Enterprise Data Science: Smarter Decisions with Big Data

Enterprise Data Science Smarter Decisions with Big Data

Enterprise Data Science Smarter Decisions with Big Data

Vidhur Gupta

Enterprise Data Science

Smarter Decisions with Big Data

Vidhur Gupta

ISBN - 9789361527357

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Preface

The book is written to keep pace with newer development in data science for enterprises and to cater to the contemporary needs of users. With the advent of the Internet, and later mobile devices and IoT, it became possible for private companies to truly use data at scale, building massive stores of consumer data based on the growing number of touchpoints they now shared with their customers. The world is firmly in the age of big data. As a result, enterprises are scrambling to integrate capabilities that can address advanced analytics such as artificial intelligence and machine learning to best leverage their data.

The need to draw out insights to improve business performance in the marketplace is nothing less than mandatory. As a result, recent data management concepts such as the data lake have emerged to help enterprises store and manage data. In many ways, the data lake was a stark contrast to its forerunner, the enterprise data warehouse. Typically, the EDW accepted data that had already been deemed useful, and its content was organized in a highly systematic way. When misused, a data lake serves as nothing more than a hoarding ground for terabytes and petabytes of unstructured and unprocessed data. Much of it is never to be used. However, a data lake can be meaningfully leveraged to benefit advanced analytics and machine learning models.

Analysis reveals that the higher failure rate for data lakes and big data initiatives has been attributed not to the technology itself but to how the technologists have applied it. For example, it often happens that a department within an organization needs a repository for its data, but its requirements are not satisfied by previous data storage efforts. So instead of attempting to reform or update older data warehouses or lakes, the department creates a new data store. The result is an assortment of data storage solutions that don't always play well together, resulting in lost opportunities for data analysis.

Obviously, new technologies can provide many tangible benefits, but those benefits cannot be realized unless the technologies are deployed and managed with care. Unlike designing a building as in traditional architecture, information architecture is not a set-it-and-forget-it prospect. While an organization can control how data is ingested, your organization can't always control how the data it needs changes over time. Organizations tend to be fragile in that they can break when circumstances change. Only flexible, adaptive information architectures can adjust to new environmental conditions. Designing and deploying solutions against a moving target is difficult, but the challenge is not impossible.

The glib assertion that garbage in will equal garbage out is treated as being pass by many IT professionals. While, in truth, garbage data has plagued analytics and decision-making for decades, mismanaged data and inconsistent representations will remain a red flag for each AI project you undertake. The level of data quality demanded by machine learning and deep learning can be significant. Like a coin with two sides, low data quality can have two separate and equally devastating impacts. On the one hand, low-quality data associated with historical data can distort the training of a predictive model. On the other, new data can distort the model and negatively impact decision-making. As a sharable resource, data is exposed across your organization through layers of services that can behave like a virus when the level of data quality is poor—unilaterally affecting all those who touch the data. Therefore, information architecture for artificial intelligence must mitigate traditional issues associated with data quality, foster data movement, and, when necessary, provide isolation.

The purpose of this book is to provide you with an understanding of how the enterprise must approach the work of building an information architecture to make way for successful, sustainable, and scalable AI deployments. The book includes a structured framework and advice that is practical and actionable toward implementing an information architecture that's equipped to capitalize on the benefits of AI technologies.

Key Features of This Book are as Follows:

●Thorough Updating: All the chapters and topics have undergone thorough revision and updating of various aspects. At the same time, most of the newer information has been inserted between the lines. In doing so, the basic accepted style of the book is simple, easy-to-understand, and reproducible of the subject matter, and emphasis on clarity and accuracy has not been changed.

●More and new figures/tables: There are several newer figures and tables in this book. All figures with proper illustrations have been placed alongside the corresponding link, respectively, enhancing the understanding of the subject for beginners in data science.

●Summary and Inquiries: Throughout the book, a unique summary of the topic has been placed at the end of every topic. The inquiries have also been placed for answering the question and a quick revision of the topics in a short time. The student can revise the entire subject quickly, turning pages of the book. The summaries are short to quickly revise the topic without searching for them, making the book truly user-friendly.

What You'll Learn

We'll begin in Chapter 1, Data Science, with a discussion of data science with an illustration of various algorithms. Chapter 2, Stepping into AI, with a discussion of the building AI, an illustrative device developed by IBM to demonstrate the steps or rungs an organization must climb to realize sustainable benefits with the use of AI. From there, Chapters 3, Forming Organizations Using AI and Chapter 4, Working with Data and AI, cover an array of considerations data scientists and IT leaders must be aware of as they traverse their way up the ladder. Finally, in Chapter 5, Smarter Learning Software, and Chapter 6, Looking Forward to Analytics, we'll explore some recent history: data warehouses and how they've given way to data lakes. Next, we'll discuss how data lakes must be designed in terms of topography and topology. This will flow into a deeper dive into data ingestion, governance, storage, processing, access, management, and monitoring.

