Big data is a simple term that covers complex volumes of both structured and unstructured data. Here are 10 big data facts that will turn data into business insights.

The significance of big data does not revolve around the quantity of data you have, but what you do with it. You can take data from almost any source, and then you can analyze it to find answers that will help your business in areas like cost bargains, time reductions, new product development and optimized offerings, and, lastly, smart decision-making. When you merge big data with high-powered analytics, you can achieve many business-related tasks such as the following:

• Determining the root causes of failures and also the issues, and defects in near-real time.
• Generating attractive coupons at the point of sale that are based on the customer’s buying habits.
• Recalculating risk portfolios in minutes.
• Detecting fraudulent behavior before it affects your company.

Big Data and Data Collection

Big data explains massive amounts of semi-structured, structured and unstructured data that is collected by organizations. But as the data is so voluminous, it takes lots of money and time to load all the big data into the traditional relational database for analysis, recent approaches for collecting and analyzing data that have emerged.

The process is long. First, you must gather and then mine big data for information, but it doesn’t stop here. The raw data, with the extended metadata, is then aggregated in a “data lake.” From there, machine learning, artificial intelligence and machine learning programs then use sophisticated algorithms to look for all the repeatable patterns that can be used over time.

Types of Data

Normally, there are two main types of data: qualitative and quantitative data. Starting with the latter type, the quantitative data is any data that is in the numerical form (e.g., percentages and statistics). Qualitative data is known as descriptive data (e.g., smell, color, appearance and the quality).

In addition to the data types, which are quantitative and qualitative data, a few organizations may also use the secondary data to help drive significant business decisions. The secondary data is also typically quantitative in nature, and, most of the times, it has already been collected by another party for an entirely different purpose. For example, a firm may use the U.S. Census data to make decisions about various marketing campaigns. While in media, a news team may use the government health studies or health statistics to drive the content strategy.

Top 10 Big Data Facts

Now that we’ve looked at what big data is, the data types and its significance, let’s move to the top facts on big data what everyone should know.

1. Big Data Needs a Diverse Culture

In order to fully take advantage of big data, it is imperative to turn your firm into an information-centric company. This cultural shift will greatly help to make much more data-driven decisions, and it will also give employees the opportunity to construct new operational, strategic and tactical plans that will be based on data instead of wild guesses and assumptions. A big data culture requires that employees be encouraged to ensure that the data is collected and stored at any moment in the customer contact journey. Essentially, they need to be encouraged and bold enough to ask the right questions and also to solve them with respective data.

2. Hadoop Isn’t the Holy Grail

The data in a Hadoop framework is divided down into many smaller pieces, which are commonly referred to as blocks and are distributed throughout the framework. In this way, many things can work as the map and reduce functions that can be easily executed on many smaller subsets of larger datasets. This also gives the scalability that is required for big data processing. When these technologies combine, they result in massive amounts of data that can be easily stored, analyzed and processed in a fraction of a second. If a top layer like Hortonworks or Cloudera is attached to it, then the real-time analytics becomes very much possible with Hadoop, giving it outstanding advantages, and making big data analytics achievable.

Although, Hadoop isn’t the Holy Grail. There are various benefits, such as linear scaling with the product hardware, which is easy to implement, and it isn’t expensive. When combined with the simple programming model, it allows the end-user only to write the Map Reduce tasks and the specific fault tolerance of the system as all the required data is copied many times over multiple nodes and clusters. Hadoop thus offers many advantages.

However, it does have a downside, too, as there are a few substantial disadvantages of Hadoop. For example, getting Hadoop operational is challenging and requires specialized engineers that are expensive. Subsequently, cluster management is hard and debugging is difficult. Organizations will need special trained IT personnel to install a complete Hadoop server.

Luckily, there are more and more big data startups that develop big data as service platforms, taking away the need to build an own Hadoop environment.

