Geschriebene Artikel über Big Data Analytics

Signavio

Process Paradise by the Dashboard Light

/0 Comments/in , , , , , , /by

The right questions drive business success. Questions like, “How can I make sure my product is the best of its kind?” “How can I get the edge over my competitors?” and “How can I keep growing my organization?” Modern businesses take their questions further, focusing on the details of how they actually function. At this level, the questions become, “How can I make my business as efficient as possible?” “How can I improve the way my company does business?” and even, “Why aren’t my company’s processes working as they should?”

Read this article in German:

Mit Dashboards zur Prozessoptimierung

To discover the answers to these questions (and many others!), more and more businesses are turning to process mining. Process mining helps organizations unlock hidden value by automatically collecting information on process models from across the different IT systems operating within a business. This allows for continuous monitoring of an organization’s end-to-end process landscape, meaning managers and staff gain specific operational insights into potential risks—as well as ongoing improvement opportunities.

However, process mining is not a silver bullet that turns data into insights at the push of a button. Process mining software is simply a tool that produces information, which then must be analyzed and acted upon by real people. For this to happen, the information produced must be available to decision-makers in an understandable format.

For most process mining tools, the emphasis remains on the sophistication of analysis capabilities, with the resulting data needing to be interpreted by a select group of experts or specialists within an organization. This necessarily creates a delay between the data being produced, the analysis completed, and actions taken in response.

Process mining software that supports a more collaborative approach by reducing the need for specific expertise can help bridge this gap. Only if hypotheses, analysis, and discoveries are shared, discussed, and agreed upon with a wide range of people can really meaningful insights be generated.

Of course, process mining software is currently capable of generating standardized reports and readouts, but in a business environment where the pace of change is constantly increasing, this may not be sufficient for very much longer. For truly effective process mining, the secret to success will be anticipating challenges and opportunities, then dealing with them as they arise in real time.

Dashboards of the future

To think about how process mining could improve, let’s consider an analog example. Technology evolves to make things easier—think of the difference between keeping track of expenditure using a written ledger vs. an electronic spreadsheet. Now imagine the spreadsheet could tell you exactly when you needed to read it, and where to start, as well as alerting you to errors and omissions before you were even aware you’d made them.

Advances in process mining make this sort of enhanced assistance possible for businesses seeking to improve the way they work. With the right process mining software, companies can build tailored operational cockpits that unite real-time operational data with process management. This allows for the usual continuous monitoring of individual processes and outcomes, but it also offers even clearer insights into an organization’s overall process health.

Combining process mining with an organization’s existing process models in the right way turns these models from static representations of the way a particular process operates, into dynamic dashboards that inform, guide and warn managers and staff about problems in real time. And remember, dynamic doesn’t have to mean distracting—the right process mining software cuts into your processes to reveal an all-new analytical layer of process transparency, making things easier to understand, not harder.

As a result, business transformation initiatives and other improvement plans and can be adapted and restructured on the go, while decision-makers can create automated messages to immediately be advised of problems and guided to where the issues are occurring, allowing corrective action to be completed faster than ever. This rapid evaluation and response across any process inefficiencies will help organizations save time and money by improving wasted cycle times, locating bottlenecks, and uncovering non-compliance across their entire process landscape.

Dynamic dashboards with Signavio

To see for yourself how the most modern and advanced process mining software can help you reveal actionable insights into the way your business works, give Signavio Process Intelligence a try. With Signavio’s Live Insights, all your process information can be visualized in one place, represented through a traffic light system. Simply decide which processes and which activities within them you want to monitor or understand, place the indicators, choose the thresholds, and let Signavio Process Intelligence connect your process models to the data.

Banish multiple tabs and confusing layouts, amaze your colleagues and managers with fact-based insights to support your business transformation, and reduce the time it takes to deliver value from your process management initiatives. To find out more about Signavio Process Intelligence, or sign up for a free 30-day trial, visit www.signavio.com/try.

