The importance of being Data Scientist

Header-Image by Clint Adair on Unsplash.

The incredible results of Machine Learning and Artificial Intelligence, Deep Learning in particular, could give the impression that Data Scientist are like magician. Just think of it. Recognising faces of people, translating from one language to another, diagnosing diseases from images, computing which product should be shown for us next to buy and so on from numbers only. Numbers which existed for centuries. What a perfect illusion. But it is only an illusion, as Data Scientist existed as well for centuries. However, there is a difference between the one from today compared to the one from the past: evolution.

The main activity of Data Scientist is to work with information also called data. Records of data are as old as mankind, but only within the 16 century did it include also numeric forms — as numbers started to gain more and more ground developing their own symbols. Numerical data, from a given phenomenon — being an experiment or the counts of sheep sold by week over the year –, was from early on saved in tabular form. Such a way to record data is interlinked with the supposition that information can be extracted from it, that knowledge — in form of functions — is hidden and awaits to be discovered. Collecting data and determining the function best fitting them let scientist to new insight into the law of nature right away: Galileo’s velocity law, Kepler’s planetary law, Newton theory of gravity etc.

Such incredible results where not possible without the data. In the past, one was able to collect data only as a scientist, an academic. In many instances, one needed to perform the experiment by himself. Gathering data was tiresome and very time consuming. No sensor which automatically measures the temperature or humidity, no computer on which all the data are written with the corresponding time stamp and are immediately available to be analysed. No, everything was performed manually: from the collection of the data to the tiresome computation.

More then that. Just think of Michael Faraday and Hermann Hertz and there experiments. Such endeavour where what we will call today an one-man-show. Both of them developed parts of the needed physics and tools, detailed the needed experiment settings, conducting the experiment and collect the data and, finally, computing the results. The same is true for many other experiments of their time. In biology Charles Darwin makes its case regarding evolution from the data collected in his expeditions on board of the Beagle over a period of 5 years, or Gregor Mendel which carry out a study of pea regarding the inherence of traits. In physics Blaise Pascal used the barometer to determine the atmospheric pressure or in chemistry Antoine Lavoisier discovers from many reaction in closed container that the total mass does not change over time. In that age, one person was enough to perform everything and was the reason why the last part, of a data scientist, could not be thought of without the rest. It was inseparable from the rest of the phenomenon.

With the advance of technology, theory and experimental tools was a specialisation gradually inescapable. As the experiments grow more and more complex, the background and condition in which the experiments were performed grow more and more complex. Newton managed to make first observation on light with a simple prism, but observing the line and bands from the light of the sun more than a century and half later by Joseph von Fraunhofer was a different matter. The small improvements over the centuries culminated in experiments like CERN or the Human Genome Project which would be impossible to be carried out by one person alone. Not only was it necessary to assign a different person with special skills for a separate task or subtask, but entire teams. CERN employs today around 17 500 people. Only in such a line of specialisation can one concentrate only on one task alone. Thus, some will have just the knowledge about the theory, some just of the tools of the experiment, other just how to collect the data and, again, some other just how to analyse best the recorded data.

If there is a specialisation regarding every part of the experiment, what makes Data Scientist so special? It is impossible to validate a theory, deciding which market strategy is best without the work of the Data Scientist. It is the reason why one starts today recording data in the first place. Not only the size of the experiment has grown in the past centuries, but also the size of the data. Gauss manage to determine the orbit of Ceres with less than 20 measurements, whereas the new picture about the black hole took 5 petabytes of recorded data. To put this in perspective, 1.5 petabytes corresponds to 33 billion photos or 66.5 years of HD-TV videos. If one includes also the time to eat and sleep, than 5 petabytes would be enough for a life time.

For Faraday and Hertz, and all the other scientist of their time, the goal was to find some relationship in the scarce data they painstakingly recorded. Due to time limitations, no special skills could be developed regarding only the part of analysing data. Not only are Data Scientist better equipped as the scientist of the past in analysing data, but they managed to develop new methods like Deep Learning, which have no mathematical foundation yet in spate of their success. Data Scientist developed over the centuries to the seldom branch of science which bring together what the scientific specialisation was forced to split.

What was impossible to conceive in the 19 century, became more and more a reality at the end of the 20 century and developed to a stand alone discipline at the beginning of the 21 century. Such a development is not only natural, but also the ground for the development of A.I. in general. The mathematical tools needed for such an endeavour where already developed by the half of the 20 century in the period when computing power was scars. Although the mathematical methods were present for everyone, to understand them and learn how to apply them developed quite differently within every individual field in which Machine Learning/A.I. was applied. The way the same method would be applied by a physicist, a chemist, a biologist or an economist would differ so radical, that different words emerged which lead to different langues for similar algorithms. Even today, when Data Science has became a independent branch, two different Data Scientists from different application background could find it difficult to understand each other only from a language point of view. The moment they look at the methods and code the differences will slowly melt away.

Finding a universal language for Data Science is one of the next important steps in the development of A.I. Then it would be possible for a Data Scientist to successfully finish a project in industry, turn to a new one in physics, then biology and returning to industry without much need to learn special new languages in order to be able to perform each tasks. It would be possible to concentrate on that what a Data Scientist does best: find the best algorithm. In other words, a Data Scientist could resolve problems independent of the background the problem was stated.

This is the most important aspect that distinguish the Data Scientist. A mathematician is limited to solve problems in mathematics alone, a physicist is able to solve problems only in physics, a biologist problems only in biology. With a unique language regarding the methods and strategies to solve Machine Learning/A.I. problems, a Data Scientist can solve a problem independent of the field. Specialisation put different branches of science at drift from each other, but it is the evolution of the role of the Data Scientist to synthesize from all of them and find the quintessence in a language which transpire beyond all the field of science. The emerging language of Data Science is a new building block, a new mathematical language of nature.

Although such a perspective does not yet exists, the principal component of Machine Learning/A.I. already have such proprieties partially in form of data. Because predicting for example the numbers of eggs sold by a company or the numbers of patients which developed immune bacteria to a specific antibiotic in all hospital in a country can be performed by the same prediction method. The data do not carry any information about the entities which are being predicted. It does not matter anymore if the data are from Faraday’s experiment, CERN of Human Genome. The same data set and its corresponding prediction could stand literary for anything. Thus, the result of the prediction — what we would call for a human being intuition and/or estimation — would be independent of the domain, the area of knowledge it originated.

It also lies at the very heart of A.I., the dream of researcher to create self acting entities, that is machines with consciousness. This implies that the algorithms must be able to determine which task, model is relevant at a given moment. It would be to cumbersome to have a model for every task and and every field and then try to connect them all in one. The independence of scientific language, like of data, is thus a mandatory step. It also means that developing A.I. is not only connected to develop a new consciousness, but, and most important, to the development of our one.

