TPX - Customising the future

Big Data Analytics

Say Hello to Open Source Big Data Technology. Amass and process data like never before.

Data Intel

Incorporate NLP and ML into structured and unstructured data.

Media Intel

Extended amounts of data by mainstream & non-mainstream media worldwide.

Social Media Intel

Move beyond social media monitoring and listening. Analyse entire conversations and the people behind it.

Geo Intel

Exploit and analyse of imagery and geospatial information for Strategic Action Plans

Intelligence Partner

Intelligence cycle outsourcing. From Intelligence gathering to content dissemination and everything in between.

Big Data Analytics

Big Data refers to extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. In recent years, Big Data was defined by the “5Vs” of Big Data which are also termed as the characteristics of Big Data.

With extremely large data sets that comprises of both structured and unstructured data, it’s not the amount of data that’s important. It’s what organisations do with the data that matters. Big data can be analysed for insights that lead to better decisions and strategic business moves.

A Different Approach

We believe everybody should have access to reliable analytics to make data-driven decisions. Our approach to Big Data takes advantage of the latest technology advancements of collecting, storing and processing huge amounts of data. We can either build you your own Big Data platform or you can use ours.

Data, Data, Data

Our focus on Big Data is not so much on the hardware but the software side of it. Largely open source, one of our strengths is on the capability to develop systems that can acquire data en mass for storage and processing. This means the ability to crawl, scrape and amass data from any relevant sources (internal and external). After all, what is Big Data without data.

Artificial Intelligence

Big Data analytics requires new and sophisticated algorithms to process data in real-time with high accuracy and efficiency.

Excelling in Bahasa Malaysia, Indonesia and English, our proven Natural Language Processing (NLP) with Semantic Modelling is at the core of our A.I together with Machine Learning (ML).

ML provides efficient and automated tools for data gathering, analysis, and assimilation. In collaboration with cloud computing superiority, the machine learning ingests agility into processing and integrates large amounts of data regardless of its source.

Data Intel

We offer a text mining and ontology based system that is able to collect, structure, analyse and distribute information from both un-structured and structured content sources by incorporating NLP, ML and DL into structured and unstructured data.

Structured data is most often categorised as quantitative data, and it’s the type of data most of us are used to working with. Think of data that fits neatly within fixed fields and columns in relational databases and spreadsheets. Examples of structured data include names, dates, addresses, credit card numbers, stock information, geolocation, and more.

Most of information published in the internet is in un-structured format. Articles, blogs, tweets, forum comments, wiki, etc, come in narrative form and require people to read in order to get the objectives, context and meaning. Small number of articles are convenient for a person to read but when it comes to thousands or even millions, it will be impossible for people to read and do manual analyses.

Natural Language Processing (NLP)

Natural Language Processing or NLP is a field of Artificial Intelligence that gives the machines the ability to read, understand and derive meaning from human languages. Nowadays it is no longer about trying to interpret a text or speech based on its keywords (the old fashioned mechanical way), but about understanding the meaning behind those words (the cognitive way). This way it is possible to detect figures of speech like irony, or even perform sentiment analysis.

Machine Learning (ML)

Machine Learning (ML) is a way to implement Artificial Intelligence. Similar to AI, ML is the design of algorithms that can provide systems with the ability to automatically learn and understand without being programmed time and again.

Deep Learning (DL)

Deep Learning (DL) is a class of ML algorithms inspired by the structure of a human brain. DL algorithms use complex multi-layered neural networks, where the level of abstraction increases gradually by non-linear transformations of input data.

We apply various methods to apply DL. Each proposed method has a specific use case like the kind of data you have, whether it is supervised or unsupervised learning you would like to apply, what type of task you would want to solve with the data. So depending on these factors, you can choose one of the methods that can best solve your problem.

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Media Intel

We live in the world where the media have gained control in many aspects of our lives. Every day we are conditioned to absorb all kinds of information published by media. News paper, magazine, TV, radio, etc have been our main references in understanding issues in our daily lives. By continuously flooding people with information, media have gained the ability to shape public perception.

Media is able to affect the world by playing around with public opinion, officials and decision makers. Many agencies react to issues after heavy media exposure. Taking a reactive position to the specific issue exposure in media will distract the focus of the agency in managing the core of the issues.

PRU 14 has been a case study for the implementation of WAR OF PERCEPTION, where media and perception played the most important role in influencing public opinion.

