Read about Big Data and what is necessary to implement it in your company

In the last weeks, I outlined several Big Data benefits by industries. The next posts, I want to outline use-cases where Big Data are relevant in any company, as I will focus on the business functions.

This post’s focus: Marketing.

Marketing is one of the use-cases for Big Data, which are discussed controversial. One the one hand, it gives opportunities to companies to adjust offers to their customers and make the offers more “individual”. I will describe the themes here before I will discuss the downsides of this.

With customer loyalty programs, companies can better “target” their customers. When the company understands the behaviour of the customer, special offers and promotions can be sent to the customer. We all know this from large online shops, where you get regular offers by e-mail. But this also applies to retail stores around you: with programs from the retailers, they also collect data about their customers and can improve the portfolio. Furthermore, they can make their advertisement more individual – and increase the revenue. Marketing gets valuable insights for all industries. Retail is the most common, but also other industries that are not in retail can gain benefits from it. Companies that work in B2B can create value from Big Data by adjusting their sales processes adjusted by data – and react to new trends before competitors find out.

On the other side, this is somewhat frightening. I am basically in favour of Big Data. However, there must be some kind of assurance that personal privacy is respected. At present, it is hard to opt-out of such programs.

The last weeks I outlined several industries that can benefit from Big Data. However, this was just a short overview on what is possible. Let me use this post to sum up the industries that benefit from Big Data. You can get an overview by this tag.

In the first post I started with manufacturing. This traditional industry sees major benefits from Big Data, especially with Industry 4.0. You can read the full post here. Big Data is already used heavily by another industry – the finance sector. Major banks, insurances and financial service providers use Big Data. I outlined the possibilities in this post.

Big Data is also a Big Deal for the public sector. Not just that the Obama administration announced to make more data available – it also gives major benefits to smart cities and alike. You can read the full post here. Often included in public sector is healthcare. Healthcare sees great benefits from using Big Data as well. I’ve summed up the benefits here.

The oil and gas industry can also benefit from Big Data by applying them to sensors while drilling. A sector where you might not expect benefits from IT or Big Data is agriculture. But Big Data can give major benefits to this industry as well – as described here.

Next week I will start to look at the functions within a company – to see where Big Data is within a company – independent from the industry.

Big Data is a disruptive technology. It is changing major industries from the inside. In the next posts, we will learn how Big Data changes different industries.

Today’s focus: Big Data for Agriculture.

Well wait – farming and IT? Really? Short answer: YES!

I believe that we are at the brink of something revolutionary in agriculture. This topic has largely been ignored in industrialization and the ongoing digitalization. Agriculture is (at least in Europe) done by many farmers that cultivate rather small land. Big Data is not about to change this in favor of few farmers on large land – the changes are more about performance, quality and quantity.

I recently had a very interesting discussion with someone from a ministry in Europe working on IT and agriculture. They expect a lot from Big Data. First, they want to improve the way how terrain is used by integrating geo-data from satellites. Analysing the terrain and former usages of the terrain gives additional benefits on what to grow on a specific place. The ministry also wants to integrate weather data in combination with what grains grow on a specific place. This would give additional informations on where water is missing. The long-term idea behind that is to make integrate drones that take care of watering grains and plants that had too little water so far. This is also useful for “premier” goods such as wine. Better quality means higher prices and profits for farmers.

At present, companies such as John Deree are working on integrating Data into their products and services. We can expect some very interesting things to happen here 😉

If you spend much time in a casino, you’ll quickly notice the familiar relationships that develop between croupiers and their regular players. Casinos are an unusual and unique world, many patrons visit to unwind or to meet a friendly community; a certain level of player/dealer intimacy certainly helps in that aim.

Online establishments can sometimes struggle to offer the same level of community experience. Many of the latest, most advanced casinos include chat functions, live games and “hosts” to help recreate the “live playing experience”. However, Big Data can also play an important part in improving the consumer’s gaming experience.

Big Data refers to the extremely large data sets available to modern companies and researchers; often derived from cookies, loyalty cards and other tracking tools. In few industries do consumers reveal as much about their preferences as they do while gambling and casinos record it all through cameras, chip scanners and loyalty cards.

An online casino will know, for example, exactly what games a customer plays, when and with what pattern. Some players will make regular, predictable deposits and play slot games with an unwavering stake, while another might play Poker tournaments and turn to roulette if winning. The biggest sectors of the online gambling market are sports, bingo and table games. Given that the games are generally provided by a small pool of developers, and sports odds set by tote, the main factor that distinguishes competing gaming brands is bonus offering. Customer data can be invaluable in crafting personalized bonus messages, arriving at relevant times.

