Challenges of #BigData

Big data opportunities-challenges

Behind the name of #BigData is hidden an astronomical amount of data produced anywhere, everywhere at any moment by men and machines to each action they perform together and separately.

This production is exploding because 90% of the available data was created only in the last two years. Big Data today is being analyzed to discover the insights that lead to better decisions and strategic business moves.

 

Big data apps are being used to improve offers, service levels and customer support and many more. The following numbers will certainly show you the economic potential of well-established data: Only 17% of companies haven’t plan at all to launch a Big Data project but over 70% of companies have already made use of Big Data, either by integrating their business or as part of a pilot project process. The Data technologies are maturing to a point in which more and more organizations are prepared to pilot and adopt big data as a core component of the information management and analytics infrastructure. It’s an area of research that is booming but still faces many challenges in leveraging the value that data have to offer.

 

Here are so called “big challenges” of Big Data.

 

Find a language for Big Data:

All sciences, chemistry and mathematics have experienced a tremendous boost by adopting a specific language. Don’t you think we must follow the same path in the area of Big Data and invent an algebraic notation and an adapted programming language to better share and facilitate its analysis?

 

Work on reliable data: 

With the explosion in the volume of available data, the challenge is how to separate the “signal” of “data” and “valuable information”. Unfortunately at this point, a lot of companies have difficulty to identify the right data and determine how to best use it. The fight against “spam data” and data quality is a crucial problem. Companies must think outside of the box and look for revenue models that are very different from the traditional business.

 

Data access: 

Data access and connectivity can be an obstacle. McKinsey survey shows that still a lot of data points aren‘t yet connected today, and companies often do not have the right platforms to manage the data across the enterprise.

 

Embedding increasingly complex data: 

If the Big Data was first concerned the “simple” data (tables of numbers, graphs …), the processed data is now more and more complex and varied: images, videos, representations of the physical world and the living world. It is therefore necessary to rethink and reinvent the big data tools and architectures to capture, store and analyze this data diversity.

 

Better integrate time variable: 

The time dimension is also an important challenge for the development of Big Data, both to analyze causalities in the long term than to treat accurate information in real time in a large data flow. Finally, the problem also arises in terms of storage. The volume of created data will exceed the storage capacities and will require careful selection.

 

IT architecture: 

 The technology landscape in the data world is changing extremely fast. Delivering valuable data means collaboration with a strong and innovative technology partner that can help create the right IT architecture that can adapt to changes in the landscape in an efficient manner.

 

Security: 

Last but not the least, we’ve security issue. Keeping such vast lake of date secure is a big challenge itself. But if companies limit data access based on a user’s need, make user authentication for every team and team member accessing the data and make a proper use of encryption on data, we can avoid a lot of problems.

 

The change of scale offered by the technologies of Big Data have generated profound paradigm shifts in scientific, economic and political fields. But it also impacts the human field.

 

Xorlogics cognitive abilities are indeed developed to treat and represent all number of data. Big Data thus puts us to the test to challenge our analytical capabilities and our perception of the world. As we change and grow, the beliefs that are most vital to us is to put the people first, follow excellence, embrace the change and act with integrity to serve the world.We at Xorlogics have exceptional expertise in the domain of Big Data like Hadoop EcoSystem (HDFS), MapReduce, Pig, Spark, Storm HBase, Cassandra, MongoDB, Hive, Sqoop, Thrift, Zookeeper, HUE, Nutch Tika, Kafka.

 

So if you are looking for more information or to gain a better understanding of big data terms, tools and methodologies don’t hesitate to contact our experts in the data field!

Machine Learning and a powerful Customer-Service

Machine learning: powerful customer service

Every business-customer couple is looking for a certain harmony. But like any other private couple, it cannot exist without first having a strong knowledge of one another.

