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Artificial Intelligence: Just a Trend or A Long-Term Growth Factor?

Artificial Intelligence Just a Trend or A Long-Term Growth Factor

Artificial Intelligence is having an impact on our world in many different ways by playing an increasingly important role in our lives and worldwide economy. The number of companies that have integrated AI in their process is growing rapidly, in sectors like health-care, retail, HR, marketing and cybersecurity industry and the war for talent acquisition and competition for value creation is on the next level between industries and nations.

 

AI is not new or just a temporary trend, history tells us that humans and the companies are remarkably successful at working together with robotics to the latest technologies such as smart machines, including 3D printing, AR and VR in order to survive in competitive era and also find new opportunities for their companies’ growth. A study by MMC Ventures revealed that 60% of the interrogated cases, which represents 1,580 companies, AI is being used in their solutions and 40% of the self-proclaimed “AI companies” in Europe had discovered that AI was not used in their solutions.

 

AI is bringing its advantages in sectors like healthcare, transportation, cyber security industry, security industry, prediction of natural disasters, farming, smart-cities, and is helping to reinforce economy. A discussion paper created by McKinsey demonstrated how AI applications are contributing for social good. According to McKinsey’s analysis, AI initiatives are having a positive impact on good health and well-being, peace, justice and strong institutions, provide quality education, protect all life on land and below water, ending poverty, industrial innovation and infrastructure, reducing inequality, climate action, decent work and economic growth, gender equality, sustainable cities and communities and responsible consumption and production, increase work efficiency and output, offers accurate future predictions, detect frauds, avoid human errors with the help of increased automation and many more.

 

Artificial intelligence is certain ally and dramatically improving the efficiencies of our workplaces and increasing the work humans can do by taking all repetitive or dangerous tasks and the human workforce to do work they are better equipped for, such as, tasks that involve creativity and empathy among others. By doing work that is more engaging to them, job satisfactions, productivity and happiness among employees is also increasing.

 

AI in its many forms offers users the ability to do tasks on a scale and at a speed that humans cannot achieve on their own. AI can also gain new insights from analytical tasks. It’s not just about collecting large amounts of data (=bigdata) and investigative it with advanced mathematics or developing amazingly complex algorithms. Once the gathered data is well extracted, managed and good algorithms are chosen, our society will gain countless hours of productivity with just the introduction of well automated tasks.

 

Thus, it seems important to clear the fuss around AI destroying jobs because it’s only fiction rather than a fact. Instead of destroying jobs, AI is bringing a gradual evolution in the job market, people are getting better at their jobs with the help of AI. According to a report from the World Economic Forum (WEF) called “The Future of Jobs 2018, and recent report called “Job creation and elimination worldwide due to AI 2022” AI, Machines and algorithms in the workplace are expected to create 2.3 million jobs. Even who does the work is changing rapidly – human, robot, or co-bot. The division of labour between humans, machine and AI is shifting quickly. By 2025, it is expected to shift to 48% human, 52% machine or algorithm. The combination of human and machine is the new normal in the workforce of the future.

 

Technology helps move society forward, and the integration of AI is doing the same. The integration of AI into healthcare, smart cities, law and other service-based industries is helping in making processes simpler and giving them enough time to focus on other issues. In this increasingly data-driven world the importance and impact of artificial intelligence is only growing and is becoming more crucial in gaining competitive advantage. Therefore, despite all the positive effects and worldwide boom in artificial intelligence industry, companies shouldn’t forget that AI is only a tool that they need to update/improve continuously instead of one-time investment in order to achieve their goals.

 

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Top Strategies to Improve and Increase Data Quality

Top Strategies to Improve and Increase Data Quality

 

Organizations face enormous amount of pressure when it comes to face the issue related to data quality. Businesses can only make the right data-driven decisions if the data they use is correct. Without sufficient data quality, data is practically useless and sometimes even dangerous.

 

Regardless of whether your data is structured or unstructured or your data is on-premises or in the cloud, it needs to be on top to deliver business value by ensuring that all key initiatives and processes are fueled with relevant, timely and trustworthy data. Because bad data quality not only costs time and money, in the worst case, it even leads to significant revenue losses.

 

But despite its importance of having data quality, the reality in many of today’s organizations, data quality has been voted among the top three problems for BI software users every year since the first issue of The BI Survey back in 2002.

