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what is good data and where do you find it
What is Good Data and Where Do You Find It?
  • what is good data and where do you find itBad data is worse than no data at all.
  • What is “good” data and where do you find it?
  • Best practices for data analysis.

There’s no such thing as perfect data, but there are several factors that qualify data as good [1]:

  • It’s readable and well-documented,
  • It’s readily available. For example, it’s accessible through a trusted digital repository.
  • The data is tidy and re-usable by others with a focus on ease of (re-)executability and reliance on deterministically obtained results [2].

Following a few best practices will ensure that any data you collect and analyze will be as good as it gets.

1. Collect Data Carefully

Good data sets will come with flaws, and these flaws should be readily apparent. For example, an honest data set will have any errors or limitations clearly noted. However, it’s really up to you, the analyst, to make an informed decision about the quality of data once you have it in hand. Use the same due diligence you would take in making a major purchase: once you’ve found your “perfect” data set, perform more web-searches with the goal of uncovering any flaws.

Some key questions to consider [3] :

  • Where did the numbers come from? What do they mean?
  • How was the data collected?
  • Is the data current?
  • How accurate is the data?

Three great sources to collect data from

US Census Bureau

U.S. Census Bureau data is available to anyone for free. To download a CSV file:

  • Go to[4]
  • Search for the topic you’re interested in. 
  • Select the “Download” button.

The wide range of good data held by the Census Bureau is staggering. For example, I typed “Institutional” to bring up the population in institutional facilities by sex and age, while data scientist Emily Kubiceka used U.S. Census Bureau data to compare hearing and deaf Americans [5]. [6] contains data from many different US government agencies including climate, food safety, and government budgets. There’s a staggering amount of information to be gleaned. As an example, I found 40,261 datasets  for “covid-19” including:

  • Louisville Metro Government estimated expenditures related to COVID-19. 
  • State of Connecticut statistics for Connecticut correctional facilities.
  • Locations offering COVID-19 testing in Chicago.


Kaggle [7] is a huge repository for public and private data. It’s where you’ll find data from The University of California, Irvine’s Machine Learning Repository, data on the Zika virus outbreak, and even data on people attempting to buy firearms.  Unlike the government websites listed above, you’ll need to check the license information for re-use of a particular dataset. Plus, not all data sets are wholly reliable: check your sources carefully before use.

2. Analyze with Care

So, you’ve found the ideal data set, and you’ve checked it to make sure it’s not riddled with flaws. Your analysis is going to be passed along to many people, most (or all) of whom aren’t mind readers. They may not know what steps you took in analyzing your data, so make sure your steps are clear with the following best practices [3]:

  • Don’t use X, Y or Z for variable names or units. Do use descriptive names like “2020 prison population” or “Number of ice creams sold.”
  • Don’t guess which models fit. Do perform exploratory data analysis, check residuals, and validate your results with out-of-sample testing when possible.
  • Don’t create visual puzzles. Do create well-scaled and well-labeled graphs with appropriate titles and labels. Other tips [8]: Use readable fonts, small and neat legends and avoid overlapping text.
  • Don’t assume that regression is a magic tool. Do test for linearity and normality, transforming variables if necessary.
  • Don’t pass on a model unless you know exactly what it means. Do be prepared to explain the logic behind the model, including any assumptions made.  
  • Don’t leave out uncertainty. Do report your standard errors and confidence intervals.
  • Don’t delete your modeling scratch paper. Do leave a paper trail, like annotated files, for others to follow. Your predecessor (when you’ve moved along to better pastures) will thank you.

3. Don’t be the weak link in the chain

Bad data doesn’t appear from nowhere. That data set you started with was created by someone, possibly several people, in several different stages. If they too have followed these best practices, then the result will be a helpful piece of data analysis. But if you introduce error, and fail to account for it, those errors are going to be compounded as the data gets passed along. 


Data set image: Pro8055, CC BY-SA 4.0 via Wikimedia Commons

[1] Message of the day

[2] Learning from reproducing computational results: introducing three …

[3] How to avoid trouble:  principles of good data analysis

 [4] United States Census Bureau

[5] Better data lead to better forecasts


[7] Kaggle

[8]Twenty rules for good graphics

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big data key advantages for food industry
Big Data: Key Advantages for Food Industry

big data key advantages for food industry

The food industry is among the largest industries in the world. Perhaps nothing serves as a better testament to its importance. The global food industry not only survived the pandemic even as pretty much every other sector suffered the wrath of shutdowns, but it thrived. The growth Zomato, Swiggy, UberEats and more managed to achieve in the past year is incredible. Now, it is clear to see that this sector has an abundance of potential to offer, but with great potential comes even greater competition. And it’s not only the humongous competition — but companies also have to contend with the natural challenges of operating in this industry. For all that and more, the sector has found great respite in various modern technologies.

However, in particular, one has evinced incredible interest from the food industry, on account of its exceptional potential, of course: Big Data. You see, this technology has increasingly proven its potential to transform the food and delivery business for the better completely. How? In countless ways, actually, for starters, it can help companies identify the most profitable and highest revenue-generating items on their menu. It can be beneficial in the context of the supply chain and allow companies to keep an eye on factors such as weather conditions for farms they work with, monitor traffic on delivery routes, and so much more. Allow us to walk you through some of the other benefits big data offers to this industry.