3. The Real Driver Behind Big Data Is the People Within the Organization

We already mentioned that a shift in the organizational culture is necessary to ensure a successful big data strategy. However, for the big data strategy to happen, it needs to be created by people. Especially the managers and executive level personnel should be aware of what big data is and how it can be applied to their organization.

One thing important to know is that big data is not an “IT party.” IT is merely a means to achieve your big data strategy, as was the case, for example, with social media. A few years back, everyone thought that social media was the Holy Grail for marketing. Today we see it, correctly, just to achieve the goals stated.

Therefore, the people who need the insights should drive a big data strategy in an organization: marketing and strategy managers. They should drive IT to build a big data system that gives marketing and strategy the answers to the questions they have.

4. There Is No Place Where Big Data Doesn’t Exist

Everything that is digital is data, and more and more items are digitalized and are connected to the internet. This results in new data flowing into your organizations from entirely new areas, previously not thought of. With the Internet of Things (IoT), any product or device can be connected to the internet and therefore provide data. Organizations should use this information with no fear to digitize products. Big data literally is up for grabs; you only need to open your eyes to understand where it can lie and how you can find it, analyze it and use it.

5. Big Data Technicians Will Disappear, You Better Start Looking Around

McKinsey predicts a shortage of about 140,000 – 190,000 Big Data engineers in America alone in 2018. They also predict a shortage of 1,500.000 big data managers who can manage the big data engineers and can connect the IT aspect of big data with the strategy aspect. So, big data engineers will be scarce in the future. The organizations should already start to train their IT personnel to become familiar with big data technologies. Universities should create big data engineering courses that prepare students for the big data future ahead of us. Luckily, more and more universities are already offering a big data study, as well as open online platforms such as Coursera, are offering a big data course.

6. Big Data Does Require Big Security Measurements

Whenever organizations gather large valuable datasets, criminals are on the lookout to steal that data and use it to their advantage. Recently large online organizations have been hacked, including Linkedin, Evernote and even Bitcoin. However, not only the online organizations are being hacked, but the government institutions and organizations are also under attack quite often. Therefore, it is key to protect any data that is collected. There are several ways to secure your data.

Correctly encrypting all your data is, of course, the most commonly known. But there are many other ways to protect this data, and whenever an organization wants to deal with big data, security should be part of the team. Organizations should also have a crisis plan ready when it does go wrong, and the organization is hacked. Surprisingly, there are still many companies who have no clue what to do in case of a crisis.

7. A Public Debate About Privacy Issues Is Inevitable

With big data, comes the big privacy issues. In the age of big data, big brother will be watching everyone whether it is online or offline. If the data is not correctly anonymized, there will be a huge risk of unauthentic data. Therefore, it is necessary for the public as well, as about how far we want the organizations to go.

8. Venture Capital Firms Are Investing Massively in Big Data Startups

The Datalfoq platform aggregates a lot of different big data startups that have developed a solution for big data. At the moment, there are dozens of startups mentioned on this website, and together they have received over $ 890 million in funding in recent years. As these big data startups are just a small part of all existing startups worldwide, the amount of money invested in this industry is enormous, and it will only grow in the coming years. Especially the United States is ahead with investing in big data startups compared to other regions in the world.

9. Governments Are Increasing Big Data Technology All Over the World

In 2012, the United States government made $200 million available for research and development in the field of big data. Also, the European Commissioner Neelie Kroes is a supporter of big data, and she wants Europe to be in the front of it. Multiple governments are starting to see the possibilities of opening up and sharing their public data sets with the public in order to develop applications that can solve problems. These datasets can be found on platforms like Infochimps or Data market where governments from around the world are sharing up to 45,000 data sets. There is still a long way to go. But for now, it is clear that the governments can also significantly benefit from the opportunities of big data.