 

What is data quality?

Defining data quality depends on the needs of each organizations. It can differ from one business to another. As a poor quality of data, especially of customer data, quickly leads to serious problems, therefore for some organizations, it can be ensuring that customer contact data is up to date so that deliveries are received in a timely manner. For other organizations, it could be filling prospects profiles that can be helpful with marketing segmentation effort. Serval factors are being used to determine the quality of data, such as accuracy, completeness, relevancy, validity, timeliness and consistency.

 

Here below are few examples to clean up and improve the consistency and reliability of your data:

 

  • Understand the purpose of your data

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IT department should work with other departments of company to align and acknowledge the problems and negative impact that company can face because of missing or erroneous data. Even though a lot of data today are generated, companies must make a strategy about what data is been collected and for which purpose the gathered data can be used because the collected data should ultimately exist for a business or mission purpose.  For this purpose, they must work to identify incomplete, faulty or multiple existing customer data, because very often, in different departments, different inventory data exists for the same customers. So, paying attention to an error free data can lead to increase data quality.

 

  • Get a Data Control Supervisor from a Qualified Department

Data Control supervisors play a crucial role in the success of a data quality mission. They come from a specialist department and know how to oversee the development and use of data systems. They can discover efficient ways to organize, store and analyze data with attention to security and confidentiality. He is also responsible for creating and enforcing policies for effective data management, formulating management techniques for quality data collection to ensure adequacy, accuracy and legitimacy of data, devising and implementing efficient and secure procedures for data management and analysis with attention to all technical aspects. His goal is to ensure that information flows timely and securely to and from the organization as well as within.

 

  • Implement a priority list of undesirable data

Today many companies are using different equipment (IOT) that records vast volumes of sensor data. Unfortunately, not all the gathered data in company is valuable. Therefore, Data Control supervisor must perform quality checks in order to reject undesirable data. To do this, he must be able to respond to following questions: How and by whom was the data generated? Which users are accessing it? For what purposes are they used by which applications? Which costs cause faulty data?

 

  • Prevent duplicate data

Duplicate data refers to when the same information is somehow input as two separate entries by different people or teams

In the presence of duplicate data, it is very hard to pull out exact results or CRM and Marketing campaigns and can create serious issues when you’re creating automations, looking up buying patterns, or putting together a target audience. So, Data Control supervisors must make sure that company is using a data management software that regularly checks the data for duplicates and cleans it to ensure that their data is clean, has quality, and is reliable to work with.

 

  • Perform regular checks on your data to uncover anomalies

If you want to understand and ensure your data quality, you have to perform regular checks to see if there’s no “bad-data”. Reviewing your data will help you to understand if the gathered data aim for organisations objectivity. As getting 100% data accuracy is not the final objective, Data Control supervisors must be able to pull-up the insights from the data to it’s main goal. Improving data quality is an ongoing process and it takes time to get it right.

The Internet of Things and Big Data Management

 

 

A new survey from Juniper Researchand Gartner Insightson How to Lead in a Connected World, has revealed that in 2020, the total number of connected IoT, will exceed 50 billion devices compared to 21 billions of connected devices in 2018.

 

Organizations are using the vast amounts of data available from IOT diverse sources such as, wearable devices, smart sensors.With the huge increase in numbers of IOT devices, companies are facing unmanageable amounts of data. By using this data and rapidly analysing it, theyare makinginsightful decisions and usingthe information in a more targeted way to deliver better service, improvetheirbusiness processes, increase operational efficiencies, and last but not the least, grow revenues.in order to extract valuable information from the gathered data, they also have to come up with a strategy over data storage and protection. Society is reliant on the storage, process and transmission of data therefore ensuring the integrity, security and privacy of this data is fundamental.

 

The IoT devices can be located anywhere in the world and are part of every imaginable living and working environment. Our phones, computers, smart home, fridge, machines, cars are the existing proof of IoT.We are living in a world where IOThas formed a massive transformation in our way of living. Not only in our everyday life but wecan seea strong expansion of IoT in all industrial sectors. IoT networks devices are being used worldwide without restrictions.

 

As experts highlighted, with more than 20 billion connected things expected to be in use by 2020, organizations can be sure that cyber-criminals will be developing new ways in order to extract data that can be converted into money.Thus, organizations must also find new ways and update their traditional approaches and existing strategies to manage and protect their data.

