Since the 1950s, scientists have been actively studying the capabilities of computer intelligence. Over the last 70 years, the concept of machine learning (ML) has developed from a theory to a technology actively used in the wild that allows program autonomy for decision-making, reducing the amount of manual work needed.

Security pros use machine learning to boost and automate malware detection, among other things. We also use machine learning against advanced email phishing. Sophisticated, accurately prepared phishing letters are effective ways to trick a specific organization or a user. Attackers disguise these messages as emails from new online services, exploiting popular events, even the pandemic. In the first quarter of 2020, there were many emails circulated with a request to transfer money to help combat COVID-19. Through the business email compromise technique, criminals gain employees’ trust via email correspondence. They disguise themselves as a third party, contractor or even a colleague and make targets do what the criminals want.

To protect users from such attacks, a security solution should quickly analyze all parameters of the email, including the content and technical characteristics, to detect if it’s malicious or not. Machine learning can take care of this.

In this case, we should use two machine learning models. One model will automatically analyze the technical parameters of emails, such as technical headers. It references hundreds of millions of metadata records from real emails and learns to recognize the combinations of technical traces that prove that the email is malicious, but it’s not enough information to make a verdict.

The second model detects the malicious nature of an email based on its content. To achieve the desired emotional effect, attackers use emotive language, as well as a clear call to action, something like: “your parcel couldn’t be delivered, update your data here” in their text. The model recognizes such words and phrases typical for phishing letters.

The two models then correlate both results and make the final decision of whether or not it’s a phishing email, saving the user from opening it.

Machine learning against mobile threats for Android

In 2020, Kaspersky researchers detected an increase of 2 million more mobile threats than in the previous year, totaling more than 5 million overall. One of the key tasks within mobile protection is to secure against unknown malicious objects which have recently appeared in the wild.

On iOS devices, it’s only possible to install apps for a wide audience from the Apple App Store. On Android devices, users can install apps from a variety of sources and app markets. Unfortunately, cybercriminals sometimes exploit this by posting malware in apps disguised as games, useful software, porn, and so on. To detect the threats effectively and quickly, security teams need machine learning.

A machine learning agent on a user’s device scans every app as it’s downloaded for specific features such as required access permissions or numbers and sizes of internal structures. The metadata gets sent to the cloud-based ML model that then decides if this set of parameters causes the app to be classified as malicious or not. The model then sends a response indicating whether it’s a malicious file or not, and the protection product on the device decides to block the app’s download and installation.

This type of machine learning analysis requires a lot of computing resources, much more than a mobile device has available, and that’s why security teams leverage the cloud for this process. 

Machine learning can prevent plant breakdowns

Equipment malfunctions, misconfigurations, human error, or hacker attacks can all cause the breakdown of industrial machinery. If any of these situations happen, it’s better to detect the deviation in production processes as soon as possible, otherwise an incident can spiral out of control.

The early symptoms of an incident are virtually impossible to detect by threshold monitoring or human operators. When thousands of telemetry readings come in every second, even an experienced operator can focus on a few patterns and overlook the rest.

Here’s where machine learning for anomaly detection (MLAD) comes in. The neural network can analyze a massive amount of telemetry data, absorb all aspects of the machine’s operation and thoroughly learn how the machine behaves under the normal conditions such as how the signals change over time and how they correlate with each other.

Once the ML model gets trained, the model switches to anomaly detection mode. It then receives telemetry in real-time, and if the divergence between the model and the observation rises above a certain threshold, the software deems the machine’s behavior anomalous and raises an alarm. The model gives an early warning of attacks, malfunctions, or mismanagement before any other instrument can spot the problem. This way, it helps to minimize damage and prevent a plant’s breakdown.

Machine learning against advanced cyberattacks

In some cases, security teams can use machine learning techniques to complement human intelligence against advanced threats, such as in managed detection and response (MDR) services.