In Chapter 7, Optimizing Disciplines on AI Ladder, we'll discuss how DevOps, DataOps, and MLOps can enable an organization to better use its data in real-time. In Chapter 8, Value Edition and Maximizing the use of data, we'll delve into the elements of data governance and integrated data management. We'll cover the data value chain and the need for data to be accessible and discoverable for the data scientist to determine the data's value. Chapter 9, Statistical analysis for valuing data, introduces different approaches for data access, as different roles within the organization will need to interact with data in different ways. The chapter also furthers the discussion of data valuation, explaining how statistics can assist in ranking the value of data.

In Chapter 11, Extend the value through data AI, we'll discuss some things that can go wrong in information architecture and the importance of data literacy across the organization to prevent such issues. Chapter 12, An IA for AI, will bring everything together with a detailed overview of developing an information architecture for artificial intelligence (IA for AI). This chapter provides practical, actionable steps to bring the preceding theoretical backdrop to bear on real-world information architecture development. Finally, Chapter 13, Modernization in data science, will bring about the case studies and practical industrial application provided.

Content

01. Data Science

Abstract 1

1.1 Analyzing the Data Science 1

1.2 Lifecycle of Data Science 2

1.3 Tools For Data Science 3

1.4 Types Of Data Science Work 4

1.5 Components of Data Science 5

1.6 Machine Learning in Data Science 7

1.7 Data science and IBM Cloud 9

1.8 Application of Data Science 10

1.9 Summary 13

1.10 Inquiries 13

02. Stepping into AI

Abstract 16

2.1 Building base data for AI 16

2.3 Choosing the Ladder rung by rung 19

2.4 Adapting to Retain Organizational

2.5 Data-Based in Modern Business 20

2.6 Developing AI-centric organization 23

2.7 Summary 23

2.8 Inquiries 24

03. Data Science Organization Using AI

Abstract 26

3.1 Artificial Intelligence cooperating with

3.2 Decision making in AI 28

3.3 Standardizing data and data science 31

3.4 Data science for the enterprise 31

3.5 Facilitating data in a reaction time 34

3.6 Summary 35

3.7 Inquiries 36

04. Working With Data And AI

Abstract 38

4.1 User-friendly data 38

4.2 Data governance 41

4.2 Data Governance 42

4.3 Encapsulation Knowledge 46

4.4 Summary 49

4.5 Inquiries 50

05. Smarter Learning Software

Abstract 52

5.1 Preaching big data imaginary 52

5.2 Powerful data and algorithms 55

5.3 New normal is big data 57

5.4 Data Management for AI 59

5.5 Summary 60

5.6 Inquiries 61

06. Looking Forward to Analytics

Abstract 63

6.1 Need for Organization 63

6.1.2 The raw zone 65

6.2 Data Topologies 69

6.3 Exploring Various Zones 72

6.4 Summary 76

6.5 Inquiries 77

07. Optimizing Disciplines on AI Ladder

Abstract 79

7.1 Operational AI 79

7.2 Time Passage 80

7.3 Create 82

7.4 Execute 83

7.5 Operating the work 85

7.6 Business-driven tools for Software

7.7 Summary 89

7.8 Inquiries 90

08. Value Edition and Maximizing the Use of Data

Abstract 92

8.1 Marching Towards Value Chain 92

8.2 Curation 95

8.3 Socializing the Data 95

8.4 Integrated Data Management 96

8.5 Multi-Tenacy 99

8.6 Summary 100

8.7 Inquiries 101

09. Statistical Analysis For Valuing Data

Abstract 104

9.1 Data Management Through Asset 104

9.2 Inexact Science 106

9.3 Data Inequality Among Users 108

9.4 Accessing the Data in Control 110

9.5 Bottom-Up Approach 111

9.6 Various Industries use Data and AI 111

9.7 Benefits from Statistics 112

9.9 Summary 115

9.10 Inquiries 116

10. Long Term Availability

Abstract 119

10.1 Avoid Hard Coding 120

10.2 Overloading 121

10.3 Locked In 121

10.4 Ownership and Decomposition 123

10.5 Avoiding Changing in Design 125

10.6 Summary 126

10.7 Inquiries 126

11. Extending Value Data Through AI

11.1 Emphasizing the AI

11.2 Polyglot Persistence 133

11.3 Profit in Data Literacy 140

11.4 Skill Sets 144

11.5 Pursuing AI 144

11.6 Creating Metadata 145

11.7 Right Movement to Data 147

11.8 Summary 147

11.9 Inquiries 148

12. An IA for AI

Abstract 152

12.1 Development Effort for AI 153

12.2 Machine Learning Model 153

12.3 Data Drift 157

12.4 Essential elements 158

12.6 Intersections 162

12.7 Interoperability Across Element 164

12.8 Driving Action 168

12.9 Keep It Simple 169

12.10 Organizing Data zones 169

12.11 Possibilities of Open Platforms 170

12.12 Summary 171

12.13 Inquiries 172

13. Data Governance for Creating Trust in Data Science Decision Outcomes

Abstract 175

13.1 Transformation of business 176

13.2 Data Science Decision-Making Outcomes 178