 

In IoT,IT security remains a major technical challengeas all the devices and people are connected with each other to provide services at any time and at any place.Mostof thesedevices are not equipped with efficient security mechanisms and are vulnerable to various privacy and security issues e.g., confidentiality, integrity, and authenticity, etc. For the IoT, some security requirements must be fulfilled to prevent the network from malicious attacks. Herebelow aresome of the most required capabilities of a secure network.

 

      • Resilience to attacks: Most enterprises take years to build up their trustworthy reputation of a reliable organisation but overnight that reputation can be irrecoverably damaged by a cyber-attack or massive data leak.Therefore, the system should be capable enough to recover itself in case if it crashes during data transmission. For an example, a server working in a multiuser environment, it must be intelligent and strong enough to protect itself from intruders. In the case, if it is down it would recover itself without intimation the users of its down status.

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    • Data Authentication: Today, attackers can easily gain access to internal networks, whether at a remote branch or in the headquarters, by taking advantage of mobile devices and BYOD policies. They do this by posing as on-site contractors or launching phishing attacks against employees.Once attackers authenticate onto the network, they can connect to the applications used for IoT. With many enterprise architectures, cybercriminals can execute network-layer attacks — even if they are unauthorized to access the application — disrupting the service.Thus, the data and the associated information must be authenticated. An authentication mechanism is used to allow data transmission from only authentic devices.

 

    • Access control: Only authorized persons are provided access control. The system administrator must control access to the users by managing their usernames and passwords and by defining their access rights so that different users can access only relevant portion of the database or programs.

 

  • Client privacy: IoT data is mostly unstructured and can therefore easily be stored in the public cloud infrastructure. All major cloud vendors provide cost-effective, scalable storage systems based on object storage solutions. With fast networks and free data access, large amounts of enterprise IoT data can be optimally stored in the public cloud.Therefore, the data and information should be in safe hands. Personal data should only be accessed by authorized person to maintain the client privacy. It means that no irrelevant authenticated user from the system or any other type of client cannot have access to the private information of the client.

 

Clearly cyber-attacks are damaging and costly, however the true cost to an enterprise is in the damage to the trust the users place inproducts and the system. Not only can cyber-attacks damage individual businesses, the knock-on effect for the whole digital economy could be devastating. Due to the multiple layers of information flow for IoT devices, enterprises are under constant pressure and have to deal with the threats in these environments and the complex conditions that are constantly changing and evolving. Faced with these turbulent conditions, in order to ensure long term survival of the business, they must embrace agility and resilience to the core of any information assurance strategy.

 

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Machine Learning: Guardian of Cloud Data Protection

Data is knowledge and knowledge is power. In the progression of digitization, the amount of data that companies process daily is growing progressively. This is also reflected in the increased use of cloud applications in recent years.

 

Corporate data is accessible around the clock, enabling efficient workflows and, at least at first glance, less administrative operating cost for the IT department. However, for organizations, this outcome is only brought into perspective when they consider cloud usage for the security or their data. The associated risks and new threats require advanced and secured technologies to ensure the protection of corporate data.

 

Malware Threat – Target to Cloud Data 

 

As the currency of the digital age, the growing amount of data in the cloud has long since become a remarkably attractive target for hackers. A frequently used attack vector is currently the introduction of malware into cloud applications via social engineering, for example through spear phishing attacks. Securing data in cloud applications is still largely left to users. While some public cloud providers provide few basic protections to detect threats in the cloud. However, as the results of a recent Bitglass experiment have shown, their effectiveness is limited: as part of their current security study – tracking cloud infection- the Bitglass Threat Research team tested integrated malware protection of the popular enterprise cloud such as Google Drive and Microsoft Office 365 cloud applications. In collaboration with Cylance, it used a previously unknown form of ransomware called Shurl0ckr, a variant of Goijdue malware. ShurL0ckr is ransomware-as-a-service, meaning the hacker generates a ransomware payload and distributes it via phishing or drive-by-download to encrypt files on disk in a background process until a Bitcoin ransom is paid. Although the malware protection of Google and Microsoft already knew the related Gojdue ransomware, both applications did not recognize Shurl0ckr as malware. In the case of an unknown threat, the protective mechanism has failed.

