Showing posts with label Data Analytic. Show all posts

Data Quality Intuitively comes along with TimeTec Parking Analytics


The quality of data is a prerequisite for a meaningful data analytics deployment. Characteristics of Data Quality are accuracy, completeness, consistency, uniqueness, and timeliness. Without Data Quality, data analytics process might paint a false picture and risking the companies to make wrong decision. 

The few elements contribute to Data Quality:

Accuracy: a company has to cross-check data with the source. If there is no data lineage, the accuracy is always questionable. 

Completeness: It doesn’t mean all data source, but it must adhere to the business requirements, especially those data is used for KPI measurement. 

Consistency: If data is flowed to multiple applications, it must have the same properties and not conflicting each other. For example, if the date format is DDMMYYYY,  make sure it should be the same across all applications. 

Validity: It should follow the business rules and parameters. For example, if the amount rounds up after 2 decimals, then it should.  

Uniqueness: There must be no duplication of data. 

Timelines: Data must be available when promised. 

There are quite some methods suggested by data experts to improve Data Quality, among them: Data Profiling, Data Standardization, Data Geocoding, Data Matching and Linking, and Data Quality Monitoring. 


To ensure Data Quality is kept, organizations have to maintain a Data Life Cycle, which involves the processes:

1. Find data through root cause analysis
2. Investigate data
3. Find potential causes
4. Perform root cause analysis
5. Apply correction
6. Monitor by continuous improvement monitoring
7. Sustain by applying fixes on sources or closest to source



Hence, to achieve Data Driven Organization is easier said than done. Since data itself generates further data, especially when raw data go into applications generate and regenerate much meaningful data, solution providers who have the capability to provide data analytics services should be the better choices for companies that take data seriously and have the plan for data transformation. 

The complication of parking operations arises from multiple parking types, such as casual parking, season parking, on-street parking, valet parking, event parking, etc., and multiple payment methods like cash and cashless transactions, which include credit cards, debit cards, Touch ‘n Go, eWallets, FPX, offline, and online payments, across various parking sites. This complexity worsen the data quality when need to consolidate multiple parking and payment methods provided by different solution providers. 


TimeTec Parking is the one-stop parking solution provider capable of fulfilling the most sophisticated parking requirements and facilitating further activities to form a smart building ecosystem. Additionally, TimeTec Parking Analytics is available as an optional module for TimeTec Parking, allowing parking operators to continue the data journey, saving costs and time without the hassle and the need for extensive data lifecycle measures mentioned earlier to maintain data quality. The data quality flows intuitively in a lineage form along with the applications, providing readily prepared templates for easy visualization and benchmark reference.

TimeTec Parking Analytics offers instantaneous data crunching and data visualization, providing a 360° view on:

• Collection breakdown by duration
• Single view and overview
• Earnings per vehicle
• Earnings per bay
• Occupancy rate
• Season Pass turnover rate
• Income per site analytics
• User behavior analysis
• Different parking methods and next activities
• And many more

For example, for bay utilization, an operator can plan their bays especially the mix ratio of casual and season parking bays properly if they know the details clearly to optimize the bay utilization and maximize the profits.   

The Benefits:
1. Increase parking occupancy based on user behavior
2. Maximize parking revenue streams by right-sizing product offerings
3. Identify dynamic pricing opportunities that enhance your parking strategy
4. Make period-over-period comparisons
5. Identify successes and pinpoint areas for improvement

Interested in learning more about TimeTec Parking Analytics and TimeTec Parking? Request your free demo of the TimeTec Parking solution now.

03-8070 9933     |     Email     |     www.timeteccloud.com     |     Interest Form
Data Analytic

Data Quality Intuitively comes along with iNeighbour Analytics


The quality of data is a prerequisite for a meaningful data analytics deployment. Characteristics of Data Quality are accuracy, completeness, consistency, uniqueness, and timeliness. Without Data Quality, data analytics process might paint a false picture and risking the companies to make wrong decision. 

The few elements contribute to Data Quality:

Accuracy: a company has to cross-check data with the source. If there is no data lineage, the accuracy is always questionable. 

Completeness: It doesn’t mean all data source, but it must adhere to the business requirements, especially those data is used for KPI measurement. 

Consistency: If data is flowed to multiple applications, it must have the same properties and not conflicting each other. For example, if the date format is DDMMYYYY,  make sure it should be the same across all applications. 

Validity: It should follow the business rules and parameters. For example, if the amount rounds up after 2 decimals, then it should.  

Uniqueness: There must be no duplication of data. 

Timelines: Data must be available when promised. 

There are quite some methods suggested by data experts to improve Data Quality, among them: Data Profiling, Data Standardization, Data Geocoding, Data Matching and Linking, and Data Quality Monitoring. 