Process mining is a powerful analysis tool, giving you the visibility, quantifiable numbers, and information you need to improve your business processes. Would you like to read more? With this guide to managing successful process mining initiatives, you will learn that how to get started, how to get the right people on board, and the right project approach.

Valentin Curtef

The importance of being Data Scientist

/0 Comments/in , , , , , , /by

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.

Digvijay Upadhyay

Glorious career paths of a Big Data Professional

/0 Comments/in , , , /by

Are you wondering about the career profiles you may get to fill if you get into Big Data industry? If yes, then Bingo! This is the post that will inform you just about that. Big data is just an umbrella term. There are a lot of profiles and career paths that are covered under this umbrella term. Let us have a look at some of these profiles.

Data Visualisation Specialist

The process of visualizing data is turning out to be critical in guaranteeing information-driven representatives get the upfront investment required to actualize goal-oriented and significant Big Data extends in their organization. Making your data to tell a story and the craft of envisioning information convincingly has turned into a significant piece of the Big Data world and progressively associations need to have these capacities in-house. Besides, as a rule, these experts are relied upon to realize how to picture in different instruments, for example, Spotfire, D3, Carto, and Tableau – among numerous others. Information Visualization Specialists should be versatile and inquisitive to guarantee they stay aware of most recent patterns and answers for a recount to their information stories in the most intriguing manner conceivable with regards to the board room. 

 

Big Data Architect

This is the place the Hadoop specialists come in. Ordinarily, a Big Data planner tends to explicit information issues and necessities, having the option to portray the structure and conduct of a Big Data arrangement utilizing the innovation wherein they practice – which is, as a rule, mostly Hadoop.

These representatives go about as a significant connection between the association (and its specific needs) and Data Scientists and Engineers. Any organization that needs to assemble a Big Data condition will require a Big Data modeler who can serenely deal with the total lifecycle of a Hadoop arrangement – including necessity investigation, stage determination, specialized engineering structure, application plan, and advancement, testing the much-dreaded task of deploying lastly.

Systems Architect 

This Big data professional is in charge of how your enormous information frameworks are architected and interconnected. Their essential incentive to your group lies in their capacity to use their product building foundation and involvement with huge scale circulated handling frameworks to deal with your innovation decisions and execution forms. You’ll need this individual to construct an information design that lines up with the business, alongside abnormal state anticipating the improvement. The person in question will consider different limitations, adherence to gauges, and varying needs over the business.

Here are some responsibilities that they play:

    • Determine auxiliary prerequisites of databases by investigating customer tasks, applications, and programming; audit targets with customers and assess current frameworks.
    • Develop database arrangements by planning proposed framework; characterize physical database structure and utilitarian abilities, security, back-up and recuperation particulars.
    • Install database frameworks by creating flowcharts; apply ideal access methods, arrange establishment activities, and record activities.
    • Maintain database execution by distinguishing and settling generation and application advancement issues, figuring ideal qualities for parameters; assessing, incorporating, and putting in new discharges, finishing support and responding to client questions.
    • Provide database support by coding utilities, reacting to client questions, and settling issues.


Artificial Intelligence Developer

The certain promotion around Artificial Intelligence is additionally set to quicken the number of jobs publicized for masters who truly see how to apply AI, Machine Learning, and Deep Learning strategies in the business world. Selection representatives will request designers with broad learning of a wide exhibit of programming dialects which loan well to AI improvement, for example, Lisp, Prolog, C/C++, Java, and Python.

All said and done; many people estimate that this popular demand for AI specialists could cause a something like what we call a “Brain Drain” organizations poaching talented individuals away from the universe of the scholarly world. A month ago in the Financial Times, profound learning pioneer and specialist Yoshua Bengio, of the University of Montreal expressed: “The industry has been selecting a ton of ability — so now there’s a lack in the scholarly world, which is fine for those organizations. However, it’s not extraordinary for the scholarly world.” It ; howeverusiasm to perceive how this contention among the scholarly world and business is rotated in the following couple of years.