10. Big Data IT Spending Will Grow But Remain Small Compared to Total IT Spending

Gartner predicts that big data IT spending will grow to $43 billion in 2016 from $28 billion in 2013. With a total IT spending of $3.7 trillion in 2013, this is only 0.75 percent of total worldwide IT spending in 2013. This is a relatively small number of a trend that can will such a massive impact on organizations and governments. Gartner furthermore predicts that social media network analysis and content analysis will grow 45 percent on an annual basis until 2016. By 2020, they expect that big data will be normal and part of the baseline of enterprise software.

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It has been reported that some people are stealing data about tigers in Indian wildlife reserves, raising concerns over data abuse.

Wildlife data tracking used to rely on field observations recorded by experts. However, the lack of standard formats makes it difficult to process such data.

Fortunately, technological advancements in tracking systems, such as the global positioning system (GPS) and other digital sensor devices, have made large, continuous and high-frequency data sets available.

These new technologies have made data collection, storage, evaluation and visualization a lot more easy. However, they have also increased the risk of data misuse.

The importance of information sharing in the push for innovations has been recognized by many people.

For example, geospatial data is essential in wildlife research, particularly in the study of animal behavior, habits and demand. Such research is used in animal conservation and prevention of disease transmission.

But there is always a need to prevent big data abuse while extracting the benefits of data mining.

While some animal monitoring centers have fully opened access to their real-time geospatial data, others only provide a small fraction of their data for fear that the data could be misused.

To solve this problem, researchers at the Massachusetts Institute of Technology have come up with Open Algorithms (OPAL), which makes a broad array of data available for inspection and analysis without violating personal data privacy.

OPAL also allows the repository owner to control raw data: only aggregate answers or “safe answers” are returned. The system can also stipulate that data must be in an encrypted state while being transmitted and during computations.

Data security is the cornerstone for innovative development in big data. I believe technology is the best solution to tackle data security issues, not more stringent regulation.

How to best utilize data resources while protecting individual privacy presents a great challenge, but it is also a great opportunity.

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Didi, the Chinese ride-hailing giant, is looking for ways build up its big data and artificial intelligence capabilities. While widening its global presence with its partners, the company is also deepening its big data technology and artificial intelligence capabilities to become a global leader in smart transportation and automotive technology.

The Beijing-based company raised $5.5 billion this April, giving the company a value of $50 billion. It now has a presence all over the world, having invested in a number of taxi hailing companies on multiple continents.

In Southeast Asia, Didi and SoftBank invested $2 billion in ride-hail company Grab this July. In August, Didi partnered with Dubai-based Careem to make a move into the Middle East and North Africa and invested in Taxify, which currently serves 2.5 million users across 18 countries in Europe and Africa. Before Careem, Didi led a $100 million investment in Brazilian company 99.

Didi hopes that predictive algorithms will minimize and eventually solve the supply-demand imbalance in mobility markets as shown in this vehicle dispatch map (Image Credit: Didi)

But it’s not only globalization that Didi is interested in. According to Didi, among their 7,000 employees, 50% are engineers and data scientists. At the 2015 TechCrunch Beijing, Stephen Zhu, VP of Strategy at Didi stated that Chinese cities are a bigger challenge than US cities, forcing engineers to seek more sophisticated technology solutions.

So how is Didi using big data to help solve China’s congestion problems? As the above visualization tool shows, Didi uses a data-driven intelligent matching technology to find drivers for riders in a way that maximizes overall transportation for a given area. Red spots show that there is an excess of demand for taxis and private cars, and the green spots show that there is an oversupply of drivers.

We visited Didi’s Beijing headquarters and talked to Paul Wang Zhanwei, a data analyst working on Didi’s urban transportation program. Here are 3 ways Didi is helping to solve China’s traffic problem.

1. Didi helps urban development economists

Didi can monitor in real time from which area a user is calling Didi (Image Credit: Didi)

“You see a China map with colorful lights glowing in 400 Chinese cities. Didi can monitor in real time, from which area a user is calling Didi,” Wang told TechNode. “Didi’s  operation is a vivid representation of a city’s or even an entire region’s economic and demographic dynamic.”