13.2 The Role of Data Governance with Regards to Data Science as a Product of Human Agency 178

13.3 The Role of Data Governance with Regards

13.4 The Role of Data Governance with Regards

13.5 The Role of Data Governance with Regards

13.7 Summary 181

13.8 Inquiries 182

14. Big Data Analytics Creates Business Value in Smart Manufacturing

Abstract 186

14.1 Cyber-Physical System 186

14.2 Big Data Analytics in Smart Manufacturing 188

14.3 Business value of IT frameworks 189

14.4 Science and Technology in Industry 189

14.5 Computing Devices and Internet 190

14.6 Context-aware Mobile computing 191

14.8 Mobile systems and services 191

14.7 Summary 192

14.8 Inquiries 193

15. Modernization of Data Science in AI

Abstract 197

15.1 Case Study 198

15.2 Biomedical Engineer’s Station 199

15.3 AI in Media Platforms 201

15.4 IBM Commercial Process 203

15.5 Hadoop Ecosystem 205

15.6 Image and Speech Recognition 207

15.7 Investing and Financing 208

15.8 Manufacturers using IoT 209

15.9 Telephonic Communication 210

15.10 Summary 212

15.11 Inquiries 213

Glossary 216

Index219

Chapter 1. Data Science

Abstract

Data science is a multidisciplinary approach to extracting actionable insights from the large and increasing volumes of data collected and created by today’s organization. Data preparation can involve cleansing, aggregating, and manipulating it to be ready for specific types of processing. In addition, analysis requires the development and use of algorithms, analytics, and AI models. Thus, data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

1.1 Analyzing the Data Science

It’s driven by software that combs through data to find patterns within to transform these patterns into predictions that support business decision making; the scientifically designed tests and experiments prediction must be validated accurately. The results should be shared through skillful data visualization tools that make it possible for anyone to see the patterns and understand trends. Data science requires computer science and pure science skill that build a data science. A data scientist must know mathematical modeling, statistics, and the scientific method.

1.2 Lifecycle of Data Science

The data science lifecycle—also called the data science pipeline—includes anywhere from five to sixteen (depending on whom you ask) overlapping, continuing processes. The processes common to just about everyone’s definition of the lifecycle include the following, right from the first step of obtaining data to analysis and result presentation. There are five important things in the data science life cycle.

Fig 1.1 Lifecycle of data science

1.2.1 Gathering Data

There are certain things to be known for gathering information from data resources. Technical skills in different programming languages to be known. Social media sites such as Facebook and Twitter let their users approach data by connecting with web servers. The most convenient way of gathering data is fetching from the files. Kaggle or preexisting information stored in Tab Separated Values (TSV) or Comma Separated Value (CSV) format can be downloaded from the files. Since these are flat text files, a specific Parser format is needed to read them.

1.2.2 Cleaning Data

The next step is to clean the data, referring to the scrubbing and filtering of data. This procedure requires the conversion of data into a different format. It is necessary for processing and analyzing information. If the files are web-locked, then it is also needed to filter the lines of these files. Moreover, cleaning data also constitute withdrawing and replacing values. In case of missing data sets, the replacement must be done properly since they could look like non-values. Additionally, columns are split, merged, and withdrawn as well.

1.2.3 Exploring data

As data has to be examined before ready to use. In business areas, data scientist has to transform the data that is available into corporate settings. The first thing is to be done an exploration of data. Different data require inspection, such as nominal and ordinal, numerical, and categorical data.

1.2.4 Modeling Data

Modeling has to deal with a few tasks. For example, models can be trained to differentiate via classification, such as mails received as ‘Primary’ and ‘Promotion’ through logistic regressions. Forecasting is also possible through the use of linear regressions. Grouping data to comprehend the logic backing these sections is also an achievable feat. For instance, E-Commerce customers are grouped to understand their behavior on a particular E-Commerce site. This is made possible with hierarchical clustering or with the aid of K-Means, and such clustering algorithms.