  1. Quicker deliveries: Ensuring timely food delivery is one of the fundamental factors for success in this industry. Unfortunately, given the myriad things that can affect deliveries, ensuring punctuality can be quite a challenge. Not with big data by your side, though, for it can be used to run analysis on traffic, weather, routes, etc. To determine the most efficient and quickest ways for delivery to ensure food reaches customers on time.
  2. Quality control: The quality of food is another linchpin of a company’s success in this sector. Once again, this can be slightly tricky to master, especially when dealing with temperature-sensitive food items or those with a short shelf life. Big data can be used in this context by employing data sourced from IoT sensors and other relevant sources. And to monitor the freshness and quality of products and ensure they are replaced, the need arises.
  3. Improved efficiency: A restaurant or any other type of food establishment typically generates an ocean-load of data, which is the perfect opportunity to put big data to work. Food businesses can develop a better understanding of their market and customers and their processes and identify any opportunities for improvement. It allows companies to streamline operations and processes, thus boosting efficiency.

To conclude, online food ordering and delivery software development can immensely benefit any food company when fortified with technologies such as big data. So, what are you waiting for? Go find a service provider and get started on integrating big data and other technologies into your food business right away!

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product innovation marketing drives global data science platforms
Product Innovation Marketing Drives Global Data Science Platforms

product innovation marketing drives global data science platforms

Data science platform market is estimated to rise with a CAGR of 31.1% by generating a revenue of $224.3 billion by 2026. Asia-Pacific holds the highest growth rate, expecting to reach $80.3 billion during the forecast period.

Data science is the preparation, extraction, visualization, and maintenance of information. Data science uses scientific methods and processes to draw the outcomes from the data. With the help of data science tools and practices one can recognize the data patterns. The person dealing with data science tools and practices uses meaningful insights from the data to assist the companies to take the necessary decision. Basically, data science helps the system to function smarter and can take autonomous decisions based on historical data.

Access to Free Sample Report of Data Science Platform Market (Including Full TOC, tables & Figure) Here! @

Many companies have a large set of data that are not being utilized.  Data science is majorly used as a method to find specific information from a large set of unstructured and structured data. Concisely, data science is a vast and new field which helps to build, asses and control the data by the user. These analytical tools help in assessing business strategies and taking decisions. The rising use of data analytics tools in data science is considered to be major driving factor for the data science platform market.

Data science is mostly used to find hidden information from the data so that business decisions and strategies can be conceived. If the data prediction goes wrong, business has to face a lot of consequences. Therefore, professional expertise are required to handle the data carefully. But as the data science platform is new, the availability of the workforce with relevant experience is considered to be the biggest threat to the market.

Service type is predicted to have the maximum growth rate in the estimated period. Service segment is projected to grow at a CAGR of 32.0% by generating a revenue of $76.0 billion by 2026. Increasing difficulties in terms of operational work in many companies and rising use of Business Intelligence (BI) tools are predicted to be major drivers for the service type segment.

Manufacturing is predicted to have the highest growth rate in the forecast period. Data scientists have acquired a key position in the manufacturing industries. Data science is being broadly used for increasing production, reducing the cost of production and boosting profit in manufacturing area. Data science has also helped the companies to predict potential problems, monitor the work and analyze the flow of work in the manufacturing work area. Manufacturing segment is expected to grow at a CAGR of 31.9% and is predicted to generate a revenue of $43.28 billion by 2026.

North Americas has the largest market size in 2018. North America market is predicted to grow at a CAGR of 30.1% by generating a revenue of $80.3 billion by 2026. The presence of large number of multinational companies and rising use of data with the help of analytical tools in these companies gives a boost to the market in this region. Asia-Pacific region is predicted to grow at a CAGR of 31.9% by generating a revenue of $48.0 billion by 2026. Asia-Pacific is accounted to have the highest growth due to increasing investments by companies and the increased use of artificial intelligence, cloud, and machine learning.

The major key players in the market are Microsoft Corporation, Altair Engineering, Inc., IBM Corporation, Anaconda, Inc., Cloudera, Inc., Civis Analytics, Dataiku, Domino Data Lab, Inc., Alphabet Inc. (Google), and Databricks among others.

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dsc weekly digest 29 march 2021
DSC Weekly Digest 29 March 2021
dsc weekly digest 29 march 2021

One of the more significant “quiet” trends that I’ve observed in the last few years has been the migration of data to the cloud and with it the rise of Data as a Service (DaaS). This trend has had an interesting impact, in that it has rendered moot the question of whether it is better to centralize or decentralize data.

There have always been pros and cons on both sides of this debate, and they are generally legitimate concerns. Centralization usually means greater control by an authority, but it can also force a bottleneck as everyone attempts to use the same resources. Decentralization, on the other hand, puts the data at the edges where it is most useful, but at the cost of potential pollution of namespaces, duplication and contamination. Spinning up another MySQL instance might seem like a good idea at the time, but inevitably the moment that you bring a database into existence, it takes on a life of its own.

What seems to be emerging in the last few years is the belief that an enterprise data architecture should consist of multiple, concentric tiers of content, from highly curated and highly indexed data that represents the objects that are most significant to the organization, then increasingly looser, less curated content that represents the operational lifeblood of an organization, and outward from there to data that is generally not controlled by the organization and exists primarily in a transient state.

