Advanced analysis of data
to face new challenges
in traffic and road safety
The challenge of ensuring the integrity, security and consistency of the
information in order to speed up the necessary formalities for the efficient
development of traffic operations and processes
page. 3 Introduction to the customer case Executive summary.
Case study file
page. 5 New challenges or how to reduce processing in mobility/traffic areas
The problem of managing and analyzing information. More agile decisions and automated processes.
page. 7 The Solution: Quiterian Analytics
Audit and advanced analysis of data at record-breaking speed. Advanced analysis for the institution that protects traffic regulations.
page. 9 Resuts of deploying Quiterian Analytics
Resources optimization and business user’s self-sufficiency. Obtained benefits.
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Executive summary
In order to reduce formalities and speed up procedures and decisions-making, the official institution regarding traffic and mobility in a Latin American country got equipped in 2010 with Quiterian’s visual data mining software, through Nase’s advice and support, which is current member of the Quiterian Business Partner Network (Quiterian’s international partners network).
This project consisted of creating a unique data register, through the integration of data from miscellaneous sources, as well as from miscellaneous suppliers, both internal and external, for a consistent and in-depth analysis to be performed by non-technical business users. On the other hand and, in order to respond to the organization’s needs - it was interested in expanding its operating coverage to a business area -, the project was aimed at processes automation and at speeding up the decisions-making, which sometimes was too slow and little informed.
The starting point, however, was full of handicaps. Managing large raw data sets was a must. Additionally, there was little time left for daily reports/analysis, and business users, though being able to easily infering business insights, had little or none technical skills.
Previously to deploying Quiterian Analytics, users requested their daily analysis to the institution’s engineers, who created PL/SQL (Oracle) sentences that, when being executed, caused delays in the engine’s response timings and in the reports delivering. For this reason, users needed to get equipped with analytical software that could complement their prior BI, already installed and which was being used for daily matters. This new software should be, moreover, intuitive, dynamic, accessible and easy to deploy.
The context demanded agility for the decision-making process, self-sufficiency for business users to develop their daily analysis, and processes automation. Through Quiterian Analytics’ advanced and predictive techniques, it was possible for non-technical users to start to perform in-depth business analysis, instantly and with no dependence on the IT department, just by crossing different pieces of information and profiles, extracting patterns and trends, as well as implicit information inside data. Its engineering techniques let them, furthermore, increase the value and consistency of data by applying metrics, aggregates or quantiles.
Results - collected until the present time - are simply amazing: since its deployment, 60 million registers and more than three million and a half transactions have been analysed and automated. The obtained benefits are equally obvious: improvement and reduction of consultation times to the database, resources optimization, optimum management and excellent processes running within the operating scope.
Customer:
National body devoted to ensure the correct accomplishment of transportation, traffic and road safety, and to render optimum service to the citizenship regarding mobility.
Challenge:
Reduction of processing and speeding up decisions.
Goal:
Creation of a unique register of data, integrating data from heterogeneous sources and agents and from internal/external organizations, for its homogeneous and in-depth analysis to be performed by business users.
Handicaps:
Large raw data sets.
Little time available for daily reports/analysis.
Business users have little technical skills (only business knowledge).
Implemented solution: Quiterian Analytics
Year of implementation: 2010
Nowadays there is vast formal culture around the documentation managed by the institution in charge of traffic monitoring in each country. Millions of licenses and certificates, procedures and transfers and technical-medical checkings are monthly submitted, performed or registered by the national traffic institution or by any of its attached organizations. In general terms, the information is miscellaneous and lacks of consistency and integrity, which usually hinders processes, internal flows and external operations.
In order to speed up transactions and improve the efficiency of processes, the official institution in charge of managing one country’s traffic information for ensuring the accomplishment of road regulation in Latin America ₁, has been equipped with Quiterian Analytics. The visual data mining software has enabled this institution to unify all its registers with three goals:
Centralize the information system.
Digitize and put together all the available information. Unify validating systems.
In spite of recent deployment, business users refer to Quiterian Analytics as a fresh, simple and comfortable platform for a high-demanding final user. Figures also demonstrate that expectations have been definitely exceeded, both in registers loading and in terms of information quality. Only during the year 2010, 60 million registers and more than three million and a half transactions were analysed and systematized ₂.
Users of the institution devoted to controlling the accomplishment of traffic rules in a Latin American country refer to Quiterian Analytics as fresh, simple and comfortable visual data mining software
for a high-demanding final user During the year 2010, 60 million registers and more than three million and a half transactions
have been analyzed and systematized
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Towards a centralized, integrated and unified register of data,
to obtain a global business view and to ensure the service’s
efficiency and efectiveness
₁ The present document is based on a success story with the national traffic and road safety institution in a Latin American country. Due to protecting our customer’s privacy, we cannot reveal the identity of this institution.