Prediction and regression are the main two devices used for classification and identification, forecasting values, and clustering groups.

1.2.5 Interpreting Data

It is one of the most important steps for the data science life cycle. It is the last phase. Generalization ability is the crux of the power of any predictive model. This model explains that it is dependent on capacity, and the future data cannot be seen and is vague.

1.3 Tools For Data Science

To create a model, data scientists must be able to create, build and run code. The most popular programming languages among data scientists are open source tools that include or support prebuilt statistical, machine learning, and graphic capabilities.

1.3.1 R

An open-source programming language and environment for developing statistical computing and graphics, R is the most popular programming language among data scientists. R provides a wide variety of libraries and tools for cleansing and prepping data, creating visualizations, and training and evaluating machine learning and deep learning algorithms. It’s also widely used among data science scholars and researchers.

1.3.2 Python

Python is a general-purpose, object-oriented, high-level programming language that emphasizes code readability through its distinctive, generous use of white space. Several Python libraries support data science tasks, including Numpy for handling large dimensional arrays, Pandas for data manipulation and analysis, and Matplotlib for building data visualizations.

1.4 Types Of Data Science Work

Data science creating paths for many job roles. Due to the over-demanding of data science, the job has different functionality.

1.4.1 Data Analyst

The data analyst means that who performs mining of huge amount of data, Patterning the data, models the data, checking the relationship and trends. At the end of the day, it comes up with the visualization and reporting of problem-solving issues and decision-making.

For becoming an analysis, one has to know mathematics, business modeling, and the basics of statistics. In addition, one should be familiar with the concepts and tools of programming languages.

1.4.2 Data engineer

It is generally an IT worker whose primary work is to prepare data for different analytical, operational users. They build different pipelines to connect different sources of systems. The amount of data an engineer works with varies with the organization, particularly with respect to its size. Data engineer work is to provide transparent relationships and enabling the business to be a trustworthy business decision.

The bigger the company, the more complex architecture analytics it requires with respect to its size.

Data Scientist

Data scientists are a specialist who makes models make predictions and answers key business questions which applies to the statistics and building machine learning models. Data scientists have more depth and expertise in these skills and will also train and optimize machine learning models. Thus, they tackle the problem with immense knowledge experience of advanced statistics and algorithms.

1.5 Components of Data Science

Fig 1.2: Data science components

1.5.1 Statistics

The essential component of Data Science is statistics. This is the method to collect and innumerate data in large amounts to get useful and meaningful insight. There are two main categories of statistics:

Descriptive statistics:

Descriptive statistics helps to organize data and only focuses on the characteristics of data-providing parameters.

Inferential Statistics:

Inferential statistics generalizes a large data set and applies probability before concluding. It also allows to infer the parameters of the population based on sample stats and build a model on it.

1.5.2 Visualization

Visualization means representing the data in visuals such as maps, graphs, etc., so that people can understand it easily. It makes it easy to access a vast amount of data. The main goal of data visualization is to make it easier to identify patterns, trends, and outliers in large data sets. The main benefit of data visualization is that it can understand the information quickly, helps to improve the insights, and quickly make a decision.

It increases understanding of the next level and to stabilizes the performance. It also provides an easy distribution of information that increases the opportunity to share insights with everyone. It also helps to find the information quickly achieve success with higher speed and fewer mistakes.

1.5.3 Data Engineering

Data engineering involves acquiring, storing, retrieving, and transforming data. The key to understanding the data depends on the engineering part. First, engineer design and build things. Data engineers should approach the design which builds pipelines that transform and transport data into a format, and it reaches the data scientist or other end users in a highly usable state. These pipelines must take data from many different sources and collect them into a single warehouse representing the data uniformly as a single source of truth.

1.5.4 Advance Computing

Advance computing has many functions. It involves designing, writing, debugging, and maintaining the source code of computer programs. In addition, advanced computing capabilities are used to handle a growing range of challenging science and engineering problems, many of which are compute and data-intensive.

Fig 1.3: Data science designing

1.6 Machine Learning in Data Science

To become a data scientist, one should also be aware of machine learning and its algorithms, as, in data science, there are various machine learning algorithms that are broadly being used. Following are the name of some machine learning algorithms used in data science:

•Regression

•Decision tree

•Clustering

•Principal component analysis

•Support vector machines

•Naive Bayes

•Artificial neural network

•Apriori

1.6.1 Linear Regression Algorithm

This Linear Regression algorithm is a popular technique on a machine learning algorithm. This algorithm is regression-based. This is a method where targets the model value based on independent variables. This algorithm is mostly used in forecasting and predictions. Since it shows the linear relationship between input and output variables, hence it is called linear regression.