Within an MDR service, an external security operation center (SOC) helps business customers respond to advanced cyberattacks. It receives alerts from the customer’s endpoints and investigates them to find traces of attacks, which it then reports back to the customer with response actions. SOC experts analyze some threat samples manually, but given the scale, they physically cannot look at each and every alert.

Machine learning can take on this burden. It automatically filters out alerts of no interest for SOC analysts, sets alert importance levels and gives hints for analysis saving their capacity and minimizing the response times.

During the training mode, model analyses alerts and scores them. The higher the score, the greater the probability that the alert should get reviewed by experts. Alerts with scores above a certain threshold are sent to SOC analysts who label them manually and enrich training data for the ML model.

In combat mode, the model resolves some alerts and prioritizes the rest for manual processing: Those with the highest score are put at the head of the queue for processing. This queue strategy reduces the average processing time of alerts and allows the offering to deliver the best SLA.

These are just a few interesting cases of how machine learning serves cybersecurity goals, but we believe that the field will continue to expand. Developing ML techniques in products can make cyber protection more intelligent, faster, and efficient.

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A mobile data handling concept developed by Germany-based specialist ViGEM should, according to the company, make 24/7 utilization of test fleets for ADAS and AD validation driving feasible.

The system is based on the fast, uncomplicated exchange and shipment of robust removable data storage devices independent from the vehicle under test. As ViGEM’s CEO, Markus Trauth, explained, “Thanks to the robustness of our removable data storages and the mobile data handling concept, our customers are able to reduce the downtime of their vehicles to a minimum compared to other data transfer methods. Test fleets can thus be utilized almost 24/7, which holds significant potential for cost savings for ViGEM customers.”

In practice this means that large amounts of test data can be made quickly available to manufacturers’ development departments while testing is still ongoing. Working in cooperation with OEMs, ViGEM says it has developed a suite of integrated hardware and software components (Car Communication Analyzer – CCA) for the efficient validation of advanced driving functions. By ensuring seamless interaction between elements such as dataloggers, robust storage media and fast copy stations, featuring transfer rates of up to 50Gb/s, maximum data reliability and security are ensured.

For example, the SSDs installed in its CCA 9010 solution, with up to 64TB storage capacity and data rates of up to 25Gb/s, can continuously record raw data for approximately eight hours. Their shock- and vibration-tested metal housings can withstand even the highest mechanical stresses and allow operating temperatures from -20°C to +65°C, meaning they can be removed from vehicles and transported safe in the knowledge the data they contain is secure.

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App Annie, the leading mobile data and analytics company helping brands and publishers create winning experiences on mobile, announces its newest innovation designed to put data-driven insights in the hands of executives — the App Annie Pulse app.

For busy executives, time is of the essence. To keep pace with the ever-changing app economy, it’s important to access critical information about trends, markets and the competition in real time. With over USD 143 billion spent on mobile and 218 billion new app downloads in 2020, unlocking mobile insights and capitalizing quickly on opportunities is more important than ever.

Now available on the Apple App Store, App Annie Pulse provides one touch access to mobile market data. This is the industry’s only answer to benchmark competition, track market movers, and identify insights that are powered by the industry’s best estimates and data science.

This is the first mobile app to fully leverage AI-driven features:

• App Annie Performance Score: A composite metric, based on sentiment, acquisition, monetization, and engagement metrics. This allows businesses to quantify and benchmark their performance. The score can be tailored to your favorite metrics.
• App Annie Insights: Automatically monitors shifts in data and identifies potential causality

“We are unmatched in our data science capabilities – the only market data provider that derives all estimates and insights from a pure AI model,” said Ted Krantz, CEO, App Annie. “App Annie Pulse marks the introduction of the industry’s first Mobile Performance Score and ability to track your own custom metrics.”

The company is also announcing the appointment of Ketaki Rao as Chief Product Officer. Ketaki will lead the way on innovation and continue to deliver the most advanced products on the market. Ketaki joins with over 20 years of experience in product development at technology companies including Salesforce, Amazon, and Sun Microsystems.

