the increased accessibility of data post-migration. A data-mature organization can better leverage cloud capabilities for innovation and resilience.Conclusion: Tying It All Together
The Alation Data Culture Maturity Model provides a comprehensive framework for organizations looking to elevate their data culture. By focusing on data search & discovery, governance, literacy, and leadership, companies can foster a data-centric environment where data is trusted, accessible, and utilized effectively. Measuring the maturity of these components helps organizations quantify their data culture and demonstrate the business value added at each stage. In partnership with Alation, Beinex delivers comprehensive data governance solutions that enhance discoverability, enforce robust access controls, and streamline data auditing processes. By leveraging Alation's industry-leading data intelligence platform, Beinex helps organizations optimize their data strategies, driving business growth and operational efficiency. Connect with us for the transformation you seek: https://beinex.com/data-governance/
TABLEAU 2019.2 RELEASE
Tableau 2019.2 Release – A Deeper Dive
Tableau has released the newest version of the platform that enhances the way people visualize and interact with data. In this article we’ll go through some of the exciting new changes included in Tableau 2019.2 and how business will benefit from these newly enhanced and released features.
The major release of Tableau has on-boarded some impressive features and functionality that will have big influence in data visualization and business productivity. In this post, we will highlight some significant and exciting features that are a part of this release.
1. Upscale your Viz with Vector Maps:
Vector Maps enhances your mapping experience by rendering a crisp output as you pan, zoom in, and zoom out to explore your geospatial data. This new feature also leverages the census data from American Community Survey (ACS) to incorporate demographics data into your dashboard.
Highlights:
Enhanced mapping interaction with sharp rendering
Improved default maps styles: Dark, Normal, and Light
New map styles: Street, Satellite, and Outdoor
New background mapping styles: subway and train stations, building footprints, terrain, and water labels
Demographics data from American Community Survey (ACS)
Usability:
Above recording is a view of the new map styles that is a part of the Tableau 2019.2 release. Here you can see how simple it is to include and switch between the newly added map layers. The maps have become crisper while zooming and panning.
2. Enable Interactivity with Parameter Actions
Improved parameter actions is another powerful feature that powers up interactivity in your dashboards and help your viewers gain deeper insights into the trends. It unlocks the ability of a viewer to visually change a parameter’s value thereby offering you endless possibilities to create a truly interactive dashboard.
Highlights:
Interactive relationships between data, dashboard, objects, other workbook sheets and web
Enhanced interactivity by creating marks on a Viz
Improved user experience by transferring control to the end user
Allows users to dynamically change SQL queries, drive reference lines, calculations and filters
Usability:
With this feature the end users can dynamically view the parameter values. In the above example, we have created a parameter ‘order date’ and placed Day of order date and sales into the view and we have added reference line for sales. When applied in a parameter action, the values get changed dynamically.
3. Left Nav, Favorites, and Recent for Enhanced Navigation
Navigate seamlessly and find the content you are searching with the improved navigation. The new navigation features will intensify the already powerful content browsing experience. You can easily find your favorite and recent content including projects and prep flow at the top.
Highlights:
Improved access to key pages and content
New quick access navigation bar with personalized content relevancy
Welcome screen with navigation walk-through for new users
Completely re-designed homepage
Usability:
4. Talk to your data with AskData
AskData is a fairly new module and growing quickly in popularity with the 2019.1 release. Tableau is continuing to expand on this by adding streamlined and innovative capabilities to AskData. You now have the option to add calculations on the fly without having to build them into the data source. You can also ask more sophisticated questions and get answers with the enhanced AskData.
Highlights:
Improved interface to allow more conversational interactions
Enhanced language processing to edit statements from an existing question
Features to execute calculations on the fly
Usability:
In the above example, you can experience the power of the natural language processing engine built into the latest version of ask data. When we ask ‘How are my sales doing by numbers?’, the system automatically understands our query and displays the sum of sales by segment. We can further segment it by year by asking further questions or adjust the values according to our requirements.
These are only a few of the many exciting updates Tableau has incorporated to continue pushing the boundaries of data analytics and prepare for the future market changes. A complete list of updates and features could be found in Tableau’s release notes: https://www.tableau.com/products/all-features
Beinex is a digital transformation organization en-rooted with ideas, innovation and unparalleled customer service. Our mission is to transform the way individuals and the organizations work with the data through innovation and experience.