Mit Dashboards zur Prozessoptimierung

Geschäftlicher Erfolg ergibt sich oft aus den richtigen Fragen – zum Beispiel: „Wie kann ich sicherstellen, dass mein Produkt das beste ist?“, „Wie hebe ich mich von meinen Mitbewerbern ab?“ und „Wie baue ich mein Unternehmen weiter aus?“ Moderne Unternehmen gehen über derartige Fragen hinaus und stellen vielmehr die Funktionsweise ihrer Organisation in den Fokus. Fragen auf dieser Ebene lauten dann: „Wie kann ich meine Geschäftsprozesse so effizient wie möglich gestalten?“, „Wie kann ich Zusammenarbeit meiner Mitarbeiter verbessern?“ oder auch „Warum funktionieren die Prozesse meines Unternehmens nicht so, wie sie sollten?“

Um die Antworten auf diese (und viele andere!) Fragen zu erhalten, setzen immer mehr Unternehmen auf Process Mining. Process Mining hilft Unternehmen dabei, den versteckten Mehrwert in ihren Prozessen aufzudecken, indem Informationen zu Prozessmodellen aus den verschiedenen IT-Systemen eines Unternehmens automatisch erfasst werden. Auf diese Weise kann die End-to-End-Prozesslandschaft eines Unternehmens kontinuierlich überwacht werden. Manager und Mitarbeiter profitieren so von operativen Erkenntnissen und können potenzielle Risiken ebenso erkennen wie Möglichkeiten zur Verbesserung.

Process Mining ist jedoch keine „Wunderwaffe“, die Daten auf Knopfdruck in Erkenntnisse umwandelt. Eine Process-Mining-Software ist vielmehr als Werkzeug zu betrachten, das Informationen erzeugt, die anschließend analysiert und in Maßnahmen umgesetzt werden. Hierfür müssen die generierten Informationen den Entscheidungsträgern jedoch auch in einem verständlichen Format zur Verfügung stehen.

Bei den meisten Process-Mining-Tools steht nach wie vor die Verbesserung der Analysefunktionen im Fokus und die generierten Daten müssen von Experten oder Spezialisten innerhalb einer Organisation bewertet werden. Dies führt zwangsläufig dazu, dass es zwischen den einzelnen Schritten zu Verzögerungen kommt und die Abläufe bis zur Ergreifung von Maßnahmen ins Stocken geraten.

Process-Mining-Software, die einen kooperativeren Ansatz verfolgt und dadurch das erforderliche spezifische Fachwissen verringert, kann diese Lücke schließen. Denn nur wenn Informationen, Hypothesen und Analysen mit einer Vielzahl von Personen geteilt und erörtert werden, können am Ende aussagekräftige Erkenntnisse gewonnen werden.

Aktuelle Process-Mining-Software kann natürlich standardisierte Berichte und Informationen generieren. In einem sich immer schneller ändernden Geschäftsumfeld reicht dies jedoch möglicherweise nicht mehr aus. Das Erfolgsgeheimnis eines wirklich effektiven Process Minings besteht darin, Herausforderungen und geschäftliche Möglichkeiten vorherzusehen und dann in Echtzeit auf sie zu reagieren.

Dashboards der Zukunft

Nehmen wir ein analoges Beispiel, um aufzuzeigen, wie sich das Process Mining verbessern lässt. Der technologische Fortschritt soll die Dinge einfacher machen: Denken Sie beispielsweise an den Unterschied zwischen der handschriftlichen Erfassung von Ausgaben und einem Tabellenkalkulator. Stellen Sie sich nun vor, die Tabelle könnte Ihnen genau sagen, wann Sie sie lesen und wo Sie beginnen müssen, und würde Sie auf Fehler und Auslassungen aufmerksam machen, bevor Sie überhaupt bemerkt haben, dass sie Ihnen passiert sind.

Fortschrittliche Process-Mining-Tools bieten Unternehmen, die ihre Arbeitsweise optimieren möchten, genau diese Art der Unterstützung. Denn mit der richtigen Process-Mining-Software können individuelle operative Cockpits erstellt werden, die geschäftliche Daten in Echtzeit mit dem Prozessmanagement verbinden. Der Vorteil: Es werden nicht nur einzelne Prozesse und Ergebnisse kontinuierlich überwacht, sondern auch klare Einblicke in den Gesamtzustand eines Unternehmens geboten.

Durch die richtige Kombination von Process Mining mit den vorhandenen Prozessmodellen eines Unternehmens werden statisch dargestellte Funktionsweisen eines bestimmten Prozesses in dynamische Dashboards umgewandelt. Manager und Mitarbeiter erhalten so Warnungen über potenzielle Probleme und Schwachstellen in Ihren Prozessen. Und denken Sie daran, dynamisch heißt nicht zwingend störend: Die richtige Process-Mining-Software setzt an der richtigen Stelle in Ihren Prozessen an und bietet ein völlig neues Maß an Prozesstransparenz und damit an Prozessverständnis.

Infolgedessen können Transformationsinitiativen und andere Verbesserungspläne jederzeit angepasst und umstrukturiert werden und Entscheidungsträger mittels automatisierter Nachrichten sofort über Probleme informiert werden, sodass sich Korrekturmaßnahmen schneller als je zuvor umsetzen lassen. Der Vorteil: Unternehmen sparen Zeit und Geld, da Zykluszeiten verkürzt, Engpässe lokalisiert und nicht konforme Prozesse in der Prozesslandschaft der Organisation aufgedeckt werden.

Dynamische Dashboards von Signavio

 Testen Sie Signavio Process Intelligence und erleben Sie selbst, wie die modernste und fortschrittlichste Process-Mining-Software Ihnen dabei hilft, umsetzbare Einblicke in die Funktionsweise Ihres Unternehmens zu erhalten. Mit Signavios Live Insights profitieren Sie von einer zentralen Ansicht Ihrer Prozesse und Informationen, die in Form eines Ampelsystems dargestellt werden. Entscheiden Sie einfach, welche Prozesse und Aktivitäten Sie innerhalb eines Prozesses überwachen möchten, platzieren Sie Indikatoren und wählen Sie Grenzwerte aus. Alles Weitere übernimmt Signavio Process Intelligence, das Ihre Prozessmodelle mit den Daten verbindet.

Lassen Sie veraltete Arbeitsweisen hinter sich. Setzen Sie stattdessen auf faktenbasierte Erkenntnisse, um Ihre Geschäftstransformation zu unterstützen und Ihre Prozessmanagementinitiativen schneller zum Erfolg zu führen. Erfahren Sie mehr über Signavio Process Intelligence oder registrieren Sie sich für eine kostenlose 30-Tage-Testversion über

Erfahren Sie in unserem kostenlosen Whitepaper mehr über erfolgreiches Process Mining mit Signavio Process Intelligence.

Why Retailers Are Making the Push for Stronger Data Science and AI

Retail relies on what the customer wants and needs at that moment, no matter the size of the company. Making judgments without consumer input would probably work for a little while but will fall flat as soon as the business model becomes outdated. In today’s technology-run world, things can become obsolete in a matter of days or even hours.

Retailers are the businesses most in need of capitalizing on what the customer wants in real-time. They have started to use data science and information from the Internet of Things (IoT) to not only stay in business, but also get ahead of other brands.

Artificial intelligence (AI) adds a new layer by using modern technology. The details of why retailers want to use these new practices are a bit more specific, though.

Data Targets Audiences

By using current customer data compared to information from the IoT, retailers can learn more about their audience and find better means of targeting them. Demographics like age, location and many other factors could affect advertising and even shopping, not to mention holidays throughout the year an audience celebrates.

Websites also need to be customized to suit the target audience. Those that are mobile-friendly and focused on what shoppers want can increase revenue, but the wrong approach can drive away new and existing customers. AI can help companies understand that data and present it back to the customer seamlessly, providing different options for various audiences.