“App Annie’s best-in-class intelligence for mobile, recent breakthroughs in AI technology, privacy-forward approach, and a bold product vision are primed to meet the new needs of a rapidly changing digital landscape. I am thrilled to join the talented and energetic App Annie team as the Chief Product Officer.” Ketaki Rao, Chief Product Officer, App Annie.

Look for the company to continue to unveil more intuitive and streamlined user experience redesigns for all its products. App Annie Intelligence redesign beta launches today and will become generally available in H2 2021. App Annie Pulse will be available on Android at the end of Q2.

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Synopsys, Inc. announced that Tenstorrent has achieved first-pass silicon success for its Grayskull AI processor system-on-chip (SoC) using Synopsys’ DesignWare PCI Express (PCIe) 4.0 Controller and PHY, ARC HS48 Processor, and LPDDR4 Controller IP. The silicon-proven DesignWare IP portfolio enabled Tenstorrent to quickly meet the critical real-time connectivity and specialized processing requirements of their dynamic artificial intelligence (AI) processor SoC for high-performance computing applications. Tenstorrent also leveraged Synopsys’ expert technical support team to ease IP integration and significantly accelerate their design schedule.

Grayskull offers differentiated capabilities, including fine-grained conditional computation, an area- and power-efficient matrix compute engine, a custom network-on-chip (NoC), and dynamic data compression. Due to the success of the Grayskull SoC, Tenstorrent intends to engage with Synopsys on their next-generation AI processor SoCs for markets such as data centers, public/private cloud servers, on-premises servers, edge servers, and automotive.

“Tenstorrent’s Grayskull AI processor SoC required a range of high-performance IP that met the aggressive compute demands of training and inferencing models,” said Drago Ignjatovic, vice president of engineering at Tenstorrent. “Synopsys’ established track record in the IP industry gave us confidence that we could quickly integrate the DesignWare PCIe 4.0 Controller and PHY, ARC HS48 Processor, and LPDDR4 IP into our AI processor SoC. In addition, Synopsys’ technical support team along with the maturity and quality of the DesignWare IP allowed our designers to focus on their core competencies and quickly achieve first-pass silicon success.”

The PCI Express 4.0 controller and PHY IP provide the required 16GT/s data rate and x16 link width while allowing more than 36dB channel loss across process, voltage, and temperature (PVT) variations for high-throughput and low-latency connectivity. A quad-core configuration of the DesignWare ARC HS48 Processor delivers high processing performance within constrained area and power budgets. To achieve power-efficiency Synopsys’ LPDDR4 Controller IP, operating at 4267 Mbps, provides automated low-power state entry and exit. The Advanced Reliability, Serviceability, and Availability (RAS) features including inline error correcting code (ECC) with address protection reduce system downtime.

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Tenstorrent Achieves First-Pass Silicon Success For AI Processor SoC Using Synopsys’ Broad DesignWare IP Portfolio

AIT News Desk  16 Jul 2020 Computing, Machine Learning, News  Leave A Comment  68 Views

High-Quality DesignWare Interface and Processor IP for Efficient Real-Time Connectivity and Machine Learning Processing Accelerate Design Schedule and Lower Risk

Synopsys, Inc. announced that Tenstorrent has achieved first-pass silicon success for its Grayskull AI processor system-on-chip (SoC) using Synopsys’ DesignWare PCI Express (PCIe) 4.0 Controller and PHY, ARC HS48 Processor, and LPDDR4 Controller IP. The silicon-proven DesignWare IP portfolio enabled Tenstorrent to quickly meet the critical real-time connectivity and specialized processing requirements of their dynamic artificial intelligence (AI) processor SoC for high-performance computing applications. Tenstorrent also leveraged Synopsys’ expert technical support team to ease IP integration and significantly accelerate their design schedule.