Data Scientist

The move of Big Data from tech publicity to business reality may have quickened, yet the move away from enrolling top Data Scientists isn’t set to change in 2020. An ongoing Deloitte report featured that the universe of business will require three million Data Scientists by 2021, so if their expectations are right, there’s a major ability hole in the market. This multidisciplinary profile requires specialized logical aptitudes, specialized software engineering abilities just as solid gentler abilities, for example, correspondence, business keenness, and scholarly interest.

Data Engineer

Clean and quality data is crucial in the accomplishment of Big Data ventures. Consequently, we hope to see a lot of opening in 2020 for Data Engineers who have a predictable and awesome way to deal with information transformation and treatment. Organizations will search for these special data masters to have broad involvement in controlling data with SQL, T-SQL, R, Hadoop, Hive, Python and Spark. Much like Data Scientists. They are likewise expected to be innovative with regards to contrasting information with clashing information types with have the option to determine issues. They additionally frequently need to make arrangements which enable organizations to catch existing information in increasingly usable information groups – just as performing information demonstrations and their modeling.

IT/Operations Manager Job Description

In Big data industry, the IT/Operations Manager is a profitable expansion to your group and will essentially be in charge of sending, overseeing, and checking your enormous information frameworks. You’ll depend on this colleague to plan and execute new hardware and administrations. The person in question will work with business partners to comprehend the best innovation ventures to address their procedures and concerns—interpreting business necessities to innovation plans. They’ll likewise work with venture chiefs to actualize innovation and be in charge of effective progress and general activities.

Here are some responsibilities that they play:

  • Manage and be proactive in announcing, settling and raising issues where required 
  • Lead and co-ordinate issue the executive’s exercises, notwithstanding ceaseless procedure improvement activities  
  • Proactively deal with our IT framework 
  • Supervise and oversee IT staffing, including enrollment, supervision, planning, advancement, and assessment
  • Verify existing business apparatuses and procedures remain ideally practical and worth included 
  • Benchmark, dissect, report on and make suggestions for the improvement and development of the IT framework and IT frameworks 
  • Advance and keep up a corporate SLA structure

Conclusion

These are some of the best career paths that big data professionals can play after entering the industry. Honesty and hard work can always take you to the zenith of any field that you choose to be in. Also, keep upgrading your skills by taking newer certifications and technologies. Good Luck 

Akshay Chavan

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

/0 Comments/in , , , , , , /by

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: https://visualqa.org/index.html. 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: https://www.cs.toronto.edu/~frossard/post/vgg16/

 

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 https://colab.research.google.com 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:

  1. https://blog.floydhub.com/asking-questions-to-images-with-deep-learning/
  2. https://tryolabs.com/blog/2018/03/01/introduction-to-visual-question-answering/
  3. https://github.com/sominwadhwa/vqamd_floyd

Digvijay Upadhyay

6 Important Reasons for the Java Experts to learn Hadoop Skills

/0 Comments/in , , , /by

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.

Conclusion

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.

Savaram Ravindra

How to Ensure Data Quality in an Organization?

/1 Comment/in , , /by

Introduction to Data Quality

Today, the world is filled with data. It is everywhere. And, the value of any organization can be measured by the quality of its data. So, what actually is the quality of data or data quality, and why is it important? Well, data quality refers to the capability of a set of data to serve an intended purpose. 

Data quality is important to any organization because it provides timely and accurate information to manage accountability and services. It also helps to ensure and prioritize the best use of resources. Thus, high-quality data will lead to appropriate insights and valuable information for any organization. We can evaluate the quality of data in certain aspects. They include accuracy, relevancy, completeness, and uniqueness. 

Data Quality Problems

As the organizations are collecting vast amounts of data, managing its quality becomes more important every single day. In the year 2016, the costs of problems caused due to poor data quality were estimated by IBM, and it turned out to be $3.1 trillion across the U.S economy. Also, a Forrester report has stated that almost 30 percent of analysts spend 40 percent of their time validating and vetting their data prior to its utilization for strategic decision-making. These statistics indicate that the scale of the problems with data quality is vast.