Monday, is your birthday. You open your emails and, wow surprise, your favorite shoe brand address their vows with a discount code. But before you go further to use this delicate attention, you note that the recipient is not good, means the mail and promo code might not be for you. And what’s more annoying than receiving a mailing from one’s favorite brand with such error? Unfortunately, this kind of mistake is not so rare in the context of written exchanges between a customer and a business. And although regrettable, this is sometimes the tip of the iceberg in terms of cutting edge of customer relationship.

 

Actually such errors in the commercial couple not only irritate, but they can also be the cause of branch of the company-client marriage contract. All loyal customers may fly away if their trusted brand is not even able to store essential information about them. Because Customers expect to be heard and acknowledged, to be treated with the utmost care and personalization, and to receive responses promptly.

As any other couple, the business couples thus have its ups and downs. However, with a little effort we can make this relation stable. And for a business, knowing a customer on the fingertips is a must.

 

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. To be able to work on its commercial couple and ensure its present and future, every company therefore needs to know how to collect and operate effectively its customer data.

Development of data gathering tools, databases, behavioral segmentation techniques, connected data feedback from the field, are just as many opportunities on which it is necessary to invest in order to create a certain connection with the customer. Only by knowing the “who”, “what”, “when”, “how” and “why” about the act of buying, companies will be able to provide personalized service.

 

But so far, the collection of data alone is not the happy ending of the story. Like any old couple who knows each other by heart, if one doesn’t anticipate the desires, expectations and limits of others and does not act according, misunderstandings and conflicts takes birth. For a long term harmonious relationship, companies must therefore capitalize on these new gathered data and to do this machine learning is the best option. By describing the ability of a computer to not only calculate but to learn without being explicitly programmed, machine learning analyzes the raw data, synthesizes and then leaves it to companies to operate according to the relevance of their “data driven strategy”.

 

Duo of anticipation and empathy, a win-win for commercial couple:

Today, the customer data volumes is exploding, more data has been created in the past two years than in the entire previous history of the human race thanks to the advent of digital channels and connected communication tool for customer interactions and business have thus become very complex. Machine learning makes it possible not only to sort and keep only the essentials, but also helps to learn about what are needs, expectations and requirements of customers, so companies can anticipate their actions and harmonize the client relationship.

 

A well-synthesized insight, driven by approaches such as machine learning, can provide opportunity for any company to predict. In particular, I would say that customer-profiling based on data from touch-points can allow companies to not only determine the stage of a customer within the sales funnel, but to predict their actions and reactions in the future. While only the biggest players have access to the technological know-how to do this well right now, it’s only a matter of time before SMEs can replicate it and take advantage of the computing power already at their disposal.

 

Customer service teams are expected not only to react to requests and questions coming their way, but to also proactively anticipate customer needs. Machine learning is also anticipation. It’s a powerful tool to analyze the actions of customers and sales assistant, but also to identify some keywords used throughout their conversations to recognize problems and find within the company’s knowledge base information a solution to the problem. Companies can identify urgent request of customers and respond quickly. A bit like when it comes to detect in the long conversations THE topic which should not be overlooked and which fully deserves our attention.

 

Beyond anticipation, machine learning also increases the empathy of any business capacity. By learning from their exchanges, companies eventually learn a lot about their customers and can offer personalized services. The challenge is then to provide new goods and services by knowing from customer purchase history, to give free shipping or reductions when it comes to a loyal customer or his birthday. A professional error can happen, in that case a company must admit their mistake and do everything to compensate the client at the right time, but also must learn from their errors and take extra precautions to reduce the risk of problem in the future.

 

So Machine learning is a predictive (and increasingly prescriptive) analytics approach that teaches computers to think and solve problems like a human, continuously adapting to new information. With machine learning, you can monitor the entire customer experience to not only gain new perspective but actual guidance on the best next steps to take, because it’s virtually impossible to grow your business over time without putting the customer first. While there are plenty of tools and services that allow you to streamline aspects of marketing and customer service, be wary of letting these resources overtake your entire business model. The only way to build a profitable business is by humanizing your brand and developing lasting connections with your customer base.