Grayskull offers differentiated capabilities, including fine-grained conditional computation, an area- and power-efficient matrix compute engine, a custom network-on-chip (NoC), and dynamic data compression. Due to the success of the Grayskull SoC, Tenstorrent intends to engage with Synopsys on their next-generation AI processor SoCs for markets such as data centers, public/private cloud servers, on-premises servers, edge servers, and automotive.

“Tenstorrent’s Grayskull AI processor SoC required a range of high-performance IP that met the aggressive compute demands of training and inferencing models,” said Drago Ignjatovic, vice president of engineering at Tenstorrent. “Synopsys’ established track record in the IP industry gave us confidence that we could quickly integrate the DesignWare PCIe 4.0 Controller and PHY, ARC HS48 Processor, and LPDDR4 IP into our AI processor SoC. In addition, Synopsys’ technical support team along with the maturity and quality of the DesignWare IP allowed our designers to focus on their core competencies and quickly achieve first-pass silicon success.”

The PCI Express 4.0 controller and PHY IP provide the required 16GT/s data rate and x16 link width while allowing more than 36dB channel loss across process, voltage, and temperature (PVT) variations for high-throughput and low-latency connectivity. A quad-core configuration of the DesignWare ARC HS48 Processor delivers high processing performance within constrained area and power budgets. To achieve power-efficiency Synopsys’ LPDDR4 Controller IP, operating at 4267 Mbps, provides automated low-power state entry and exit. The Advanced Reliability, Serviceability, and Availability (RAS) features including inline error correcting code (ECC) with address protection reduce system downtime.

“Innovations in machine learning algorithms and neural network processing for high-performance computing applications are driving new technology requirements for AI SoCs,” said John Koeter, senior vice president of marketing and strategy for IP at Synopsys. “Synopsys provides companies such as Tenstorrent with a comprehensive IP portfolio that addresses the performance, latency, memory and connectivity requirements of AI chips for cloud, IoT, mobile, and automotive designs, while accelerating their development time.”

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Optus mobile subscriber data was used by all levels of government to understand changes in behaviour that corresponded to the COVID-19 lockdowns.

The data also proved to be a useful measure of foot traffic during the bushfire crisis last Summer, with the federal-run Tourism Research Australia using it “in preparation for discussions around bushfire recovery initiatives”.

It was reported by The Sydney Morning Herald in April that Vodafone had contributed aggregated and anonymised mobile data on its subscribers to governments to help them understand population movement trends during COVID-19.

The same report quoted Optus as saying it was “working with our Commonwealth and state governments to assist their efforts in navigating our country through these challenging times” though it declined to disclose “the requests [for data] we receive.”

It’s now clear there was government interest in Optus subscriber data, which it combines with third party datasets and packages up via a data mining operation called DSpark Australia.

iTnews first reported on DSpark’s formation back in April 2017, though at the time even the name of the operation was unknown.

DSpark generally has kept a fairly low public profile, though this has changed in the past couple of months as crisis events emerged that proved good use cases for its product.

Country head Paul Rybicki told an SA Government-run event last week that DSpark’s data holdings had found use by all three layers of government.

“How does COVID-19 actually impact the mobility of people? It certainly does by the fact that there are social distancing measures in place, people are working from home, [and] we’re all living that new reality,” he said.

“A lot of this data is now being used at a federal level, state level and even local level with councils, to help understand the changes in behaviour.”

Rybicki did not discuss specific use cases but showed off an Australia-wide – as well as South Australia-specific – snapshot of DSpark’s movement data during the COVID lockdowns.

He said that in general Australians spent more time at home and travelled much shorter distances than usual.

Bushfire recovery

Rybicki provided a more detailed look at how DSpark’s data was used to aid federal understanding of bushfire-affected areas in Australia that were hardest hit from an international tourist perspective.

The data was largely taken from an unlisted video posted by Dspark in mid-March that apparently includes data analysis fed to Tourism Research Australia.

Rybicki confirmed the federal use of this data, noting that it had helped inform authorities about areas in most critical need of financial assistance.

