The Big Data Experts generally agree that the amount of data generated will grow exponentially. A recent report by independent analyst IDC predicts that the global data set will reach around 175 zettabytes by 2025. What is the reason for this growth? There is a steady rise in Internet users conducting their lives online, from business communications to shopping to social networking. IDC estimates that within five years, 75 percent of the world’s population will be interacting with online data daily. And it’s not just people driving the growth, as billions of connected devices and embedded systems are now helping to transform the new science of IoT-Form data analysis.

Data analysis has come a long way in a short time. Understanding what can be achieved with data has evolved, as has the maturity of the tools that leverage it. As a result, their value increases in innovative and exciting new ways. Entirely new avenues of data science are opening up, from IoT analytics and advanced analysis of large amounts of data to DataOps.

Diverse Areas Of Application For Analytics

Online retailers can already use analytics to follow the customer journey from initial interest to purchase decision. Each step of the trip is quantifiable and measurable in one way or another. A single customer’s data becomes part of a larger dataset composed of the preferences of thousands of consumers. Analytics professionals leverage the latest software platforms to uncover insights for a more targeted and relevant customer experience.

Modern analytics’s value lies in the important unveiling of information present in the data but was previously inaccessible or invisible. This breaks up and changes the dynamics of an otherwise fixed market. Gartner cites the example of banks and their focus on wealth management services. The traditional view here has been that older customers are likely to be most interested in these products. However, with advanced analysis, the banks found that younger customers, aged 20 to 35, are more likely to use such services. Thorough analysis removed distortion and wrong thinking in one fell swoop.

An even more recent example of the power of analytics is the scientists and researchers working worldwide to find a cure for COVID-19. Scientific computing platforms not least support this vital work. Such platforms accelerate progress, from data analysis to simulation and visualisation to AI and edge processing.

Supercomputers And GPUs As A Basis

For example, Oxford Nanopore Technologies was able to sequence the virus’s genome in just seven hours using fast graphics processors. Using GPU-accelerated software, the US National Institutes of Health and the University of Texas could generate a 3D structure of the virus protein using cryogenic electron microscopy. GPU-driven AI accurately classified COVID-19 infection rates based on lung scans, speeding up treatment plans. And in drug development, Oak Ridge National Laboratory used an InfiniBand-connected, GPU-accelerated supercomputer to study a billion potential drug combinations in just 12 hours.

In developing even faster and more powerful analytics, standards and limits are constantly being broken. One of the most important benchmarks in data analysis is called TPCx-BB. The value includes queries that combine SQL with machine learning on structured data with natural language processing and unstructured data, reflecting the diversity of modern data analysis workflows.

The record for TPCx-BB performance was recently surpassed by almost 20x with the RAPIDS suite of open-source data science software libraries based on 16 NVIDIA DGX-A100 systems. The benchmark was completed in just 14.5 minutes, compared to a previous best result of 4.7 hours on a CPU-powered computer cluster.

Accelerated visualisation solutions that span terabytes of data have applications in other areas of science as well. For example, NASA has used the technology to visualise the landing of the first human-crewed mission to Mars interactively and in real-time in the world’s largest volume rendering.

With the digital transformation, data is now the beating heart of every company. But only with the right technology can these organisations determine which data matters most, unlock the most important insights from that data, and decide what actions to take to leverage that data.

Also Read: Big Data: Definition, Challenges And Applications

The post Big Data Is Bigger Than Ever appeared first on Tech Cults.

There is no end to the flood of data in the corporate environment. The efficient evaluation of the available information is increasingly becoming a competitive advantage or even leading to entirely new business models. The pitfalls are diverse and widespread: According to current studies, more than half of the managers responsible do not even know what kind of data their own company collects.

There are also several obstacles to be overcome on the technical side. Legacy environments were often completely geared towards structured data and are now overwhelmed with the deluge of unstructured data. Existing information treasures are usually stored in data silos and can only be lifted with great effort or not at all. A modern extensive data infrastructure requires scalable computing, storage and network capacities, powerful analysis tools and the appropriately qualified specialist staff to ensure the smooth interaction of all factors. All of this generates considerable costs, which medium-sized companies, in particular, cannot always bear.

BDaaS Provides The Way Out

Cloud-based Big Data as a Service platform (BDaaS) can help. They not only provide the necessary storage space for large amounts of data but also offer the necessary analytics solutions at the same time. This has several advantages for the user: It can be set up quickly compared to setting up a corresponding infrastructure on-premises. In addition, both implementation and ongoing operation are significantly cheaper than providing and maintaining the necessary technology yourself. Usage-based fees have been established.

Customers only pay for their resources, such as storage space or computing power. Last but not least, this ensures planning security and prevents unnecessary expenses that would be incurred, for example, by providing additional storage space or additional servers in your own data centre. In addition, the costs can be calculated, which ensures planning security. In this context, there is another advantage: The architecture of the BDaaS offers are usually designed to be highly scalable – they grow with the respective company’s requirements.

