Skip to main content

5 Key Challenges In Today’s Era of Big Data




Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes.

Modern enterprises face 5 key challenges in today’s era of big data

1. Handling a multiplicity of enterprise source systems
The average Fortune 500 enterprise has a few hundred enterprise IT systems, all with their different data formats, mismatched references across data sources, and duplication
2. Incorporating and contextualising high frequency data
The challenge gets significantly harder with increase in sensoring, resulting inflows of real time data. For example, readings of the gas exhaust temperature for an offshore low-pressure compressor are only of limited value in of itself. But combined with ambient temperature, wind speed, compressor pump speed, history of previous maintenance actions, and maintenance logs, this real-time data can create a valuable alarm system for offshore rig operators.
3. Working with data lakes
Today, storing large amounts of disparate data by putting it all in one infrastructure location does not reduce data complexity any more than letting data sit in siloed enterprise systems. 
4. Ensuring data consistency, referential integrity, and continuous downstream use
A fourth big data challenge is representing all existing data as a unified image, keeping this image updated in real-time and updating all downstream analytics that use these data. Data arrival rates vary by system, data formats from source systems change, and data arrive out of order due to networking delays.
5. Enabling new tools and skills for new needs
Enterprise IT and analytics teams need to provide tools that enable employees with different levels of data science proficiency to work with large data sets and perform predictive analytics using a unified data image.

Let’s look at what’s involved in developing and deploying AI applications at scale

Data assembly and preparation
The first step is to identify the required and relevant data sets and assemble them. There are often issues with data duplication, gaps in data, unavailable data and data out of sequence.
Feature engineering
This involves going through the data and crafting individual signals that the data scientists and domain experts think will be relevant to the problem being solved. In the case of AI-based predictive maintenance, signals could include the count of specific fault alarms over the trailing 7 days,14 days and 21 days, the sum of the specific alarms over the same trailing periods; and the maximum value of certain sensor signals over those trailing periods. 
Labelling the outcomes
This step involves labeling the outcomes the model tries to predict. For example, in AI-based predictive maintenance applications, source data sets rarely identify actual failure labels, and practitioners have to infer failure points based on a  combination of factors such as fault codes and technician work orders.
Setting up the training data
For classification tasks, data scientists need to ensure that labels are appropriately balanced with positive and negative examples to provide the classifier algorithm enough balanced data. Data scientists also need to ensure the classifier is not biased with artificial patterns in the data.
Choosing and training the algorithm
Numerous algorithm libraries are available to data scientists today, created by companies, universities, research organizations, government agencies and individual contributors.
Deploying the algorithm into production
Machine learning algorithms, once deployed, need to receive new data, generate outputs, and have some actions or decisions be made based on those outputs. This may mean embedding the algorithm within an enterprise application used by humans to make decisions – for example, a predictive maintenance application that identifies and prioritizes equipment requiring maintenance to provide guidance for maintenance crews. This is where the real value is created – by reducing equipment downtime and servicing costs through more accurate failure prediction that enables proactive maintenance before the equipment actually fails. In order for the machine learning algorithms to operate in production, the underlying compute infrastructure needs to be set up and managed. 
Close-loop continuous improvement
Algorithms typically require frequent retraining by data science teams. As market conditions change, business objects and processes evolve, and new data sources are identified. Organizations need to rapidly develop, retrain, and deploy new models as circumstances change.
Therefore, problems that have to be addressed to solve AI computing problems are nontrivial. Massively parallel elastic computing and storage capacity are prerequisites. In addition to the cloud, there is a multiplicity of data services necessary to develop, provision, and operate applications of this nature. However, the price of missing a transformational strategic shift is steep. The corporate graveyard is littered with once-great companies that failed to change.
This article originally appeared on Makeen Technologies.

Comments

Popular posts from this blog

How Big Data Analytics Can Help You Improve And Grow Your Business?

Big Data Analytics There are certain problems that can only solve through big data. Here we discuss the field big data as "Big Data Analytics". The big data came into the picture we never thought how commodity hardware is used to store and manage the data which is reliable and feasible as compared to the costly sources. Now let us discuss a few examples of how big data analytics is useful nowadays. When you go to websites like Amazon, Youtube, Netflix, and any other websites actually they will provide some field in which recommend some product, videos, movies, and some songs for you. What do you think about how they do it? Basically what kind of data they generated on these kind websites. They make sure to analyze properly. The data generated is not small it is actually big data. Now they analysis these big data they make sure whatever you like and whatever you are the preferences accordingly they generate recommendations for you. If you go to Youtube you have noticed it kn…

AI Vs Machine Learning Vs Deep Learning

AI Vs Machine Learning Vs Deep Learning Artificial intelligence, deep learning and machine learning are often confused with each other. These terms are used interchangeably but do they do not refer to the same thing. These terms are closely related to each other which makes it difficult for beginners to spot differences among them. The reason I think of this puzzle is that AI is classified in many ways. It is divided into subfields with respect to the tasks AI is used for such as computer vision, natural language processing, forecasting and prediction, with respect to the type of approach used for learning and the type of data used. Subfields of Artificial Intelligence have much in common which makes it difficult for beginners to clearly differentiate among these areas. Different approaches of AI can process similar data to perform similar tasks. For example Deep learning and SVM both could be used for object detection task. Both have pros and cons. In some cases Machine Learning is …

How Computers Understand Human Language?

How Computers Understand Human Language? Natural languages are the languages that we speak and understand, containing large diverse vocabulary. Various words have several different meanings, speakers with different accents and all sorts of interesting word play. But for the most part human can roll right through these challenges. The skillful use of language is a major part what makes us human and for this reason the desire for computers that understand or speak human language has been around since they were first conceived. This led to the creation of natural language processing or NLP.
Natural Language Processing is a disciplinary field combining computer science and linguistics. There is an infinite number of ways to arrange words in a sentence. We can't give computers a dictionary of all possible sentences to help them understand what humans are blabbing on about. So, an early and fundamental NLP problem was deconstructing sentences into small pieces which could be more easily…

Introduction to Data Science: What is Big Data?