Wang noted that after the Pearl River Delta, Yangtze River Delta, and the Beijing-Tianjin-Hebei triangle, Didi’s data shows that Sichuan is coming up to become China’s next hub for growth and regional integration.

Didi is rapidly expanding beyond China’s first- and second-tier cities, even expanding into the underdeveloped counties and townships. Didi’s service covers 518 out of China’s 823 underdeveloped counties. In lower-tier cities, Didi is welcomed because it takes away the pervasive verbal price haggling process between taxi drivers and passengers.

“This makes a difference to local employment and economic vitality,” Wang said. “Affordable and efficient transportation is a way to help smaller cities to develop together with the main centers.”

2. Didi is getting closer to predicting and eliminating traffic jams

The Holy Grail of big-data scientists, Wang said, is the power of forecast and therefore intervention. With the value of data analytics, Didi can predict the traffic congestion, by analyzing accumulated previous traffic data combined with their real-time data.

“Today, we can forecast demand about 15 minutes in advance, with 85% accuracy, within a specific region. This allows us to start building our predictive dispatching models, sending vehicles to those hot spots of congestion a bit sooner,” Wang told us. “What if we can do this for entire cities, for longer time periods? When our algorithms are trained with more data, the technology will improve, the traffic manager will be close to the point of a perfect forecast, when a traffic jam is anticipated and prevent it from happening.”

3. Didi is working with traffic police to cut urban congestion

Chinese cities, like their counterparts from Singapore to Helsinki, are working on building sustainable “smart cities”, where Didi believes that it could play a key role. Didi is now working with 20 cities including Nanjing, Shenzhen, Jinan, and Wuhan to improve traffic management with its data capacities.

Physical screens along Jinan’s expressways help drivers plan routes better according to real-time traffic situation (Image Credit: Didi)

One such experiment took place in Jinan, where Didi adapted its city dispatch and monitoring system for local traffic police. Besides providing real-time transportation data, the company is also working on projects like smart traffic lights, physical traffic directing screens and reversible vehicle lanes. When applied to traffic management along the city’s main avenue, Wang said, the technology is already cutting journey lengths by 10 –  20%. “We are working on traffic lights at about 100 out of 900 intersections in the city,” Wang said. “If we cover all the traffic lights in the city, then traffic congestion will be hugely improved.”

Didi said data insights are even helping local traffic chiefs better evaluate the performance of their precincts, in terms of traffic operation and management.

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BRISBANE, Australia, Aug. 7, 2017 /PRNewswire/ — Artificial intelligence (AI) and big data are pervasive and disruptive in today’s world. They transform the way people work and live, decision making processes and the landscape of industries. The language service industry is not an exception to this. What revolutionary changes have AI and big data brought to society? How would they reshape the language service industry? How would they benefit and inspire practitioners, service providers and clients in the future?

At the 21st International Federation of Translators Congress ( FIT 2017) held from August 3 to 5 in Brisbane, Global Tone Communication Technology Co., Ltd. (GTCOM), the exclusive strategic partner of the event, worked with some 1,000 professionals and experts to find out answers for the above questions and many other questions.

GTCOM CEO Eric Yu addressed the opening ceremony, sharing with participants how the advances in machine translation and AI technology have had a disruptive impact on the language industry, and how their application will greatly improve industry-wide efficiency and provide easier access to smarter language services.

During the event, GTCOM unveiled the YEEKIT, a language tool integrating both AI and language technology, and presented YEESIGHT and other products incorporating the latest development in AI and cross-language big data technology, attracting great attention from local businesses and media.

At the forums sponsored by GTCOM, the management of the company, including the experts from world leading language service companies and organizations, and professors from prestigious higher-education institutions exchanged their views on the roles technology has played in the delivery of language services. This also included translation instruction and research, and they offered thought-provoking insights into the new challenges and opportunities for the language service industry.

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