Part of the challenge is gathering useful data from as many sources as possible. Fortunately a new generation of social games is emerging which will likely connect directly into social media. If they take off, gambling businesses will be able to plumb Facebook and Twitter accounts for telling indicators. Betting sites often offer hospitality packages and prize draws to reward loyal players and incentivise regular play; they would have much more emotional draw if they could be individually targeted to a customer’s favourite team or band.

Casinos already offer bonuses tailored to a player’s favourite game type. However, Big Data at sites including Uptown Aces, promise the ability to tailor bonuses on a completely individual level. For example, some casinos are now timing bonus emails to coincide with player’s return from work, and adding extra offers for their favourite teams.

Sports betting has long been the richest online gambling market, with the vast majority of gambling advertising focused on, and screened around major sporting events. Online sportsbooks have adopted a strategy of competing indirectly on “insurance” or “money back” deals – which play to natural superstition and avoid damaging price competition.

The problem is, we each have our individual bugbears and bogey men when sports betting. One player may be constantly undone by last minute penalties, while another may constantly see his team reduced to 10 men. The ultimate objective of Big Data will be achieved when sportsbooks can seamlessly anticipate our worries and choices, providing individual markets based on the fate of previous bets.

Big Data has become a buzzword over the last decade, but for good reason. The human race now generates vastly more data than it can currently find a use for, finding that use will undoubtedly create many more efficiencies in our lives. As companies learn how to simplify and sort their vast Excel spreadsheets, they should take some pointers from the gaming industry – where information has been successfully leveraged for long term profit.

Big Data is a disruptive technology. It is changing major industries from the inside. In the next posts, we will learn how Big Data changes different industries.

Today’s focus: Big Data for Oil and Gas.

There are several benefits for the Oil & Gas industry with Big Data. A key benefit comes with real-time monitoring of sensors in the production chain. This starts with monitoring during a drill and continues when hauling oil. It is possible to react immediately to changed pressure and other factors. Big Data can also largely improve results during the refining phase. Last but not least, it is possible to adjust global operations in the oil&gas industry by using data.

Big Data is a disruptive technology. It is changing major industries from the inside. In the next posts, we will learn how Big Data changes different industries.

Today’s focus: Big Data for Healthcare.

Big Data offers several benefits for the healthcare industry. Decoding the human genome was one of the first Big Data applications in IT. It took years to decode the DNA sequence the first time, nowadays it is a matter of hours! This gives entirely new approaches to research in the health industry, enabled by Big Data algorithms.

However, Big Data in healthcare is not only about decoding the DNA. There are several other benefits. Analyzing data brings large benefits to illnesses we don’t know enough yet. There is a large number of chronic illnesses where doctors are still not sure where they come from and how to best treat them. This can be done by collecting large amounts of data from a specific illness and compare it on a broad base with different factors. However, it is necessary to keep the data anonymous and respect the data rights and privacy of individuals. The target should be to improve the healthcare.

Another benefit of Big Data in Healthcare is about medical devices. There are a large number of devices today that are used in the healthcare environment. Outages of these devices are often a problem, as they are always connected to very important functions. When a device that is used for analysis has an outage, problems will occur. It is either necessary to have more of the same devices in case of a failure or to simply wait for the devices to come back. In recent years, I had several projects in the predictive maintenance area, where Big Data analytics were integrated to improve the stability of devices and to predict when a failure might occur. I saw several companies that could reduce the time a device “stands still” from several days to only hours by applying such algorithms.

Big Data is a disruptive technology. It is changing major industries from the inside. In the next posts, we will learn how Big Data changes different industries.

Today’s focus: Big Data in Finance.

The finance sector heavily benefits from Big Data analytics. First, there is banking. Banks have large amounts of data on transactions that have to be processed every day. This data needs to be checked for fraud. Real-time analytics such as Apache Storm play a vital role in that process. To improve the security and detect fraud before it can happen largely decreases financial loss for them. But not only banks adapt Big Data for that: credit card companies such as Visa or MasterCard also apply these techniques in order to prevent fraud. A sample is when you travel: imagine you travel from New York to London. You didn’t pay anything during the trip with your credit card nor did you pay the travel itself with your credit card. Once in London, you get a coffee at the airport, but your credit card is rejected (or at least, authorization is required). In case you paid the trip with your credit card, the credit card company knows that you will be in London and can accept the payment.

Insurance companies face a similar problem with financial fraud. By analyzing data, the validity of a claim can be checked. Insurance companies can also lower their risk by analyzing data (and thus increase our bill)

Big Data is a disruptive technology. It is changing major industries from the inside. In the next posts, we will learn how Big Data changes different industries.

Today’s focus: Big Data in Manufacturing.

