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#MachineLearning: How #PredictiveAnalytics reinvents Customer’s Satisfaction

Billions and trillions of data is collected on customer behavior from the huge platform called internet. To these are added valuable information gathered by every organization from different sectors. In this mine of information, Machine learning, pursues an ultimate goal: to better understand customers in order to offer them the best experience possible by offering them the product or service most likely to their need. It’s analytical power and the advance in artificial intelligence allows companies to take advantage of the wealth of data they collect.

At this point we all know that #bigdata is worth nothing, nada, without proper decryption. This is where machine learning or “automatic learning” comes into action. With its power of analysis, this field of artificial intelligence extracts the valuable information from the mass data. In other words: it enables to turn the lead into gold by simplifying the life of the customer and improving its satisfaction thanks to the precise analysis of its way of purchase.

 

Artificial Intelligence: algorithms and insights

Since its first use in the general public in the late 1990s, the machine learning have never stopped to make talk about it. Its recent victory was in March 2016 via AlphaGo, the software of Google, against the legendary Lee Sedol. We’ve witnessed AlphaGo’s most notable examples of deep learning, which was, the ability of a machine to independently analyze sums of data with an extremely high level of performance.

If such technological power remains exceptional, all of us daily experience the small machine learning without knowing it. How? Well, just surf on Amazon, LinkedIn, Spotify or even Netflix to see these platforms automatically offer suggestions according to their precise taste. These associations of ideas remain pertinent on subjects as fine as the interest for a film, a song, a service or a cross purchase. It is a much less superficial intelligence than it seems but with concrete results.

 

From big data to automatic learning

Well-resourced with quality data, the algorithm analyze deeply in the vast meadows of digital world. They cross distant data from each other to reveal information never brought to light. These algorithms bring us the astonishing results which a human mind would have swept away. For example, in a customer journey, deep learning allows to discover that the intention of purchase can be correlated with an action at precise moment of purchasing action. With automatic learning, one can therefore target with precision every important thing that human understanding can escape.

 

Machine learning: better tracking of customer routes

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According to Salesforce’s state-of-the-art survey published in 2016, customer engagement is a top priority for organizations. Customer satisfaction is the main reason for success, even surpassing revenue growth and the acquisition of new customers. In this context, Machine learning is thus a major ally.

From an operational point of view, most of the machine learning applications used today are subject to a pre-learning phase. A large amount of data is thus processed, during algorithm design, to better guide the search and automate more easily the answers that will be offered to online surfers. It comes to deal with a combination between human intelligence and artificial intelligence. The goal still to be reached, for each organization, is a user experience that is as simple and fluid as possible. The machine learning has already made it possible to take a major step forward thanks to the ultra-segmentation of the profiles for a refined follow-up of the customer routes.

 

Sharing Data: the essence of war

In order to function at full capacity, machine learning must benefit from first-class information. How it’s possible? By adapting an omnivorous diet. Depending on the project, companies use the information they collect through cookies, geolocation, social networks, loyalty programs (which typically collect data on age, location, purchase history …).

Contrary to popular belief, consumers are rather inclined to share their data, but not at any price. This is evidenced by the Columbia Business School’s “What is the future of data sharing” study conducted by the Columbia Business School Center for Global Brand Leadership in 2015 with 8,000 Internet users in the United Kingdom, the United States, Canada, France and India. “Consumers are much more knowledgeable about the issue of data sharing than we originally suspected. According to our study, one of the determining factors in the decision to share them is trust in the brand, “says Matthew Quint, director of the Center for Global Brand Leadership. Researchers at Columbia Business School have come to the conclusion that more than 75% of Internet users more readily share their data with a brand they trust.

 

Customer data: Give and Take

Beyond trust, the sharing of information is based on a give-and-take approach. According to the same Columbia Business School study, 80% of consumers agree to share confidential information in exchange for a reward. It must be a “valuable offer, but this value must be clearly defined and easy to understand to hope for the best possible return on investment,” says Matthew Quint. Young consumers would be more favorable than their elders to concede their personal information. What promises beautiful days to machine learning.

 

All the above points ends on the same conclusion that organizations can get a better understanding and add a new layer of intelligence on their customers behavior by using predictive analysis.