SaaS apps contained malware

Many anti-malware mechanisms are still reactive and detect malware based on file properties stored in a database. The whole thing has to be something like a puzzle game: the malware protection checks whether the new malware fits into an existing puzzle game. In the present case it was like a puzzle piece where an edge or corner was slightly changed. Since it did not fully fit the existing “malware template”, it was considered safe, although it would have met the majority of the necessary properties. In the long run, if organizations won’t consider a security approach for this progressive professionalization of cybercriminals they risk having ever more sophisticated attacks.

 

Agile protection of cloud data with machine learning

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Many roads lead to the cloud – and there are many ways to inject and distribute malware. The multiplicity of users and accessibility, as well as increasingly sophisticated security threats, require a dynamic security approach that can make a far-reaching risk assessment and automatically apply appropriate policies. Machine Learning is currently the most effective approach to effectively protecting enterprise data on and off the cloud.

 

Machine learning algorithms are already being used in speech recognition software or in ERP systems for managing data. This technology is now also finding its way into cloud security solutions, such as Cloud Access Security Broker. Instead of risk assessing traditional signature-based solutions built solely on specific data profiles, machine learning uses in-depth property and behavior analysis and makes a decision that automatically applies the implemented policies. If a file is considered a likely threat, it can be blocked if users attempt to upload it to the cloud or download it to a device. In this way, Machine Learning provides a holistic approach to enterprise data across all enterprise cloud applications and provides advanced threat control capabilities.

 

For example, if a user downloads a malware-infected file from a Web site, saves the file to the cloud and creates a potential corporate vulnerability, it will be automatically detected and marked. Machine learning solutions continuously monitor all files and applications in the cloud. They automatically check every upload and download of files for malware. Once the malware risk has been reported through machine learning protection and eliminated by the security team, the solution automatically grants users full write access. In this way, the solution provides security, but at the same time ensures a high degree of user-friendliness, since no interruption of the work processes is required.

 

Data-driven cloud guard

 

For cloud applications, machine learning algorithms are ideal, since large amounts of data are the most important prerequisite for their reliability. Most algorithms do not operate data-efficiently if only a small amount of data is available, they lack the necessary experience, in a specific case to make the right decision and to apply the appropriate directive. People only need to look at an object once – for example, a laptop – in order to be able to recognize it as such in the future in a modified form. Machines, on the other hand, require a wealth of experience, that is, dealing with many laptops in order to reliably identify. For example, machine learning solutions that receive less data are not as “intelligent” as solutions that handle a high volume of data from different environments. The more files that are analyzed and the more malware detected, the better the accuracy.

 

Thus, the use of machine learning marks the logical response to the growing amount of data and the changing security situation through cloud usage. Likewise, the automation of security mechanisms is the next step in the digitization process.

 

While malware is not a new threat, many companies fail to defend against its modern forms; relying solely upon endpoint or native cloud security is no longer adequate. Organizations must now adopt cloud solutions that defend against known and unknown malware as they are uploaded to applications, downloaded to devices, and resting in the cloud.

The #BigData Evolution and Revolution in 2017

Big data, a buzz word of overloaded information, gathers a set of technologies and practices for storing large amount of data and analyse in a blink of an eye. Nowadays, Big data is shaking our ways of doing business and the ability to manage and analyse this data depends on the competitiveness of companies and organisations. The phenomenon of Big Data is therefore considered one of the great IT challenges of the next decade.

 

4 major technological axes are at the heart of the digital transformation:

 

  • Mobile and Web: The fusion of the real and virtual worlds,
  • Cloud computing: The Web as a ubiquitous platform for services,
  • Big Data: The data revolution,
  • Social empowerment: The redistribution of roles.

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Interconnected and feeding each other, these 4 axes are the foundations of digital transformation. Data, global or hyperlocal, enables the development of innovative products and services, especially through highly personalised social and mobile experiences. As such, data is the fuel of digital transformation.

The intelligent mobile terminals and the permanent connectivity form a platform for social exchanges emergence new methods of work and organisation. Social technologies connect people to each other, to their businesses and to the world, based on new relational models where power relations are profoundly altered. Finally, cloud computing makes it possible to develop and provide, in a transparent way, the information and services needed by users and companies.