To ensure Data Quality is kept, organizations have to maintain a Data Life Cycle, which involves the processes:

1. Find data through root cause analysis
2. Investigate data
3. Find potential causes
4. Perform root cause analysis
5. Apply correction
6. Monitor by continuous improvement monitoring
7. Sustain by applying fixes on sources or closest to source


Hence, to achieve Data Driven Organization is easier said than done. Since data itself generates further data, especially when raw data go into applications generate and regenerate much meaningful data, solution providers who have the capability to provide data analytics services should be the better choices for companies that take data seriously and have the plan for data transformation. 

Technological advancements that consolidate all activities into a single super app in recent years are aiding residential property management in its digital transformation journey. TimeTec is the prominent smart community solution provider capable of fulfilling the most sophisticated residential property management needs through its iNeighbour super app, and now furthering the journey on data transformation.

iNeighbour Analytics is available as an optional module for the iNeighbour super app, allowing JMB/JMC/MC/RA/Developers to continue the data journey, saving costs and time without the hassle and need for extensive data lifecycle measures mentioned earlier to maintain data quality. Data quality flows intuitively in a lineage form along with the applications, providing readily prepared templates for easy visualization and benchmark references.


By logging into iNeighbour Analytics, JMB/JMC/MC/RA/Developers and management can obtain a clear picture of their neighborhood activities overview and a single view easily in multiple presentation formats on the web and app, providing all the essentials for building management to improve the health of their property management and better portfolio of their owners and tenants.

iNeighbour Analytics offers instantaneous data crunching and data visualization, providing a 360° view on:

• Units and user analytics 
• Occupancy rates 
• Accounting analytics 
• Outstanding debt analytics 
• Facility booking analytics
• Visitors analytics 
• Patrol management analytics 
• E-info, e-Form analytics 
• Maintenance analytics 
• Parking analytics 
• Feedback/Inquiry analytics 
• Admin KPI 
• Many more

The Benefits:
- Increase unit owners' and tenants' satisfaction
- Detect and remedy blind spots easily
- Better accounting control
- More accurate advice on tenants and residents
- Enhance neighborhood security
- Review and improve property management
- Informed decision-making driven by data
- Boost property value

Interested in learning more about iNeighbour Analytics and iNeighbour? Request your free demo of the iNeighbour Residential Property Management solution now.

03-8070 9933     |     Email     |     www.timeteccloud.com     |     Interest Form
Data Analytic

Data Quality Intuitively comes along with TimeTec HR Analytics


The quality of data is a prerequisite for a meaningful data analytics deployment. Characteristics of Data Quality are accuracy, completeness, consistency, uniqueness, and timeliness. Without Data Quality, data analytics process might paint a false picture and risking the companies to make wrong decision. 

The few elements contribute to Data Quality:

Accuracy: a company has to cross-check data with the source. If there is no data lineage, the accuracy is always questionable. 

Completeness: It doesn’t mean all data source, but it must adhere to the business requirements, especially those data is used for KPI measurement. 

Consistency: If data is flowed to multiple applications, it must have the same properties and not conflicting each other. For example, if the date format is DDMMYYYY,  make sure it should be the same across all applications. 

Validity: It should follow the business rules and parameters. For example, if the amount rounds up after 2 decimals, then it should.  

Uniqueness: There must be no duplication of data. 

Timelines: Data must be available when promised. 

There are quite some methods suggested by data experts to improve Data Quality, among them: Data Profiling, Data Standardization, Data Geocoding, Data Matching and Linking, and Data Quality Monitoring. 


To ensure Data Quality is kept, organizations have to maintain a Data Life Cycle, which involves the processes:

1. Find data through root cause analysis
2. Investigate data
3. Find potential causes
4. Perform root cause analysis
5. Apply correction
6. Monitor by continuous improvement monitoring
7. Sustain by applying fixes on sources or closest to source



Hence, to achieve Data Driven Organization is easier said than done. Since data itself generates further data, especially when raw data go into applications generate and regenerate much meaningful data, solution providers who have the capability to provide data analytics services should be the better choices for companies that take data seriously and have the plan for data transformation. 

TimeTec HR Analytics comes as an option module for TimeTec HR suite, for companies to continue to complete the data journey to save cost and time, without the pains and the needs of hassle and data life cycle measures as mentioned above to upkeep the data quality. The data quality flows intuitively in a lineage form along with the applications to readily prepared templates for easy visualization and benchmarks reference. 

HR analytics is the collection and application of talent data is to improve critical talent and business outcomes. TimeTec HR analytics enable business owners to develop data-driven insights of their talent pool, improve workforce processes and promote positive employee experience.