As a result of managing large volumes of data from miscellaneous origins (transactions, databases, registers…), the traffic institution’s team required to integrate the information for its comprehensive analysis. Creating reports implied, furthermore, putting various business rules into practice in front of a database with hundreds of tables and millions of different registers. For this purpose, the engineering team used to create PL/SQL (Oracle) sentences that, when being executed, brought many disadvantages:
High-variable responses in front of different business conditions.
Affectation in the processes flows and, therefore, variation in information requirements. High rates in reponse times compared to the production of reports, which included from the query-making to the report’s tuning in data quality.
Impossibility of identifying weaknesses in stored data.
PL/SQL (Oracle) sentences implied, therefore, delays in the time needed by the engine to respond to those queries, causing additional workload to its running, and delays in the reports issuing. For example, making monthly analysis - even with tamed information - took various hours.
In this context, it was absolutely necessary to speed up the processes of data collection for in-depth analysis, to gain efficiency and to reduce costs and time. And, obviously, the internal team needed a tool that could cowork with the traditional Business Intelligence systems that had been deployed in the past.
Reveals what has happened, why and what will happen next.
Intuitive and self-service advanced analytics for business users.
The fastest and most user-friendly visual data mining.
Business insights from raw data analysis.
Quiterian Analytics complements traditional BI
2.1. The problem of managing and
analyzing information
2.2. More agile
decisions and
automated processes
Previously to deploying Quiterian Analytics, data was unstructured, the strategic decisions-making was slow and little informed, and valuable time was unavoidably being lost. New organizational needs were added to these starting problems. It was interested, for example, in expanding its operating coverage to other business areas, through technological tools for information management, that could be dynamic, accessible, intuitive and easy to deploy inside the organization.
Speeding up the decisions-making, delivering depth and speed, became urgent. Bearing this in mind, it was necessary to ensure the quality of daily analysis and their fast production, so that they could turn into a more trusted decision-making.
On the other hand, achieving efficiency implied to automate processes. The generation of reports was made by hand, affecting the performance of other urgent tasks.
The organization’s new challenges were added to the need of integrating miscellaneous data, of speeding up decisions and of
fulfilling with in-depth daily analysis, reducing time and costs. The institution wanted to expand the institution’s operating coverage
to other business areas PL/SQL (Oracle) sentences generated additional workload to the engine and caused delays in the
reports delivery. Therefore, the traffic institution’s team needed a tool that could be compatible with already deployed
of data in real time
Quiterian’s cutting-edge technology enables users to load and integrate miscellaneous data to create an analytical repository with no need of having classical modeling, without cubes, dimensions, measures neither metadata. Data is at the business user’s disposal; it can be freely analysed, in its maximum granularity, quickly and intuitively, at one clic of the mouse. In every step of the process data is being shown in grid and in graphic form, so that they can be easily interpreted. To analyse data, the visual data mining software is equipped with advanced and predictive analytical techniques: crosstabs for crossing different pieces of information (Pivot Table), comparatives and hidden concurrences (Venn diagram), profiles to identify features of groups and patterns (Profile), group analysis to identify trends (Bubble), geographical samples (Mapping), etc.
Furthermore, to increase the consistency and value of data, Quiterian Analytics has multiple engineering techniques to detect duplicates, to clean, audit and standardize the available data, enriching it through metrics, aggregates, quantiles, ranks, etc.
Quiterian Analytics validates the quality of data in a very transparent way, and shortly solves the questions
that are built around the business
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Venn Diagram. Analytical technique that combines two or more segments in order to find connections or exclusions that dont’ seem obvious.
Profile. Enables users to draw the profile of a group of registers according to selected features. For example, to get to know the profile of drivers who have caused more accidents.
Decision Tree. Predictive analytical technique used to classify data and make predictions.
Quiterian iWorkflow. Starting from the analysis of data in real time, iWorkflow monitors key facts, detecting variables out of the expected value, and triggers the appropiate actions. This lets users see and be anticipative, make faster and more trusted decisions, reduce risks and therefore, gain efficiency and competitiveness.
The visual data mining software has advanced and predictive analytical techniques for business users:
Pivot Table, Profile, Venn Diagram, Bubble, Mapping, Decision Tree, KMeans, Time Series, etc.
Mapping. Shows through the areas in a map the value of a function according to a wide range of colours. It lets users see, for example, the places with a bigger number of accidents.
3.2. Advanced and predictive analysis
to respond to traffic questions
Analysis of incomes and cash collected by traffic organizations, attached to the national institution, in terms of traffic certificates, technical-mechanical certificates, driving licenses delivery, etc.
Integration and validation of information related to compulsory insurance policies against traffic accidents, taking into account the essential role played by the different actors: insured, insurance company, preventive detentions, vehicles and historic policies per vehicle.
Processing and analysis of daily requested data by the Transportation Ministry (which depends on the institution that coordinates traffic issues), to prepare and publish statistics of the national transportation sector.
For example, profile of drivers with bigger number of caused/suffered accidents, or identification of places with the highest number of accidents. Or forecasting the national growth of the number of vehicles, based on the number of new registration numbers and new requests made by vehicle dealers to legalize those vehicles.