In addition to maintaining their environments, the BDaaS providers usually also ensure that compliance and data protection regulations are observed. This is an important point, especially for small and medium-sized companies. You can quickly start analyzing large amounts of structured and unstructured data without taking incalculable risks.

Booming Market

The constantly increasing demand for powerful analysis platforms also drives the BDaaS market. According to market researchers from Mordor Intelligence, the market volume last year was 13.21 billion US dollars. By 2026, the experts expect a magnitude of 52.75 billion US dollars. This would correspond to a compound annual growth rate (CAGR) of 26.2 per cent.

The USA is still the most important market since most BDaaS providers are there. In addition, the regional acceptance of extensive data services and analytics is very pronounced there. On the other hand, Europe, Asia, and Oceania are particularly well represented in potential growth markets.

The post The Most Popular Big Data As Service Providers appeared first on Tech Cults.

However, statistics have often exploited new, massive data sources in recent years, commonly called big data. This has meant that the distance between digital technologies and social research has drastically shortened. But trying to unite these two areas involves a discussion about the nature of both. A debate often slips precipitously towards drastic and pessimistic scenarios, such as the “crisis of empirical sociology” or the fear that data scientists may soon replace sociologists, anthropologists, economists, and even psychologists.

Will The Data-Driven Approach Mean The End Of The Theory?

For some, big data is not just a powerful tool but a fundamental paradigm shift. The use of big data is considered by many to be a new form of empiricism. According to this point of view, it would be obsolete and useless for a scientist to start from a hypothesis, verify them through experiments, and finally formulate a theory; on the contrary, adopting a data-driven approach, one could begin to directly from the study of the data, leveraging their quantity, allowing the algorithms to find the theory that best describes the phenomenon in question.

This kind of process would represent a kind of Copernican counter-revolution on the scientific method. The approach is no longer the starting point but, on the contrary, the final achievement of the given analysis process. This epistemological shift reduces the importance of causality to a significant correlation, enough to formulate a theory in a big data-driven approach.

Work On Data And Not With Data

Obviously, this is only a scenario, and it is by no means certain that the paradigm shift will take place, and from a theoretical point of view, it is not even clear how to frame it. This new empiricism represents the full positivist (and post-positivist) achievement for many scientists: the realization of a project of social control and prediction made possible by the valuable amount of data available.

On the other hand, I am not confident that the advent of big data will erase, with a single stroke of the sponge, all the problems of the social sciences on the quantification of human behaviour.

Working on big data could help solve methodological problems that characterize quantitative studies, such as social desirability, the interviewer effect, or the economic sustainability of surveys. Working on big data and not with big data could therefore be a viable path. A subtle but substantial difference is that the social researcher exploits the information potential of big data without negotiating its crucial role in the process.

A New Digital Epistemology For Social Research Is In Step With The Times

Sociology has undoubtedly recognized the potential of digital media and big data, adopting a perhaps too pessimistic point of view, as in critical digital sociology, sometimes adopting a too “idyllic” point of view, thinking of new digital media as the answer to all the open questions posed by traditional sociology. Both of these points of view are likely too drastic and could therefore be misleading.

What is certain is that the progressive use of big data in social research must, however, be supported by a plural and interdisciplinary epistemological framework, which allows to include new data and tools within the different paradigmatic traditions that coexist in the social sciences. To affirm this “digital epistemology”, researchers must also adopt a unique methodological point of view, trying to exploit the advantages that digital techniques entail, alongside digital methods with traditional ones, both qualitative and quantitative.

The post Big Data And Social Research: Methodological And Theoretical Implications appeared first on Tech Cults.

What Is Big Data?

Big Data: The Simple Definition

With the development of new technologies, the Internet, and social networks, the production of digital data is constantly growing. The expression “Big Data” (translated into French by “mégadata” or “massive data”) designates the heterogeneous mass of digital data produced by companies and individuals whose characteristics (huge volume, diversity of form, speed of processing ) require increasingly sophisticated specific IT storage and analysis tools.

Where Does Big Data Come From?

The digital data produced comes in part from devices connected to mobile telephone networks and the Internet.

Thus, smartphones, tablets and computers transmit data relating to their users during the following actions: emission of GPS signals from smartphones, internet browsing, use of search engines, messages left on social networks, download and use of applications, an online publication of photos and videos, purchases on e-commerce sites, etc. Likewise, bank cards transmit data when used for withdrawals or payments, for example.

The intelligent objects connected transmit data on the use made of some consumer’s everyday objects (e.g., for a car, chip connected indicating the route and the distance travelled and the average speed).