Customer Base Expansion

Customer success should mean business success, as well. Growing a client base is something data science can assist with. However, helping customers grow is another type of service few companies provide but all people appreciate. A business can expand by offering new products and services that are relevant to their audience through the use of data.

Once a company learns what current customers want and begin to fit their needs, it can expand to more audiences. With data science, a business can ensure it does so slowly to give more of what current customers want while also finding new ones. The data can tell what sort of interests they all share so companies can capitalize on the venture.

AI Helps Customer Service

AI helps out customer service on both ends. Employees don’t have to focus on common problems that could easily be resolved, and clients often walk away happier than if they were to speak to a real person. This doesn’t work for every problem, especially ones that are specific in nature, but they can assist with more common issues. This is where chatbots enter the stage.

An AI-supported chatbot can give immediate support, provide suggestions, answer direct questions and offer almost any other form of help needed. Customers get personalized attention, and businesses can work faster toward customer loyalty.

Again, speaking to a real person when they have problems is a big plus for customers, but not for issues they know could be resolved in the time it takes to wait on the line for a representative.

Supply and Demand

Price optimization has taken on a bigger role than it has in the past. Mostly, data science is looking at supply and demand in real-time rather than having price fluctuations occur months after the business loses money. Having the right price can also help create more promotions for products and services, rewarding loyal customers for their shopping.

The data has to be gained from multiple channels by using price optimization tools, which focus on using data correctly in a company’s favor. The information doesn’t just look at supply and demand, but also examines locations, times, customer attitudes, competitor pricing and many other factors. All these pieces of information can be delivered in real-time so prices can be changed accordingly.

Taking the Competition

The thing about data science is that businesses are already utilizing it to their full potential and getting more customers than ever. The only way to get ahead of the competition is to at least start using the tools they’ve had at their disposal for years.

Target was one such company that took up the data helm. During 2012 and 2013, it saw a pretty sizeable dip in sales, but its online sales went up by almost 30% during the same time.

Data and Retail

When running a retail business, especially one that’s branching off into a franchise, using data is imperative. Data science and AI have become extremely important to companies both big and small.

Applying it correctly can help enterprises of any size and in every industry take things to the next level.

Even if a company is just starting out, sticking the first landing with a target audience is a fantastic way to begin the adventure and find success.

Interview: Data Science im Einzelhandel

Interview mit Dr. Andreas Warntjen über den Weg zum daten-getriebenen Unternehmen – Data Science im Einzelhandel

Zur Einführung der Person:

Dr. Andreas Warntjen arbeitet seit Juli 2016 bei der Thalia Bücher GmbH, aktuell als Senior Manager Advanced and Predictive Analytics. Davor hat Herr Dr. Warntjen viele Jahre als Sozialwissenschaftler an ausländischen Universitäten geforscht. Er hat selbst langjährige Erfahrung in der statistischen Datenanalyse mit Stata, SPSS und R und arbeitet im Moment mit der in-memory Datenbank SAP HANA sowie Python und SAP’s Automated Predictive Library (APL).

Data Science Blog: Herr Dr. Warntjen, welche Bedeutung hat die Data Science für Sie und Ihren Bereich bei Thalia? Und wie ordnen Sie die verwandten Begriffe wie Predictive Analytics und Advanced Analytics im Kontext der geschäftlichen Entscheidungsfindung ein?

Data Science spielt bei Thalia in unterschiedlichsten Bereichen eine zunehmend größer werdende Rolle. Neben den klassischen Themen wie Betrugserkennung und Absatzprognosen ist für Thalia als Buchhändler Text Mining von zentraler Bedeutung. Das größte Potential liegt aus meiner Sicht darin, besser auf die Wünsche unserer  Kunden eingehen zu können.

Bei Thalia werden in schneller Taktung Innovationen eingeführt. Sei es die Filialabholung, bei der online bestellte Bücher innerhalb von 2 Stunden in einer Buchhandlung abgeholt werden können. Oder das Beratungs- und Bezahl-Tablet für die Mitarbeiter vor Ort. Oder Innovationen im Webshop. Bei der Beurteilung, ob diese Neuerungen tatsächlich Kundenwünsche effektiv und effizient erfüllen, kann Advanced Analytics helfen. Im Gegensatz zur klassischen Business Intelligence – die weiterhin eine wichtige Rolle bei der Entscheidungsfindung im Unternehmen spielen wird – berücksichtigt Advanced Analytics stärker die Vielfalt des Kundenverhaltens und der unterschiedlichen Situationen in den Filialen. Verfahren wie etwa multivariate Regressionsanalyse, Entscheidungsbäume und statistische Hypothesentest können die in Unternehmen etablierte Analyse von deskriptiven Statistiken – etwa der Vergleich von Umsatzzahlen zwischen Pilot- und Vergleichsfilialen mit Pivot-Tabellen – ergänzen.

Predictive Analytics kann helfen verschiedenste Geschäftsprozesse individuell für Kunden zu gestalten. Generell können auf Grundlage von automatischen, in Echtzeit erstellten Vorhersagen Prozesse im Unternehmen optimiert werden. Außerdem kann Predictive Analytics Mitarbeiter bei wiederkehrenden Tätigkeiten unterstützen, beispielsweise in der Disposition.

Data Science Blog: Welche Fähigkeiten benötigen gute Data Scientists denn wirklich zur Geschäftsoptimierung? Wie wichtig ist das Domänenwissen?

Die wichtigsten Eigenschaften eines Data Scientist sind große Neugierde, eine sehr analytische Denkweise und eine exzellente Kommunikationsfähigkeit. Um mit Data Science erfolgreich Geschäftsprozesse zu optimieren, benötigt man ein breites Wissensspektrum: vom Geschäftsprozess über das IT-Datenmodell und das Know-how zur Entwicklung von Vorhersagemodellen bis hin zur Prozessintegration. Das ist nur im Team machbar. Domänenwissen spielt dabei eine wichtige Rolle, weshalb es für den Data Scientist essentiell ist sich mit den Prozessverantwortlichen und Business Analysten auszutauschen.

Data Science Blog: Sie bearbeiten Anwendungsfälle für den Handel. Können sich Branchen die Anwendungsfälle gegenseitig abschauen oder sollte jede Branche auf sich selbst fokussiert bleiben?

Es gibt sowohl Anwendungsfälle, die für den Einzelhandel und andere Branchen gleichermaßen relevant sind, als auch Themen, die für Thalia als Buchhändler besonders wichtig sind.

Die Individualisierung im eCommerce ist ein branchenübergreifendes Thema. Analytisches CRM, etwa das zielsichere Ausspielen von Kampagnen oder eine passgenaue Kundensegmentierung, ist für eine Versicherung oder Bank genauso wichtig wie für den Baumarkt oder den Buchhändler. Die Warenkorbanalyse mit statistischen Algorithmen ist ein klassisches Data Mining-Thema, das für den Einzelhandel generell interessant ist.

Natürlich muss man sich vorab über die Besonderheiten des jeweiligen Geschäftsumfeldes Gedanken machen, aber prinzipiell kann man von Unternehmen oder Branchen lernen, die Advanced und Predictive Analytics schon seit Jahren oder Jahrzehnten nutzen. Die passende IT-Infrastruktur und das entsprechende Interesse vom Fachbereich vorausgesetzt, eignen sich diese Anwendungsfälle damit besonders für den Einstieg in Advanced und Predictive Analytics – auch für Mittelständler.