Instantaneous Data Crunching & Data Visualization to provide 360° on:
- Employee statistics and profiles
- Turnover & Retention Rate 
- Salary and career path history
- Staff Performance 
- Demographic data 
- Attendance 
- Absenteeism
- Leave pattern
- Claim pattern 




The Benefits of TimeTec HR Analytics: 
1. Improve talent acquisition
2. Increases talent retention
3. Prevent workplace misconduct 
4. Increase productivity 
5. Uncover skill gaps
6. Improve employee experience 
7. Build highly engaged workplace
8. Reduce attrition rate
9. Machine learning spots the patterns that you might miss 

Interested to know more about TimeTec HR Analytics and TimeTec HR Suite? Request your Free Demo of TimeTec HR solutions now. 


03-8070 9933     |     Email     |     www.timeteccloud.com     |     Interest Form
Data Analytic

Differentiating Dashboards and Analytics for Informed Business Decisions

 
Lots of solution providers use the system dashboard that comes along with their solutions as data analytics module to deceive customers. In fact, data analytics is much more than that.

In brief, a dashboard is a visual display of key performance indicators (KPIs) and other relevant information, presented in a consolidated and easy-to-understand format. It provides a real-time snapshot of data and helps users monitor and make decisions based on that data. In short, dashboard is more for operational.  

On the other hand, data analytics involves the process of examining raw data to extract insights, identify patterns, and make informed conclusions. It goes beyond visualization to explore data trends, correlations, and anomalies, often using statistical and machine learning techniques. In short, data analytics is more for management to understand in-depth situation and to enhance operation, but more importantly to make strategical decision that would affect the future of the organization.  

In terms of benefits, Dashboards may deliver the followings:

1. Real-time Monitoring:
Dashboards provide a real-time overview of key metrics, enabling quick and informed decision-making as you can track operational performance instantly.

2. Visual Representation:
Visual elements like charts and graphs make it easier to comprehend complex data. Dashboards condense large amounts of information into a visually digestible format.

3. User-Friendly:
Dashboards are designed for ease of use, making them accessible to a broad audience within an organization. Users can quickly grasp trends and performance without needing in-depth data analysis skills.

4. Efficiency:
Dashboards save time by providing a consolidated view, eliminating the need to sift through extensive reports. This promotes efficiency in operational decision-making processes.

And for Data Analytics, it delivers different types and more impactful benefits to the businesses:

1. In-Depth Analysis:
Data analytics allows for a deeper exploration of data, uncovering patterns, trends, and insights that might not be immediately apparent on a dashboard.

2. Predictive Modeling:
Advanced analytics, including machine learning, enables predictive modeling. Organizations can forecast future trends and make proactive decisions based on these predictions.

3. Data-driven Decision Making:
Analytics provides a robust foundation for making strategic decisions. By relying on data rather than intuition, organizations can enhance decision accuracy when planning the future moves.

4. Identification of Opportunities and Risks:
Analytics helps in identifying both opportunities for growth and potential risks by analyzing historical and current data. This proactive approach supports better risk management.

5. Continuous Improvement:
Analytics fosters a culture of continuous improvement. By regularly analyzing data, organizations can refine strategies, optimize processes, and stay agile in response to changing conditions.

In summary, they complement each other; dashboards are excellent for quick, visual insights and monitoring, while data analytics requires more expertise to offer an in-depth exploration of data, supporting strategic decision-making and long-term planning.

TimeTec solutions, be it HR suite, property management, smart security, and smart parking, the applications all come with dashboards on their own to help customers in quick visual performance for operation. While we provide TimeTec Analytics modules across platforms, customizable and consolidated in an ecosystem, it allows management to access anytime, anywhere on the web or on the app without the need to log into their individual operation system, to obtain deeper analysis and interpretation of data for actionable insights.


 
 About TimeTec:
TimeTec Group was established in 2000. Over the past 20 years, the Group has developed three homegrown, globally recognized IT brands: FingerTec, TimeTec, and iNeighbour. These brands specialize in workforce management, security, smart parking, smart office, smart residential, and smart township solutions, harnessing the power of biometrics, cloud and edge computing, IoT, and AI technologies. All these solutions connect and reshape the landscape of work life and home life within a larger ecosystem.

Through an extensive network, TimeTec Group distributes its biometric hardware products and 18 cloud applications, including IoT devices, to more than 150 countries worldwide. Visit our company websites at TimeTec Cloud, FingerTec, iNeighbour, and TimeTec Building.

Various renowned clients have subscribed to TimeTec's solutions, including IOI Properties, Putrajaya Holdings, Ibraco, Binastra, Thriven, Hock Seng Lee, QSR Brands, Central Sugars Refinery (CSR), Sunway Constructions, Mamee, Yakult, Nano Malaysia Berhad, and many more. The versatility and feasibility of TimeTec products also attract international customers from around the world, including Hong Kong, Dubai, Australia, South Africa, and beyond.

03-8070 9933     |     Email     |     www.timeteccloud.com     |     Interest Form
Dashboard
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