Critical challenges of #DataProtection and #CyberSecurity within your Organization

#DataProtection and #CyberSecurityData breaches are a constant threat to all organizations. And the risk keeps growing: By 2016, the total number of exposed identities by data violations has increased by 23%, with a record of 100,000 incidents, of which 3,141 were confirmed data breaches.  The data now is corrupted/compromised in a few minutes and their exfiltration takes only some days.

 

The worst part is that detecting a violation can take months, with an average discovery of 201 days. Unable to respond quickly, organizations face the risk of exposing valuable data and confidential information. The recovery process can be incredibly costly, and the damage in terms of reputation is incalculable.

 

Why companies must stay alert?

Why companies must stay alert?

The increasingly digital revolution requires companies to constantly be on their guard in order to detect attacks and respond to potential incidents. However, after several years of constant vigilance, many companies are wondering if their investments will one day be sufficient. Some of them even think that they’ve solved the problem with devices to counter conventional attacks (such as phishing, for ex) or to fill in the most important flaws (the identity and access management system, for ex). In reality, that’s not the only thing they must do in order to protect their valuable data.

 

While most companies have laid the foundations for proper cybersecurity, most of them haven’t realized that these measures are only the beginnings of a much wider and proactive policy, and the digital world needs continuous investments on security matters. An enterprise may consider that it has implemented sufficient cybersecurity measures when it will be able to remain permanently within the limits of its risk appetite.

 

Demonstrating the contribution of cybersecurity investments can be challenging. Nevertheless, when a company reaches a high level of maturity in this area, it becomes easier to justify ongoing vigilance by demonstrating the contribution and value of investments: whenever the Security Operations Center identifies a potential attack, the evaluation of the costs generated by the different attack scenarios (particularly the least favorable one) justifies the made investments.

 

How organizations can unfold threats and vulnerabilities?

  • All vulnerability and incident data are retrieved in a single system. By the automation of simple security tasks and correlating intelligence data against threats with security incidents, analysts have all the information they need to protect your business.
  • Through the integration with the CMDB, analysts can quickly identify affected systems, their locations, and their vulnerability to multiple attacks.
  • Workflows are essential to ensure compliance with your security runbook. Predefined processes allow 1st level personnel to perform real security work, while more experienced security professionals can focus on tracking complex threats.
  • By managing an overload alert via applying priorities based on their potential impact on your organization. Analysts need to know precisely which systems are affected, as well as any subsequent consequences for related systems.
  • By improving controls and processes to identify, protect, detect, respond and recover data
  • By creating cyber security awareness within your employees

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How organizations can improve their CyberSecurity?

A company must establish a solid foundation of cybersecurity to protect its present environment. For example by carrying out a safety assessment and building a roadmap; review and update security policies, procedures and standards; establishing a security operations center; testing business continuity plans and incident response procedures; designing and implementing cybersecurity mechanisms.

 

As a business holder, you must consider that your basic safety measures will become less effective over time, so don’t forget to focuses on the changing nature of business environment. At certain point you must highlight the actions needed to enable your company to keep up with the demands and developments of the market. It can be by designing a transformation program to improve cybersecurity maturity, using external assistance, in order to accelerate its implementation. You can decide what will be maintained internally and what will be outsourced and define a RACI matrix for Cybersecurity.

 

Last but not the least, the company must proactively develop tactics to detect and neutralize potential cyber-attacks. It must focus on the future environment and have more confidence in its ability to manage predictable and unexpected threats/attacks. Few companies are at this level, and today it is necessary for them to design and implement a cyber threat strategy (Cyber Threat Intelligence), define and integrate a global cybersecurity ecosystem, a cyber-economic approach, Usage of data analysis techniques for investigations, as well as monitoring cyber threats and preparation for the worst by developing a comprehensive intrusion response strategy.

 

Sources :

Verizon’s 2016 Data Breach Investigations Report

Whitepaper: Insights on governance, risk and compliance

Big Data: 2017 Major Trends

big data trends 2017

Over the past year, we’ve seen more and more organizations store, process and exploit their data. By 2017, systems that support a large amount of structured and unstructured data will continue to grow. The devices should enable data managers to ensure the governance and security of Big Data while giving end-users the possibility to self-analyze these data.