 

Internet of Things, a booming connectivity for Africa

African economic pulse has quickened, infusing the continent with a new commercial technology known as The Internet of Things. This new technology offers many prospects for the continent, and can help solve many problems.
In terms of IT infrastructure, Africa is currently very behind compared to other more developed nations. However, more than half sub-Saharan African population has a mobile phone. Therefore, the Internet of Things is the logical continuation in terms of connectivity for the African continent.

IoT in Africa

A driver for economic growth for Africa:
The Internet of Things is much more than a simple technology. This is a product and services ecosystem, from the simple device to the technology of Cloud. As you might know an efficient connectivity adds real value to businesses. This value provides an exciting prospect for Africa, and could result in significant economic growth. The African IT could also quickly catch up and align with the rest of the world.

 

A booming connectivity: 
The adoption of the Internet of Things in Africa is nothing fancy. According to a McKinsey study, the penetration of internet in Africa will triple by 2025 to exceed 50%. This represents 600 million regular Internet users. The study also predict a strong potential for the Internet of Things in developing countries. By 2020, these countries could represent 40% of the global market value of the IoT.
Currently 15% of the world population lives in Africa. More than half of global population growth between now and 2050 is expected to occur in Africa. Therefore, the deployment of a connected system is essential to this.

 

IoT promises: 
The Internet of Things has the potential to solve many problems on the African continent. Many African countries have already embarked on the IoT adventure. Caregivers in Ethiopia monitor patient health status to adapt and adjust their treatment. Nairobi’s connected Traffic lights helps to regulate traffic. In South Africa, utilities suppliers use connected measuring tools to prevent possible overloads. Wildlife is monitored and maintained through connected DNA analysis applications and satellite imagery. DNA analysis has proved a game changer in wildlife.
The potential of the Internet of Things in Africa is unlimited. As technology advances and integrates daily life of most of citizens, we always expect more from IoT solutions to solve the problems.

 

A solution to the agricultural problems: 
In sub- Saharan Africa, 95% of usable land depend on rain. Therefore, food crops are often minimal, and the risk of famine continues to loom as a threat. With the IoT, wireless sensors can monitor the growth, soil moisture levels and water tanks. Smart vehicles can reduce the required physical labor. Thus, cultures can be more prolific, for a lower cost. According to the United Nations Food and Agriculture Organization, agricultural production needs to increase by 60% to feed the entire population expected to reach nine billion by 2050.
For example, John Deere has partnered with SAP to use the Internet of Things and Big Data in the fields to increase the yield per used hectare. The interconnectivity between owners, operators, vendors and agricultural consultants help farmers increase their productivity and efficiency.
The sensors on their equipment help farmers to manage their vehicles and tractors, reduces time usage while saving fuel. The information is combined with historical and meteorological data, or data relating to ground conditions.

 

Limiting the effects of natural disasters and epidemics: 
Connected Robots can help limit the effects of natural disasters. Still in development pharse, future robots, connected IoT technologies and control mechanisms as SORMAS of SAP could reduce the impact of epidemics such as Ebola. We all know when the powerful earthquake in March 2011 triggered a tsunami that devastated Japan’s Fukushima-Daiichi nuclear plant and raised radiation to alarming levels, authorities contemplated sending in robots first to inspect the facility, assess the damage and fix problems where possible. Ever since, Defense Advanced Research Projects Agency (DARPA), an agency under the U.S. Department of Defense, has been working to improve the quality of robots. It is now conducting a global competition to design robots that can perform dangerous rescue work after nuclear accidents, earthquakes and tsunamis.

 

Several obstacles:
The future looks bright, but there are still many obstacles to overcome. The implementation cost of the IoT infrastructure is very high, and the investments will likely come from outside. Moreover, the hacking risk is a major threat. In addition, it is imperative to deploy training programs to educate and enable it to exploit the opportunities offered by this new technology.