Apart from connected devices, Big Data data comes from a wide variety of sources: demographic data, climate data, scientific and medical data, energy consumption data, data from transport networks, frequentation of public places, etc. An important new source of data: open data, i.e. data sharing of the state, public institutions and communities.

All of this data provides information on the users of the devices, their movements, their centers of interest, their consumption habits, their hobbies, their projects, etc. But also information on how infrastructure, machines and devices are used.

With the constant increase in the number of Internet and mobile phone users, the volume of digital data is growing dramatically.

Also Read: How And Why Companies Use Big Data And Analytics

Big Data Analysis

What Is Big Data Analysis?

Innovative storage solutions (cloud computing, hybrid supercomputers, etc.) coupled with software using sophisticated computer algorithms allow the analysis of these large volumes of digital data. These tools are designed to detect relevant information and establish correlations between it. If data analysis, also called “Data Mining,” already existed in many companies, this activity took on a new dimension with the arrival of Big Data. Today we speak of data science.

One of the current challenges of Big Data is the development of complex tools making it possible to process and better visualize, analyze and catalogue vast flows of data.

What Is Extensive Data Analysis Used For?

This analysis allows, for example:

• Understand the needs of individuals and the constraints of users;
• To adapt infrastructures, networks and services (in particular public services) according to their use;
• To assist the decision-making of the various economic actors (companies, administration);
• Analyze and anticipate consumer behaviour (predictive analysis);
• To facilitate the evaluation of services;
• Improve the use of machines and devices (improvement of performance, prevention of breakdowns, maintenance).

Concrete Applications Of Big Data

In the health sector, for example, Big Data promotes preventive and personalized medicine. Thus, the analysis of Internet users’ searches on a search engine has already made it possible to detect the arrival of an influenza epidemic more quickly. Shortly, connected devices should enable the continuous analysis of patient biometric data.

In the transport sector, the analysis of Big Data data (data from public transport passes, geolocation of people and cars, etc.) makes it possible to model populations’ movements to adapt infrastructures and services. (timetables and frequency of trains, for example).

In energy management, data analysis from Big Data is used to manage complex energy networks via intelligent grids that use computer technologies to optimize the production, distribution, and consumption of electricity. ”electricity.

Likewise, data analysis from sensors on aircraft (flight data) combined with weather data makes it possible to modify flight lanes to achieve fuel savings and improve aircraft design and maintenance.

There are concrete uses of Big Data in many other fields: scientific research, marketing, sustainable development, commerce, education, leisure, security, etc.

Also Read: Will Big Data Also Revolutionize The World Of Drugs?

Should We Be Afraid Of Big Data?

The use of Big Data is strictly regulated. In France, the operators involved in collecting and analyzing data are subject to the supervision of the National Commission for Informatics and Liberties ( CNIL ). The Data Protection Act regulates the use of personal data. This law specifies that personal data must be collected and processed with a specific objective: only relevant data for a defined use can be collected. The law also recognizes the right of everyone to be informed about the collection and use of their data. In principle, each person can decide for himself the use of the data concerning him.

Therefore, big data is subject to the requirements of the CNIL, and its uses are directly concerned by the legislative framework in force.

Note: new European regulation on protecting personal data will come into force on May 25, 2018. This regulation provides for increased transparency on the use of personal data collected. In particular, new obligations will be imposed on operators collecting personal data: they will have an obligation to ensure the consent of individuals (and to prove it) for the collection and processing of their data. Data. They will also have to put all the necessary devices to secure this data against risks such as loss, theft or even disclosure.

Right To Be Forgotten And The Internet

The Court of Justice of the European Union obliges search engines to implement a “right to be forgotten,” i.e. a deletion of personal data at the request of users.

The challenges of Big Data in the banking sector

Data To Improve Customer Relations

The banks’ Big Data strategy aims to improve their customers’ knowledge and establish a closer link to respond more appropriately to their needs (customer satisfaction).

Concretely, this involves the immediate personalization of the services and products offered by using the data sources to which the customer has authorized access. We talk about “predictive marketing.”

Certain banking products (for example, a mortgage loan offer ) can thus be highlighted on the bank’s website during its consultation by the client according to his projects identified thanks to Big Data (for example, a project of acquisition of real estate identified because the client has consulted real estate ad websites).

Customer satisfaction is also improved thanks to the adaptation of communication processes depending, in particular, on the customer’s use of social networks. Therefore, big data is part of banks omni channel communication strategy to adapt to their customers’ communication habits and preferences.

Also Read: The Influence Of Big Data On Search Engine Optimisation

The Fight Against Bank Fraud Thanks To Big Data

In the fight against credit card fraud, thanks to Big Data, the banks aim to cross-check the information related to a request for payment authorization by credit card with the customer’s history and buyer profile. And its activity on social networks (which can allow it to be geolocated in particular). In case of doubt, additional authentication would then be necessary.