According to Eric Schmidt, Chairman of Google, we are currently creating in two days as much information as we had created since the birth of civilisation until 2003. For companies, the challenge is to process and activate the available data in order to improve their competitiveness. In addition to the “classical” data already manipulated by companies and exploited by Business Intelligence techniques, there is now added informal data, essentially stemming from crowdsourcing, via social media, mobile terminals and, increasingly via the sensors integrated in the objects.

 

Why Big and why now?

 

3 factors explain the development of Big Data:

    • The cost of storage: the latter is constantly decreasing and is becoming less and less a relevant criterion for companies. Cloud computing solutions also allow for elastic data management and the actual needs of enterprises.
    • Distributed storage platforms and very high-speed networks: with the development of high speed network and cloud computing, the place of data storage is no longer really important. They are now stored in distinct, and sometimes unidentified, physical locations.
    • New technologies for data management and analysis: among these Big Data-related technological solutions, one of the references is the Hadoop platform (Apache Foundation) allowing the development and management of distributed applications addressing huge and scalable amounts of data.

 

These 3 factors combined tend to transform the management and storage of data into a “simple” service.

 

Sources of Data: 

 

To understand the phenomenon of Big Data, it is interesting to identify the sources of data production.

 

    • Professional applications and services: these are management software such as ERP, CRM, SCM, content and office automation tools or intranets, and so on. Even if these tools are known and widely mastered by companies, Microsoft has acknowledged that half of the content produced via the Office suite is out of control and is therefore not valued. This phenomenon has experienced a new rebound with the eruption of e-mail. 200 million e-mails are sent every minute.
    • The Web: news, e-commerce, governmental or community-based websites, by investing the Web, companies and organizations have created a considerable amount of data and generated ever more interactions, making it necessary to develop directories and search engines, the latter generating countless data from users’ queries.
    • Social media: by providing crowdsourcing, Web 2.0 is at the root of the phenomenal growth in the amount of data produced over the past ten years: Facebook, YouTube and Twitter, of course, but also blogs, sharing platforms like Slideshare, Flickr, Pinterest or Instagram, RSS feeds, corporate social networks like Yammer or BlueKiwi, etc. Every minute, more than 30 hours of video are uploaded to YouTube, 2 million posts are posted on Facebook and 100,000 Twitter tweets.
    • Smartphones: as the IBM specifies, the mobile is not a terminal. The mobile is the data. There are now 4 times more mobile phones in use than PCs and tablets. A “standard” mobile user has 150 daily interactions with his smartphone, including messages and social interactions. Combined with social media and Cloud Computing services, mobile has become the first mass media outlet. By the end of 2016, Apple’s App Store and Google Play had over 95 billion downloaded apps.
    • IOT: mobile has opened the way to the Internet of Things. Everyday objects, equipped with sensors, in our homes or in industry, are now a potential digital terminal, capturing and transmitting data permanently. The industrial giant General Electric is installing intelligent sensors on most of its products, from basic electrical equipment to turbines and medical scanners. The collected data is analysed in order to improve services, develop new ones or minimise downtime.

 

Data visualization:

 

An image is better than a big discourse … Intelligent and usable visualization of analytics is a key factor in the deployment of Big Data in companies. The development of infographics goes hand in hand with the development of data-processing techniques.

 

The data visualization allows to:

 

    • show “really” the data: where data tables are rapidly unmanageable, diagrams, charts or maps provide a quick and easy understanding of the data;
    • reveal details: data visualization exploits the ability of human view to consider a picture as a whole, while capturing various details that would have gone unnoticed in a textual format or in a spreadsheet;
    • provide quick answers: by eliminating the query process, data visualization reduces the time it takes to generate business-relevant information, for example, about the use of a website;
    • make better decisions: by enabling the visualization of models, trends and relationships resulting from data analysis, the company can improve the quality of its decisions;
    • simplify the analyzes: datavisualizations must be interactive. Google’s Webmaster tools are an example. By offering simple and instinctive functionality to modify data sets and analysis criteria, these tools unleash the creativity of users.

 

Big Data Uses: 

 

The uses of Big Data are endless, but some major areas emerge.

