Big Data in Logistics


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: Logistics.

Big Data is a key driver for logistics. By logistics, companies that provide logistics solutions and companies that take advantage of logistics are meant. On the one hand, Big Data can significantly improve the supply chain of a company. For years – or even decades – companies rely on the “just in time” delivery. However, “just in time” wasn’t always “just in time”. In many cases, the time an item spent on stock was simply reduced but it still needed to be stored somewhere – either in a temporary warehouse on-site or in the delivery trucks themselves. The first approach is capital intensive, since these warehouses need to be built (and extended in case of growth). The second approach is to keep the delivery vehicles waiting – which creates expenses on the operational side – each minute a driver has to wait, costs money. With analytics, the just in time delivery can be further improved and optimized to lower costs and increase productivity.

Another key driver for Big Data and logistics is the route optimization. Routes can be improved by algorithms and make them faster. This lowers costs and on the other hand significantly saves the environment. But this is not the end of possibilities: routes can also be optimized in real-time. This includes traffic prediction and jam avoidance. Real-time algorithms will not only calculate the fastest route but also the environmental friendliest route and cheapest route. This again lowers costs and time for the company.

Header Image by  Nick Saltmarsh / CC BY

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Big Data for Customer Services


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: Customer Services.

Big Data is great for customer services. In customer services, there are several benefits for it. A key benefit can be seen in the IT help desk. IT help desk applications can greatly be improved by Big Data. Analysing past incidents and calls, their occurrence and impact can give great benefits for future calls. On the one hand, a knowledge base can be built to give employees or customers an initial start. For challenging cases, trainings can be developed to reduce the number of tickets opened. This reduces costs on the one side and improves customer acceptance on the other side.

Big Data can have a large impact here. When a customer feels treated well, the customer is very likely to come back and buy more at the company. Big Data can serve as an enabler here.

Big Data for Sales


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: Sales.

Las week I outlined Marketing possibilities (and downsides) with Big Data. Very similar to Marketing is Sales. Often,  those two things come together. However, I would say it needs to be stated separately. In this post, I won’t discuss the Sales opportunities in Big Data from Webshops and alike. Today, I want to focus on Big Data opportunities that respect privacy but still have an impact.

Last year, I attended a conference where a company outlined their big data case. It was about analysing bills issued in their chain stores. The data from the bills included no personal details like credit card number, bonus card number and alike. It was only about what was in the basket. With the help of that, they could figure out what products get more attention at a specific store and how it differs from other stores. This data was joined with open data from public sources and other data about demographics. They could also find out that specific products get bought with another products – which means that if customer X buys product C, the customer is very likely to buy product D. An example of that for instance is that if you buy a skirt, you are also likely to buy a top.

The later example focused on analysing data for fashion stores. However, most stores can benefit from Big Data. I recently had the chance to talk to the CIO of a large supermarket chain. They also have some Big Data algorithms that improve their chain stores. The company’s policy is to accept their customer’s privacy and they don’t work on their personal data. They figured out when the neighbourhood changes – e.g. because a university was built. They could see that other products are demanded and changed the assortment of goods accordingly.

There are many opportunities where Big Data can improve Sales, and as shown in these two examples, they don’t necessarily need to violate someone’s privacy.

Big Data for Marketing


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.

Big Data is everywhere! In all major industries


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 in Agriculture


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 😉

How is Big Data used by online casinos?


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.