Here below the hot predictions for 2017.

 

The year of the Data Analyst – According to forecasts, the Data Analyst role is expected to grow by 20% this year. Job offers for this occupation have never been more numerous before. Similarly, the number of people qualified for these jobs is also higher than ever. In addition, more and more universities and other training organizations offer specialized courses and deliver diplomas and certifications.

 

Big Data becomes transparent and fast – It is obviously possible to implement machine learning and perform sentiment analysis on Hadoop, but what will be the performance of interactive SQL? After all SQL is one of powerful approach to access, analyze, and manipulate data in Hadoop. In 2017, the possibilities to accelerate Hadoop will multiply. This change has already begun, as evidenced by the adoption of high performance databases such as Exasol or MemSQL, storage technology such as Kudu, or other products enabling faster query execution.

 

The Big Data is no longer confined to Hadoop – In recent years, we have seen several technologies developing with the arrival of Big Data to cover the need to do analysis on Hadoop. But for companies with complex and heterogeneous environments, the answers to their questions are distributed across multiple sources ranging from simple file to data warehouses in the cloud, structured data stored in Hadoop or other systems. In 2017, customers will ask to analyze all their data. Platforms for data analytics will develop, while those specifically designed for Hadoop will not be deployable for all use cases and will be soon forgotten.

 

An asset for companies: The exploitation of data lakes – A data lake is similar to a huge tank, it means one needs to build a cluster to fill up the tank with data in order to use it for different purpose such as predictive analysis, machine learning, cyber security, etc. Until now only the filling of the lake mattered for organizations but in 2017 companies will be finding ways to use data gathered in their reservoirs to be more productive.

 

Internet of Objects + Cloud = the ideal application of Big Data – The magic of the Internet of Objects relies on Big Data cloud services. The expansion of these cloud services will allow to collect all the data from sensors but also to feed the analyzes and the algorithms that will exploit them. The highly secure IOT’s cloud services will also help manufacturers create new products that can safely act on the gathered data without human intervention.

 

The concentration of IoT, Cloud and Big Data generates new opportunities for self-service analysis – It seems that by 2017 all objects will be equipped with sensors that will send information back to the “mother server”. Data gathered from IoT is often heterogeneous and stored in multiple relational or non-relational systems, from Hadoop cluster to NoSQL databases. While innovations in storage and integrated services have accelerated the process of capturing information, accessing and understanding the data itself remains the final challenge. We’ll see a huge demand for analytical tools that connect natively and combine large varieties of data sources hosted in the cloud.

 

Data Variety is more important than Velocity or Volume – For Gartner Big Data is made of 3 V: Large Volume, Large Velocity, Large Variety of Data. Although these three Vs evolve, the Variety is the main driver of investment in Big Data. In 2017, analysis platforms will be evaluated based on their ability to provide a direct connection to the most valuable data from the data lake.

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Spark and Machine Learning makes Big Data undeniable – In a survey for Data Architect, IT managers and analysts, almost 70% of respondents favored Apache Spark compared to MapReduce, which is batch-oriented and does not lend itself to interactive applications or real time processing. These large processing capabilities on Big Data environments have evolved these platforms to intensive computational uses for Machine Learning, AI, and graph algorithms. Self-service software vendor’s capabilities will be judged on the way they will enable the data accessible to users, since opening the ML to the largest number will lead to the creation of more models and applications that will generate petabytes of data.

 

Self-service data preparation is becoming increasingly widespread as the end user begins to work in a Big Data framework – The rise of self-service analytical platforms has improved the accessibility of Hadoop to business users. But they still want to reduce the time and complexity of data preparation for analysis. Agile self-service data preparation tools not only enable Hadoop data to be prepared at source, but also make it accessible for faster and easier exploration. Companies specialized in data preparation tool for Big Data end-user, such as, Alteryx, Trifacta and Paxata are innovating and consistently reducing entry barriers for those who have not yet adopted Hadoop and will continue to gain ground in 2017.

