- 1 Digital Leadership Series
- 2 What is Big Data?
- 3 Big Data Analytics
- 4 Big Data Technology
- 5 Big Data Career Demand
- 6 Big Data Manager Salary
- 7 Big Data Manager Job Description
- 8 Big Data Startups
- 9 Big Data Project Management – A Practical Case
- 10 Big Data dangerous
- 11 Big Data Wiki
- 11.1 Analytics
- 11.2 Algorithm
- 11.3 Behavioral analytics
- 11.4 Big data
- 11.5 Business intelligence (BI)
- 11.6 Clickstream analytics
- 11.7 Dashboard
- 11.8 Data aggregation
- 11.9 Data analyst
- 11.10 Data analytics
- 11.11 Data governance
- 11.12 Data mining
- 11.13 Data repository
- 11.14 Data scientist
- 11.15 ETL (extract, transform, and load)
- 11.16 Hadoop
- 11.17 HANA
- 11.18 Legacy system
- 11.19 MapReduce
- 11.20 System of record (SOR) data
Digital Leadership Series
Welcome to the Digital Leadership Series. This Big Data career insight guide is part of the Digital Leadership Series written by Angel Berniz. Following you can find all the Digital Leadership Series guides available:
- Big Data
- Internet of Things
- Industry 4.0
- Scaled Agile
- Machine Learning
- Lean Startup
- Design Thinking
CALL FOR LESSONS LEARNED TIPS by Pros, for writing 10 new books. The challenge is the following:
- If you are a Pro in one of these 10 areas, please contribute with one Lesson Learned tip that you would love someone else had taught you when you started. I mean a 1-page project management tip on Robotics, Big Data, Blockchain, etc.
- You can also help me with this challenge by sharing to your friends, colleagues, Twitter followers, Facebook friends, and LinkedIn connections, asking them for Pros to contribute one tip to be included in these books.
All contributors will be mentioned in the book and gain career exposure!
Send your contributions to [email protected]
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What is Big Data?
Big data describes the complete information management strategy which includes and integrates many new kinds of data and knowledge management alongside traditional data.
Big data has additionally been based on the 4 Vs:
The quantity of data. While volume signifies more data, it’s the granular nature from the data that’s unique. Big data requires processing high volumes of low-density, unstructured Hadoop data-that’s, data of unknown value, for example Twitter data feeds, click streams on the web site along with a mobile application, network traffic, sensor-enabled equipment recording data in the speed of sunshine, and much more. It’s the task of massive data to transform such Hadoop data into valuable information. For many organizations, this can be many terabytes, for other people it might be countless petabytes.
The rate where information is received and possibly applied. The greatest velocity data normally streams straight into memory versus being written to disk. Some Internet of products (IoT) applications have safety and health ramifications that need real-time evaluation and action. Other internet-enabled smart products operate instantly or near real-time. For instance, consumer eCommerce applications aim to combine mobile phone location and private preferences to create time-sensitive marketing offers. Operationally, mobile application encounters have large user populations, elevated network traffic, and also the expectation for fast response.
New unstructured data types. Unstructured and semi-structured data types, for example text, audio, and video require additional processing to both derive meaning and also the supporting metadata. Once understood, unstructured data has most of the same needs as structured data, for example summarization, lineage, auditability, and privacy. Further complexity arises when data from the known source changes without warning. Frequent or real-time schema changes are a massive burden for transaction and analytical environments.
Data has intrinsic value-but it should be discovered. There are a number of quantitative and investigative strategies to derive value from data-from finding someone preference or sentiment, to creating another offer by location, or identifying a device that is going to fail. The technological breakthrough would be that the price of data storage and compute has tremendously decreased, thus supplying a good amount of data that record analysis around the entire data set versus formerly only sample. The technological breakthrough makes a lot more accurate and precise decisions possible. However, finding value also requires new discovery processes involving clever and insightful analysts, business users, and executives. The actual big data challenge is really a human one, that is understanding how to ask the best questions, recognizing patterns, making informed assumptions, and predicting behavior.
Big Data Analytics
Big data analytics is the activity of collecting, organizing and analyzing large teams of data (known as big data) to uncover patterns along with other helpful information. Big data analytics might help organizations to higher comprehend the information contained inside the data as well as help find out the data that’s most significant towards the business and future business decisions. Analysts dealing with big data essentially want the understanding which comes from analyzing the information.
Big Data Requires High-Performance Analytics
To evaluate this type of large amount of data, big data analytics is usually performed using specialized software programs and applications for predictive analytics, data mining, text mining, forecasting and knowledge optimization. With each other these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables a company to process very bulk of information that the business has collected to find out which information is relevant and could be examined they are driving better business decisions later on.