Efficient data management means recognizing that there is both a cost and a benefit to data authority. A manufacturer’s data about its products is unique to that company, and as such, it should be seen as being authoritative. This data and metadata about what it produces has significant value both to itself and to the users of those products, and this tier usually requires significant curational management but also represents the greatest value to that company’s customers.

Customer databases, on the other hand, may seem like they should be essential to an organization, but in practice, they usually aren’t. This is because customers, while important to a company from a revenue standpoint, are also fickle, difficult to categorize, and frequently subject to change their minds based upon differing needs, market forces, and so forth beyond the control of any single company. This data is usually better suited for the mills of machine learning, where precision takes a back seat to gist.

Finally, on the outer edges of this galactic data, you get into the manifestation of data as social media. There is no benefit to trying to consume all of Google or even Twitter without taking on all of the headaches of being Google or Twitter without any of the benefits. This is data that is sampled, like taking soundings or wind measurements in the middle of a boat race. The individual measurements are relatively unimportant, only the broader term implications.

From an organizational standpoint, it is crucial to understand the fact that the value of data differs based upon its context, authority, and connectedness. Analytics, ultimately, exists to enrich the value of the authoritative content that an organization has while determining what information has only transient relevance. A data lake or operational warehouse that contains the tailings from social media is likely a waste of time and effort unless the purpose of that data lake is to hold that data in order to glean transient trends, something that machine learning is eminently well suited for. 

This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.

In media res,
Kurt Cagle
Community Editor,
Data Science Central

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how big data can improve your golf game
How Big Data Can Improve Your Golf Game

how big data can improve your golf game

Big data and data analytics have become a part of our everyday lives. From online shopping to entertainment to speech recognition programs like Siri, data is being used in most situations. 

Data and data analytics continue to change how businesses operate, and we have seen how data has improved industry sectors like logistics, financial services, and even healthcare. 

So how can you use data and data analytics to improve your golf game? 

It Can Perfect Your Swing 

Having proper posture on your swing helps maintain balance and allows a golfer to hit the ball squarely in the center of the club. A good setup can help a golfer control the direction of the shot and create the power behind it. 

Using big data and data analytics, you’re able to analyze your swing and identify areas they could improve upon. This allows you to understand how your shoulder tilts at the top of every swing and when it connects with the ball, and your hip sways when the club hits the ball. 

All this information can help a golfer see where their swing is angled and how the ball moves. This will help identify areas that can be worked on, leading to better balance, a better setup, and a sound golf swing. 

It Can Help You Get More Distance on the Ball 

Every golfer would love to have more distance on the ball, and it’s completely possible to gain that extra distance.  Golfers can use data to get the following information: 

  • Swing speed
  • Tempo
  • Backswing position
  • % of greens hit

By using data analytics, you’d be able to tell which part of the clubface you’re striking the ball with or if you’re hitting more towards the toe or heel. You’ll also get a better understanding of your shaft lean, which can help you get your shaft leaning more forward. This can help you gain distance just by improving your impact. 

When it comes to tempo, analytics can help you gain more speed in your backswing so that you get an increase in speed in your downswing. This will lead to more speed and can help you gain more distance.

The goal of using data is to get the golfer to swing the club faster without swinging out of control. 

How Can You Track Your Data?

There are a few ways in which a golfer can track and analyze their golf swing. The first is by attaching golf sensors like the Arccos Caddie Smart Sensors to your golf clubs. 

This will record the golfer’s swing speed, tempo, and backswing position on every club used on every hole. Once you’re done with the round of golf, you’d upload the information to your PC, and this would give the golfer the statistics of their game.

You can also use your mobile phone to record your swing shot and then use an app like V1 to analyze the video. This will allow you to see your down line or front line and show you the swing angle. 

You can also use golf simulators like Optishot, which has 32 sensors and tracks both the swing and your face. It’s also pre-loaded with key data points to track your swing speed, tempo, and backswing position. This simulator also lets you play golf against your friends online. 

Benefits of Using Data in Golf

Practice will help your game improve, but our daily lifestyles don’t always allow us to practice regularly. Using data, you’re getting unbiased feedback, which allows a golfer to evaluate their strengths and weaknesses. 

This will allow you to customize your practice time to what you need to focus on, making sure you make efficient use of the practice time. You can also set realistic goals where you can track and measure your progress. 


Big data is here to stay, and it’s found its way into almost every aspect of life. Why not include it in your golf game if you’re looking for a way to improve and make more efficient use of your practice time? 

Author bio:

Jordan Fuller is a retired golfer, mentor, and coach. He also owns a golf publication site,, where he writes about a lot of stuff on golf. 

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important skills needed to become a successful data scientist in 2021
Important Skills Needed to Become a Successful Data Scientist in 2021

important skills needed to become a successful data scientist in 2021

The use of Big Data as an insight-generating engine has opened up new job opportunities in the market with Data scientists being in high demand at the enterprise level across all industry verticals. Organizations have started to bet on the data scientist and their skills to maintain, expand, and remain one up from their competition, whether it’s optimizing the product creation process, increasing customer engagement, or mining data to identify new business opportunities.

The year 2021 is the year for data science, I bet you. As the demand for qualified professionals shoots up, a growing number of people are enrolling in data science courses. You’ll also need to develop a collection of skills if you want to work as a data scientist in 2021. In this post, we will be discussing the important skills to have to be a good data scientist in the near future.

But first what is data science?