 

Data management policies in hybrid cloud’s favor – Knowing where the data come from (not just which sensor or system, but from which country) will enable governments to implement more easily national data management policies. Multinationals using the cloud will face divergent interests. Increasingly, international companies will deploy hybrid clouds with servers located in regional datacenters as the local component of a wider cloud service to meet both cost reduction objectives and regulatory constraints.

 

New safety classification systems ensures a balance between protection and ease of access- Consumers are increasingly sensitive to the way data is collected, shared, stored – and sometimes stolen. An evolution that will push to more regulatory protection of personal information. Organizations will increasingly use classification systems that organize documents and data in different groups, each with predefined rules for access, drafting and masking. The constant threat posed by increasingly offensive hackers will encourage companies to increase security but also to monitor access and use of data.

 

With Big Data, artificial intelligence finds a new field of application – 2017 will be the year in which Artificial Intelligence (AI) technologies such as automatic learning, natural language recognition and property graphs will be used routinely to process data. If they were already accessible for Big Data via API libraries, we will gradually see the multiplication of these technologies in the IT tools that support applications, real-time analyzes and the scientific exploitation of data.

 

Big Data and big privacy – The Big Data will have to face immense challenges in the private sphere, in particular with the new regulations introduced by the European Union. Companies will be required to strengthen their confidentiality control procedures. Gartner predicts for 2018 that 50% of violations of a company’s ethical rules will be data-related.

 

Sources:

Top 10 Big Data Trends 2017 – Tableau

Big Data Industry Predictions for 2017 – Inside Bigdata

#InternetOfObjects and the Emerging Era of #CloudComputing

Big data and connected objects represent an important source of economic growth according to numerous studies. They open the possibility to connect people or objects in a more relevant way, to provide the right information to the right person at the right time, or to highlight information that is useful for decision-making. Allied to Big Data, connected objects give professionals new opportunities to better understand customer needs and better satisfy them.

 

According to McKinsey, the overall economic potential of the IoT universe could be between $ 3,900 billion (US ‘trillion’) and 11,100 billion per year by 2025! So with 30 billion connected objects by 2020 it’s now necessary, more than ever, to rethink the use of Cloud.

 

The explanation of this boom?
Connected objects are already very widespread and are gradually taking over all sectors. The general public sees it as a way to improve everyday life, while companies are already using it to control and improve industrial processes and propose new services. Cities and vehicles are becoming smart by using different types of sensors.

 

Nearly all manufactured goods entering the market – vehicles, equipment for energy or water supply, health sector equipment, scientific and technical research facilities, machine tools and robots, etc. – all are bound to be connected and, for a good part, to be interconnected.

 

We are only on the premises but very well equipped with advanced technologies, the only thing to do is to imagine their great usage that will respond to every real expectations and will bring real added value. This ability to make our environment much smarter is linked to sensors, to the data collected by these sensors and to the speed of processing of this data. The triangle of Connected Objects, Big Data and Cloud will become essential to transform this universe of connected objects into intelligent systems.

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Future of IOT Data 
The continuous flow of data generated by IOT is challenging the networks. All of these billions of objects that can be interconnected via the Internet are accelerating the real tsunami of announced data. The cloud is a simple and flexible way to deal economically with this mass of data that will continue to grow with time and new uses. And to cope with this huge data, the computing power will have to be adjusted. With the successful adoption of IoT, manufacturers will work on new systems architectures, especially those that are “hyper-integrated”, “hyper-convergent”, and can bring very high performances.

 

Cloud, indispensable for the development of the internet of objects
Connected objects are synonymous with capturing very large masses of valuable data. The gathered information via IoT will have to be stored, transmitted and analyzed for which the choice of Cloud infrastructure is the most appropriate method. Firstly because of the flexibility afforded by this type of offer only a Cloud solution allows real-time adaptation of the infrastructure capacity according to the level of demand. A flexibility for the management of all the connected objects devoted to knowing peaks of load and allows connected devices to interact with powerful back-end analytic and control capabilities. 

Furthermore, this flexibility can play more decisive role for commercial success, a situation in which it is essential to adapt its infrastructure quickly to meet demand. A necessity that affects the companies of moderate sizes seeking to contain their investments in technical infrastructures.