The Challenges of Big Data Analytics
For many organizations, big data analysis is really a challenge. Think about the sheer amount of data and also the different formats from the data (both structured and unstructured data) that’s collected over the entire organization and various ways various kinds of data could be combined, contrasted and examined to locate patterns along with other helpful business information.
The very first challenge is within breaking lower data silos to gain access to all data a company stores in various places and frequently in various systems. Another big data challenge is within creating platforms that may get unstructured data as quickly as structured data. This massive amount of information is typically so large it’s hard to process using traditional database and software methods.
How Big Data Analytics is Used Today
Because the technology that can help a company to interrupt lower data silos and evaluate data improves, business could be transformed in many ways. Based on Datamation, today’s advances in analyzing big data allow researchers to decode human DNA within a few minutes, predict where terrorists intend to attack, pick which gene is mainly apt to be accountable for certain illnesses and, obviously, which ads you’re probably to reply to on Facebook.
Another example originates from among the greatest mobile carriers on the planet. France’s Orange launched its Data for Development project by releasing subscriber data for purchasers within the Ivory Coast. The Two.5 billion records, that have been made anonymous, incorporated information on calls and texts exchanged between 5 million users. Researchers utilized the information and sent Orange proposals for the way the information could help as the building blocks for development projects to enhance public safety and health. Suggested projects incorporated one which demonstrated how you can improve public safety by tracking mobile phone data to map where individuals attacked emergencies another demonstrated using cellular data for disease containment.
The Benefits of Big Data Analytics
Enterprises are more and more searching to locate actionable insights to their data. Many big data projects result from the necessity to answer specific business questions. With the proper big data analytics platforms in position, a company can boost sales, increase efficiency, and improve operations, customer support and risk management.
Webopedia parent company, QuinStreet, surveyed 540 enterprise decision-makers involved with big data purchases to understand which business areas companies intend to use Big Data analytics to enhance operations. About 50 % of respondents stated these were applying big data analytics to enhance customer retention, assist with product and obtain a competitive advantage.
Particularly, the company area obtaining the most attention pertains to growing efficiency and optimizing operations. Particularly, 62 percent of respondents stated they use big data analytics to enhance speed and lower complexity.
Big Data Technology
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
The benefits of Hadoop are the following:
- Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT), that’s a key consideration.
- Computing power. Hadoop’s distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
- Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
- Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
- Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
- Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.
A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data. The data structure and requirements are not defined until the data is needed.
Master Data Management (MDM)
Master data management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference. When properly done, MDM streamlines data sharing among personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications.
Big Data Career Demand
The demand of the Big Data career is very high, with exponential increase (according to IT Jobs Watch):
Big Data Manager Salary
The salary of Big Data is high because there lack of professionals in this field:
Big Data Manager Job Description
The following Big Data Project Manager job description for an employment opportunity was published at Linkedin by everis:
Would you like to join us?
We offer you a career plan, to be involved in innovative projects with top customers in EMEA, a training and certification plan, access to enterpreneur networks and forums, etc.
everis belongs to NTT DATA Group, the sixth company of IT services in the world, with 70,000
professionals and presence in Asia-Pacific, Middle East, Europe, Latin America and North America.
Within everis, the Big Data Technology area aims to support our customers with their business decisions making based on both architectures OpenSource and large market vendors. Our Big Data team has a differential knowledge regarding methodologies, technologies for capturing, processing and storing information (both real-time and batch; NoSQL, File Systems, in-memory), data government (quality, security, audit) and visualization and discovery tools with which to provide relevant information for our customers´ business decisions.
Specifically, everis BigData Technology develops business strategies based on analysis of data, structured and unstructured. To do this, it relies on a robust methodology and a reference architecture based on OpenSource technologies, big vendors technologies or a combination of both. All this is allowing us to position ourselves as a Big Data system integrator reference at a European level.
BigData technology also offers tools and ad-hoc developed assets, managed from the BigData Data Innovation Center (eDIC), allowing us to increase productivity throughout the development life cycle of projects and our clients concept tests.
What are we looking for?
Project Manager (Knowledge Leaders) for our BigData Technology Area in our offices in Madrid, Barcelona and London.
What do we offer?
- Professional career development based on the management and implementation of sophisticated projects in major enterprises worldwide, in each industrial sector
- Joining the team of NTT Data / Everis experts. Integration in the BigData Technology Excellence Centers located in Tokyo (Japan) and Barcelona (Spain)
- Continuous training at prestigious universities worldwide. Continued development of internal training programs. Support with the development of Master programs and any kind of post-graduation
- Continued support to the generation of entrepreneurship around BigData Technologies. Access to financing sources, knowledge, analytical solutions, infrastructures, methodologies, etc., as a way to develop innovative ideas that apply to BigData solutions
- Access to key entrepreneurship networks worldwide. Access to the largest startups Portal in the world.