So, why do these data quality problems occur? The main reasons include manual entry of data, software updates, integration of data sources, skills shortages, and insufficient testing time. Wrong decisions can be taken due to poor data management processes and poor quality of data. Because of this, many organizations lose their clients and customers. So, ensuring data quality must be given utmost importance in an organization. 

How to Ensure Data Quality?

Data quality management helps by combining data, technology, and organizational culture to deliver useful and accurate results. Good management of data quality builds a foundation for all the initiatives of a business. Now, let’s see how we can improve the data quality in an organization.

The first aspect of improving the quality of data is monitoring and cleansing data. This verifies data against standard statistical measures, validates data against matching descriptions, and uncovers relationships. This also checks the uniqueness of data and analyzes the data for its reusability. 

The second one is managing metadata centrally. Multiple people gather and clean data very often and they may work in different countries or offices. Therefore, you require clear policies on how data is gathered and managed as people in different parts of a company may misinterpret certain data terms and concepts. Centralized management of metadata is the solution to this problem as it reduces inconsistent interpretations and helps in establishing corporate standards.  

The next one is to ensure all the requirements are available and offer documentation for data processors and data providers. You have to format the specifications and offer a data dictionary and also provide training for the providers of data and all other new staff. Make sure you offer immediate help for all the data providers.

Very often, data is gathered from different sources and may include distinct spelling options. Hence, segmentation, scoring, smart lists, and many others are impacted by this. So, for entering a data point, a singular approach is essential, and data normalization provides this approach. The goal of this approach is to eliminate redundancy in data. Its advantages include easier object-to-data mapping and increased consistency.

The last aspect is to verify whether the data is consistent with the data rules and business goals, and this has to be done at regular intervals. You have to communicate the current status and data quality metrics to every stakeholder regularly to ensure the maintenance of data quality discipline across the organization.

Conclusion

Data quality is a continuous process but not a one-time project which needs the entire company to be data-focused and data-driven. It is much more than reliability and accuracy. High level of data quality can be achieved when the decision-makers have confidence in data and rely upon it. Follow the above-mentioned steps to ensure a high level of data quality in your organization. 

Haroon Bhutta

The Future of AI in Dental Technology

/0 Comments/in , , /by

As we develop more advanced technology, we begin to learn that artificial intelligence can have more and more of an impact on our lives and industries that we have gotten used to being the same over the past decades. One of those industries is dentistry. In your lifetime, you’ve probably not seen many changes in technology, but a boom around artificial intelligence and technology has opened the door for AI in dental technologies.

How Can AI Help?

Though dentists take a lot of pride in their craft and career, most acknowledge that AI can do some things that they can’t do or would make their job easier if they didn’t have to do. AI can perform a number of both simple and advanced tasks. Let’s take a look at some areas that many in the dental industry feel that AI can be of assistance.

Repetitive, Menial Tasks

The most obvious area that AI can help out when it comes to dentistry is with repetitive and menial simple tasks. There are many administrative tasks in the dentistry industry that can be sped up and made more cost-effective with the use of AI. If we can train a computer to do some of these tasks, we may be able to free up more time for our dentists to focus on more important matters and improve their job performance as well. One primary use of AI is virtual consultations that offices like Philly Braces are offering. This saves patients time when they come in as the Doctor already knows what the next steps in their treatment will be.

Using AI to do some basic computer tasks is already being done on a small scale by some, but we have yet to see a very large scale implementation of this technology. We would expect that to happen soon, with how promising and cost-effective the technology has proven to be.

Reducing Misdiagnosis

One area that many think that AI can help a lot in is misdiagnosis. Though dentists do their best, there is still a nearly 20% misdiagnosis rate when reading x-rays in dentistry. We like to think that a human can read an x-ray better, but this may not be the case. AI technology can certainly be trained to read an x-ray and there have been some trials to suggest that they can do it better and identify key conditions that we often misread.

A world with AI diagnosis that is accurate and quicker will save time, money, and lead to better dental health among patients. It hasn’t yet come to fruition, but this seems to be the next major step for AI in dentistry.