He said DSpark’s data on Kangaroo Island – “a critical tourism spot, in particular for international visitors – [showed] probably one of the highest impacts we saw across our whole country in terms of a tourism region”.

“At its worst in the end of January period, [there was] a decline of nearly 80 percent [international] visitation,” Rybicki said.

“How this was used – Minister [Simon] Birmingham from the federal office of tourism effectively looking at how to allocate recovery funds and how to prioritise that based on actual volume of impact and percentage of impact.

“This sort of data can certainly help with understanding visitation and impact in events like the bushfires.”

Unpacking DSpark

Rybicki said that DSpark sits “very closely to Optus” and had “access to Optus’ customer base” data, which it combined with other third-party datasets.

“We take mobile phone data, we take GPS data from various apps, we look at the roads network, we have some data in on immigration so that we can also look at how people who are from abroad traveling in Australia are moving throughout the country, [and] we ingest public transport information,” he said.

“It’s the power of all of these datasets combined, enhanced, extrapolated that allow us to effectively then see the population moving 24×7.”

“It’s around looking at how people move, looking at data that they generate when they move, seeing where they work, where they live, what trips they make, what modes of transport they take, and being able to apply those insights and that type of information to various problems.”

DSpark has some ties to South Australia, given it and Optus are part of Lot Fourteen, which Rybicki said was “effectively a ‘Silicon Valley’ in South Australia”.

“It will attract lots of exciting businesses and startups and creative minds and ideas, and hopefully it will generate together some really good stuff,” he said.

Rybicki said that the skill set most in-demand in his area of business right now is data engineering, not data science per se.

“It’s less around the analysis and extracting the value, but more around processing raw datasets, bringing them together and extracting attributes out of them, in order to then be able to do the data science on the backend of that. 

“Certainly one observation we’re seeing in the last few years is that data engineering as a function, as a skillset, is more difficult to find and is more difficult to hone.

“It’s certainly in very high demand in businesses like ours and in many other entities who are playing with data or analysing data and using it for insight and action.”

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While many European states are currently setting up the 5th generation of mobile communication, scientists are already working on its optimization. Although 5G is far superior to its predecessors, even the latest mobile communication standard still has room for improvement: Especially in urban areas, where a direct line of sight between emitter and transceiver is difficult, the radio link does not yet function reliably. Within the recently launched EU project ARIADNE, eleven European partners are researching how an advanced system architecture “beyond 5G” can be developed by using high frequency bands and artificial intelligence.

A major advantage of 5G is its high frequencies and consequently its high transmission rate, which ensures an almost latency-free connection and fast data transfer. However, high frequencies require a directed system, which in most cases relies on a line of sight (LOS). This means that transmitter and receiver must be able to see each other. Unfortunately, the LOS principle can lead to connection problems, especially in urban and heavily developed areas.

One of the issues responsible for these connection problems in local 5G networks is the cancelling effect. This effect occurs when a signal is transmitted over a LOS connection and simultaneously copied via reflections. The copy overrides the signal from the LOS and cancels it. The result: the signal does not reach the receiver. This multipath propagation via non-line of sight (NLOS) remains a problem for 5G, as it did with its predecessor 4G. For this reason, one of the main aims of ARIADNE is the development of new concepts for better control of LOS and NLOS scenarios to massively improve the reliability of mobile communication links.

Higher efficiency and reliability of 5G

The EU Project, with the full title “Artificial Intelligence Aided D-band Network for 5G Long Term Evolution” brings together partners from research and industry from five countries. The aim is to develop energy-efficient and reliable mobile communication links based on frequencies in the D-band (130—174,8 GHz). With its aggregated bandwidth of more than 30 GHz, the D-band is perfectly suited for fast data transmission. However, this newly used band is divided into several sub-bands and requires an adaptation of the previously used system architecture and corresponding network control.