The Data Science domain is majorly responsible for all of the massive databases, as well as figuring out how to make them useful and incorporating them into real-world applications. With its numerous industry, science, and everyday-life benefits, digital data is considered one of the most important technological advancements of the twenty-first century. 

Data Scientists’ primary task is to sift through a wide variety of data. They are adept at providing crucial information, which opens the path for better decision-making. Most businesses nowadays have become the flag bearers of data science and make use of it. It is a defined data science precisely. In a larger context, data science entails the retrieval of clean data from raw data, as well as the study of these datasets to make sense of them, or, in most terms, the visualization of meaningful and actionable observations.

What is a Data Scientist, and how can one become one?

Extracting and processing vast quantities of data to identify trends and that can support people, enterprises, and organizations are among the duties of a Data Scientist. They employ sophisticated analytics and technologies, including statistical models and deep learning, as well as a range of analytics techniques. Reporting and visualization software is used to show data mining perspectives, which aids in making better customer-oriented choices and considering potential sales prospects, among other things.

Now let’s find out how to get started with Data science

First thing first, start with the basics

Though not a complicated step, but still many people skip it, because- math.

Understanding how the algorithms operate requires one to have a basic understanding of secondary-level mathematics.

Linear Algebra, Calculus, Permutation and Combination, and Gradient Descent are all concerned. 

No matter how much you despise this subject, it is one of the prerequisites and you must make sure to go through them to have a better standing in the job market.

Learn Programming Language

R and Python are the most widely used programming languages. You should start experimenting with the software and libraries for Analytics in any language. Basic programming principles and a working knowledge of data structures are important.

Python has rapidly risen to the top of the list of most common and practical programming languages for data scientists. However, it is not the only language in which data scientists can work.

The more skills you have, the more programming languages you will learn; however, which one do you choose?

The following are the most important ones:

  • JavaScript 
  • SQL (Structured Query Language)
  • Java 
  • Scala is a programming language.

Read regarding the advantages and disadvantages in both — as well as where they’re more often found — before deciding which would fit better with your ventures.

Statistics and Probability

Data science employs algorithms to collect knowledge and observations and then makes data-driven decisions. As a result, things like forecasting, projecting, and drawing inferences are inextricably linked to the work.

The data industry’s cornerstone is statistics. Your mathematical abilities would be put to the test in every career interview. 

Probability and statistics are fundamental to data science, and they’ll assist you in generating predictions for data processing by allowing you in:

  • Data exploration and knowledge extraction
  • Understanding the connections between two variables
  • Anomalies of data sets are discovered.
  • Future trend analysis based on historical evidence

Data Analysis

The majority of Data Scientists’ time is spent cleaning and editing data rather than applying Machine Learning in most professions.

The most critical aspect of the work is to understand the data and look for similarities and associations. It will give you an idea of the domain as well as which algorithm to use for this sort of query.

‘Pandas’ and ‘Numpy’, two popular Python data analysis applications, are also popular.

Data Visualization 

Clients and stakeholders would be confused by the mathematical jargon and the Model’s forecasts. Data visualization is essential for presenting patterns in a graphic environment using different charts and graphs to illustrate data and study behavior.

Without a question, data visualization is one of the most essential skills for interpreting data, learning about its different functions, and eventually representing the findings. It also assists in the retrieval of specific data information that can be used to create the model.

Machine learning

Machine learning will almost always be one of the criteria for most data scientist work. There’s no denying Machine learning’s influence. And it’s just going to get more and more common in the coming years.

It is unquestionably a skill to which you can devote time (particularly as data science becomes increasingly linked to machine learning). And the combination of these two inventions is yielding some fascinating, leading-edge insights and innovations that will have a big effect on the planet.

Business Knowledge

Data science necessitates more than just technological abilities. They are, without a doubt, necessary. However, when employed in the IT field, don’t forget about market awareness, as driving business value is an important aspect of data science.

As a data scientist, you must have a thorough understanding of the industry in which your firm works. And you need to know what challenges your company is trying to fix before you can suggest new ways to use the results.

Soft Skills

As a data scientist, you are responsible for not only identifying accurate methods to satisfy customer demands, but also for presenting that information to the company’s customers, partners, and managers in simple terms so that they understand and follow your process. As a result, if you want to take on responsibilities for some vital projects that are critical to your business, you’ll need to improve your communication skills.

Final Thoughts

As the number of people interested in pursuing a career in data science increases, it is crucial that you master the fundamentals, set a firm base, and continue to improve and succeed throughout your journey.

Now that you’ve got the run, the next step is to figure out how to learn Data Science. Global Tech Council certification courses are a common option since they are both short-term and flexible. The data analytics certification focuses on the information and skills you’ll need to get a job, all bundled in a versatile learning module that suits your schedule. It’s about time you start looking for the best online data science courses that meet your requirements and catapult you into a dazzling career.

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big data in the healthcare industry definition implementation risks
Big Data in the Healthcare Industry: Definition, Implementation, Risks

big data in the healthcare industry definition implementation risks

How extensive must data sets be to be considered as big data? For some, a slightly larger Excel spreadsheet is “big data”. Fortunately, there are certain characteristics that allow us to describe big data pretty well.

According to IBM, 90% of the data that exists worldwide today was created in the last 2 years alone. Big Data Analysis in Healthcare could be helpful in many ways. For example, such analyzes may also counteract the spread of diseases and optimize the needs-based supply of medicinal products and medical devices.