Artificial Intelligence Assistants

Once it has been demonstrated that AI can perform a range of tasks that are useful to dentists, the next logical step is to combine those skills to make a fully-functional AI dental assistant. A machine like this has not yet been developed, but we can imagine that it would be an interface that could be spoken to similar to Alexa. The dentist would request vital information and other health history data from a patient or set of patients to assist in the treatment process. This would undoubtedly be a huge step forward and bring a lot of computing power into the average dentist office.

Conclusion

It’s clear that AI has a bright future in the dental industry and has already shown some of the essential skills that it can help with in order to provide more comprehensive and accurate care to dental patients. Some offices like Westwood Orthodontics already use AI in the form of a virtual consult to diagnose issues and provide treatment options before patients actually step foot in the office. Though not nearly all applications that AI can provide have been explored, we are well on our way to discovering the vast benefits of artificial intelligence for both patients and practices in the dental healthcare industry.


Lisa Gao, DDS, MS | Westwood Orthodontics 1033 Gayley Ave #106, Los Angeles California 90024, 310-870-1823

Benjamin Aunkofer

Interview – Customer Data Platform, more than CRM 2.0?

/0 Comments/in , , , , , , , , /by

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.

 

 

Sergey Yevsiukov

Marketing Attribution Models

/0 Comments/in , , /by

Why do we need attribution?

Attribution – is the process of distributing the value of a purchase between the various channels, used in the funnel chain. It allows you to determine the role of each channel in profit. It is used to assess the effectiveness of campaigns, to identify more priority sources. The competent choice of the model makes it possible to optimally distribute the advertising budget. As a result, the business gets more profit and less expenses.

What models of attribution exist

The choice of the appropriate model is an important issue, because depending on the business objectives, it is better to fit something different. For example, for companies that have long been present in the industry, the priority is to know which sources contribute to the purchase. Recognition is the importance for brands entering the market. Thus, incorrect prioritization of sources may cause a decrease in efficiency. Below are the models that are widely used in the market. Each of them is guided by its own logic, it is better suited for different businesses.

First Interaction (First Click)

The value is given to the first touch. It is suitable only for several purposes and does not make it possible to evaluate the role of each component in making a purchase. It is chosen by brands who want to increase awareness and reach.

Advantages

It does not require knowledge of programming, so the introduction of a business is not difficult. A great option that effectively assesses campaigns, aimed at creating awareness and demand for new products.

Disadvantages

It limits the ability to analyze comprehensively all channels that is used to promote a brand. It gives value to the first interaction channel, ignoring the rest.

Who is suitable for?

Suitable for those who use the promotion to increase awareness, the formation of a positive image. Also allows you to find the most effective source.

Last Interaction (Last Click)

It gives value to the last channel with which the consumer interacted before making the purchase. It does not take into account the actions that the user has done up to this point, what marketing activities he encountered on the way to conversion.

Advantages

The tool is widely used in the market, it is not difficult. It solves the problem of small advertising campaigns, where is no more than 3 sources.

Disadvantages

There is no way to track how other channels have affected the acquisition.

Who is suitable for?

It is suitable for business models that have a short purchase cycle. This may be souvenirs, seasonal offers, etc.

Last Non-Direct Click

It is the default in Google Analytics. 100% of the  conversion value gives the last channel that interacted with the buyer before the conversion. However, if this source is Direct, then assumptions are counted.

Suppose a person came from an email list, bookmarked a product, because at that time it was not possible to place an order. After a while he comes back and makes a purchase. In this case, email as a channel for attracting users would be underestimated without this model.

Who is suitable for?

It is perfect for beginners who are afraid of making a mistake in the assessment. Because it allows you to form a general idea of ​​the effectiveness of all the involved channels.

Linear model attribution (Linear model)

The value of the conversion is divided in equal parts between all available channels.

Linear model attribution (Linear model)

Advantages

More advanced model than previous ones, however, characterized by simplicity. It takes into account all the visits before the acquisition.