If you are interested in learning more about the latest Tableau 2019.2 features and updates, please contact us at training@beinex.com/ info@beinex.com and we would be happy to schedule a Tableau demo or training for you and your company.
Advanced Analytics: Facilitating One Giant Leap to The Future
1. Advanced Analytics in HR
HR teams always struggle with mammoth amounts of data which can be transformed into meaningful insights with the help of Advanced Analytics. The applications of Advanced Analytics can spearhead the domains of recruitment, learning and development of the employees and help manage their performance.
It can aid HR to identify the shortcomings in the existing resources and help decide to improve the skills of existing employees or hire competent new talent. The added advantage of HR analytics is that it is efficient in analysing employee performance by categorising them according to their individual calibre.
2. Advanced Analytics in Banking
Advanced Predictive Analytics has the potential to gear up the banking sector by assisting banks to adopt better strategies assessing and predicting customer behaviour. It can safeguard customer accounts from cyber-attacks and can detect fraudulent activities. Real-time alert messages are the most exclusive of the features which can shield against cyber-attacks to a big extent.
3. Advanced Analytics in Retail
The uncertain economy creates challenging scenarios for retailers. But Advanced Analytics functions act as a handy tool to make use of customer insights from the silos of data and provide a personalised and tailored marketing experience. It can be both cost saving and can unleash business growth to a great extent.
4. Advanced Analytics in Manufacturing
Manufacturing Analytics harvests revolutionary output from the manufacturing process, supply chain production, and customer delivery. The global manufacturing market is predicted to grow up to $28.5 billion by 2026 and the growth can be multiplied using the insights generated by analytics. Tools for asset management, product development, inventory management, and predictive maintenance can generate insightful options to rake in revenue amidst the limitations of the times.
5. Advanced Analytics in Finance
Financial sector produces a huge quantity of structured and unstructured data. The finance consultants or analysts should exploit this data with the help of Advanced Analytics to tackle financial troubles and to identify customer related requirements and issues. Advanced Analytics also helps to implement business plans and eliminate fraudulent activities. Financial institutions like loaning bodies, banks, trading firms, and insurance companies can improve their performance and revenue by deploying Advanced Analytics.
6. The Future of Advanced Analytics
Advanced Analytics is evolving and has become the indispensable requirement to run businesses. Due to the worldwide reception and adoption by the business community, analytics and information services sectors are predicted to have the fastest growth rate from 16% to 18% over the next five years starting 2022. As the market is getting competitive, planning and implementing strategic investments with data analytics is crucial to create new services and added revenue.
How Beinex can act as a torchbearer
Beinex has impacted and accelerated the growth of its customers and clients. With its impeccable offerings, it has partaken to build, market and expand businesses worldwide. The services provided by Beinex are enlisted below bracketed into respective domains:
• Predictive Analytics
This outstanding service offers you predictive insights to make resolutions which in turn can power your business.
• Artificial Intelligence
To cut short expenses and to attain target within a short period of time, make use of our AI tools or applications. It is beneficial to aggregate and automate data processing.
• Machine Learning
A vast quantity of data can be analysed using algorithms which can accelerate the decision-making process. With Machine Learning, specific and relevant data can be extracted and utilised to leverage businesses.
• RPA
Robotic Process Automation or software robots can accelerate digital transformation by emulating human actions. Automation of workflows, greater degree of accuracy, better productivity, high efficiency, and lesser costs are a few major takeaways from RPA which can accelerate business growth.
Conclusion
Advanced Analytics is here to stay, as organisations have started to rely on analytics more than ever before to predict business outcomes. It is sure to govern the business world as it has gained trust as a superpower which is accessible to all but chosen by only a few.
Governing Agentic AI: How Organizations Can Balance Autonomy, Risk, and Accountability
Agentic AI is redirecting AI to autonomous systems that can plan, decide, and execute tasks independently. These AI agents can manage workflows, interact with software systems, and trigger actions without continuous human input. While this autonomy promises efficiency and scalability, it also introduces new governance challenges.
Research shows that 86% of enterprises expect higher risk levels with agentic AI, yet only 2% of organizations currently meet responsible AI standards. This gap highlights a critical reality: organizations must adopt a structured agentic AI governance framework to balance autonomy with accountability.