Comprehensive Coverage

Our system is capable of adding more media sources from all over the world. However, we are focusing on South East Asia thus majority of our media points are from the region. In Malaysia, we have more than 600 sources. This is not counting the ones in other countries eg. Indonesia, Singapore. Additionally, we have been collecting data from these sources since 2011 non-stop.

Real-time

Data in the form of news clippings, articles, anything published by the media is captured and stored for eventual analysis. This perpetual cycle feeds information into the Media Dashboard in real-time for easy monitoring and analysis.

Automated Clustering

We have the capability to collect data from various sources, for both structured and un-structured format of information, from the media as well as legacy systems. Utilising both clipping and crawling techniques to extract the data. Once data is collected, it will be processed and re-structured in a standard format for further analysis.

Discovery (Content, Person, Organization, Media, Twitter, FB)
Topic Management
Analysis Dashboard
Influencer Analysis
Sentiment Analysis
Ontology Analysis
Timeline Analysis
Top Person Analysis
Media Share Analysis
Trending and Clustering Analysis
Location Analysis
Dependency Analysis

Social Media Intel

There is a vast amount of data being generated by social networks every single day. Many companies quite rightly monitor the data for information that impacts them, for example the number of mentions or the sentiment towards their brand. Social media monitoring is absolutely essential for brands, big and small, to know what’s being said about them.

By itself, social media listening is not intelligence. It is not enough to just monitor the number of likes a post received. Discovering what consumers are saying about brands and if they have any interest in the products, is the easiest way to gather real-time and honest feedback. Rather than solely monitoring and listening to social data, it is important to go a step further and gain actionable insights. How valuable is it to know that 10% of comments about your brand are negative? Not that helpful. It would be better to take this information and understand why some of your posts are receiving negative feedback and how to make changes to prevent negative comments in the future.

Listening vs. Intelligence

Social media listening is simply presenting what has happened, but social media intelligence is asking why. Without social media intelligence, businesses cannot leverage the power of their social data. This tends to happen when social listening is used without the additional layer of social media intelligence.

Taking your social listening to the next level is the essence of building a strong social media intelligence program. Quite simply, real business success depends on having actionable customer insights instead of just collecting social listening data.

Social Media is Full of Noise

Social media ”noise” can be defined as information not of value to you. “Noise” type of information is very biased as different people are looking for different types of information via various social media channels. The challenge is to filter the voice from the noise to get to the root of the matter.

We Look at Entire Conversations

Monitoring social media through keywords or sentiment is a thing of the past. Part of social media intelligence comes from looking at entire conversations and the people behind it. Build social media ontology and profiling for effective decision making.

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Geo Intel

We provide independent research derived from the analysis of satellite, aerial and drone imageries. We apply additional, sophisticated data sources and advanced technologies, including Machine Learning, to produce evidence-based alternative data that enables better decision making.

We provide a wide network of satellite data provider partners, which cover all aspects of our clients’ data requirement needs. This network enables us to access more than 250 satellites currently in orbit. Our satellite operator partners allow us to offer both optical and radar satellite imagery at a range of resolutions and sensing modes.

Bespoke Deployment

The range of implementation is vast and varied and the possibilities are endless. Generally speaking if it can be monitored from above there’s a good chance that we provide a solution.

Visual intelligence derived from satellite, aerial and drones
Geospatial Analysis
Machine Learning
Data Analytics
Remote Sensing
Product Development
Business Transformation

Catastrophe Response

Visual Intelligence can be used to rapidly assess the impact of a catastrophic event. Examples include determination of peak flood extent before the waters recede or to analyse the immediate impacts of an explosion, inform emergency services and identify hazards or pinpoint their epicentres.

Loss Evaluation

Geospatial Intelligence is now frequently used to enable rapid response to major disaster events but it can also play a significant role in day-to-day insurance claims handling and loss adjustment.

Intelligence Partner

Now more than ever, intelligence cycle process is vital to ensure business longevity and competitiveness. However, the tasks involved can be daunting and overwhelming. We can assist you in managing the entire cycle so you can focus on your daily tasks.

From data acquisition, strategy formation, development of narratives, content creation, message dissemination to campaign management, we provide end-to-end solutions for efficient and effective engagements.

Customised Campaigns

From one-off engagements to yearly subscriptions, we employ customised campaigns based on your needs and requirements.

Scaleable

Scale your intelligence network. From localised communities to worldwide audiences, you decide how far to go.

Outcomes

Analyses and recommendations are presented in the form and interval set by you. Results and key indicators in the form of physical and digital reports, web access and/or command centre virtualisation.

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