Das Kerngeschäft des Buchhändlers  Thalia ist es, Kunden mit für sie interessanten Geschichten zusammen zu bringen. Die Geschichten selber bestehen aus Text. Die Produktbeschreibungen („Klappentexte“) und -besprechungen liegen in Textform vor. Und Kundenfeedback – sei es auf oder in sozialen Medien – erreicht uns als Text. Erkenntnisse aus Texten abzuleiten (Text Mining) ist deshalb für Thalia wichtiger als für andere Einzelhändler.

Data Science Blog: Welche Algorithmen und Tools verwenden Sie für Ihre Anwendungsfälle? Womit machen Sie eher gute, womit eher schlechte Erfahrungen?

Die Palette bei Thalia reicht von A wie Automated Machine Learning bis Z wie Zeitreihenanalyse. Ich selber arbeite aktuell mit verschiedenen Klassifikationsalgorithmen (z.B., regularisierte logistische Regression,  Random Forest, XGB, Naive Bayes, SAP’s Automated Predictive Library). Im Bereich Text Mining beschäftigen wir uns im Moment unter anderem mit Topic Models und Word2Vec.

Sowohl Algorithmus als auch die Software muss zum Verwendungszweck passen. Bei der Auswahl des Algorithmus gibt es häufig einen Trade-off zwischen Interpretierbarkeit und Prognosegüte. Das muss zusammen mit der Fachabteilung je nach Anwendungsfall abgewogen werden.

Mit flexibler Open Source-Software wie etwa R oder Python lassen sich schnell Proof-of-Concept-Projekte verwirklichen. Für die Integration in bestehende Prozesse sind manchmal kommerzielle Software-Lösungen besser.

Data Science Blog: Soviel zum kurz- und mittelfristigen Start in die Datennutzung. Wie sieht es für die langfristige Verankerung von Advanced/Predictive Analytics im Unternehmen aus? Was muss hier im Rahmen der IT-Infrastruktur bedacht und verankert werden?

Ohne Daten keine Datenanalyse. Je flexibler man auf unterschiedliche Daten im Unternehmen zugreifen kann, desto höher die Innovationsgeschwindigkeit durch Advanced/Predictive Analytics. „Datensilos“ abzubauen bzw. zu vermeiden ist also ein sehr wichtiges Thema. Hohe Datenqualität und die umfassende Dokumentation von Daten sind auch essentiell. Das gilt natürlich nicht nur für Advanced und Predictive Analytics sondern auch für Business Intelligence.

Die langfristige Verankerung von Advanced und Predictive Analytics im Unternehmen verlangt den Aufbau und die kontinuierliche Weiterentwicklung von Infrastruktur in Form von Hardware, Software, Kompetenzen und Wissen, sowie Organisationsformen und Prozessen. Wertschöpfung durch Advanced bzw. Predictive Analytics erfordert das konstruktive Zusammenspiel von Domänenexpertise aus der Fachabteilung, Wissen über Datenstrukturen und -modellen  aus der IT-Abteilung bzw. BI/BW-Systemen und tiefem statistischem Know-how. Nur durch die Zusammenarbeit verschiedener Unternehmensbereiche entstehen Erfolge für das gesamte Unternehmen.

Data Science Blog: Auch organisatorisch sollte langfristig sicherlich einiges bedacht werden. Wann sollten Projekte in den jeweiligen Fachbereichen direkt umgesetzt werden? Wann vielleicht besser in einer zentralen Daten-Abteilung?

Das hängt von einer Reihe von Faktoren ab. Bei hochgradig spezialisiertem Know-how, von dem unterschiedliche Fachbereiche profitieren können, kann es Synergie-Effekte geben, wenn dies zentral organisiert ist. Eine zentrale Einheit kann vielleicht auch Innovationen breiter in ein Unternehmen tragen. Wenn bestimmte Anwendungsszenarien von Advanced/Predictive Analytics für eine Fachabteilung hingegen eine zentrale Rolle spielen oder sie sich ein einem sehr schnelllebigen Umfeld bewegt, dann wäre eine fachliche und organisatorische Verankerung im Fachbereich wichtig.

Treffen Sie bessere Entscheidungen

Entscheidungen prägen unseren Alltag, dies beginnt schon bei der Frage, was man anziehen oder essen soll. Andere hingegen mögen auf den ersten Blick unbedeutend erscheinen, können das Leben aber gravierend verändern, wie beispielsweise die Entscheidung, ob die Überquerung einer Straße sicher ist. Je größer die relative Macht eines Entscheidungsträgers ist, desto größer ist natürlich auch die Auswirkung seiner Entscheidungen.

Read this article in English: 
“How to Make Better Decisions”

Auch der Unternehmensalltag ist geprägt durch Entscheidungen. Tatsächlich kann man ein Unternehmen als die Summe großer und kleiner Entscheidungen betrachten: Welche neuen Märkte erschlossen werden sollen, über die nächste große Werbekampagne bis hin zur Wandfarbe für das neue Büro. Im Idealfall wäre jede einzelne Entscheidung innerhalb einer Organisation Teil einer konsistenten, kohärenten Unternehmensstrategie.

Leider ist eine derartige Konsistenz für viele Unternehmen schwer umsetzbar. Den Überblick darüber zu behalten, was in der gestrigen Sitzung beschlossen wurde, geschweige denn vor Wochen, Monaten oder gar Jahren, kann schwierig sein. Die Erkennung, Kategorisierung und Standardisierung der Entscheidungsfindung kann daher eine Möglichkeit sein, diese Herausforderung zu meistern.

Strategische, taktische und operative Entscheidungen

Grundsätzlich gibt es in einem Unternehmen drei Entscheidungsebenen: Strategische Entscheidungen haben einen großen Einfluss auf das gesamte Unternehmen, wie bspw. Fusionen und Übernahmen oder die Aufgabe eines leistungsschwachen Geschäftsbereichs. Taktische Entscheidungen werden zu bestimmten Themen getroffen, z. B. wo und wie eine Marketingkampagne durchgeführt werden soll.

Und schließlich gibt es noch die operativen Entscheidungen, auf die jeder Mitarbeiter täglich in jedem Unternehmen trifft: Beispielsweise wie viele Treuepunkte ein Kunde erhält, bei welchem ​​Lieferanten Materialien und Dienstleistungen gekauft werden oder ob ein Kunde einen Kredit erhält. Millionen dieser Entscheidungen werden jeden Tag getroffen.

Der kumulative Effekt dieser operativen Entscheidungen hat einen enormen Einfluss auf die geschäftliche Leistung eines Unternehmens. Nicht unbedingt in dem Maße wie sich strategische oder taktische Entscheidungen auswirken, aber sie nehmen Einfluss darauf, wie reibungslos und effektiv die Dinge innerhalb des Unternehmens tatsächlich erledigt werden.