In this article, we will define what is Big Data and discuss ways it could be applied in Healthcare.

The easiest way to say is: Big data is data that can no longer only be processed by one computer. They are so big that you have to store and edit them piece by piece on several servers.

A short definition can also be expressed by three Vs:

  1. Volume – describes the size of the data
  2. Variety – a variety of data
  3. Velocity – the speed of the data

Volume – The Size of Data

As I said before, big data is most easily described by its sheer volume and complexity. These properties do not allow big data to be stored or processed on just one computer. For this reason, this data is stored and processed in specially developed software ecosystems, such as Hadoop.

Variety – Data Diversity

Mass data is very diverse and can also be structured, unstructured or semi-structured.

These data also mostly have different sources. For example, a bank could store transfer data from its customers, but also recordings of telephone conversations made by its customer support staff.

In principle, it makes sense to save data in the format in which it was recorded. The Hadoop Framework enables companies to do just that: the data is saved in the format in which it was recorded.

With Hadoop, there is no need to convert customer call data into text files. They can be saved directly as audio calls. However, the use of conventional database structures is then also not possible.

Velocity – The Speed of Data

This is about the speed at which the data is saved.

It is often necessary that data be stored in real-time. For companies like Zalando or Netflix, it is thus possible to offer their customers product recommendations in real-time.

There are three most obvious, but fundamentally revolutionizing ways of Big Data usage coupled with artificial intelligence.

  1. On the one hand, the monitoring. Significant deviations in essential body data will be automatically enhanced in the future: Is the increased pulse a normal sequence of the staircase just climbed? Or does he point to cardiovascular disease in combination with other data and history? Thus, diseases can be detected in their early stages and treated effectively.
  1. Diagnosis is the second one. Where it depends almost exclusively on the knowledge and the analysis capacity of the doctor, whether, for example, the cancer metastasis on the X-ray image is recognized as such, the doctor will use artificially intelligent systems, which become a little smarter with each analyzed X-ray image because of Big Data technology. The error probability in the diagnosis decreases, the accuracy in the subsequent treatment increases.
  1. And third, after all, Big Data and artificial intelligence have the potential to make the search for new medicines and other treatment methods much more efficient. Today, countless molecular combinations must first be tested in the Petri dish, then in the animal experiment, and finally in clinical trials on their effectiveness, maybe a new drug in the end. A billion company roulette game, in which the winning opportunities can be significantly increased by computer-aided forecasting procedures, which in turn access a never-existed wealth of research data.

As with every innovation in the health system, it’s about the hopes of people to a longer and healthier life. For the urgent that you could be torn from life prematurely through cancer, heart attack, stroke, or another insidious disease from life.

If you want to examine the case of Big Data in practice, you can check this Big Data in the Healthcare Industry article.

Apache Hadoop Framework

To meet these special properties and requirements of big data, the Hadoop framework was designed as open-source. It basically consists of two components:


First: It stores data on several servers (in clusters) as so-called HDFS (Hadoop Distributed File System). Second: it processes this data directly on the servers without downloading it to a computer. The Hadoop system processes the data where it is stored. This is done using a program called MapReduce.


MapReduce processes the data in parallel on the servers, in two steps: first, smaller programs, so-called “mappers”, are used. Mappers sort the data according to categories. In the second step, so-called “reducers” process the categorized data and calculate the results.


The operation of MapReduce requires programming knowledge. To make this requirement a little easier, another superstructure was created on the Hadoop framework – Hive. Hive does not require any programming knowledge and is based on the HDFS and MapReduce framework.  The commands in Hive are reminiscent of the commands in SQL, a standard language for database applications, and are only then translated in MapReduce in the second step.

The disadvantage: it takes a little more time because the code is still translated into MapReduce.

The amount of data available is increasing exponentially. At the same time, the costs of saving and storing this data also decrease. This leads many companies to save data as a precaution and check how it can be used in the future.  As far as personal data is concerned, there are of course data protection issues.

 In this article, I don’t mean to call a Big Data groundbreaking shot today. I believe it’s something that should be adopted widely, and that already has been taken by a lot of world-famous companies.

In the course of the digitization of the health system in general and currently, also with the corona crisis in particular, there are also new questions for data protection. The development and use of ever further technologies, applications, and means of communication offer a lot of benefits but also carries (data protection) risks. Medical examinations in video chat, telemedicine, attests over the internet and a large number of different health apps mean that health data does not simply remain within an institution like a hospital, but on private devices, on servers of app developers, or other places.

 Firstly we have to deal with the question of which data sets are actually decisive for the question that we want to answer with the help of data analysis. Without this understanding, big data is nothing more than a great fog that obscures a clear view through technology-based security.

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knowledge organization make semantics explicit
Knowledge Organization: Make Semantics explicit

knowledge organization make semanticsThe organization of knowledge on the basis of semantic knowledge models is a prerequisite for an efficient knowledge exchange. A well-known counter-example are individual folder systems or mind maps for the organization of files. This approach to knowledge organization only works at the individual level and is not scalable because it is full of implicit semantics that can only be understood by the author himself.

To organize knowledge well, we should therefore use established knowledge organization systems (KOS) to model the underlying semantic structure of a domain. Many of these methods have been developed by librarians to classify and catalog their collections, and this area has seen massive changes due to the spread of the Internet and other network technologies, leading to the convergence of classical methods of library science and from the web community.