ARIADNE aims to create an intelligent communication system “beyond 5G” by combining an innovative high-frequency radio architecture and a new network processing concept based on artificial intelligence. By 2022, the project consortium plans to realize and demonstrate a radio link with extremely high data rates in the 100 Gbit/s range at almost zero latency. The European Union supports the project as part of the Horizon 2020 program. ARIADNE focuses on three major research areas: the development of hardware components, the research of metasurfaces and the adaptation of the network control based on artificial intelligence or machine learning.

Devices for a reliable D-band connection

Fraunhofer IAF contributes its expertise in the field of high-frequency electronics to the development of hardware components: together with partners, the Freiburg scientists are developing new radio technology for communication in the D-band (139—174,8 GHz). “Our focus lies on the development of new radio modules with highest spectral efficiency that capitalize on the frequency diversity and provide a control interface for optimization in the network. For this purpose, we will use our 20 nm InGaAs HEMT technology on silicon for the first time,” states Dr. Thomas Merkle, scientist and project manager on the part of Fraunhofer IAF.

Reflecting surfaces

In order to prevent network disturbances in NLOS connections, ARIADNE is researching metasurfaces and their potential for optimizing radio connections. Metasurfaces are adjustable reflectors for radio waves and are intended to counteract network-processing problems in urban areas. When there is no line of sight between base stations on rooftops and users in urban canyons, the metasurfaces will reflect the radio waves and thus ensure propagation outside the line of sight. A central network control will manage the metasurfaces.

“The concept of metasurfaces is already partially being used in 5G, but so far only for low frequencies. The higher the frequencies of the radio link, the finer the microstructures on the surface have to be. This makes the production of such structures very difficult for frequencies in the D-band,” explains Thomas Merkle. For this reason, the project team is researching the development of metasurfaces suitable for both high frequencies and industrial production. At Fraunhofer IAF, the scientists are working on so-called reflect arrays. These are small metasurfaces on antennas used for beam steering and focusing.

AI-based network control

In order to provide a constant and reliable radio link in all weather conditions, machine learning and artificial intelligence (AI) methods will be utilized for network management. Currently, classical mathematical methods are used for most mobile radio management. ARIADNE will employ AI-based algorithms for problem solutions in radio communication. While machine learning aims at a profound data analysis, AI will serve to develop a network control system that not only detects and reacts to problems, but can also predict and avoid them.

The ultimate goal of the project partners is to bring the individual project modules together in a test system and demonstrate its functionality. At the end of the project, they want to present two demonstrators as result of their research: The first demonstrator should achieve a reliable connection over 100 meters with a data rate of 100 Gbit/s in any weather condition. The second demonstrator is intended as a proof of concept under laboratory conditions, to show how a metasurface can improve the propagation condition of radio transmissions. This should prove the functionality of metasurfaces at high frequencies in the laboratory. At this point, the software development should demonstrate that the AI-based network control system can increase the reliability over the whole D-band network and guarantee the control of the metasurfaces.

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Big data and analytics are topics firmly embedded in our business dialogue. The amount of data we’re now generating is astonishing. Cisco predicts that annual global IP traffic will reach 3.3 ZB per year by 2021 and that the number of devices connected to IP networks will be more than three times the global population by 2021, while  Gartner predicts $2.5M per minute in IoT spending and 1M new IoT devices will be sold every hour by 2021. It’s testament to the speed with which digital connectivity is changing the lives of people all over the world.

Data has also evolved dramatically in recent years, in type, volume, and velocity – with its rapid evolution attributed to the widespread digitisation of business processes globally. Data has become the new business currency and its further rapid increase will be key to the transformation and growth of enterprises globally, and the advancement of employees, ‘the digital natives’.

The Cisco Global Cloud Index points to the Cloud as the top driver as exponential data centre growth with cloud centre traffic quadrupling in the next five years. Data generated by IoT applications (such as connected homes, smart cities and healthcare) will be 600ZB (zettabytes) per year by 2020, 39 times higher than current data centre traffic which is 15.3 ZB.