 

The overall connectivity is essential. For now, many African nations are lagging behind in this area. The lack of infrastructure, however, can be beneficial for Africa. Instead of incremental updates techniques, the continent can directly jump into the wagon of new technologies in a way that is not possible for developed countries.
The Internet of Things happen in Africa, and African companies cannot ignore this novelty. Also, be prepared to face challenges in terms of security, and be able to articulate the return on investment are two key points to enjoy this new boom.

 

Sources:
World Population Prospects
What’s driving Africa’s growth
Lions go digital: The Internet’s transformative potential in Africa

Artificial Intelligence Techniques to detect Cyber Crimes

Artificial Intelligence Techniques to detect Cyber Crimes

When we talk about artificial intelligence, many imagine a world of science fiction where robots dominate. In reality, artificial intelligence is already improving current technologies such as online shopping, surveillance systems and many others.

 

In the area of ​​cyber security, artificial intelligence is being used via machine learning techniques. Indeed, the machine learning algorithms allow computers to learn and make predictions based on available known data. This technique is especially effective for daily process of millions of malware. According to AV-Test statistics, security analysts must examine more than 400,000 new malicious programs every day.

 

Security experts affirms that the traditional detection methods (the signature-based systems) are no longer really proactive in most cases. The task is even more difficult as, in a world dominated by copy-paste exploit cloning, security vendors must also manage third-party services, and focus on detecting the obfuscated exploit variant, to be able to provide protection to their customers. Attackers are numerous, but the automatic learning balance the chances of struggle.

 

Applying Artificial Intelligence to cyber Security: More and more technology companies and security vendors are beginning to look for ways to integrate artificial intelligence to their cyber security arsenal. Many clustering and classification algorithms can be used to quickly and correctly answer the crucial question: “This file is it healthy or malicious?” For example, if a million files must be analyzed, the samples can be divided into small groups (called clusters) in which each file is similar to the others. The security analyst only has to analyze later, a file in each group and apply the results to others.

More importantly, machine learning gets a high detection rate for new malicious software in circulation as the famous ransomware malware and zero-day, and against whom, a security solution must be as efficient as possible. In order to be practical, each machine learning classifiers used for malware detection must be set to obtain a very small amount, preferably zero, of false positives. It is also a way to form with very large databases (using the graphics processor or parallelism).

The fundamental principle of machine learning is to recognize the trends of past experiences, and make predictions based on them. This means that security solutions can react more effectively and more quickly to new invisible cyber threats compared to traditional techniques and automated cyber-attack detection systems that were used before. Artificial Intelligence is also suitable to fight against sophisticated attacks such as APT (Advanced Persistent Threats), where attackers take special care to remain undetected for indefinite periods of time.

 

Man against the machine:  breaking the boundaries between man and machine, artificial intelligence is a very important cyber weapon, but cannot alone take on any fight against cyber threats. As I’ve mentioned in previous paragraphs, the machine learning systems can get false positives, the decision of a human is needed to sort algorithms with appropriate data.

Les algorithmes d’apprentissage automatique sont, dans l’ensemble, plus précis dans l’évaluation des menaces potentielles de malwares au sein de grandes quantités de données de renseignement, que leurs homologues humains. Ils savent aussi repérer plus rapidement les intrusions.

The machine learning algorithms are, overall, more accurate in assessing potential malware threats in large quantities of intelligence data, than humans. They also know how to quickly detect breach. The current hybrid approach that is generally used today is to oversee automatic learning by human analysts. This allowed better results so far.

 

Regarding the future of AI, it is almost impossible to predict the future. Who knows that may be next year, machine learning will most likely focus on the creation of specific profiles for each user. Where an action or a user’s behavior does not correspond to the predefined templates, the user will be informed. For example, a peak of downloads in a short time will be marked as suspect, and analyzed closely by a human expert.