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Big Data Era

 Big Data: New Marketing Concept Or Genuine Market Trend?

big data - a new era

The Era of Big Data

The Era of Big Data Has Come

Over the past decade, the massive increase in digital data has forced researchers to find new ways to analyze the real world and to anticipate the future. The concept of “Big Data” was born. It mainly consists of storing a huge amount of real-world information in digital form.

 

What is Big Data?

The term “Big Data” refers to a very large volume of data that no conventional data management and processing system could not really understand. Nrearly 3 billion bytes of data are created every day, information from research or shopping online, videos, weather information, etc. The Big Data term refers to this huge volumes of data. The major online companies like Amazon, Google, Yahoo! and Facebook were the first to develop this technology for their own use.

The advent of Big Data is now seen by many as a new industrial revolution similar to the advent of electricity or steam during the 19th century. Whatever the comparison, Big Data can clearly be seen as a profound source of disruption to our modern society.

Technologies

The Big Data solutions must meet the high requirements of big data: (1) their enormous volume, (2) the variety of information they represent, both structured and unstructured, and (3) the speed they demand to be created, collected and distributed.

In recent years, the new technologies on the market have complied with 3VS Big Data: volume, variety and velocity. The first storage technologies, in particular have lead to cloud computing. Then came new technologies for processing and managing database adapted to unstructured data (Hadoop) and high performance computing modes (MapReduce).

Several technologies may be required to optimize the access times to large databases such as NoSQL databases such as MongoDB or Cassandra, server infrastructure for the distribution of treatments on the nodes and storage of data memory:

The first solution can implement storage systems considered more effective than traditional SQL for mass data analysis.

The second is called the massively parallel processing. The Hadoop framework is one example. This combines the HDFS distributed file system, based NoSQL HBase and MapReduce algorithm. Regarding the latter, it speeds up the processing time of applications.

Evolution of Big Data

The development of the Spark and the end of MapReduce

Spark gradually replaces MapReduce: As in all technologies, big data are a constantly changing environment. The technology landscape evolves rapidly, new solutions are frequently required in order to optimize existing technologies. MapReduce and Spark are very concrete examples of this evolution trend.

Developed by Google in 2004, MapReduce was then used for the Nutch project Yahoo !, to become later on the Apache Hadoop project in 2008. This algorithm has a large data storage capacity. The only problem is its relative slowness particularly visible on volumes relatively small. Despite this, solutions aiming at providing almost instantaneous treatments on these volumes begin to reduce the influence of MapReduce. In 2014, Google announced that it would replace MapReduce by a SaaS solution called Google Cloud Dataflow.

Spark is also a symbolic solution for writing distributed applications with conventional processing libraries. It is also one of the Apache projects with a speed of rapid development. In short, it is an obvious solution as the successor of MapReduce, especially since it has the advantage of combining many tools required in a Hadoop cluster.
The main market players

The Big Data industry has attracted many companies, including the incumbent suppliers of software solutions such as Oracle, SAP and IBM. Large web companies like Google, Facebook, Twitter. And Big Data specialists like MapR, Hortonworks or Teradata. IT integrators include the big names in this sector with CapGemini, Atos and Accenture. Many startups rapidly emerging such as Criteo, Squid, Ysance, Hurence, Dataiku … Not to mention the schools, universities and training organizations that provide partial or complete courses around these Big Data technologies. Read more on these Big Data companies.

Professionals on these big data technologies are still scarce on the market (see Big Data University). Yet demand is growing, and many big data job offers are available online, both in USA, Europe and worldwide (see big data jobs worldwide).

3Vs of Big Data

The following 3 dimensions are commonly used to define more precisely what these Big Data are and to know the challenges that companies are facing (these dimensions are usually called the 3 Vs of Big Data):

The 3 dimensions of Big Data

– Volume
– Variety
– Velocity

Volume

Volumes of Big Data data are huge and constantly growing. These volumes are expressed in terabytes (noted Tb) and even petabytes (noted Po).