 

Understand customer and customize services

This is one of the obvious applications of Big Data. By capturing and analyzing a maximum of data flows on its customers, the company can not only generate generic profiles and design specific services, but also customize these services and the marketing actions that will be associated with them. These flows integrate “conventional” data already organized via CRM systems, as well as unstructured data from social media or intelligent sensors that can analyze customer behavior at the point of purchase. Therefore, the objective is to identify models that can predict the needs of clients in order to provide them with personalized services in real time.

 

Optimize business processes

Big Data have a strong impact on business processes. Complex processes such as Supply Chain Management (SCM) will be optimized in real time based on forecasts from social media data analysis, shopping trends, traffic patterns or weather stations. Another example is the management of human resources, from recruitment to evaluating the corporate culture or measuring the commitment and needs of staff.

 

Improve health and optimize performance

Big Data will greatly affect individuals. This is first of all due to the phenomenon of “Quantified Self”, that is to say, the capture and analysis of data relating to our body, our health or our activities, via mobiles, wearables ( watches, bracelet, clothing, glasses, …) and more generally the Internet of the Objects. Big Data also allow considerable advances in fields such as DNA decoding or the prediction of epidemics or the fight against incurable diseases such as AIDS. With modeling based on infinite quantities of data, clinical trials are no longer limited by sample size.

 

Making intelligent machines

The Big Data is making most diverse machines and terminals more intelligent and more autonomous. They are essential to the development of the industry. With the multiplication of sensors on domestic, professional and industrial equipment, the Big Data applied to the MTM (MachineTo Machine) offers multiple opportunities for companies that will invest in this market. Intelligent cars illustrate this phenomenon. They already generate huge amounts of data that can be harnessed to optimize the driving experience or tax models. Intelligent cars are exchanging real-time information between them and are able to optimize their use according to specific algorithms.

Similarly, smart homes are major contributors to the growth of M2M data. Smart meters monitor energy consumption and are able to propose optimized behaviors based on models derived from analytics.

Big Data is also essential to the development of robotics. Robots are generating and using large volumes of data to understand their environment and integrate intelligently. Using self-learning algorithms based on the analysis of these data, robots are able to improve their behavior and carry out ever more complex tasks, such as piloting an aircraft, for example. In the US, robots are now able to perceive ethnic similarities with data from crowdsourcing.

 

Develop smartcities

The Big (Open) Data is inseparable from the development of intelligent cities and territories. A typical example is the optimization of traffic flows based on real-time “crowdsourced” information from GPS, sensors, mobiles or meteorological stations.

The Big Data enable cities, and especially megacities, to connect and interact sectors previously operating in silos: private and professional buildings, infrastructure and transport systems, energy production and consumption of resources, and so on. Only the Big Data modeling makes it possible to integrate and analyze the innumerable parameters resulting from these different sectors of activity. This is also the goal of IBM’s Smarter Cities initiative.

 

In the area of ​​security, authorities will be able to use the power of Big Data to improve the surveillance and management of events that threaten our security or predict possible criminal activities in the physical world (theft, road accidents , disaster management, …) or virtual (fraudulent financial transactions, electronic espionage, …).

#CloudComputing: Fix The Present Before You Plan The Future

Cloud computing is leading to a major transformation in the terms of digital technology by companies in all economic sectors. The associated challenges relate not only to activity and job creation among digital players, but also to a competitive gain that can be realized by all user companies.

 

The cloud computing model consists of providing remote and on-demand computing resources, infrastructure, platforms or application software. The advantages in terms of cost reduction and ease of access lead to this rapid adoption, which results in a gradual but decisive change in the information systems, activities and related markets.

 

However, complexity and lack of integration is slowing down companies’ adoption of the cloud, according to a study conducted by Oracle on the EMEA area. The wide gap between central IT and the rest of the organization is directing many companies towards a bad approach of the cloud.

 

While many European companies are adopting the Cloud Computing, nearly half of them are facing difficulties due to increased integration costs and data storage. One of the main reasons for this situation is that more than 60% of a company’s total IT spending is now directly managed by the different business units, instead of IT department, which prevents companies from benefiting from cloud services to which they subscribe to. To avoid these problems, IT department must be the one responsible for providing the funds to keep other departments running. Because the budget is an important tool for identifying and executing the IT initiatives that are crucial to each department, therefore it should be well discussed between IT department together with CIOs.