When we talk about KOSs today, we primarily mean Networked Knowledge Organization Systems (NKOS). NKOS are systems of knowledge organization such as glossaries, authority files, taxonomies, thesauri and ontologies. These support the description, validation and retrieval of various data and information within organizations and beyond their boundaries.

Let’s take a closer look: Which KOS is best for which scenario? KOS differ mainly in their ability to express different types of knowledge building blocks. Here is a list of these building blocks and the corresponding KOS.

Building blocks  




Emmental = Emmental cheese

Glossary, synonym ring


Emmental (cheese) is not same as
Emmental (valley)

Authority file


Emmental is a cow’s-milk cheese

Cow’s-milk cheese is a cheese

Emmental (valley) is part of Switzerland



Emmental cheese is related to cow’s milk

Emmental cheese is related to Emmental (valley)



Emmental is of class cow’s-milk cheese

Cow’s-milk cheese is subclass of cheese

Any cheese has exactly one country of origin
Emmental is obtained from cow’s milk


The Simple Knowledge Organization System (SKOS), a widely used standard specified by the World Wide Web Consortium (W3C), combines numerous knowledge building blocks under one roof. Using SKOS, all knowledge from lines 1–4 can be expressed and linked to facts based on other ontologies.

Knowledge organization systems make the meaning of data or documents, i.e., their semantics, explicit and thus accessible, machine-readable and transferable. This is not the case when someone places files on their desktop computer in a folder called “Photos-CheeseCake-January-4711” or uses tags like “CheeseCake4711” to classify digital assets. Instead of developing and applying only personal, i.e., implicit semantics, that may still be understandable to the author, NKOS and ontologies take a systemic approach to knowledge organization.

Basic Principles of Semantic Knowledge Modeling

Semantic knowledge modeling is similar to the way people tend to construct their own models of the world. Every person, not just subject matter experts, organizes information according to these ten fundamental principles:

  1. Draw a distinction between all kinds of things: ‘This thing is not that thing.’
  2. Give things names: ‘This thing is a cheese called Emmental’ (some might call it Emmentaler or Swiss cheese, but it’s still the same thing).
  3. Create facts and relate things to each other: ‘Emmental is made with cow’s milk’, Cow’s milk is obtained from cows’, etc.
  4. Classify things: ‘This thing is a cheese, not a ham.’
  5. Create general facts and relate classes to each other: ‘Cheese is made from milk.’
  6. Use various languages for this; e.g., the above-mentioned fact in German is ‘Emmentaler wird aus Kuhmilch hergestellt’ (remember: the thing called ‘Kuhmilch’ is the same thing as the thing called ‘cow’s milk’—it’s just that the name or label for this thing that is different in different languages).
  7. Putting things into different contexts: this mechanism, called “framing” in the social sciences, helps to focus on the facts that are important in a particular situation or aspect. For example, as a nutritional scientist, you are more interested in facts about Emmental cheese compared to, for example, what a caterer would like to know. (With named graphs you can represent this additional context information and add another dimensionality to your knowledge graph.)
  8. If things with different URIs from the same graph are actually one and the same thing, merging them into one thing while keeping all triples is usually the best option. The URI of the deprecated thing must remain permanently in the system and from then on point to the URI of the newly merged thing.
  9. If things with different URIs contained in different (named) graphs actually seem to be one and the same thing, mapping (instead of merging) between these two things is usually the best option.
  10. Inferencing: generate new relationships (new facts) based on reasoning over existing triples (known facts).

Many of these steps are supported by software tools. Steps 7–10 in particular do not have to be processed manually by knowledge engineers, but are processed automatically in the background. As we will see, other tasks can also be partially automated, but it will by no means be possible to generate knowledge graphs fully automatically. If a provider claims to be able to do so, no knowledge graph will be generated, but a simpler model will be calculated, such as a co-occurrence network.

Read more: The Knowledge Graph Cookbook

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unleashing the business value of technology part 2 connecting to value
Unleashing the Business Value of Technology Part 2: Connecting to Value

unleashing the business value of technology part 2 connecting to value

Figure 1: Unleashing the Value of Technology Roadmap

In part 1 of the blog series “Unleashing the Business Value of Technology Part 1: Framing the Cha…”, I introduced the 3 stages of “unleashing business value”:  1) Connecting to Value, 2) Envisioning Value and 3) Delivering Value (see Figure 1).

We discussed why technology vendors suck at unleashing the business value of their customers’ technology investments because they approach the challenge with the wrong intent.  We then discussed some failed technology vendor engagement approaches; product-centric approaches that force the customer to “make the leap of faith” across the chasm of value.

We also introduced the Value Engineering Framework as a way to “reframe the customer discussion and engagement approach; a co-creation framework that puts customer value realization at the center of the customer engagement” (see Figure 2).

unleashing the business value of technology part 2 connecting to value 1

Figure 2: Value Engineering Framework

The Value Engineering Framework is successful not only because it starts the co-creation relationship around understanding and maximizing the sources of customer value creation, but the entire process puts your customer value creation at the center of the relationship.