Variety

These Big Data can be found in varied formats: What is common between a video, a song, a message on Twitter, a photo posted online, a post on a Facebook page or a measure on an electric meter for example.

Velocity

The velocity dimension is the speed at which must be collected, analyzed and used big data. More than often, these data are to be processed in real time.

2 other dimensions

Two other dimensions were added later to these 3 initials dimensions of Big Data (all five dimensions are also referred to as as the “5 V Big Data”):

Veracity

Veracity refers to data reliability and accuracy. With so many various forms of data, it becomes more and more difficult to control data quality and accuracy (just imagine Facebook or Twitter messagess with abbre8viations, slang words, hash tags and typos). But today, thanks to data and analysis technology it is possible to work with this type of data.

Value

This dimension focuses on a surprising aspect of Big Data. In many situations, a small amount of data may not have much interest, whereas a huge data volumme can make sense. This is the case for example of a tweet on a current event. The content of the tweet in itself may not have great importance, but if the tweet is commented thousands of times, the number of messages associated with this tweet that becomes valuable information (an opinion shared by many, a subject that is consensus or controversy …).

What is Big Data ?

Big Data can be defined as a large amount of structured and unstructured data that is meant to be mined for information.

Big Data is a generic term refering to very large volumes of data that companies have to deal with. The arrival of these large data called “Big Data” has become a real challenge for companies whose current systems were not designed for such massive treatments.

Today the production of data has never been more important. Every day, human activities generate data on a volume of over 2.5 trillion bytes, or more than 2.5 trillion data.

The exponential growth of data is unprecedented in our societies: the development of e-commerce, the generalization of digital marketing, the proliferation of connected devices and sensors, social networks on the Internet, images and videos posted online, geolocation are among the new practices that contribute to this growth. The growth is such that today 90% of the data generated were only in the last two years, and this proportion continues to grow.

The challenge brought about by these Big Data is not only linked to large volumes of data but also to their diversity: in organizations, the traditional structured data must now cohabit with unstructured data of unconventional formats. The challenge is to make sense from these massive amounts of data. Companies have to master these massive data, analyzing them to better understand them so that they represent a real meaning and value.

big data search

Big Data

You can’t escape Big Data

Big Data is a new IT and business phenomenon whose implications go far beyond the sole tech sphere. It is a durable trend that is here to stay for decades. The Big Data trend reflects new uses of information in our societies. Few companies nowadays ever doubt about the real interest of such massive data or see in the current fever sort of a new professional marketing concept only meant to create new business opportunities and sell more IT solutions. For several years, market studies anticipated a major market trend in information technology: the arrival of massive structured and unstructured data, which would really flood the market. This trend is real and it is accelerating. Companies always want to better understand the data and control them to keep in touch with their markets. In the early 2010s, there was no real solution to integrate these large volumes of heterogeneous data in their information systems. However in recent years, manufacturers, software companies and integrators have developed new solutions that now fully meet the needs of companies, collecting and integrating these massive data into their systems. Big Data is a generic term for this new and lasting phenomenon. It also refers to solutions offered by information professional to help organizations better control their data growth, both in volume and diversity. This site is meant to help you get an overall understanding of this inevitable trend.

Volume of Big Data

Big Data: What Volume?

Every 48 hours we create as much data as all those created from 2003 to today. Over 90% of the data generated in the world have been during the last two years.

 And here are some other statistics that help measure the large volumes of Big Data today:
Big Data Volumes

204 million emails are sent every minute
1.8 million “likes” on Facebook every minute
278,000 tweets created on Twitter every minute
200,000 photos uploaded to Facebook every minute
698 000 searches launched on Google every minute
100 hours of video uploaded to YouTube every minute
35 million videos are watched on YouTube every minute
In 2012, 13 billion connected objects. In 2020, 50 billion
In 2011, 12 million RFID sensors sold. In 2021, 209 billion
Big Data market in 2013: $ 10.2 billion. In 2017: $ 54.3 billion
It would take 15 years to watch all the videos posted on YouTube in 24 hours
Amount of digital data in the world: 2.7 zettabytes (2.7 x 1021, noted Zo)
All data generated and stored in the world are doubled every 1.2 years

 

And in view of the very first figures given at the top of this page, these statistics, as impressive as they are, are much lower than those that will be in effect next year and the following year.