Disadvantages

Not suitable for reallocating the budget between the channels. This is due to the fact that the effectiveness of sources may differ significantly and evenly divide – it is not the best idea. 

Who is suitable for?

It is performing well for businesses operating in the B2B sector, which plays a great importance to maintain contact with the customer during the entire cycle of the funnel.

Taking into account the interaction duration (Time Decay)

A special feature of the model is the distribution of the value of the purchase between the available channels by increment. Thus, the source, that is at the beginning of the chain, is given the least value, the channel at the end deserves the greatest value.  

Advantages

Value is shared between all channel. The highest value is given to the source that pushed the user to make a purchase.

Disadvantages

There is no fair assessment of the effectiveness of the channels, that have made efforts to obtain the desired result.

Who is suitable for?

It is ideal for evaluating the effectiveness of advertising campaigns with a limited duration.

Position-Based or U-Shaped

40% receive 2 channels, which led the user and pushed him to purchase. 20% share among themselves the intermediate sources that participated in the chain.

Advantages

Most of the value is divided equally between the key channels – the fact that attracted the user and closed the deal..

Disadvantages

Underestimated intermediate channels.It happens that they make it possible to more effectively promote the user chain.. Because they allow you to subscribe to the newsletter or start following the visitor for price reduction, etc.

Who is suitable for?

Interesting for businesses that focus on attracting new audiences, as well as pushing existing customers to buy.

Cons of standard attribution models

According to statistics, only 44% of foreign experts use attribution on the last interaction. Speaking about the domestic market, we can announce the numbers are much higher. However, only 18% of marketers use more complex models. There is also evidence which demonstrates that 72.4% of those who use attribution based on the last interaction, they use it not because of efficiency, but because it is simple.

What leads to a similar state of affairs?

Experts do not understand the effectiveness. Ignorance of how more complex models work leads to a lack of understanding of the real benefits for the business.

Attribution management is distributed among several employees. In view of this, different models can be used simultaneously. This approach greatly distorts the data obtained, not allowing an objective assessment of the effect of channels.

No comprehensive data storage. Information is stored in different places and does not take into account other channels. Using the analytics of the advertising office, it is impossible to work with customers in retail outlets.

You may find ways to eliminate these moments and attribution will work for the benefit of the business.

What algorithmic attribution models exist

Using one channel, there is no need to enable complex models. Attribution will be enough for the last interaction. It has everything to evaluate the effectiveness of the campaign, determine the profitability, understand the benefits for the business.

Moreover, if the number of channels increases significantly, and goals are already far beyond recognition, it will be better to give preference to more complex models. They allow you to collect all the information in one place, open up limitless monitoring capabilities, make it clear how one channel affects the other and which bundles work better together.

Below are the well-known and widely used today algorithmic attribution models.

Data-Driven Attribution

A model that allows you to track all the way that the consumer has done before making a purchase. It objectively evaluates each channel and does not take into account the position of the source in the funnel. It demonstrates how a certain interaction affected the outcome. Data-Driven attribution model is used in Google Analytics 360.

With it, you can work efficiently with channels that are underestimated in simpler models. It gives the opportunity to distribute the advertising budget correctly.

Attribution based on Markov’s Chains (Markov Chains)

Markov’s chain has been used for a long time to predict weather, matches, etc. The model allows you to find out, how the lack of a channel will affect sales. Its advantage is the ability to assess the impact of the source on the conversion, to find out which channel brings the best results.

A great option for companies that store data in one service. To implement requires knowledge of programming. It has one drawback in the form of underestimating the first channel in the chain. 

OWOX BI Attribution

OWOX BI Attribution helps you assess the mutual influence of channels on encouraging a customer through the funnel and achieving a conversion.

What information can be processed:

  • Upload user data from Google Analytics using flexible built-in tools.
  • Process information from various advertising services.
  • Integrate the model with CRM systems.

This approach makes it possible not to lose sight of any channel. Analyze the complex impact of marketing tools, correctly distributing the advertising budget.