Risiken einer schlechten Entscheidungsfindung

Auf operativer Ebene können sich selbst kleine Entscheidungen erheblich auf das gesamte Unternehmen auswirken – vor allem dann, wenn sich diese Entscheidungen wiederholen. In vielen Fällen bedeutet dies:

  • Compliance-Verstöße: Mitarbeiter und Systeme wissen nicht, was das Management erwartet, oder welches das richtige Verfahren ist. Mit der Zeit kann dies dazu führen, dass Richtlinien generell nicht eingehalten werden.
  • Weniger Agilität: Unkontrolliert oder unstrukturiert getroffene Entscheidungen lassen sich nur schwer ändern, um schnell auf neue interne oder externe Umstände reagieren zu können.
  • Reduzierte Genauigkeit: Ohne einen klaren Entscheidungsrahmen können sich unklar und unpräzise ausgerichtete Prozesse und Praktiken weiterverbreiten.
  • Mangelnde Transparenz: Mitarbeiter und Management können möglicherweise die Faktoren nicht erkennen und verstehen, die jedoch für eine effektive Entscheidungsfindung zu berücksichtigen sind.
  • Zunehmende Nichteinhaltung gesetzlicher Vorschriften: Viele Entscheidungen betreffen Themen wie Steuern, Finanzen und Umwelt, sodass falsch getroffene Entscheidungen zu potenziellen Verstößen gegen Gesetze und Vorschriften und damit letztlich zu Straf- und Rechtskosten führen können.

Diese Risiken können sich wiederholen, wenn Entscheidungen nicht prozessbasiert, sondern aus dem Bauch heraus getroffen werden oder wenn Entscheidungsträger erst Anwendungsfälle, Berichte und Prozesse durchsuchen müssen.

Treffen Sie bessere Entscheidungen

Die richtige Entscheidung zur richtigen Zeit zu treffen, ist für den Geschäftserfolg entscheidend; doch nur wenige Unternehmen verwalten ihre Entscheidungen als separate Instanzen. Die meisten Unternehmen nutzen KPIs oder Ähnliches, um die Auswirkungen ihrer Entscheidungen zu messen, statt die eigentlichen Entscheidungsprozesse im Vorfeld zu definieren.

Hier kommt Business Decision Management (BDM) ins Spiel, mit dem Entscheidungen identifiziert, katalogisiert und modelliert werden können – insbesondere die bereits genannten operativen Entscheidungen. BDM kann zudem ihre Auswirkungen auf die Leistung quantifizieren und Metriken und Schlüsselindikatoren für die Entscheidungen erstellen.

Mit einem effektiven BDM-Ansatz und der Decision Model and Notation (DMN) können Unternehmen Modelle zur Entscheidungsfindung erstellen. DMN bietet ein klares, benutzerfreundliches Notationssystem, das Geschäftsentscheidungen einschließlich der zugrunde liegenden Richtlinien und Daten beschreibt.

Bessere Entscheidungen mit Signavio

Die Signavio Business Transformation Suite unterstützt nicht nur den DMN-Standard, sondern auch den Aufbau einer umfassenden Umgebung zur kollaborativen Ermittlung, Verwaltung und Verbesserung Ihrer Entscheidungen.

Mit dem Signavio Process Manager können Sie Entscheidungen über mehrere Geschäftsbereiche hinweg standardisieren, replizieren und wiederverwenden und diese Entscheidungen mit Ihren Geschäftsprozessen verknüpfen. Der Signavio Process Manager ermöglicht es Ihren Mitarbeitern, stets die beste Entscheidung für ihre Arbeit zu treffen – egal, wie komplex die Aufgaben sind.

Profitieren Sie von den vielen Vorteilen wie verbesserte Leistung und geringere Risiken und trennen Sie die Entscheidungsfindung von unklaren Prozessen und unsicheren Technologien. Registrieren Sie sich noch heute für eine kostenlose 30-Tage-Testversion und lernen Sie die Signavio Business Transformation Suite und ihre Vorteile kennen. Mehr zum Thema lesen Sie in unserem kostenlosen Whitepaper.

How can AI and Machine learning impact healthcare industry?

Healthcare industry is a recession-proof one. Even in times of economic meltdown and financial distress, the healthcare industry can hold its own because mankind will always need healthcare. In fact, during the Great Depression in the US, when the economy was facing a severe slowdown, the healthcare industry expanded, adding 852,000 jobs.

Healthcare AI in the US is slated to reach $6.6 billion in value by 2021.

From clinical trials to new drug research & development, and from innovative medical devices to technology like nanoparticles, AI, and ML has touched every point and has the power to transform them completely.

In fact, according to a study by Accenture, AI applications in healthcare can result in global savings to the tune of $150 billion by 2026.

The possibilities are endless, and the results unthinkable if AI can be properly used.

Here are some of the ways AI and ML can impact the healthcare industry:

1. Solving the Iron Triangle

A problem that has plagued the world for many years the triangle aims to solve a fundamental healthcare problem: that of good quality, accessible treatment at low cost.

Providing all three at the same time is a major challenge in healthcare, as the cost of healthcare is usually high. Here, trying to improve one factor harms another.

But AI can solve this problem in the near future without breaking the triangle, by improving the current healthcare cost-structure. The key to it is AI, and smart machines, that the patient can use for self-treatment for the majority of times, cutting down treatment costs drastically, by reducing human contact and improving quality of life.

2. Diagnostics and Imaging

The US FDA has drastically increased investment on AI in radiology and diagnostics. And it’s not without reason.

The IDx-DR became the first AI system cleared by the US FDA to provide diagnostic decisions. It was a breakthrough discovery to detect early mild diabetic retinopathy. The device was accurate 87.5% of the times, and also detected patients who didn’t have the condition, correctly up to 89.5% of times.

The US FDA also permitted marketing of the Viz.AI a type of clinical decision support system designed to analyze CT scan results to identify possibilities of a stroke in the patients and send the results to a specialist to identify any block.

In fact, diagnostics is fast becoming one of the significant drivers of AI investment in healthcare.

These advances can impact the healthcare industry in a novel way. As more and more devices become AI-enabled, the landscape of healthcare delivery will change.

3. Early screening 

Early screening in case of most diseases can drastically improve the mortality rates of patients and cut down treatment costs by over 50%.

Let’s take the example of colorectal cancer.

The 5-year survival for Stage 1 CRC is around 90%, as compared to only 10% for Stage 4.

Early detection of CRC can be ideally treated with a minimally invasive endoscopy at a low cost of less than $5,000 per year. However, in the case of late-stage CRC, it requires multidisciplinary treatment with multiple surgeries, chemotherapy, and radiation, skyrocketing the costs.

And that is why early detection is essential, and that’s exactly what AI can do. There are already apps on the market that are doing this. For example, Autism & Beyond is a revolutionary app that leveraged the power of Apple’s ResearchKit to gather videos of children and detect their preference for the development of autism, using AI software.

AI used for early screening can save billions of taxpayer dollars of taxpayer money every year, and reduce out of pocket expenditure in the US drastically.

4. Drug research & development

According to the California Biomedical Research Association, it takes around 12 years for a drug to be conceived in the laboratory and go to the patient.

Only 1 out of 5000 drugs that are selected for pre-clinical testing are then used for human testing, and only 20% of them make it to the market for human use.


The cost to develop a new drug now is more than $2.5 billion.

It is only recently that AI is being used in drug research and discovery. The power of AI can be leveraged to streamline the drug discovery and drug repurposing processes. It can identify patients best suited to the trial, can identify patients in the most need for new medications and can predict any side-effects and idiosyncrasies beforehand.