Big Data And Personal Data Protection

In line with the requirements formulated by the CNIL, companies wishing to use Big Data technologies must respect certain principles in the use of personal data:

• Bank transparency on the use of stored and analyzed data;
• Respect for confidentiality and privacy (the data remains internal to the bank and is not subject to commercial processing);
• Development of sophisticated security systems to limit the risk of data hacking.

Big Data at LCL

At LCL, algorithms study customer browsing on the LCL.fr bank website.

One of the objectives pursued is to improve customer knowledge to respond in the most appropriate way to their needs.

Another objective is to develop the bank’s website by identifying the most visited pages, the most used functions, etc. Highlighting the strengths of the site allows for a better customer experience.

In addition, to improve its customer relationship, LCL offers an efficiency assessment. For customers, this involves evaluating their satisfaction with their banking relationship.

The Effectiveness Review questionnaire has 3 questions. Through the first question, the customer rates their predisposition to recommend the bank to those around them on a scale of 0 to 10. The following two questions are open and allow the customer to detail the reasons for which he gave the mark on the one hand, and on the other hand, to provide suggestions for improvement. The comments collected are then analyzed using algorithms to identify, from the verbatim, the reasons for satisfaction and dissatisfaction to improve processes and capitalize on good practices.

Also Read: Big Data Marketing: Opportunities, Challenges And Management

The post Big Data: Definition, Challenges And Applications appeared first on Tech Cults.

We examine companies and their relationship with big data, analytics. We can classify two categories of companies:

1. Those that analyze the data by generic sectors and make them available by publishing the studies carried out and that offer analysis tools to obtain the study of the data of their customers;
2. Those who directly use the results of these existing research for their sector turn to specialists in communication and data analysis to obtain specific reports for their company. Some of these organizations have learned to analyze data through analysis software, setting up an internal figure dedicated to this task.

We find the sector pioneers such as Google and its tool Google Analytics, SAS, SAP, IBM, Adobe Analytics, and Nielsen in the first category. The latter studies the markets and consumer habits by providing a clearer world view of the sector analyzed through periodic publications on their portal. Published material can be a good starting point to consult before devising a marketing strategy. Very useful, for example, to consult this data before the launch of a new product or to change strategy on the positioning of a product already present on the market for some time and which is approaching the point of “maturity.” As is well known, when a product becomes mature, and the market becomes saturated, by the laws of marketing, if a relaunch does not take place, the product will inevitably start towards the phase of decline.

Analysis tools: Software and Tools Online

To obtain valuable information from big data, it is essential to use software and online tools created specifically for this purpose. The common goal is always to create personalized statistics and extrapolate useful data to follow your market and target audience’s evolution.

Thus, more and more companies are investing in this sector and make available, free or for a fee, platforms to study the data collected and keep track of them. We find the most popular tools: Analytics Software and Solutions (SAS), Adobe Analytics, Google Analytics, Google Analytics 360, Simple Analytics, Matomo, Woopra, and we could name many others.

A web analytics tool, after being connected to the website to be analyzed, keeps track of the actions on the site through a summary panel that allows you to have the collected data under control, showing the metrics observed, through a general overview and often through easy to consult and interpret graphs.

You can observe the habits and profile of website visitors over time, such as the duration of the session, the origin of the visit, the pages that generate the most traffic, the keywords for which you are searched, the bounce rate. These are just a few examples of the observable factors. Besides the basic statistics, the most interesting part is to analyze the conversion ratio of the site visitors to understand how a conversion is achieved.

Not only that, you can understand the average age of those who visit our pages, the geographic data, that is, from which location our site is reached, the average time of stay, the sources of traffic, to understand where to direct their investments. And understand which devices are the most visited.

A Growing Trend: The use of Big Data Analytics

Big data analytics can become a real guide in your digital marketing strategy if used to its full potential. The purpose of analyzing the data and integrating them into a business strategy or using them to plan online campaigns is always to achieve a goal to conclude a sale, obtain a lead generation, or more view a landing page. In practice, it allows a company to stand out in its sector, make a difference and consequently increase the awareness of the brand or the annual turnover.

Big data analytics also concretely helps companies develop predictive analyses for a given market or reach a new target of potential customers. All this is possible because: first of all, companies choose to resort more and more to this new science, secondly the software and tools that can be purchased or used free of charge offer increasingly complete functionality and last but not least, new professionals to manage this trend, such as those of data scientist, given engineer and data analyst.

Why it is important to analyze them

By analyzing big data, it is possible to obtain information on the profile of users who use a particular service, purchase habits and preferences for a specific product category, and obtain information on the geolocation of users.

Every time we access a website, perform a search through a search engine, or simply use an application on our smartphone or in our home automation, we generate information that, when appropriately intercepted, can be processed and become valuable for companies that offer in selling products or services.

