{"id":10,"date":"2024-02-09T07:52:00","date_gmt":"2024-02-09T07:52:00","guid":{"rendered":""},"modified":"2024-10-15T07:34:33","modified_gmt":"2024-10-15T07:34:33","slug":"data-quality-intuitively-comes-along-with-timetec-parking-analytics","status":"publish","type":"post","link":"https:\/\/www.timeteccloud.com\/blog\/data-quality-intuitively-comes-along-with-timetec-parking-analytics\/","title":{"rendered":"Data Quality Intuitively comes along with TimeTec Parking Analytics"},"content":{"rendered":"
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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.<\/p>\n<\/div>\n

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The few elements contribute to Data Quality:<\/span><\/div>\n
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Accuracy<\/span><\/b>: a company has to cross-check data with the source. If there is no data lineage, the accuracy is always questionable.\u00a0<\/span><\/div>\n
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Completeness<\/span><\/b>: It doesn\u2019t mean all data source, but it must adhere to the business requirements, especially those data is used for KPI measurement.\u00a0<\/span><\/div>\n
\u00a0<\/span><\/div>\n
Consistency<\/span><\/b>: 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,\u00a0 make sure it should be the same across all applications.\u00a0<\/span><\/div>\n
\u00a0<\/span><\/div>\n
Validity<\/span><\/b>: It should follow the business rules and parameters. For example, if the amount rounds up after 2 decimals, then it should.\u00a0\u00a0<\/span><\/div>\n
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Uniqueness<\/span><\/b>: There must be no duplication of data.\u00a0<\/span><\/div>\n
\u00a0<\/span><\/div>\n
Timelines<\/span><\/b>: Data must be available when promised.\u00a0<\/span><\/div>\n
\u00a0<\/span><\/div>\n
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.\u00a0<\/span><\/div>\n
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<\/a><\/div>\n
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To ensure Data Quality is kept, organizations have to maintain a Data Life Cycle, which involves the processes:<\/p>\n<\/div>\n

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1. Find data through root cause analysis<\/span><\/div>\n
2. Investigate data<\/span><\/div>\n
3. Find potential causes<\/span><\/div>\n
4. Perform root cause analysis<\/span><\/div>\n
5. Apply correction<\/span><\/div>\n
6. Monitor by continuous improvement monitoring<\/span><\/div>\n
7. Sustain by applying fixes on sources or closest to source<\/span><\/div>\n
\u00a0<\/span><\/div>\n
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<\/div>\n
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.\u00a0<\/span><\/div>\n
<\/div>\n
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 \u2018n 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.\u00a0<\/span><\/div>\n
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<\/a><\/div>\n
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TimeTec Parking<\/a> 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.<\/p>\n<\/div>\n

<\/div>\n
TimeTec Parking Analytics offers instantaneous data crunching and data visualization, providing a 360\u00b0 view on<\/span><\/b>:<\/b><\/span><\/span><\/div>\n
\u00a0<\/span><\/div>\n
\u2022 <\/span>Collection breakdown by duration<\/span><\/div>\n
\u2022 <\/span>Single view and overview<\/span><\/div>\n
\u2022 <\/span>Earnings per vehicle<\/span><\/div>\n
\u2022 <\/span>Earnings per bay<\/span><\/div>\n
\u2022 <\/span>Occupancy rate<\/span><\/div>\n
\u2022 <\/span>Season Pass turnover rate<\/span><\/div>\n
\u2022 <\/span>Income per site analytics<\/span><\/div>\n
\u2022 <\/span>User behavior analysis<\/span><\/div>\n
\u2022 <\/span>Different parking methods and next activities<\/span><\/div>\n
\u2022 <\/span>And many more<\/span><\/div>\n
\u00a0<\/span><\/div>\n
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.\u00a0 \u00a0<\/span><\/div>\n
\u00a0<\/span><\/div>\n
The Benefits:<\/b><\/span><\/div>\n
1. Increase parking occupancy based on user behavior<\/span><\/div>\n
2. Maximize parking revenue streams by right-sizing product offerings<\/span><\/div>\n
3. Identify dynamic pricing opportunities that enhance your parking strategy<\/span><\/div>\n
4. Make period-over-period comparisons<\/span><\/div>\n
5. Identify successes and pinpoint areas for improvement<\/span><\/div>\n
\u00a0<\/span><\/div>\n
Interested in learning more about TimeTec Parking Analytics and TimeTec Parking<\/a>? Request your free demo of the TimeTec Parking solution now.<\/span><\/div>\n
03-8070 9933\u00a0\u00a0\u00a0\u00a0 |\u00a0\u00a0\u00a0\u00a0\u00a0<\/span>Email<\/a>\u00a0\u00a0\u00a0\u00a0 |\u00a0\u00a0\u00a0\u00a0\u00a0<\/span>www.timeteccloud.com<\/a>\u00a0\u00a0\u00a0\u00a0 |\u00a0\u00a0\u00a0\u00a0\u00a0<\/span>Interest Form<\/a><\/div>\n
<\/div>\n","protected":false},"excerpt":{"rendered":"

The quality of data is a prerequisite for a meaningful data analytics deployment. Characteristics of Data Quality are accuracy, completeness, consistency, uniqueness, and timeliness. 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