In Part 2, we are going to provide some techniques that enable technology vendors to connect to “value”, but that is “value” as defined by the customer, not “value” as defined by product or services capabilities.  The Value Engineering Framework helps transition the customer engagement discussion away from technology outputs towards meaningful and material customer business outcomes (see Figure 3).

unleashing the business value of technology part 2 connecting to value 2

Figure 3: Modern Data Governance:  From Technology Outputs to Business Outcomes

So, how do technology vendors “connect to value” in their conversations with customers’ business executives?  They must invest the upfront time to understand where and how value is created by the customer.  Here are a couple of tools and techniques that technology vendors can use to understand and connect with the customer’s sources of value creation.

I’m always surprised by how few technology vendors take the time to read their customers’ financial statements to learn what’s important to their customers. Financial reports, press releases, quarterly analyst calls, corporate blogs and analyst websites (like are a rich source of information about an organization’s strategic business initiatives – those efforts by your customers to create new sources of business and operational value.

But before we dive into an annual report exercise, let’s establish some important definitions to ensure that we are talking about the same things:

  • Charter or Mission is why an organization exists. For example, the mission for The Walt Disney Company is to be “one of the world’s leading producers and providers of entertainment.”
  • Business Objectives describe what an organization expects to accomplish over the next 2 to 5 years. The Business Objectives for The Disney Company might include MagicBand introduction, launch “Black Widow” movie, and launch the new Disney World “Star Wars – Rise of the Resistance Trackless Dark Ride” attraction.
  • Business Initiative is a cross-functional effort typically 9-12 months in duration, with well-defined business metrics, that supports the entity’s business objectives. For The Disney Company example, it might be to “leverage the MagicBand to increase guest satisfaction by 15%” or “leverage the MagicBand increase cross-selling of Class B attractions by 10%.”
  • Decisions are defined as a conclusion or resolution reached after consideration or analysis that leads to action. Decisions address what to do, when to do it, who does it and where to do it.  For The Disney Company example, “Offer FastPass+ to these guests for these attractions at this time of the day” is an example of a decision.
  • Use Cases are a cluster of Decisions around a common subject area in support of the targeted business initiative. The Disney Company use cases supporting the “Increase Class B Attraction Attendance” Business Initiative could include:
    • Increase Class A to Class B attraction cross-promotional effectiveness by X%
    • Optimize Class B attraction utilization using FastPass+ by X%
    • Increase targeted guest park experience using FastPass+ by X%
    • Optimize FastPass+ promotional effectiveness by time-of-day by X%

Using these definitions, let’s examine the Starbucks’ 2019 Annual Report to identify their key business objectives (see Figure 4).

unleashing the business value of technology part 2 connecting to value 3

Figure 4: Reading the 2019 Starbucks Annual Report

From Figure 4, we can see that one of Starbucks business objectives is “Providing each customer a unique Starbucks experience.”  (Note:  the annual report is chockfull of great opportunities for technology vendors to co-create value with their customers). Let’s triage Starbuck’s “Unique Starbucks Experience” business objective to understand our technology product and service capabilities can enable their “Providing each customer a unique Starbucks experience”.   Welcome to the “Big Data Strategy Document”.

The Big Data Strategy Document decomposes an organization’s business objective into its potential business initiatives, desired business outcomes, critical success factors against which progress and success will be measured, and key tasks or actions. The Big Data Strategy Document provides a design template for contemplating and brainstorming the areas where the technology vendor can connect to the customer’s sources of value creation prior to ever talking to a customer’s business executives. This includes the following:

  1. Business Objective. The title of the document states the 2 to 3-year business strategy upon which big data is focused.
  2. Business Initiatives. This section states the 9 to 12-month business initiatives that supports the business strategy (sell more memberships, sell more products, acquire more new customers).
  3. Desired Outcomes. This section contains the Desired Business or Operational Outcomes with respect to what success looks like (retained more customers, improved operational uptime, reduced inventory costs).
  4. Critical Success Factors (CSF). Critical Success Factors list the key capabilities necessary to support the Desired Outcomes.
  5. Use Cases. This section provides the next level of detail regarding the specific use cases (“how to do it”) around which the different part of the organizations will need to collaborate to achieve the business initiatives.
  6. Data Sources. Finally, the document highlights the key data sources required to support this business strategy and they key business initiatives.

(Note:  the Big Data Strategy Document is covered in Chapter 3 my first book “Big Data: Understanding How Data Powers Big Business.”  The book provides worksheets to help organizations to determine where and how big data can derive and drive new sources of business and operational value.  Still a damn relevant book!)

See the results of the Starbucks triage exercise in Figure 5.

unleashing the business value of technology part 2 connecting to value 4

Figure 5:  Starbucks Big Data Strategy Document

To learn more about leveraging the Big Data Strategy Document, check out this oldie but goodie blog “Most Excellent Big Data Strategy Document”.

The challenge most technology vendors face when trying to help their customers unleash the business value of their technology investments, is that vendors don’t intimately understand how their customers create value.  Once the technology vendor understands how the customer creates value, then the technology vendor has a frame against which to position their product and service capabilities to co-create new sources of value for both the customer and the technology vendor.

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defining and measuring chaos in data sets why and how in simple words
Defining and Measuring Chaos in Data Sets: Why and How, in Simple Words

defining and measuring chaos in data sets why and how in simple words

There are many ways chaos is defined, each scientific field and each expert having its own definitions. We share here a few of the most common metrics used to quantify the level of chaos in univariate time series or data sets. We also introduce a new, simple definition based on metrics that are familiar to everyone. Generally speaking, chaos represents how predictable a system is, be it the weather, stock prices, economic time series, medical or biological indicators, earthquakes, or anything that has some level of randomness. 