Big Data therefore has a far-reaching impact and meaning. But how do we understand it and its benefits, along with analytics on the journey to Digital Transformation? Understanding the value of Data is key to the successful implementation of operational strategies that facilitate agile and effective business growth.

Big data means better business 

Data is an enabler of future strategies and immediate change, thanks to the power of predictive analytics and advanced data science. Correctly harnessing data can help to achieve better, fact-based decision-making and improve the overall customer experience. By using new Big Data technologies, organisations can answer questions in seconds rather than days, and in days rather than months. This acceleration allows businesses to enable the type of quick reactions to key business questions and challenges that can build competitive advantage and improve performance, and provide answers for complex problems or questions that have resisted analysis.

Big Data and analytics are becoming closely intertwined and need to work together to deliver the promised results of Big Data. Traditionally, Data management and analytics have resided in different parts of the organisation. Breaking down organisational boundaries and creating better integration between the IT and business departments is a critical step on the road to successful transformation.

There is also a widespread realisation of the need for better Business Analytics (BI).Business Analytics are the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. The key is integrating Big Data with traditional Business Analytics to create a data ecosystem that allows the business to generate new insights while executing on what it already knows.

Keep learning. Skills are everything.

Proficiency with data mining and visualisation tools ranks as one of the most important skills in determining project success.

All organisations need to consistently develop new data mining skills to fully realise the business potential. A key trend in big data is machine learning. Big data experts who can harness machine learning technology to build and train predictive analytic apps such as classification, recommendation, and personalisation systems are in high demand. Statistical and Quantitative Analysis, which aims to understand or predict behaviour or events through the use of mathematical measurements and calculations, statistical modelling and research, is also imperative to accomplishment. Other key data mining techniques that are employed industry wide include:

• Association –  one of the best-known data mining techniques. With association, a pattern is discovered based on a relationship between items in the same transaction.
• Classification is a classic data mining technique based on machine learning.
• Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique.
• Prediction is one of a data mining techniques that discovers the relationship between independent variables and relationship between dependent and independent variables.
• Sequential patterns analysis seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period.
• Decision tree technique; the root of the decision tree is a simple question or condition that has multiple answers.

Educate your stakeholders 

All stakeholders need to be educated and made aware of Data’s value and understand that it’s essential to business continuity and growth. But they may feel overwhelmed (and under informed) to the power and complexity of the data if it is not properly communicated and presented. Regular meetings, ideally face to face will enforce the importance of the issue and the need for their buy-in.

Deliver Digital Ready networks – it makes financial sense 

All today’s businesses must, via Network Function Virtualisation (decreasing the amount of proprietary hardware needed to launch and operate network services), and Software Defined Networking (that allows updates to be made in real time or as the business demands, in just a few clicks) deliver Digital Ready networks to gain competitive advantage.

The increased simplicity and reduced costs associated with deploying and maintaining a more digital-ready network are core benefits and therefore should be employed as a necessity to improve and enhance business efficiency.

Automation is a high priority 

Automation is a high priority in accelerating Digital Transformation, allowing organisations to optimise their existing processes. Automation technology is IT system and process agnostic, allowing businesses to build on their systems within the existing IT environment.

In order to create a transformative environment and improve speed and quality of delivery, organisations need to integrate automation into their existing processes to increase the ability to frequently release high-quality products –  and to enable revenue and profit growth.

Automation also improves operational efficiency and allows employees to focus on more rewarding tasks. With automation, cost-effective solutions are enabled for repetitive, rules-based tasks. In addition, the prospect of human error is eliminated, delivering outcomes that are 100% accurate. By automating tasks, companies can significantly reduce the overall process cycle.

The road towards digital transformation is a business critical one. Organisations embarking on this journey need to consider how each aspect of their business can be optimised to fulfil new digital objectives and new growth potential.  Big data and analytics play a pivotal role in digital transformation, enabling organisations to optimise their existing processes and stay ahead of the competition.

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