Sometimes, we do not even realize that we are generating useful data during navigation, even if we often give consent to be “tracked” through the acceptance of cookies. The indication of the use of cookies is mandatory on every web portal and informs the user on which information the site and the connected analysis tools will keep track, subject to the explicit consent that occurs by clicking on “Accept cookies,” “ok” or in some sites “accept and continue browsing.” These are just a few examples.

From the analysis of the information retrieved through web browsing cookies, data entered in contact forms or assistance e-mails, or customer cards registered by a company, useful information can be obtained for establishing successful marketing strategies.

As already mentioned, we live in a hyper-connected world where data is generated at every moment of the day. However, these data remain a simple bit of traffic on the network or a storage device if not processed. For this reason, more and more companies to improve their performance and increase their sales are investing in big data analysis.

Also Read: Big Data Marketing: Opportunities, Challenges And Management

The post How And Why Companies Use Big Data And Analytics appeared first on Tech Cults.

And it is in SEO and digital marketing where this technology can be very beneficial for us.

Anyone who knows what SEO means and what it is for knows that it consists of using different techniques and configurations on the web to achieve the best possible positioning of our different pages in Google search results.

Thus, the optimum is to enter the 1st page in certain keywords, which you will choose in relation to search density and competition, since this way you will get a high number of visits, it is recommended to be able to position ourselves in the first 3 places.

And this is where Big Data can be of great help since you can use large amounts of user-generated data to design our SEO strategies based on this.

Let’s see some recommendations that can significantly improve your SEO.

1. Google Analytics

A simple data source to use that can constantly improve and evolve your website is Google Analytics.

Thanks to its metrics, you can analyze:

• Marketing campaigns that are aligned with the tastes and needs of your visitors.
• Understand the preferences of your users.
• Identify the reference sources of your traffic and know from where they access your website.
• Identify unusual traffic sources.

2. Google Search Console

Google Search Console is another tool with a large amount of data generated by your website, a very useful Google application for webmasters that will help you, among other things, to:

• Know the keyword combinations that are generating a greater number of visits to your website.
• Verify the loading time of the web and its indexing.
• Verify the most and least crawled URLs on your website.
• Identify the pages you are interested in that crawlers do not index.
• Check the usability of your pages.
• Indexed pages with broken links.

3. SEMrush

If you want to delve into SEO, SEMrush contains a series of tools that thanks to the Big Data it has can facilitate the positioning of your website.

It is very versatile since you can check the positioning you have in the different keywords that you use on your website, how your competition is positioned with respect to you, or the main backlinks (reference pages) where the traffic from websites you consult.

It is paid so we recommend that if you use it, you do a preliminary analysis of the information you need to get the best possible performance from it.

4. Web Audits

If you take advantage of Big Data during an SEO audit (we recommend doing one every so often) you will be able to obtain detailed information about the state and performance of your website and thus be able to use that data to know more about the authority of the page, a number of external links and per page or other factors.

Here you should also analyze from load times, the weight of images on your website, javascript optimization on your website, .. factors that are taken into account to position your pages and that thanks to these data obtained you will be able to improve in an obvious way.

You will also have to apply redirects to pages that you have changed or have removed, check the sitemap of your website and that everything is correct so that Google correctly indexes your pages, check the robots.txt so that certain sections of your pages are not blocked. website, as well as using the most suitable meta descriptions for your different pages.

Applying all these actions after reviewing the Big Data of your website, you will make the SEO of your website more effective.

Optimization And Relevance Of The Content

Because search engines have the ability to very carefully identify content, it is important for content creators to optimize their content. This should be done in a way that is accessible to search engines that used specific keywords with the intention of finding this type of content that must be relevant to the keywords, must address the search question or problem, and must do so effectively.

Google prioritizes the search for the most relevant and useful data through its algorithms, so your company should focus on providing its visitors with value and relevant information to be more likely to get the first places on the results pages of its engines. search. Your company will greatly benefit from the way Big Data helps SEO.

As you can see, Big Data makes it easier for search engines to analyze content and deliver the most relevant results for search engine needs. As a result, if your SEO is focused on creating valuable content that people are looking for, we will be in a privileged position to benefit from all this data that we have managed.

Final Conclusion

Therefore, if you want to increase visits to our website, get closer to the type of user you want to reach, and improve conversions, you should use Big Data tools.

Currently, with the enormous amount of information that exists and the increasing competition in the globalized world in which we are, you have to have control over all these aspects in order to analyze and execute the necessary changes so that your SEO strategy is the most effective. Something that you have been able to verify that Big Data achieves.

The post The Influence Of Big Data On Search Engine Optimisation appeared first on Tech Cults.

What Is Big Data Marketing?