What Is Big Data First, we will discuss how big data is evaluated step by step process. Evolution of Data How the data evolved and how the big data came. Nowadays the data have been evaluated from different sources like the evolution of technology, IoT(Internet of Things), Social media like Facebook, Instagram, Twitter, YouTube, many other sources the data has been created day by day. 1. Evolution of  Technology We will see how technology is evolved as we see from the below image at the earlier stages we have the landline phone but now we have smartphones of Android, IoS, and HongMeng Os (Huawei)  that are making our life smarter as well as our phone smarter. Apart from that, we have heavily built a desktop for processing of Mb's data that we were using a floppy you will remember how much data it can be stored after that hard disk has been introduced which can stored data in Tb. Now due to modern technology, we can be stored data in the cloud as well. Similarly, nowadays we noticed …

The Limits of Artificial Intelligence

If you are here, it means that you are familiar with term artificial intelligence. Either you have read about it in school or have seen it in sci-fi movies or somewhere else. Talking about the limitations of AI, let me ask you one simple question first, do you know the definition of AI? You might be thinking to answer me with a yes, yes I know what is artificial intelligence. But what if I tell you that AI is a buzzword and it is almost impossible to properly define. It is this way because the definition of artificial intelligence is moving. People don’t call the things AI that they used to call. For example, a problem that seemed too complex to be solved by human and was solved by AI algorithm is no longer a problem of AI. Playing chess, is one of the examples. It was considered the peek level of artificial intelligence back in previous century. Now it hardly fits the criteria for AI. It is presented to the world as a super power that when given to a computer, it magically starts li…

How To Become A Successful Programmer?

How To Become A Successful Programmer? I have heard many novice programmers saying I want to get better at programming but there is hardly a slight improvement in their skills. I have observed that most of them say they want to get better but that is just a wish. They do not really mean it. They mere wish to improve their skills. They do not work for it. Your wish does not guarantee that you will become a successful programmer. Many other people who have developed an interest in computer programming do not know how to reach to a point where they will be called successful programmers. They either keep wandering in the middle of nowhere or just give up. The same response is for them too as it was for the wishers. Your interest does not guarantee that you will succeed. Programming is a field which requires intensive work to master. Along with improving your technical knowledge of programming, you need to work on your interest. You need to develop a habit of not giving up. You need to…

What is Multithreading? JAVA Multithreading Tutorial

It is almost end of 2017. The computer has evolved throughout its age from a simple, huge machine which was used for just simple numerical calculations to a small and swift electronic device which is affecting almost every aspect of our life. There are a lot of efforts involved in these enhancements in both hardware and software. Powerful hardware has been invented, and robust software techniques have been designed to improve hardware efficiency. One of these methods is multithreading and this is what we are going to talk about.
Multithreading is the ability of a single processing unit to execute multiple programs concurrently, apparently supported by the operating system. Multithreading is achieved either by multithreaded architecture or by software techniques or by both. All processors and OSs today support multi-thread execution.
We are talking about multithreading but what actually a thread is? A thread is a single unit a single processor can execute. The group consists of the sh…

5 Tips for Computer Science Students

You are in college now so I am skipping the basics the go to class do your homework study for tests stay out of the hospital. These are not all important pieces of advice but I am sure you have heard them. Instead, let’s talk computer science. Here are some tips I have specially collected by talking to students who wish they’d heard them when they were students. Listen up.

Seek help when you need it. Your classes are going to get harder, they are going to test your knowledge but that’s why you are there for. Some people find attending office hours or seeking extra help to be embarrassing. But these resources are there for a reason. Taking advantage of the help you are offered will not only help you prepare for future classes and learn the material better but a lot less harmful than bad grades or any other consequences of struggling.Don’t let yourself intimidated by large projects. The best thing to do, sit down a day at the assignment and break it up into smaller tasks. A lot of times…

Supervised Learning vs Unsupervised Learning vs Reinforcement Learning

Supervised Learning vs Unsupervised Learning vs Reinforcement Learning Machine learning models are useful when there is huge amount of data available, there are patterns in data and there is no algorithm other than machine learning to process that data. If any of these three conditions are not satisfied, machine learning models are most likely to under-perform. Machine learning algorithms find patterns in data and try to learn from it as much as it can. Based on the type of data available and the approach used for learning, machine learning algorithms are classified in three broad categories. Supervised learningUnsupervised learningReinforcement learning An abstract definition of above terms would be that in supervised learning, labeled data is fed to ML algorithms while in unsupervised learning, unlabeled data is provided. There is a another learning approach which lies between supervised and unsupervised learning, semi-supervised learning. Semi supervised learning algorithms are giv…

Machine Learning: A Truthy Lie?

For all these years, we all have been misguided by the term machine learning. We have been told that machines learning makes a machine capable of how to think, how to act like a human. Machine learning is the most misused term. It does not really mean what it sounds like. It is a lie, a truthy lie. What is meant by a truthy lie? Each year Merriam-Webster releases a top 10 list of most searched words. In 2003, the top word in the list was democracy. In 2004, the word blog made it to the top. The winning word for the year 2006 was trustiness, "Truth coming from the gut, not books; preferring to believe what you wish to believe, rather than what is known to be true". A word which could be a lie is used so often that it eventually feels like truth. "Bet on the jockey, not the horse" is a truthy lie. Similarly, "machine learning" has been used over time for any kind of activity to train a machine or a computer so it could think or act like a human. The word i…