All of these, for a start, can lead to much safer clinical trials with no unwanted drug reactions.

And then, there is the question of lowering costs. In fact, a study by Carnegie Mellon and a German university estimated that AI could lower drug discovery costs by as much as 70%.

This, in turn, will be transferred to patients in the form of lower drug prices, which will increase accessibility to better medications for patients and improve population health in general.

5. Surgery 

AI-enabled robotic-assisted surgeries are taking over the US. They are increasingly being used to reduce surgeon variations and improve quality.

‘Artificial intelligence can help surgeons perform better’ quotes Dr. John Birkmeyer, a chief clinical officer at Sound Physicians.

Advanced analytics and machine learning techniques are being used concomitantly used to unleash critical insights from the billions of data elements associated with robotic-assisted surgery. If used properly, this can help overcome attendant inefficiencies and improve patient health outcomes.

Artificial intelligence helps surgeons make better clinical decisions in real-time during surgery, and helps them understand the dynamics of the patient, especially during complex operations. It also reduces the length of stay of patients by 21%.

This is ultimately reflected in the patient’s post-operative care and long-term health. It also prevents patient readmissions, saving millions of dollars annually.

A study involving 379 orthopedic patients found out that AI-assisted robotic surgery resulted in five times fewer complications as compared to surgeons working alone.

According to Accenture, AI-assisted robotic surgery could save the US healthcare industry $40 billion annually, by 2026.

6. AI-assisted virtual nurses

AI-assisted virtual nurses could well end up saving the US healthcare industry $20 billion annually, by 2026.

They are available 24/7 to answer any patient queries, monitor patients, and guide them in any way they might want.

Currently, they act as a bridge for information exchange between care providers (doctors) and care receivers (patients), to decide what medications to start, the current health status, the most recent test results, and many other things.

It can save the patient many physical appointments with doctors, and also prevent high hospital readmission rates through simple, engaging, and intelligent care.

Care Angel is one of the finest virtual nurses around. Apart from all of the above, it can also provide wellness checks through voice and AI.


AI and ML in healthcare are still at its infancy. Adoption at a large-scale is missing as of yet. To be successful in the healthcare domain, AI and ML need the endorsement of healthcare providers like physicians and nurses.

However, considerable investment is being made in AI in healthcare, and its increasing at a good rate.

AI in healthcare is currently aimed at improving patient outcomes, taking care of the interests of various stakeholders involved, increasing accessibility, and reducing healthcare costs.

In the near future, however, AI and ML, along with technologies like Data Science will take up a much more holistic role to drive healthcare forward.

Visual Question Answering with Keras – Part 2: Making Computers Intelligent to answer from images

Making Computers Intelligent to answer from images

This is my second blog on Visual Question Answering, in the last blog, I have introduced to VQA, available datasets and some of the real-life applications of VQA. If you have not gone through then I would highly recommend you to go through it. Click here for more details about it.

In this blog post, I will walk through the implementation of VQA in Keras.

You can download the dataset from here: All my experiments were performed with VQA v2 and I have used a very tiny subset of entire dataset i.e all samples for training and testing from the validation set.

Table of contents:

  1. Preprocessing Data
  2. Process overview for VQA
  3. Data Preprocessing – Images
  4. Data Preprocessing through the spaCy library- Questions
  5. Model Architecture
  6. Defining model parameters
  7. Evaluating the model
  8. Final Thought
  9. References

NOTE: The purpose of this blog is not to get the state-of-art performance on VQA. But the idea is to get familiar with the concept. All my experiments were performed with the validation set only.

Full code on my Github here.

1. Preprocessing Data:

If you have downloaded the dataset then the question and answers (called as annotations) are in JSON format. I have provided the code to extract the questions, annotations and other useful information in my Github repository. All extracted information is stored in .txt file format. After executing code the preprocessing directory will have the following structure.

All text files will be used for training.


2. Process overview for VQA:

As we have discussed in previous post visual question answering is broken down into 2 broad-spectrum i.e. vision and text.  I will represent the Neural Network approach to this problem using the Convolutional Neural Network (for image data) and Recurrent Neural Network(for text data). 

If you are not familiar with RNN (more precisely LSTM) then I would highly recommend you to go through Colah’s blog and Andrej Karpathy blog. The concepts discussed in this blogs are extensively used in my post.

The main idea is to get features for images from CNN and features for the text from RNN and finally combine them to generate the answer by passing them through some fully connected layers. The below figure shows the same idea.


I have used VGG-16 to extract the features from the image and LSTM layers to extract the features from questions and combining them to get the answer.

3. Data Preprocessing – Images:

Images are nothing but one of the input to our model. But as you already may know that before feeding images to the model we need to convert into the fixed-size vector.

So we need to convert every image into a fixed-size vector then it can be fed to the neural network. For this, we will use the VGG-16 pretrained model. VGG-16 model architecture is trained on millions on the Imagenet dataset to classify the image into one of 1000 classes. Here our task is not to classify the image but to get the bottleneck features from the second last layer.

Hence after removing the softmax layer, we get a 4096-dimensional vector representation (bottleneck features) for each image.

Image Source:


For the VQA dataset, the images are from the COCO dataset and each image has unique id associated with it. All these images are passed through the VGG-16 architecture and their vector representation is stored in the “.mat” file along with id. So in actual, we need not have to implement VGG-16 architecture instead we just do look up into file with the id of the image at hand and we will get a 4096-dimensional vector representation for the image.

4. Data Preprocessing through the spaCy library- Questions:

spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. As we have converted images into a fixed 4096-dimensional vector we also need to convert questions into a fixed-size vector representation. For installing spaCy click here

You might know that for training word embeddings in Keras we have a layer called an Embedding layer which takes a word and embeds it into a higher dimensional vector representation. But by using the spaCy library we do not have to train the get the vector representation in higher dimensions.


This model is actually trained on billions of tokens of the large corpus. So we just need to call the vector method of spaCy class and will get vector representation for word.

After fitting, the vector method on tokens of each question will get the 300-dimensional fixed representation for each word.

5. Model Architecture:

In our problem the input consists of two parts i.e an image vector, and a question, we cannot use the Sequential API of the Keras library. For this reason, we use the Functional API which allows us to create multiple models and finally merge models.

The below picture shows the high-level architecture idea of submodules of neural network.

After concatenating the 2 different models the summary will look like the following.

The below plot helps us to visualize neural network architecture and to understand the two types of input:


6. Defining model parameters:

The hyperparameters that we are going to use for our model is defined as follows:

If you know what this parameter means then you can play around it and can get better results.

Time Taken: I used the GPU on and hence it took me approximately 2 hours to train the model for 5 epochs. However, if you train it on a PC without GPU, it could take more time depending on the configuration of your machine.

7. Evaluating the model:

Since I have used the very small dataset for performing these experiments I am not able to get very good accuracy. The below code will calculate the accuracy of the model.


Since I have trained a model multiple times with different parameters you will not get the same accuracy as me. If you want you can directly download mode.h5 file from my google drive.


8. Final Thoughts:

One of the interesting thing about VQA is that it a completely new field. So there is absolutely no end to what you can do to solve this problem. Below are some tips while replicating the code.