In most applications, various statistical models (or data-driven, model-free techniques) are used to make predictions. Model selection and comparison can be based on testing various models, each one with its own level of chaos. Sometimes, time series do not have an auto-correlation function due to the high level of variability in the observations: for instance, the theoretical variance of the model is infinite. An example is provided in section 2.2 in this article  (see picture below), used to model extreme events. In this case, chaos is a handy metric, and it allows you to build and use models that are otherwise ignored or unknown by practitioners.  

defining and measuring chaos in data sets why and how in simple words

Figure 1: Time series with indefinite autocorrelation; instead, chaos is used to measure predictability

Below are various definitions of chaos, depending on the context they are used for. References about how to compute these metrics, are provided in each case.

Hurst exponent

The Hurst exponent H is used to measure the level of smoothness in time series, and in particular, the level of long-term memory. H takes on values between 0 and 1, with H = 1/2 corresponding to the Brownian motion, and H = 0 corresponding to pure white noise. Higher values correspond to smoother time series, and lower values to more rugged data. Examples of time series with various values of H are found in this article, see picture below. In the same article, the relation to the detrending moving average (another metric to measure chaos) is explained. Also, H is related to the fractal dimension. Applications include stock price modeling.

defining and measuring chaos in data sets why and how in simple words 1

Figure 2: Time series with H = 1/2 (top), and H close to 1 (bottom)

Lyapunov exponent

In dynamical systems, the Lyapunov exponent is used to quantify how a system is sensitive to initial conditions. Intuitively, the more sensitive to initial conditions, the more chaotic the system is. For instance, the system xn+1 = xn – INT(xn), where INT represents the integer function, is very sensitive to the initial condition x0. A very small change in the value of x0 results in values of xn that are totally different even for n as low as 45. See how to compute the Lyapunov exponent, here.

Fractal dimension

A one-dimensional curve can be defined parametrically by a system of two equations. For instance x(t) = sin(t), y(t) = cos(t) represents a circle of radius 1, centered at the origin. Typically, t is referred to as the time, and the curve itself is called an orbit. In some cases, as t increases, the orbit fills more and more space in the plane. In some cases, it will fill a dense area, to the point that it seems to be an object with a dimension strictly between 1 and 2. An example is provided in section 2 in this article, and pictured below. A formal definition of fractal dimension can be found here.

defining and measuring chaos in data sets why and how in simple words 2

Figure 3: Example of a curve filling a dense area (fractal dimension  >  1)

The picture in figure 3 is related to the Riemann hypothesis. Any meteorologist who sees the connection to hurricanes and their eye, could bring some light about how to solve this infamous mathematical conjecture, based on the physical laws governing hurricanes. Conversely, this picture (and the underlying mathematics) could also be used as statistical model for hurricane modeling and forecasting. 

Approximate entropy

In statistics, the approximate entropy is a  metric used to quantify regularity and predictability in time series fluctuations. Applications include medical data, finance, physiology, human factors engineering, and climate sciences. See the Wikipedia entry, here.

It should not be confused with entropy, which measures the amount of information attached to a specific probability distribution (with the uniform distribution on [0, 1] achieving maximum entropy among all continuous distributions on [0, 1], and the normal distribution achieving maximum entropy among all continuous distributions defined on the real line, with a specific variance). Entropy is used to compare the efficiency of various encryption systems, and has been used in feature selection strategies in machine learning, see here.

Independence metric 

Here I discuss some metrics that are of interest in the context of dynamical systems, offering an alternative to the Lyapunov exponent to measure chaos. While the Lyapunov exponents deals with sensitivity to initial conditions, the classic statistics mentioned here deals with measuring predictability for a single instance (observed time series) of a dynamical systems. However, they are most useful to compare the level of chaos between two different dynamical systems with similar properties. A dynamical system is a sequence xn+1 = T(xn), with initial condition x0. Examples are provided in my last two articles, here and here. See also here

A natural metric to measure chaos is the maximum autocorrelation in absolute value, between the sequence (xn), and the shifted sequences (xn+k), for k = 1, 2, and so on. Its value is maximum and equal to 1 in case of periodicity, and minimum and equal to 0 for the most chaotic cases. However, some sequences attached to dynamical systems, such as the digit sequence pictured in Figure 1 in this article, do not have theoretical autocorrelations: these autocorrelations don’t exist because the underlying expectation or variance is infinite or does not exist. A possible solution with positive sequences is to compute the autocorrelations on yn = log(xn) rather than on the xn‘s.

In addition, there may be strong non-linear dependencies, and thus high predictability for a sequence (xn), even if autocorrelations are zero. Thus the desire to build a better metric. In my next article, I will introduce a metric measuring the level of independence, as a proxy to quantifying chaos. It will be similar in some ways to the Kolmogorov-Smirnov metric used to test independence and illustrated here, however, without much theory, essentially using a machine learning approach and data-driven, model-free techniques to build confidence intervals and compare the amount of chaos in two dynamical systems: one fully chaotic versus one not fully chaotic. Some of this is discussed here.

I did not include the variance as a metric to measure chaos, as the variance can always be standardized by a change of scale, unless it is infinite.

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About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). You can access Vincent’s articles and books, here.

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