The model uses CRM information, which makes it possible to do end-to-end analytics. Each user is assigned an identifier, so no matter what device he came from, you can track the chain of actions and understand that it is him. This allows you to see the overall effect of each channel on the conversion.

Advantages

Provides an integrated approach to assessing the effectiveness of channels, allows you to identify consumers, even with different devices, view all visits. It helps to determine where the user came from, what prompted him to do so. With it, you can control the execution of orders in CRM, to estimate the margin. To evaluate in combination with other models in order to determine the highest priority advertising campaigns that bring the most profit.

Disadvantages

It is impossible to objectively evaluate the first step of the chain.

Who is suitable for?

Suitable for all businesses that aim to account for each step of the chain and the qualitative assessment of all advertising channels.

Conclusion

The above-mentioned Ad Roll study shows that 70% of marketing managers find it difficult to use the results obtained from attribution. Moreover, there will be no result without it.

To obtain a realistic assessment of the effectiveness of marketing activities, do the following:

  • Determine priority KPIs.
  • Appoint a person responsible for evaluating advertising campaigns.
  • Define a user funnel chain.
  • Keep track of all data, online and offline. 
  • Make a diagnosis of incoming data.
  • Find the best attribution model for your business.
  • Use the data to make decisions.

Signavio

The Power of Analyzing Processes

/0 Comments/in , , , , /by

Are you thinking BIG enough? Over the past few years, the quality of discussion regarding a ‘process’ and its interfaces between different departments has developed radically. Organizations increasingly reject guesswork, individual assessments, or blame-shifting and instead focus on objective facts: the display of throughput times, process variants, and their optimization.

But while data can hold valuable insights into business, users, customer bases, and markets, companies are sometimes unsure how best to analyze and harness their data. In fact, the problem isn’t usually a lack of data; it’s a breakdown in leveraging useful data. Being unsure how to interpret, explore, and analyze processes can paralyze any go-live, leading to a failure in the efficient interaction of processes and business operations. Without robust data analysis, your business could be losing money, talent, and even clients.

After all, analyzing processes is about letting data tell its true story for improved understanding.

The “as-is” processes

Analyzing the as-is current state helps organizations document, track, and optimize processes for better performance, greater efficiency, and improved outcomes. By contextualizing data, we gain the ability to navigate and organize processes to negate bottlenecks, set business preferences, and plan an optimized route through process mining initiatives. This focus can help across an entire organization, or on one or more specific processes or trends within a department or team.

There are several vital goals/motivations for implementing current state analysis, including:

  • Saving money and improving ROI;
  • Improving existing processes or creating new processes;
  • Increasing customer satisfaction and journeys;
  • Improving business coordination and organizational responsiveness;
  • Complying with new regulatory standards;
  • Adapting methods following a merger or acquisition.

The “to-be” processes

Simply put, if as-is maps where your processes are, to-be maps where you want them to… be. To-be process mapping documents what you want the process to look like, and by using the as-is diagram, you can work with stakeholders to identify developments and improvements of the current process, then outline those changes on your to-be roadmap.

This analysis can help you make optimal decisions for your business and innovative OpEx imperatives. For instance, at leading data companies like Google and Amazon, data is used in such a way that the analysis results make the decisions! Just think of the power Recommendation Engines, PageRank, and Demand Forecasting Systems have over the content we see. To achieve this, advanced techniques of machine learning and statistical modeling are applied, resulting in mechanically improved results from the data. Interestingly, because these techniques reference large-scale data sets and reflect analysis and results in real-time, they are applied to areas that extend beyond human decision-making.

Also, by analyzing and continuously monitoring qualitative and quantitative data, we gain insights across potential risks and ongoing improvement opportunities, too. The powerful combination of process discovery, process analysis, and conformance checking supports a collaborative approach to process improvement, giving you game-changing insights into your business. For example:

  • Which incidents would I like to detect and act upon proactively?
  • Where would task prioritization help improve overall performance?
  • Where do I know that increased transparency would help the company?
  • How can I utilize processes in place of gut feeling/experience?