  1. Start with a very small subset of data: When you start implementing I suggest you start with a very small amount of data. Because once you are ready with the whole setup then you can scale it any time.
  2. Understand the code: Understanding code line by line is very much helpful to match your theoretical knowledge. So for that, I suggest you can take very few samples(maybe 20 or less) and run a small chunk (2 to 3 lines) of code to get the functionality of each part.
  3. Be patient: One of the mistakes that I did while starting with this project was to do everything at one go. If you get some error while replicating code spend 4 to 5 days harder on that. Even after that if you won’t able to solve, I would suggest you resume after a break of 1 or 2 days. 

VQA is the intersection of NLP and CV and hopefully, this project will give you a better understanding (more precisely practically) with most of the deep learning concepts.

If you want to improve the performance of the model below are few tips you can try:

  1. Use larger datasets
  2. Try Building more complex models like Attention, etc
  3. Try using other pre-trained word embeddings like Glove 
  4. Try using a different architecture 
  5. Do more hyperparameter tuning

The list is endless and it goes on.

In the blog, I have not provided the complete code you can get it from my Github repository.

9. References:


6 Important Reasons for the Java Experts to learn Hadoop Skills

You must be well aware of the fact that Java and Hadoop Skills are in high demand these days. Gone are the days when advancement work moved around Java and social database. Today organizations are managing big information. It is genuinely big. From gigabytes to petabytes in size and social databases are exceptionally restricted to store it. Additionally, organizations are progressively outsourcing the Java development jobs to different groups who are as of now having big data experts.

Ever wondered what your future would have in store for you if you possess Hadoop as well as Java skills? No? Let us take a look. Today we shall discuss the point that why is it preferable for Java Developers to learn Hadoop.

Hadoop is the Future Java-based Framework that Leads the Industry

Data analysis is the current marketing strategy that the companies are adopting these days. What’s more, Hadoop is to process and comprehend all the Big Data that is generated all the time. As a rule, Hadoop is broadly utilized by practically all organizations from big and small and in practically all business spaces. It is an open-source stage where Java owes a noteworthy segment of its success

The processing channel of Hadoop, which is MapReduce, is written in Java. Thus, a Hadoop engineer needs to compose MapReduce contents in Java for Big data analysis. Notwithstanding that, HDFS, which is the record arrangement of Hadoop, is additionally Java-based programming language at its core. Along these lines, a Hadoop developer needs to compose documents from local framework to HDFS through deployment, which likewise includes Java programming.

Learn Hadoop: It is More Comfortable for a Java Developer

Hadoop is more of an environment than a standalone innovation. Also, Hadoop is a Java-based innovation. Regardless of whether it is Hadoop 1 which was about HDFS and MapReduce or Hadoop2 biological system that spreads HDFS, Spark, Yarn, MapReduce, Tez, Flink, Giraph, Storm, JVM is the base for all. Indeed, even a portion of the broadly utilized programming languages utilized in a portion of the Hadoop biological system segments like Spark is JVM based. The run of the mill models is Scala and Clojure.

Consequently, if you have a Java foundation, understanding Hadoop is progressively easier for you. Also, here, a Hadoop engineer needs Java programming information to work in MapReduce or Spark structure. Thus, if you are as of now a Java designer with a logical twist of the brain, you are one stage ahead to turn into a Hadoop developer.

IT Industry is looking for Professionals with Java and Hadoop Skills

If you pursue the expected set of responsibilities and range of abilities required for a Hadoop designer in places of work, wherever you will watch the reference of Java. As Hadoop needs solid Java foundation, from this time forward associations are searching for Java designers as the best substitution for Hadoop engineers. It is savvy asset usage for organizations as they don’t have to prepare Java for new recruits to learn Hadoop for tasks.

Nonetheless, the accessible market asset for Hadoop is less. Therefore, there is a noteworthy possibility for Java designers in the Hadoop occupation field. Henceforth, as a Java designer, on the off chance that you are not yet arrived up in your fantasy organization, learning Hadoop, will without a doubt help you to discover the chance to one of your top picks.

Combined Java and Hadoop Skills Means Better Pay Packages

You will be progressively keen on learning Hadoop on the off chance that you investigate Gartner report on big information industry. According to the report, the Big Data industry has just come to the $50 billion points. Additionally, over 64% of the main 720 organizations worldwide are prepared to put resources into big information innovation. Notwithstanding that when you are a mix of a Java and Hadoop engineer, you can appreciate 250% pay climb with a normal yearly compensation of $150,000.It is about the yearly pay of a senior Hadoop developer.

Besides, when you change to Big Data Hadoop, it very well may be useful to improve the nature of work. You will manage unpredictable and greater tasks. It does not just give you a better extension to demonstrate your expertise yet, in addition, to set up yourself as a profitable asset who can have any kind of effect.

Adapting Big Data Hadoop can be exceptionally advantageous because it will assist you in dealing with greater, complex activities a lot simpler and convey preferable yield over your associates. To be considered for examinations, you should be somebody who can have any kind of effect in the group, and that is the thing that Hadoop lets you be.

Learning Hadoop will open New Opportunities to Other Lucrative Fields

Big data is only not going to learn Hadoop. When you are in Big information space, you have sufficient chance to jump other Java and Hadoop engineer. There are different exceedingly requesting zones in big information like Artificial Intelligence, Machine Learning, Data Science. You can utilize your Java and Hadoop engineer expertise as a springboard to take your vocation to the following level. In any case, the move will give you the best outcome once you move from Java to Hadoop and increase fundamental working knowledge.

Java with Hadoop opens new skylines of occupation jobs, for example, data scientist, data analyst business intelligence analyst, DBA, etc.

Premier organizations prefer Hadoop Developers with Java skills

Throughout the years the Internet has been the greatest driver of information, and the new data produced in 2012 remained at 2500 Exabyte. The computerized world developed by 62% a year ago to 800K petabytes and will keep on developing to the tune of 1.2 zeta bytes during the present year. Gartner gauges the market of Hadoop Ecosystem to $77 million and predicts it will come to the $813 million marks by 2016.

A review of LinkedIn profiles referencing Hadoop as their abilities uncovered that just about 17000 individuals are working in Companies like Cisco, HP, TCS, Oracle, Amazon, Yahoo, and Facebook, and so on. Aside from this Java proficient who learn Hadoop can begin their vocations with numerous new businesses like Platfora, Alpine information labs, Trifacta, Datatorrent, and so forth.


You can see that combining your Java skills with Hadoop skills can open the doors of several new opportunities for you. You can get better remuneration for your efforts, and you will always be in high demand. It is high time to learn Hadoop online now if you are a java developer.

Industrial IoT erreicht die Fertigungshalle

Lumada Manufacturing Insights nutzt KI, Machine Learning und DataOps, um digitale  Innovationen für Manufacturing 4.0 bereitzustellen

Dreieich/ Santa Clara (Kalifornien), 17. September 2019 Mit Lumada Manufacturing Insights kündigt Hitachi Vantara eine Suite von IIoT-Lösungen (Industrial IoT) an, mit der Fertigungsunternehmen auf ihren Daten basierende Transformationsvorhaben umsetzen können. Die Lösung lässt sich in bestehende Anwendungen integrieren und liefert aussagekräftige Erkenntnisse aus Daten, ohne dass Fertigungsanlagen oder -anwendungen durch einen „Rip-and-Replace”-Wechsel kostspielig ersetzt werden müssen. Lumada Manufacturing Insights optimiert Maschinen, Produktion und Qualität und schafft dadurch die Basis für digitale Innovationen, ohne die Manufacturing 4.0 unmöglich wäre. Die Plattform unterstützt eine Vielzahl von Bereitstellungsoptionen und kann On-Premise oder in der Cloud ausgeführt werden.