A flexible cloud service for connected devices can facilitate critical business decisions and strategies process by allowing you to connect your devices to the cloud, analyze data from those devices in real time, and integrate your data with enterprise applications, web services etc.

 

New skills and infrastructure needed
Applications linked to IOT are limited only by the human imagination. From automotive to home automation, to medical and healthcare industry, entertainment and education, IOT is pervasive and growing rapidly and transforming all economic sectors. To operate these innovative devices, it will be necessary to develop applications capable of collecting and processing the data that they will generate. The manufacturers of connected objects and the service providers responsible for the management of these applications must therefore provide themselves with appropriate skills and infrastructures.

Value Creation with #BigData and #ConnectedObjects

The Internet of Things and the Big Data have extended the digital revolution to all parts of the economy. With the Internet of objects (IoT) and gathered data we are at the dawn of a new digital revolution. If #BigData helps companies to understand the behavior and expectations of their customers, the connected objects are contributing to the process.

 

Three aspects of the digital revolution in particular are shaking up technology, industry and the economy with profound social consequences: “the decrease of computing and telecommunication costs, which are gradually becoming cheap resources and easily accessible to everyone, IOT evolutions leading into an era of continuous and never-ended innovation and the desire to create something outside the box, a new economic mechanisms which in particular enables the development of activities with increasing returns that redefine the competitive rules of the game”.

IOT

 

One by one, all economic sectors are switching to the digital age by threatening disappearance of businesses that won’t evolve. Companies must consider their positioning in this new paradigm, rethink their business model, to develop new competitive advantages – those of the previous era becoming partially obsolete – and then to transform to implement the new vision.

 

Positioning and competitive advantages: Companies must first understand the potential value creation of connected objects and Big Data in their markets. Here are four key capabilities of connected objects combined with Big Data:

 

  • Monitoring: The sensors placed on the connected objects will provide with more information and control in order to identify and fix these problems. The data can also be used indirectly to better contemplate the design of future objects, to better segment the market and prices, or to provide a more efficient after-sales service;
  • Control: use of the gathered data by algorithms placed in the product or in the cloud makes it possible to remotely control the objects if they are equipped with actuators;
  • Optimization: the analysis of the current and past operating data of an object, crossed with all the other environmental data and the possibility of controlling them, makes it possible to optimize the efficiency of the object;
  • Autonomy: the combination of all previous capabilities and the latest developments in artificial intelligence allows to achieve a high level of autonomy of individual objects (such as household vacuum robots) or complete systems (such as smartgrid).

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In addition, connected objects require companies to re-evaluate their environment, as the data produced and the services and platforms that accompany them allow for system optimization on a large scale. For example, public transport is already being considered in the context of a wider mobility market, in which the aim is no longer to operate a bus or subway network, but to help a Customer to go from point A to point B.

The ecosystem then expands to include transportation facilities in and around the city (bus, metros, individual car, taxis, car-sharing, etc.). .), GPS and mobile applications, social networks of users and infrastructures of the city (road, car parks, etc.).

CONNECTED OBJECTS

Transformation of the business model: Once measured the appearance of connected objects and their impact on a defined market, companies must think of their transformation to excel in this new paradigm. First, the company must evolve most of its functions and their expertise, in terms of:

 

  • Design: connected objects are more scalable, more efficient and less energy-consuming. Greater collaboration is needed between software teams and hardware teams to design new products and services that integrate more intelligence, sensors and remote capabilities in the cloud using Big Data;
  • Marketing: the new data created by the connected objects make it possible to better segment the market and individualize the customer relationship. This individualized marketing also makes it possible to design services more easily adaptable while preserving economies of scale;
  • Customer services: the role of customer services is gradually evolving towards the prevention of breakdowns, sometimes at a distance. The analysis of the data also allows these services to understand the causes of breakdown, in particular to improve the design.

 

We are witnessing a new era of the Internet of Things that, along with Big Data and cloud computing, is one of the key foundations for companies of the future. To do their best, companies will have to acquire much more robust technological infrastructure as these objects should be created within a safe environment where we trust digital technology. More fundamentally, companies need to evolve their structure and governance to gain agility and adaptability.

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