Big data marketing is the set of implicit and explicit data that comes to the company through web analytics, CRM or social networks and that constitutes in itself a great opportunity to take a closer look at the experience of its clients, from the beginning in order. This allows companies to:

• Segment leads intelligently.
• Market efficiently and effectively.
• Personalize each interaction and the entire customer journey.
• Optimize the marketing budget and maximize its impact.

It all starts with listening to the data. And that is the great marketing challenge.

What Are The Challenges Big Data Marketing Faces?

The data that is collected from customers is disparate and comes from a wide variety of marketing channels and sources. The challenge posed by this heterogeneous set of raw data is related to how to obtain clean, complete and reliable customer data and associate it with accurate profiles.

If this is already complicated in terms of big data, it is even more complicated when it comes to multiple sources, with different names, email addresses and devices, and is riddled with incomplete forms, significant data breaches, duplicates and other problems of quality.

Many times, companies end up accepting a fragmented view of the customer due to their inability to overcome this challenge. It is the price they pay for marketing, however, in reality, they should avoid this conformity since solving the problem is within their reach.

When Challenge Equals Big Data Marketing Opportunity?

As can be seen, the data that most companies work with is exactly the same as that used by others. Big data marketing will make the difference, between leaders and laggards, based on their ability to harness the value of that information.

Thus, the competitive advantage will be consolidated in organizations that know how to carry out optimized marketing data management, something that has to do with collecting, cleaning and validating, enriching, using and governing in this way:

1. Collect: Incorporate all the data into a data lake, for example, a Hadoop cluster, hosted on virtual machines in the data centre itself or through web services. Put in place a log marketing automation system, which will help create programs, feed streams, log pages, capture response data, and load all that activity into the data lake, without wasting a bit of information.

2. Clean and validate: Combining so much data from different sources means that there will be a lot of data duplication and possible conflicts with small variations in names, among other quality problems. Master Data Management (MDM) is the best ally, as it is an automated process guided by the business matching rules themselves. Thus, if the system sees two records for the same individual, it will automatically collapse them together, as long as the confidence level is above the established threshold.

If you are not sure, you will throw the exception to a data manager who can decide. In addition to this, it is essential to clean the data. You can never assume that the data you collected is really correct and usable: People make mistakes by entering their addresses, give you incorrect phone numbers and email addresses put the state and postal code in a field … and the result can lead to disaster. Once again, you have to use the right tools to correct these kinds of errors, so common in big data marketing.

3. Enrich: With the help of partners and suppliers, the data is enriched with additional information. This process is quite simple: it is loaded, compared to existing records, the information is combined, and the sets are imported using a data integration platform. Then it is convenient to validate and clean the data once more, to ensure its quality.

4. Use Deploying data-driven big data marketing programs targeting specific segments is a key step: One way to segment is based on product interest, which helps target more precisely and increase engagement.

5. Govern: If data is only cleaned and cared for once, that strategic asset will quickly depreciate. To avoid this impairment, you must assess the state of the data, act accordingly, and define a clear set of policies supported by good communication and training, so that everyone who interacts with data knows the rules and understands why they are important.

Big data marketing is a source of opportunities for companies looking to increase their income and customer base, but if the data is not clean, complete or reliable, information management will lead the business in the wrong ways.

The post Big Data Marketing: Opportunities, Challenges And Management appeared first on Tech Cults.

IoT and Big Data: New Era Of Remote Controlling

Thanks to its many advantages, telemonitoring has gained popularity in the industry with both plant managers and service companies. With the advent of the Internet of Things, this technology is entering the era of remote control. Connected services not only make it possible to view the status of equipment in real-time but also to reduce downtime, repair time, reduce costs and the risks associated with a malfunction.

PTC, the publisher of the ThingWorx IoT platform has published a white paper on remote monitoring. This document details the four reasons why telemonitoring is the best approach for better service. Greg Eva, IoT Business Development Manager at PTC, explained the challenges of remote monitoring in the connected industry.

What Is Remote Monitoring In The Connected Industry?

Remote monitoring was born with increasingly intelligent and communicating equipment. Concretely, this allows them to be monitored remotely. Thus, we get to know their functioning, we receive diagnostic reports, notifications during problems. These features are particularly interesting in the field of IoT and service. Remote monitoring can notify a failure in real-time. The repair company is even able to react before users are aware of the problem.

In addition, we can predict or even predict future failures thanks to the information reported by different types of sensors. We then have a continuous vision of a set of assets that can be spread over a given geographic region. Finally, it makes it easier to compare a group of assets with other groups and thus know which machines are the most efficient and optimize, improve those that are lagging behind.

How Important Are The Relationship Between IoT & Remote Monitoring?

In itself, remote monitoring is not a recent technology. Historically it was used on equipment of large sizes, very complex and very expensive. There have been issues of maintenance costs and logistics for a long time. Spare parts did not arrive until several months after the breakdown and expert repairers had to systematically take international flights to repair these machines on the other side of the world.