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Study also revealed that organizations continue to finance their IT investments without taking into account potential revenue and innovative projects: 2/3 decision-makers claim that funding their IT is too traditional and penalizes innovation, and 1/3 decision-maker admit that the IT funding models of their organization are hindering IT innovation. As IT budget can be divided across various categories, depending on the complexity and sophistication of your company/department and its structure, it must reflect benefits of IT strategy. For example, if you’ve been communicating a strategy of migrating to the cloud and highlighting the operational savings, you should reflect those advantages and use them as justification for budget allotment.

 

Companies need to rethink their IT financing models and undertake a profound cultural transformation in order to fully exploit all the benefits of the cloud. 33% of respondents say that an inadequate model of IT funding is currently penalizing their business. 33% are also convinced that their company’s IT culture is insufficient for the cloud age. As a result, 72% of respondents say that a new cloud financing model will allow IT to offer more cloud services to the company, and 70% say it will allow the company to reduce its costs.

 

Problems that companies are facing in cloud computing adoption are less about technology but it’s about the difficulties of synchronization between the different business units. Managers of each department are increasingly making cloud purchasing decisions without involving the CIO or the IT department advice, especially because these purchases are very easy to make. So to be successful with digital business transformation and optimization, CIOs and leaders must brainstorm and communicate the strategy to allow IT spending and functional resource costs to be connected to business processes, outcomes and goals. By developing multiple views of the IT budget and resource allocations per department they can provide a better IT service supply on demand.

Managing Data Traceability: Impact and Benefits

Data is at the heart of digital transformation. A company can’t support data lacking integrity if they aim to advance in their digital transformations initiatives.  The integrity of the data lies primarily in the confidence that users can have in the latter. Most of this trust rests on the traceability of data. In the absence of traceability, it is not possible to know if these data are trustworthy.

 

Data traceability is a concept that companies have been trying to understand for some time.  You might be asking for which reasons do today’s companies need more traceability? Well with a large amount of data from unmanaged external sources (sensors, data streams, Internet of objects), it is essential, for companies, to monitor these data when they are collected, processed and moved to be able to use them effectively. Digital transformation requires higher levels of data integrity. Indeed, companies need to have better data, which can be a basis they can trust.

 

Previously, data traceability was based on two dimensions: “where” and “how”. The need for better analysis and exploitation of the data leads to new demands and extends the definition of data, adding the following dimensions: “what”, “when”, “why” and “who”. Faced with these new requirements, it is necessary to master the bases of the primary components of the type: “where” and “how, especially as regards the impact and the value to be realized.

 

The “where” component of traceability focus on the origin of the data. The “how” component focus on how the data source was manipulated to produce its result. It is also possible to refine these two types of dimension via their level of granularity: “low” granularity and “high” granularity. The “where” component at the “low” granularity level focus on defining an upstream output dataset at the point of consumption to understand which dataset have been used to produce a result. The “how” component of the “low” granularity level focus on the transformations applied to the set of data source to produce the output dataset. On the other hand, “high” granularity level of traceability is concerned with the values ​​of data in the “low” level granularity: instead of where they were created and how they were modified to produce the result.

 

An example will better illustrate the types and granularities of traceability. Let’s take an accounting report, showing the total amount paid to suppliers over a given period. The “where” component of the “low” granularity level would trace all output data from the source invoice to the supplier tables from the accounting application. The traceability component “how” of “low” granularity level would look at how the supplier and billing tables were linked together with the calculation functions that were performed on the billing table to produce the total amount paid to each supplier. Traceability “where” at the “high” granularity level could (to search for the amount paid to a vendor) trace back to the invoices provided by the supplier. In order to take interest in the entire process, traceability at the “high” granularity level could also link to the original request: the purchase order, the receipt operations, in addition to the payment approvals.

 

Benefits of using data traceability

 

Declined in many forms, traceability can provide many benefits in terms of impact and added value to the companies that implement it. Such as:

  • Governance: Ensure the traceability of upstream data to provide owners and data sources with quality and access control results. This will allow data owners to manage their procedure in addition to downstream traceability (integrated with a corporate glossary, data traceability can allow data managers to control current definitions and understanding of terms and fields of data).
  • Conformity: Provide regulatory authorities with information to govern data sources, users and their behavior.
  • Change Management: Enabling users and developers to understand the impact of modifying certain data on downstream systems and reports.
  • Development of Solutions: Improve design, testing and deliverables of better quality. This is achieved through the sharing of traceability metadata, glossaries, and relationship among distributed development teams.
  • Storage Optimization: Provide as an input to archive decisions and provisions, an overview of the data being accessed and indicate where, how often and by whom access is permitted.
  • Data Quality: Improvement of the quality scores defined by the application of business rules and standardization on data, added to the metadata population as input of algorithms and decision making.
  • Problems Resolution: Helps in the analysis of root causes in repair-type processes.