„Daten und Analytics können Produktionsprozesse modernisieren und transformieren. Aber für zu viele Hersteller verlangsamen bestehende Legacy-Infrastrukturen und voneinander getrennte Software und Prozesse die Innovation”, kommentiert Brad Surak, Chief Product und Strategy Officer bei Hitachi Vantara. „Mit Lumada Manufacturing Insights können Unternehmen die Basis für digitale Innovationen schaffen und dabei mit den Systemen und der Software arbeiten, die sie bereits im Einsatz haben.” 

Lumada Manufacturing Insights wird weltweit ab dem 30. September verfügbar sein. Weitere Informationen:

Bei der deutschen Version handelt es sich um eine gekürzte Version der internationalen Presseinformation von Hitachi Vantara.

Hitachi Vantara
Hitachi Vantara, eine hundertprozentige Tochtergesellschaft der Hitachi Ltd., hilft datenorientierten Marktführern, den Wert ihrer Daten herauszufinden und zu nutzen, um intelligente Innovationen hervorzubringen und Ergebnisse zu erzielen, die für Wirtschaft und Gesellschaft von Bedeutung sind. Nur Hitachi Vantara vereint über 100 Jahre Erfahrung in Operational Technology (OT) und mehr als 60 Jahre in Information Technology (IT), um das Potential Ihrer Daten, Ihrer Mitarbeitern und Ihren Maschinen zu nutzen. Wir kombinieren Technologie, geistiges Eigentum und Branchenwissen, um Lösungen zum Datenmanagement zu liefern, mit denen Unternehmen das Kundenerlebnis verbessern, sich neue Erlösquellen erschließen und die Betriebskosten senken können. Über 80% der Fortune 100 vertrauen Hitachi Vantara bei Lösungen rund um Daten. Besuchen Sie uns unter

Hitachi Ltd. Corporation
Hitachi, Ltd. (TSE: 6501) mit Hauptsitz in Tokio, Japan, fokussiert sich auf Social Innovation und kombiniert dazu Information Technology, Operational Technology und Produkte. Im Geschäftsjahr 2018 (das am 31. März 2019 endete) betrug der konsolidierte Umsatz des Unternehmens insgesamt 9.480,6 Milliarden Yen (85,4 Milliarden US-Dollar), wobei das Unternehmen weltweit rund 296.000 Mitarbeiter beschäftigt. Hitachi liefert digitale Lösungen mit Lumada in den Bereichen Mobility, Smart Life, Industry, Energy und IT. Weitere Informationen über Hitachi finden Sie unter



Hitachi Vantara
Bastiaan van Amstel 


Public Footprint 
Thomas Schumacher
+49 / (0) 214 8309 7790



Interview – Customer Data Platform, more than CRM 2.0?

Interview with David M. Raab from the CDP Institute

David M. Raab is as a consultant specialized in marketing software and service vendor selection, marketing analytics and marketing technology assessment. Furthermore he is the founder of the Customer Data Platform Institute which is a vendor-neutral educational project to help marketers build a unified customer view that is available to all of their company systems.

Furthermore he is a Keynote-Speaker for the Predictive Analytics World Event 2019 in Berlin.

Data Science Blog: Mr. Raab, what exactly is a Customer Data Platform (CDP)? And where is the need for it?

The CDP Institute defines a Customer Data Platform as „packaged software that builds a unified, persistent customer database that is accessible by other systems“.  In plainer language, a CDP assembles customer data from all sources, combines it into customer profiles, and makes the profiles available for any use.  It’s important because customer data is collected in so many different systems today and must be unified to give customers the experience they expect.

Data Science Blog: Is it something like a CRM System 2.0? What Use Cases can be realized by a Customer Data Platform?

CRM systems are used to interact directly with customers, usually by telephone or in the field.  They work almost exclusively with data that is entered during those interactions.  This gives a very limited view of the customer since interactions through other channels such as order processing or Web sites are not included.  In fact, one common use case for CDP is to give CRM users a view of all customer interactions, typically by opening a window into the CDP database without needing to import the data into the CRM.  There are many other use cases for unified data, including customer segmentation, journey analysis, and personalization.  Anything that requires sharing data across different systems is a CDP use case.

Data Science Blog: When does a CDP make sense for a company? It is more relevant for retail and financial companies than for industrial companies, isn´t it?

CDP has been adopted most widely in retail and online media, where each customer has many interactions and there are many products to choose from.  This is a combination that can make good use of predictive modeling, which benefits greatly from having more complete data.  Financial services was slower to adopt, probably because they have fewer products but also because they already had pretty good customer data systems.  B2B has also been slow to adopt because so much of their customer relationship is handled by sales people.  We’ve more recently been seeing growth in additional sectors such as travel, healthcare, and education.  Those involve fewer transactions than retail but also rely on building strong customer relationships based on good data.

Data Science Blog: There are several providers for CDPs. Adobe, Tealium, Emarsys or Dynamic Yield, just to name some of them. Do they differ a lot between each other?

Yes they do.  All CDPs build the customer profiles I mentioned.  But some do more things, such as predictive modeling, message selection, and, increasingly, message delivery.  Of course they also vary in the industries they specialize in, regions they support, size of clients they work with, and many technical details.  This makes it hard to buy a CDP but also means buyers are more likely to find a system that fits their needs.

Data Science Blog: How established is the concept of the CDP in Europe in general? And how in comparison with the United States?

CDP is becoming more familiar in Europe but is not as well understood as in the U.S.  The European market spent a lot of money on Data Management Platforms (DMPs) which promised to do much of what a CDP does but were not able to because they do not store the level of detail that a CDP does.  Many DMPs also don’t work with personally identifiable data because the DMPs primarily support Web advertising, where many customers are anonymous.  The failures of DMPs have harmed CDPs because they have made buyers skeptical that any system can meet their needs, having already failed once.  But we are overcoming this as the market becomes better educated and more success stories are available.  What’s the same in Europe and the U.S. is that marketers face the same needs.  This will push European marketers towards CDPs as the best solution in many cases.

Data Science Blog: What are coming trends? What will be the main topic 2020?

We see many CDPs with broader functions for marketing execution: campaign management, personalization, and message delivery in particular.  This is because marketers would like to buy as few systems as possible, so they want broader scope in each systems.  We’re seeing expansion into new industries such as financial services, travel, telecommunications, healthcare, and education.  Perhaps most interesting will be the entry of Adobe, Salesforce, and Oracle, who have all promised CDP products late this year or early next year.  That will encourage many more people to consider buying CDPs.  We expect that market will expand quite rapidly, so current CDP vendors will be able to grow even as Adobe, Salesforce, and Oracle make new CDP sales.

You want to get in touch with Daniel M. Raab and understand more about the concept of a CDP? Meet him at the Predictive Analytics World 18th and 19th November 2019 in Berlin, Germany. As a Keynote-Speaker, he will introduce the concept of a Customer Data Platform in the light of Predictive Analytics. Click here to see the agenda of the event.