With IoT, we use the same type of technologies at much lower costs. The reduction in the cost of sensors, communication with Sigfox or LoRa, on-board computers and infrastructures facilitates the emergence of this type of solution. Let us add that the Internet of Things is a technological whole that promotes convergence.

As such, we can directly interface an IoT monitoring solution to other systems such as ERP. This is extremely important in order to make the link between the availability of assets and the order book.

Who Do You Think Needs Remote Monitoring Services?

Previously, remote control involved companies that deployed high-cost assets. Today, there is more talk of reducing the downtime of machinery and equipment. So any company that encounters problems of unavailability of equipment due to a breakdown or other phenomena has an interest in installing an IoT remote monitoring solution.

Companies that are not in this case also endow themselves with this type of solution. Through data readings, they better understand the operation of the machines, which allows optimization of the production phases.

For their part, service companies that offer a remote control solution can improve customer service, better understand these needs and offer additional services with high added value while evolving their business model.

Finally, service companies that must respect maintenance contracts have every interest in implementing a solution of this type. Remote monitoring makes it possible in particular to comply as best as possible with the instructions given by their customers and thus avoid paying heavy penalties. Risk management is thus refined.

What Are The Prerequisites For Setting Up A Remote Control Solution?

Our IoT remote monitoring solutions are tailor-made according to customer needs. We have a lot of customers who started with simple remote connectivity which allows them to manually connect to a machine in order to troubleshoot a remote client. This simple solution speeds up repair time and dramatically lowers travel costs. Our customers may spend 30 minutes at the bedside instead of spending 20 hours counting travel time.

As time goes by, customers will add monitoring points, collect additional data and thus improve remote control. This phenomenon is progressive: it depends on the maturity of the client. There are other customers who do not have connected machines, but who have very advanced service management solutions.

We can interface with it to create orders and automatically call a technician when a machine breaks down. We can treat simple requests as more complicated. The important thing is that it is flexible enough to be adapted to the needs of the client.

The sooner this type of remote monitoring system is installed, the sooner the savings can be seen. So, it is very attractive to start small to grow the solution as needed. Finally, for each problem that requires intervention, this allows us to better understand the needs of the service company and for them to better understand their customers.

The post How Remote Control Takes Advantage Of Big Data & IoT? appeared first on Tech Cults.

1. Artificial Intelligence

In 2020, artificial intelligence will continue its technological evolution and new use cases will emerge. Learn about AI trends and predictions for the year ahead.

Artificial intelligence saw a spectacular evolution in 2019, and the exploits achieved thanks to this technology did not stop making headlines. Here’s how AI should continue to evolve in 2020

Artificial intelligence and machine learning will automate many professional and everyday tasks, from truck driving to ship piloting, handling customer calls – and even conducting rudimentary conversations with them.

What we don’t talk enough about, however, is the real impact on the jobs of AI creators and administrators themselves – developers, analysts and data administrators and everyone in the business. the orbit of information technology is responsible for developing these revolutionary systems. In fact, AI will play a role in overcoming the challenges of AI development.

IT and data professionals have a lot to gain from the AI revolution. Some observers see the roles of IT managers and IT professionals becoming greater commercial responsibilities as they have been relieved of much of the tedious work of AI.

2. Internet Of Things (IoT)

Internet of Objects can be defined as a “global infrastructure. Which allows having advanced services by interconnecting objects (physical or virtual) thanks to information technologies and communication ”mentions the International Telecommunication Union.

In detail, the Internet of Things device offers, on the one hand, objects equipped with sensors (temperature, pressure, brightness, consumption measurement, etc.) connected to the Internet in order to collect remote data regularly. data. In addition, the network which allows the connection and the transport of the data produced by the connected objects are in the full structure.

This network can be dedicated. It is then characterized by a mesh of low-consumption, long-range antennas and relaying a low bit rate. It can also be joined to current data networks, such as Edge or 4G, within the framework of 3GPP specifications. Objects connected by an Internet network can also interact with each other.

Taking up the basics of M2M (Machine 2 Machine), this process promises to set up an ecosystem of software objects working in an automated manner. The first applications of the Internet of Things are found in the logistics sectors (RFID and NFC chips). The field of smart cities should take full advantage of the uses of the Internet of Things (air quality, temperature, noise level, condition of buildings).

Home automation is also a sector fond of the Internet of Things (thermostat, smoke, presence detectors, etc.), especially on the aspects of smart meters and security systems. Finally, the health sector could be upset by the establishment of an ecosystem of connected objects.

3. Virtual Reality (VR)

Virtual reality or VR is an immersive, visual, sound and/or haptic interactive computer simulation of real or imaginary environments.

