Big Data &
Data-Driven Business
As a business leader, you are use Data to design more precise marketing campaigns, to detect unusual incidences of fraud, to identify indicators of risk, or to find the next major oil field, each of these problems presents unique analysis challenges.

Data-Driven Business

Today we are witnessing dynamic shifts in business strategy — enabled by Digital Technology. Digital transformation creates extraordinary opportunities for consumers, companies, industries and countries. We examining how industries embrace the Internet of Things, cloud and cognitive computing. It's clear, we are embarking on something truly life changing as we enter the Cognitive Business Era.
Cognitive Computing

Like many emerging technologies, cognitive computing is not easy. First, cognitive computing represents a new way of creating applications to support business and research goals. Second, it is a combination of many different technologies that have matured enough to become commercially viable. Some technologies or methods such as machine learning algorithms and natural language processing (NLP) have been seen in artificial intelligence applications for many decades. Other technologies such as advanced analytics have evolved and grown more sophisticated over time. Dramatic changes in deployment models such as cloud computing and distributed computing technology have provided the power and economies of scale to bring computing power to levels that were impossible only a decade ago.
Predictive Analytics
Predictive analytics is a bright light bulb powered by your data. You can never have too much insight. The more you see, the better the decisions you make — and you never want to be in the dark. You want to see what lies ahead, preferably before others do. It's like playing the game "Let's Make a Deal" where you have to choose the door with the hidden prize. Which door do you choose? Door 1, Door 2, or Door 3? They all look the same, so it's just your best guess — your choice depends on you and your luck. But what if you had an edge — the ability to see through the keyhole? Predictive analytics can give you that edge.
Process Mining Assessment
Process mining techniques are able to extract knowledge from event logs commonly available in today's information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments.
Data Cleaning Assessment
Big data, data mining, machine learning, and visualization—it seems like data is at the center of everything great happening in computing lately. From statisticians to software developers to graphic designers, everyone is suddenly interested in data science. The confluence of cheap hardware, better processing and visualization tools, and massive amounts of freely available data means that we can now discover trends and make predictions more accurately and more easily than ever before. What you might not have heard, though, is that all of these data science hopes and dreams are predicated on the fact that data is messy. Usually, data has to be moved, compressed, cleaned, chopped, sliced, diced, and subjected to any number of other transformations before it is ready to be used in the algorithms or visualizations that we think of as the heart of data science.

Missing Data Recovery Assessment
Missing data are ubiquitous throughout the social, behavioral, and medical sciences. For decades, researchers have relied on a variety of ad hoc techniques that attempt to "fi x" the data by discarding incomplete cases or by fi lling in the missing values. Unfortunately, most of these techniques require a relatively strict assumption about the cause of missing data and are prone to substantial bias.

Big Data Analytics

With huge advancements in technology in the last 30 years, the ability to gain insights and actions from data hasn't changed much. In general, applications are still designed to perform predetermined functions or automate business processes, so their designers must plan for every usage scenario and code the logic accordingly. They don't adapt to changes in the data or learn from their experiences. Computers are faster and cheaper, but not much smarter. Of course, people are not much smarter than they were 30 years ago either. That is about to change, for humans and machines. A new generation of an information system is emerging that departs from the old model of computing as process automation to provide a collaborative platform for discovery.
Behavior Statistics
Statistics plays a crucial role in scientific research, especially social science research based on observational (nonexperimental, survey) data. In addition, statistics plays an increasingly important role in our daily lives. We are constantly inundated with statistics in the news in the form of political polls and general information about one or another aspect of life, and these statistics routinely find their way into politics — often as sound bites—as well as into policy. At the same time that the use of statistics permeates our lives, statistics used as a evidence for a position or argument are much maligned.
Social Network Analysis
Computational Social Network is a new emerging field that has overlapping regions from Mathematics, Psychology, Computer Sciences, Sociology, and Management. Emails, blogs, instant messages, social network services, wikis, social bookmarking, and other instances of what is often called social software illustrate ideas fromsocial computing. Social network analysis is the study of relationships among social entities. Very often, all the necessary information is distributed over a number of Web sites and servers, which brings several research challenges from a data mining perspective. Real-world social networks are very dynamic and constantly evolving. Sometimes, it is much harder for us to comprehend how users in a network are connected and how influential some connections are. Visualization helps us with a better understanding of how networks function.
Data-Driven Organisation
Many organizations think that simply because they generate a lot of reports or have many dashboards, they are data-driven. Although those activities are part of what an organization does, they are typically backward-looking. That is, they are often a declaration of past or present facts without a great deal of context, without causal explanation of why something has or has not happened, and without recommendations of what to do next. In short, they state what happened but they are not prescriptive. As such, they have limited upside.

Topological Data Analysis
As many areas in science and engineering are relying more and more heavily on computational science, the analysis of extremely large and complex data sets is becoming ever more crucial. Even though they are a comparatively recent development, topological techniques have proven highly valuable in this context as they can provide a high level, abstract view of data which is well aligned with human intuition. As a result, topological concepts often translate directly into features of interest in various applications and thus can reduce the time-to-insight. The quick rise in popularity of topological techniques has led to an interest and exciting state of the art that spans the entire gamut of analysis of extremely large data to fundamental, theoretical work.
Stochastic Optimization

In order to cope with these uncertainties, a basic procedure in the engineering/economic practice is to replace first the unknown parameters by some chosen nominal values, e.g., estimates, guesses, of the parameters. Then, the resulting and mostly increasing deviation of the performance (output, behavior) of the structure/system from the prescribed performance (output, behavior), i.e., the "tracking error", is compensated by (online) input corrections. However, the online correction of a system/structure is often time-consuming and causes mostly increasing expenses (correction or recourse costs). Very large recourse costs may arise in case of damages or failures of the plant. This can be omitted to a large extent by taking into account already at the planning stage the possible consequences of the tracking errors and the known prior and sample information about the random data of the problem. Hence, instead of relying on ordinary deterministic parameter optimization methods—based on some nominal parameter values—and applying then just some correction actions, stochastic optimization methods should be applied: Incorporating stochastic parameter variations into the optimization process, expensive and increasing online correction expenses can be omitted or at least reduced to a large extent.

Business Process Optimization
Business process optimization is one of four core business process management (BPM) enabled by service-oriented architecture (SOA) services that are interchangeable and can be deployed independently or in combination - so there's no set entry point. These services are designed to ensure business processes are optimized and aligned with company objectives and will help you:

1. Assess and document current processes and model and simulate new processes using "what if" scenarios;

2. Govern the entire lifecycle of business processes using policies, standards and guidelines;

3. Evaluate the deployed business processes against the stated operational and performance objectives to identify areas of potential improvement;

4. Deliver critical, real-time business data to business leaders using dashboards and scorecards.

Probabilistic Models
Chance surrounds our lives with uncertainty. Trying to making sense of this uncertainty leads some to seek solace in religion, others to abandon religion altogether. Realization of the haphazardness of life can lead certain individuals to depression and despair. Such people may see chance as at the root of something undesirable, because it is associated with uncertainty and unpredictability, and hence with danger. Perhaps they still believe that if things are not certain — if the harvests fail and the rains do not come — then there are serious consequences that are readily identifiable with divine punishment.