Manufacturing is a traditional industry relevant to almost any country in the world. It started to emerge in the industrial revolution, when machines took over and production became more and more automated. Big Data has the possibility to substantially change the manufacturing industry again – with various opportunities.

Manufactures can utilize Big Data for various reasons. First, it is all about quality. When we look at production chains, may it be producing a car or just some metal works, quality is key. Who wants to buy a car that is broken? Exactly, nobody. Improving quality is a key aspect in Big Data for manufacturers. As of Big Data, this can come with several aspects. First of all, it is necessary to collect data about the production line(s) and all devices that are connected or connect-able. When errors occur or a product isn’t as desired, the production data can be analyzed and reviewed. Data scientists basically do a great job on that. Real-Time analytics allow the company to improve the material quality and product quality again. This can be done by analyzing images of products or materials and removing them from the production line in case they don’t fulfill certain standards.

A key challenge today in manufacturing is the high degree of product customization. When buying a new car, the words by Henry Ford (you can have any type of the T-model as long as it is black) are not true any more. When customers order whatever type of product, customers expect that their own personality is reflected by the product. If a company fails to deliver that, they might risk loosing customers. But what is the affiliation with Big Data now? Well, this customization is a strong shift towards Industry 4.0, which is heavily promoted by the German industry. In order to make products customize able, it is necessary to have an automated product line and to know what customers might want – by analyzing recent sales and trends from social networks and alike.

Changing the output of a production line is often difficult and ineffective. Big Data analytics allow manufacturers to better understand future demands and they can reduce production pikes. This enables the manufacturer to better plan and act in the market – and get more efficient.

Self-driving cars are getting more and more momentum. In 2014, Tesla introduced the “Autopilot” feature for it’s Model S, which allows autonomous driving. The technology for self-driving cars has been around for years though – there are other factors why it is still not here. It is mainly a legal question and not a technical one.

However, autonomous systems will be here in some years from now, and they will have a positive impact on cloud computing and big data. The use-cases were already described partially with smart cities in an earlier post. However, there are several other use-cases. Positive effects of self-driving cars are the advanced security: sensors need milliseconds to react to threads whereas humans need a second. This gives more time for better reactions. Autonomous systems can then also communicate with other cars and warn them in advance. This is called “Vehicle to Vehicle communication”. But communication is also done with infrastructure (which is called Vehicle to Infrastructure communication). A street for instance can warn the car that there are problems ahead – e.g. that the street itself is getting worse.

The car IT itself doesn’t need the cloud and big data – but services around that will heavily use cloud and big data services.

Self-driving cars also brings a side-effect: Smart Logistics. Smart Logistics are fully automated logistic devices that drive without the need of a driver and deliver goods to a destination. This can start in china with a truck that brings a container to a ship. This ship is also fully automated and works independent. The ship drives to New York, where the goods are picked up by a self-driving truck again. The truck brings the container to a distribution center, where robots unload the container and drones deliver the goods to the customers. All of that is handled by cloud and big data systems that often operate in real-time.

According to various sources, we are in the middle of the so-called 4th industrial revolution. This revolution is basically lead by a very high degree of automation and IT systems. Until recently, IT played mainly a support role in the industry, but with new technologies the role will change dramatically: it will lead the industry. Industry 4.0 (or Industrie 4.0) is mainly lead by Germany which places a high bet on that topic. The industrial output of germany is high and in order to maintain it’s global position, the german industry has to – and will – change dramatically.

Let’s first look at the past industrial revolutions:

  • The first industrial revolution took place in the 18th century. This happend when the mechanical loom was introduced.
  • The second industrial revolution took place in the early 20th century, when assembly lines were introduced
  • In the 70th and 80th of the last century, the 3rd industrial revolution took place. Machines could now work on repeatable tasks and robots were first introduced

The 4th industrial revolution is now lead dramatically by the IT industry. It is not only about supporting the assembly lines but it is about replacing them. The customer can define it’s own product and make it really individual. Designers can offer templates in online stores and the product then knows how it will be produced. The product selects in what fabric it will be produced and tells the machines how it should be handled.

Everything in this process is fully automated. It starts by ordering something online. The transportation process is automated as well – autonomous systems deliver individual parts to the fabrics and this goes well beyond traditional just-in-time delivery. This is also a democratization of design: just like individuals can now write their books without a publisher as e-books, designers can provide their designs online on new platforms. This gives new opportunities to designers as well as customers.

As with Smart Homes and Smart Cities, this produces not only a lot of data – it also requires sophisticated back-end systems in the cloud that take care of this complex processes. Business processes need to be adjusted to the new challenges and they are more complex than ever. This can’t be handled by one single system – this needs a complex system running in the cloud.