To achieve its immersive objective, the vector of virtual reality takes the form of glasses or a helmet connected to a smartphone or a computer.

Virtual reality became a commercial reality in 2016 with the launch of several consumer products such as Facebook’s Oculus Rift, and Vive d’HTC which offer very advanced experiences, particularly in games or for professional applications through headsets connected to PCs. Samsung offers the Gear VR, a cheaper device connected to a smartphone.

4. Blockchain

Blockchain is a shared registry technology. Unalterable, it is decentralized and does not need a single controlling authority. There are public blockchains, open to everyone, and private blockchains, with limited and controlled access.

By definition, Blockchain is a technology for storing and transmitting information, the advantages of which are transparency, security and decentralized control. It is a database which contains the history of all the exchanges made between its users since its creation.

Among the projects made possible by the Blockchain we can cite cryptocurrencies (bitcoin, ethereum, monero etc.) but the blockchain can be used for other applications. Many experts believe today that it will be the basis of the next technological revolution.

5. Cybersecurity

Also called IT security or systems security, cybersecurity consists in protecting the integrity of data, sensitive or not, within a digital infrastructure.

From the installation of antivirus to the configuration of servers, multi-factor authentication when connecting to an account or even the guarding of data centres, cybersecurity is a vast field. For businesses, the law today requires the implementation of technical and organizational means aimed at protecting information.

In recent years, cyberattacks have multiplied, taking new forms, from simple viruses to ransomware, which is particularly growing.

6. Cloud Computing

Cloud computing is a technology that allows remote access – via the Internet – to IT resources. These resources are part of the infrastructure (server, storage and network; it is then an IaaS offer for “Infrastructure as a Service”) or software (we then speak of SaaS for “Software as a Service”).

This new way of consuming IT leads to a change in the economic model: resources are rented and billing smoothed in the form of a subscription. It also leads to an increase in the number of data centres.

If the public cloud defines elastic infrastructures made available by a service provider outside the company, the private cloud evokes a logic of consumption of internal resources on demand. Finally, the hybrid cloud is a model that brings together the two types of cloud previously mentioned in an entity that combines the advantages and limits of the two architectures.

In this same field, we see multicoloured developing more and more, allowing companies to juggle between the services of several suppliers, and posing the problem of supplier interoperability.

7. Big Data

Big Data is a concept born in the late 1990s and it describes the storage of infinite information on a digital basis. It also means such a large set of data that no conventional database management tool can fully analyze it.

If no precise or universal definition is given to Big Data, it can be conceptualized in the form of a giant and evolving database. Big Data relies on data storage technologies and cloud computing to host this information, as well as data processing technologies to analyze it. Oracle, IBM, Google, Atos, SAS or Criteo are all big data players, each intervening at different levels.

8. Networks & Telecoms

A telecommunications network is a network of telecommunications links and nodes (switches, routers, etc.), set up so that messages can be transmitted from one end of the network to the other through multiple links.

The fixed telephone network is called a switched telephone network and is based on copper links. It is expected to disappear around the 2020s replaced by an all IP (Internet Protocol) network.

Today, there are mainly two types of networks: the fixed Internet network (ADSL, optical fibre) of boxes, which allows Wi-Fi and Ethernet connections, and the cellular network (3G, 4G and soon 5G ) mobile devices to connect to the Internet from anywhere.

9. Robotics

It’s an exciting time to work in the robotics industry. Driven by the growing diversification of the industry, the global sector, whose revenues today total more than $ 100 billion, has experienced rapid growth in recent years.

Industrial robots are no longer the exclusive pre-square of heavy industry or large factories. Collaborative robots, in particular, have helped to broaden the corporate customer base to include medium and even small businesses.

But is this golden age coming to an end? We spoke with Chris Harlow, director of product development at Realtime Robotics, about his predictions for 2020 and beyond. For him, if the good period that the sector is going through should continue, it will not last in all markets.

Particularly with regard to collaborative robots (or “cobots”), these small units have contributed to the expansion of industrial automation beyond large factories. “Demand for robots has peaked due to reduced functionality and capacity,” said Chris Harlow. According to him “by 2025, manufacturers will no longer invest in these systems, and traditional cobots will be replaced by better technology for the human-robot work cell”.

10. Mobility

Mobility means who can move, change place, function. In terms of technology, mobility, therefore, covers all devices that do not have a fixed location and can be used in several places: laptop, smartphone, tablet, wearable …

The most mobile devices for professional use are smartphones and tablets, which guarantee access wherever you are, especially thanks to cellular communication technologies (3G, 4G and soon 5G). There are three operating systems for these mobile devices: iOS (Apple), Android (Google), and Windows (Microsoft), for Surface tablets only.

The post Top 10 Trending Technologies You Must Learn In 2020 appeared first on Tech Cults.