Soaring Big Data Market

ig Data, Big Market

Most studies from experts such as IDC and MarketsandMarkets end up with what everyone of us already knows: a big increase of Big Data volume in our daily lives for the years to come.

Big Data Market

Between 2012 and 2014, the Big Data industry more than doubled in revenue as well as in size. In 2016, the big data market size grew up to 38 billion U.S. dollars in revenue. This big data market is expected to grow steadily for the next few years, offering great potential to businesses of any kind.

By 2017 the global big data industry should be worth around 43 billion U.S. dollars. And by 2017 revenue from professional services should climb the highest to 15 billion U.S. dollars.

 

Among the leading big data vendors in terms of revenue, IBM (2.1 billion U.S. dollars), SAP (890M$), Oracle (745M$), HPE (680M$) and Palantir (672M$) are the top five competitors.

Big Data Analytics

Big Data Analytics At The Heart of Business Strategies

Big Data Data analysis is the combined process of collecting, organizing and analyzing large volumes of data (big data). The ultimate goal of this data analysis is to draw strategic information or models on which companies are going to make their decisions.

 

Big data analysis is very promising because it helps companies better understand the information in their data warehouses, whether structured or not. Analysis of these data also helps to clearly identify those that are important and play a decisive role in the decisions of leaders. Analysts in charge of this activity have a role whose importance grows as the big data is needed within enterprises.

big data dangerous
Big Data Analysis Requires a Performance Environment

To work on large volumes of data, big data analytics requires a performance environment with machines and software tools dedicated to predictive analysis, and upstream, to data mining, text mining, organizing and optimizing data, etc. These tools previously used separately have been increasingly integrated into single dedicated environments, making possible the processing of large enterprise data volumes. Predictive analytics on these data will help identify trends and models that the company are always in need of in order to make better decisions.

 

Analyses Challenges

In order for the analysis to be relevant, it should address a number of challenges. The first two challenges are the large data sets associated with the heterogeneity of data available in different formats within the company. We must therefore have the necessary platforms to integrate these large data, regardless of their formats.

Another challenge is to overcome the inevitable silos in which are confined business data in a company that for example has many subsidiaries, services and systems. We must therefore strive to “flatten the data” and find the different links between them so as to establish a global, coherent and interconnected data set.
Growing Use of Big Data

Technologies are increasingly contributing to shape companies, eliminating data silos and improving data analysis. Commpanies can detect an emerging trend on a market and anticipate a strong demand for a certain type of product (analysis of messages on social networks and forums, for example). Big data technologies can help government agencies, for example, to prevent and fight against the risk of epidemics, fire, theft, or against the development of a disease by further analysis of the genome of a person, etc..
Benefits of Predictive Analytics

Businesses are hungry for information that make sense for their business and can give them safe guidance for the months and years ahead. Ability to anticipate the future with great reliability, this is a question to which all companies try to answer, and more and more nowadays with the help of big data. Therefore they tend to adopt the best environments (machines, software and specific solutions) to ensure their data further analysis and help them increase their sales, improve the efficiency of their internal processes, and more generally, to reduce risks associated with the uncertainty of their markets.

Dangerous Big Data?

Beware of Big Data?

WHAT ARE THE MAIN RISKS OF BIG DATA?

Some people may ask: Is Big Data dangerous? Any business project can fail for some reasons, the most frequent ones being a lack of budget, bad team and project management, poor skills. But big data projects can also fail because of specific reasons.

Even if there’s no need to be afraid of big data, you need to be aware of potential risks with such new technologies. So here are the biggest and most common risks of big data projects.