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Traceability also brings a deeper advantage such as focus on the changed values ​​of the basic data entities that are shared between processes, services, and applications. For example, the impact that a change in, position, service, address or even the employer of a contact might have on the marketing, business or maintenance service of a business. According to the “U.S. Bureau of Labor Statistics,” an employee has on average 11 different employers throughout his career. Taking into account the speed at which US residents move and change their professions each year, the potential change in baseline data may be important in light of the adequacy of these statistics to the population of basic data within a company. The ability to collect, validate, distribute and track these changes in a timely manner could lead to better protection of existing revenue streams and the ability to capitalize on new revenue in B2B or B2C business relationships.

So, the companies which take advantage of traceability, are able to find data faster and are better able to support security and privacy requirements.

IT Challenge n°2: Rise of new Partnership Models

 

 

2020 companies will be totally interlinked organizations within an ecosystem in which new strategic partnerships and associations will be formed, as well with customers, suppliers and competitors!

 

A profound transformation of the ecosystem

The growth of value creation is a major trend in digital era. We witness more and more companies opening up, thanks to the multiplication of the interactions allowed by cloud, data repositories, connected objects … This condition requires companies to rethink their business partnership strategies within their ecosystem in order to succeed in the age of digitization.

This ecosystem is very extensive, with an interesting diversity of actors, such as, GAFA (Google, Apple, Facebook, Amazon), start-ups, innovative SMEs, communities, customers, employees, self-entrepreneurs, suppliers, public and local authorities and political institutions… In the era of “co-something”, a company can no longer succeed alone in its market, particularly because of the rapid emergence of new business models, competitors “out of nowhere” and an accelerated renewal technology.

 

The challenges: anticipate and ally

Controlling the ecosystem depends on anticipating the evolution of it’s different actors, to be noted that actors in the traditional IT are not necessarily those of the current ecosystem, nor, of tomorrow. Some will disappear or merge, others will emerge, many will become partners.

Establishing a good relationship with the right partner, which can be a supplier, requires joint sharing of opportunities and risks, commitment to common goals, and shearing value. And this sharing of value aims to bring something new and positive to the partners, and ultimately to help them grow. Strategic partnerships can be established when there are common objectives for value creation. With this perspective, the partnership is strategic and is quite different from the traditional customer-supplier relationship (even major), in which the parties are bound by a contract for the providing services.

The objectives of each party must be the same and the balance of the relationship arises precisely because of that different but valuable opinions and ideas.

 

Challenges: Negotiation and Confidence

  • Collaborate: one of the typical challenges of partnerships will be to manage the paradox between internal resources (including CIOs) that are experiencing difficulties and struggles, collaborating and, on the other hand, the market, which requires close collaboration to better innovate.
  • Dialogue: companies are confronted with a cultural interoperability challenge in order to engage all the actors involved, even if they do not share a common language.
  • Establishing trust: a partnership relation is always based on trust. Thus, it is not a question of “collaborating to collaborate”, but of collaborating to win together, in order to create communities that engage clients and collaborators.

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R-Link is the result of a long-term partnership between Renault and Tom-Tom. R-Link is an integrated multimedia tablet, driven by a tactile control or an advanced voice command. It combines, the various functions related to multimedia in the car such as, navigation, radio, telephony, messaging, well-being, eco-driving. Renault’s interest in combining with Tom-Tom was to increase the value for its customers, to know them better and to improve the level of service.
This example illustrates the idea of a service platform: Renault added services to its products by developing the customer experience.

 

To conclude, I’ll say that the success of these new partnership model depends only on the business taking much greater role in designing, building, and exploiting the technology, platforms, and data it needs to succeed. Overcoming challenges of traditional IT management is a step forward of bringing IT closer to its true mission and succeeding in all IT collaborations.

#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.

#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.

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