Analysis per segments of vehicles.
For example, the segment of heavy vehicles: number of vehicles and volume
transported through national roads, according to the vehicle’s features, the number of renewals, technical-mechanical checkings, etc.
Flows automation in which people and institutions that shape or take part in the organization’s running are involved.
Identification of the excessive delivery of licenses, to regularize practices. Calculation of driving licenses and traffic licenses.
4.1. Resources optimization and
business users self-sufficiency
The processes that are currently developed with Quiterian Analytics are essentially three: identification of inconsistencies in data, validation of business rules and implementation of new businesses.
The project’s managers have noticed clear optimization in the times needed to give answer to users’ requests, through the delivery of reports and analysis:
Infrastructure costs reduction, since analysis require less technical and infrastructural involvement.
More user-friendliness and more operating capacity, and less waiting times, since all the analysis and reports are simply and effectively generated.
Non-technical user’s self-sufficiency in daily analysis, so that users can prepare the reports, themselves, through the website, without any need of technical involvement. This implies a dramatic reduction of the IT department’s and the data mining team’s workload.
Quiterian Analytics is being used at the moment to identify inconsistencies in data, to validate business rules and to implement and model new businesses
4.2. The resulting benefits
The deployment of Quiterian Analytics has led, within very little time, to speed up formalities, procedures and processes that are established with different stakeholders in the institution, internal and external, optimizing resources and automating workflows. The managed information is now more consistent, truthful and integrated, and its analysis more consistent and deeper, faster and easier to deploy. Daily reports do not generate workload neither additional costs. Within minutes, any business user with minimum technical skills can forecast the number of vehicles in the country for the next years.Improvement and time reduction in the cycle of requests to the database. Optimization of time and availability of human recources.
Optimum process management, aimed at the operating sphere, and covergae expansion, in order to cover new businesses.
In short time transactions, operations and processes with the different stakeholders within the institution, internal and external, have been established,
optimizing resources and automating workflows
info@quiterian.com www.quiterian.com
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About Nase
Founded in 1997, NASE Ltda. is a company devoted to implementing integrated IT solutions, aimed at providing added value to its customers’ business processes.
Its services are focused on implementing end-to-end solutions using products from leading vendors, such as: SAP, IBM and Microsoft, companies with whom Nase has business relationships for different product and service lines.
Nase’s team counts on broad experience in deploying projects with national customers, as well as with customers from Venezuela, Mexico and the US, within the following areas: ERP, Business Intelligence (BI), Market Intelligence, Data Quality and Customer Relationship Management (CRM and XRM).
In 2000, the company started the Business Intelligence area and, from that moment on, it has used tools for data visualization (Crystal Reports, Hyperion, Business Objects), for data warehousing (IBM DB2, Essbase, SQL Server), for Data Warehouse’s building and nurturing (IBM Warehouse Manager, MS Integration Services, Business Objects Data Integrator, etc.) and for integrating and analysing large volumes of data instantly (Quiterian); becoming today a company with deep expertise in data exploitation.
To learn more, visit: NASE LTDA www.nase-it.com
Calle 100 N° 17ª – 36 of. 803 + 57 (1) 621 9390
www.nase-it.com Bogotá - Colombia
Quiterian develops, markets and supports Quiterian Analytics, Visual Data Mining software that complements traditional BI and that analyses large volumes of raw data at record-breaking speeds. Quiterian includes advanced analytics and predictive techniques and works on Big Data (at least 4TB). The simplicity and intuitiveness of the platform enables any business user to make use of it independently from the IT or data mining teams.
Beyond traditional and predefined BI and visualization-based tools that focus on corporate and dynamic reporting, Quiterian Analytics was featured in Gartner’s ‘Magic Quadrant for BI platforms 2011’ for “easing the use of Data Mining and Statistical Analysis, and thus making these capabilities more broadly available.” Taking advanced and predictive analytical techniques as basis, Quiterian designs specific business solutions that are customized to each one of its customers, regardless of their operating sector (including financials, insurance, ecommerce, utilities, retail, public transportation, logistics), delivering strategic value, competiti-veness and efficiency to their users.
Among its customers are leading companies in their sectors (El Corte Inglés, BBVA, La Caixa, Inversis, Telefo-nica, Vodafone, Orange, Telepizza, Volkswagen, Travel Club, Bayer, Sanofi), plus some of the most advanced government institutions (Governments of Andalusia, Catalonia, Valencia, Extremadura, Madrid City Council, Bilbao City Council, Metro of Madrid, Metro of Bilbao, ATM, Muface, Muprespa, ENESA).
US headquarters is located in Miami, FL. The company has major offices in Los Angeles (CA), Barcelona, Madrid, Seville, Valencia, Lisbon and Mexico City. In 2010, Quiterian commenced an international expansion, broadening their reach through the development of the Quiterian Business Partner Network. To learn more, visit: www.quiterian.com