Costs

Collecting data, storing them and getting reports out of them cost a lot of money. These costs should be mitigated in advance by a thorough budgeting study at the very first step of the project. This approach will avoid spiralling costs. A clearly defined strategy and a clear knowledge of what you intend to achieve and the benefits that are expected from this project will give you the chance to succeed. In short, if you don’t want to be pull the plug on a big data project, keep costs under control and and you’ll achieve your objectives.

Bad Data

Always try to start off on the right foot with relevant and up to date data. try to spend sufficient time on designing your project strategy. Far too often, the big data frenzy has led to collect everything and think about analyzing these data later, a poor strategy that can only lead to project failure, adding to the growing cost of storing the data, and leading to large amounts of data that are or can become quicky outdated.

You need to be drawing the right insights from your data and thus running ahead of your competitors. If you are getting it right, you will take the lead. So in short, don’t create irrelevant background noise with bad data.

Bad Analytics

An frequent pitfall of big data projects comes from a wrong interpretation of your data. You can sometimes draw causal links where there is only random coincidence. Sales data showing a rise after a major event, such as a live music event, may lead you to draw a link between music fans and your products, when in fact the rise is only based on there being more people in town due to summer vacation or sunny weather, or something else.

Data Security

Of course, as in any IT project, data theft is a real and big concern. The bigger your data, the bigger the risk. A growing number of companies have seen their customer data stolen with credit and debit card information as well as their email and geographical addresses. So make sure that there’s no security breach in your system, and invest the money needed to prevent your company from such data theft.

Data Privacy

This is an issue closely related to security. Ensure that your customers and other people’s personal data are safe from criminals but also from misusing by your team in charge of data analyzing and reporting.

These are just some of the most frequent risks and dangers that every company conducting big data projects has to take into consideration before spending any cent. Companies should now engage with big data project in order to prevent the serious risk of being left behind by their competitors. But they also should be well aware of the potential dangers of such projects and take action to prevent them.

Challenges To Come

Big Data Challenges

Big Data: Why You Should Care?

 

In recent years Big Data has become a subject that fascinates and, at the same time, raises many questions. It’s mostly a subject of interrogation for companies: should they engage in a Big Data promising approach but whose benefits can’t be measured in the medium term? Is Big data yet another tech fad whose return on investment is more than hypothetical? Should companies adopt a wait and watch attitude observing how competitors deal with that before investing in technologies whose benefits, on paper, are far from being proven?

One thing is certain, the Big Data rising importance leads companies to think more seriously about it. For Big Data is in aaccordance with the need of companies for a more extensive customer knowledge in order to anticipate his demands, provide better service, design products and services that respond without fail to anticipated needs. In short, the Big Data seem to meet the needs of organizations to react as early and as quickly as possible to market demands, ideally in real time and even predict market demand for better answer.
What are the Big Data challenges for companies?

 

What are the challenges and the expected benefits of Big Data? Undoubtedly, Big Data is a factor of innovation for enterprises by new uses it creates. On the client side, big data promises better quality service, more personalized and responsive services. On the business side, Big Data makes it mossible to have this 360-degree vision that all decision-makers dream of, a vision that acts on all communication channels and company’s interaction with its customers. A 360-degree vision that brings unparalleled knowledge of the client, with all the benefits that can be expected from information as precise and sharp, but that requires putting the customer at the heart of the organization.

But current systems are not organized in this way. Each channel of communication with customers is managed with a dedicated business application with its own customer data. This silo architecture does not expect to disclose all business applications hitherto autonomous. Put the given customer at the heart of business practice involves communicating these business applications, which poses a number of challenges, both technical and organizational.

The implementation of Big Data projects requires above all the involvement of the business directions, general management and business managers. It requires centralize all analytical processes around a single repository (customer data) to rethink the respective business models and value chains. Finally, the implementation of Big Data projects makes it essential transverse steering between business applications for better coordination.

One of the major challenges of Big Data is abandoning a silo vision of an information system in favor of a multi-business cross customer-centric vision. A major site for any business that wants to provide the means to enjoy the benefits of Big Data.