Further, as the economic environment continues to change rapidly, and modern organizations keep adopting process-based approaches to ensure they are achieving their business goals, process analysis naturally becomes the perfect template for any company.

With this, process mining technology can help modern businesses manage process challenges beyond the boundaries of implementation. We can evaluate the proof of concept (PoC) for any proposed improvements, and extract relevant information from a homogenous data set. Of course, process modeling and business process management (BPM) are available to solve the potentially tricky integration phase.

Process mining and analysis initiatives

Process mining and discovery initiatives can also provide critical insights throughout the automation and any Robotic Process Automation (RPA) journey, from defining the strategy to continuous improvement and innovation. Data-based process mining can even extend process analysis across teams and individuals, decreasing incident resolution times, and subsequently improving working habits via the discovery and validation of automation opportunities.

A further example of where process mining and strategic process analysis/alignment is already paying dividends is IT incident management. Here, “incident” is an unplanned interruption to an IT service, which may be complete unavailability or merely a reduction in quality. The goal of the incident management process is to restore regular service operation as quickly as possible and to minimize the impact on business operations. Incident management is a critical process in Information Technology Library (ITIL).

Process mining can also further drive improvement in as-is incident management processes as well as exceptional and unwanted process steps, by increasing visibility and transparency across IT processes. Process mining will swiftly analyze the different working habits across teams and individuals, decreasing incident resolution times, and subsequently improving customer impact cases.

Positive and practical experiences with process mining across industries have also led to the further dynamic development of tools, use cases, and the end-user community. Even with very experienced process owners, the visualization of processes can skyrocket improvement via new ideas and discussion.

However, the potential performance gains are more extensive, with the benefits of using process mining for incident management, also including:

  • Finding out how escalation rules are working and how the escalation is done;
  • Calculating incident management KPIs, including SLA (%);
  • Discovering root causes for process problems;
  • Understanding the effect of the opening interface (email, web form, phone, etc.);
  • Calculating the cost of the incident process;
  • Aligning the incident management system with your incident management process.

Robotic Process Automation (RPA)

Robotic process automation (RPA) provides a virtual workforce to automatize manual, repetitive, and error-prone tasks. However, successful process automation requires specific knowledge about the intended (and potential) benefits, effective training of the robots, and continuous monitoring of their performance and processes.

With this, process mining supports organizations throughout the lifecycle of RPA initiatives by monitoring and benchmarking robots to ensure sustainable benefits. These insights are especially valuable for process miners and managers with a particular interest in process automation. By unlocking the experiences with process mining, a company better understands what is needed today, for tomorrow’s process initiatives.

To further upgrade the impact of robot-led automation, there is also a need for a solid understanding of legacy systems, and an overview of automation opportunities. Process mining tools provide key insights throughout the entire RPA journey, from defining the strategy to continuous improvement and innovation.

Benefits of process mining and analysis within the RPA lifecycle include:

  1. Overviews of processes within the company, based on specific criteria;
  2. Identification of processes suitable for RPA implementation during the preparation phase;
  3. Mining the optimal process flow/process path;
  4. Understanding the extent to which RPA can be implemented in legacy processes and systems;
  5. Monitoring and analysis of RPA performance during the transition/handover of customization;
  6. Monitoring and continuous improvement of RPA in the post-implementation phase.

The process of better business understanding

Every organization is different and brings with it a variety of process-related questions. Yet some patterns are usually repeated. For example, customers who introduce data supported process analysis as part of business transformation initiatives will typically face challenges in harmonizing processes from fragmented sectors and regional locations. Here it helps enormously to base actions on data and statistics from the respective processes, instead of relying on the instincts and estimations of individuals.

With this, process analysis which is supported by data, enables a fact-based discussion, and builds a bridge between employees, process experts and management. This helps avoid siloed thinking, as well as allowing the transparent design of handovers and process steps which cross departmental boundaries within an organization.

In other words, to unlock future success and transformation, we must be processing… today.

Find out more about process mining with Signavio Process Intelligence, and see how it can help your organization uncover the hidden value of process, generate fresh ideas, and save time and money.