- Participation in the main forums (associations, communities, etc.) on the subject, in Europe and the world, such as STRATA (Barcelona and London), Cloudera Summit (London), etc
What do we ask for?
- Proven experience implementing projects that have involved the development and use of BigData solutions.
- Experience in project planning and controlling. Knowledge of project management methodologies.
- Provable skills in team management as well as the development and training of them
- Provable skills for the proper performance and quality in the delivery of services
- Requirements elicitation from business users and IT to design end-to-end technologica solutions (data extraction, processing, storage and display)
- Knowledge development and research around BigData technologies
- Commissioning, initial configuration, Big Data technology architectures
- Lead the implementation of BigData technological solutions
- Project management and coordination of several teams of 2-3 people
- Management of relationships with business areas.
- Proven ability to identify technology solutions to business problems, proposing in each case the best solution to the client
- Demonstrated strengths with the project planning and management, achieved results and deadlines. Strong sense of belonging and commitment to continuous improvement
- Strong communication and presentation skills; analysis and ability to convey concepts effectively to different audiences with different needs of detail.
- Ability to lifelong learning and updated view of the evolution of BigData technologies, maintaining partnership relations with the leading players in the market.
- 5+ years of experience managing Business Intelligence projects
- 1+ years of experience leading teams and PoC de BigData projects
- BigData solutions knowledge based on different technologies on the market (Hadoop, Spark, Hbase, Mongo DB, Cassandra, Redis …), knowing how to argue the pros and cons of the applicability of each, based on the functionality they provide
- Knowledge of programming languages: Java (Desirable: Scala and Python)
- Experience with SQL and Linux Administration (shell)
- Experience in the implementation of BI projects using market solutions (Microstrategy, Oracle BI, Pentaho, Cognos, Microsoft, QlikView, Tableau, etc.)
- Specific knowledge in any industry (Telecommunications, Financial, Healthcare, Public Sector)
- Software Engineer, Systems Engineer, Telecommunications Engineer …
- English Proficiency
Big Data Startups
Big Data universe is very wide, with a lot of specialization of the companies competing in this market so that they shine in one specific area. The Big Data universe is the following:
Big Data Project Management – A Practical Case
Following in the following practice case you can see how does Big Data Mroject Management look like:
Big Data dangerous
Big Data dangerous problems are also present, as follows:
Challenges how businesses are run and the business models
This really is both negative and positive. For many companies, this underlying change will signal huge chance and trigger massive growth. For other people who cannot change and change using the occasions, it’ll signal the start of the finish. I predict we will have a lot more cases of upstart companies arriving and altering the whole dynamic of the particular field or market, the way in which Netflix disrupted video rentals and Uber has disrupted taxi run. Established “old school” companies should awaken and be aware. And these kinds of disruptions might have major potential economic implications.
Everything is tracked and analysed. EVERYTHING.
Since everything about us could be tracked, it is also employed for dubious purposes. Privacy law hasn’t stored track of we’ve got the technology and the kinds of data being collected. The master of the information that’s collected in regards to you – you, or the organization that collects it? The solution determines how that data could be shared and used, whether sturdy your buying habits online or even more private maters. Additionally, the greater data we collect, the simpler it’s to parse lower and employ it to promote (or otherwise) to specific segments of people, developing a new type of discrimination. We already have accounts of information-driven discrimination happening vehicle insurance providers, for instance, have a tendency to penalise individuals who drive late into the evening, however that could affect otherwise safe motorists who occur to work a swing shift, and who are usually lower-earnings to begin with.
Privacy problems and data-driven discrimination
Since everything about us can be tracked, it can also be used for nefarious purposes. Privacy law has not kept up with the technology and the types of data being collected. Who owns the data that is collected about you – you, or the company that collects it? The answer will determine how that data can be shared and used, whether it’s about your buying habits online or more private maters. In addition, the more data we collect, the easier it is to parse down and use it to market (or not) to particular segments of the population, creating a new kind of discrimination. There are already accounts of data-driven discrimination happening; car insurance companies, for example, tend to penalise people who drive late at night, but that can impact otherwise safe drivers who happen to work a swing shift, and who tend to be lower-income to start with.
Data about can be used to spy people
In fact, it’s already happening. We all know organisations such as the NSA are utilizing data to monitor people. However it may go much further. China is promoting a “social credit score” that’s influenced by not only that which you say and do personally, what your social networking buddies say and do too. And Russia’s Red Web is basically a mystery to the web, allowing the Russian intelligence agencies free use of every Russian ISP. Where does national security finish and privacy begin? It’s an issue which has not yet been resolved.
Danger from hacking and cyber crime
Getting all of our data somewhere within the cloud (or around the oceans) leaves it susceptible to attacks and misuse. Recall the traditional days when criminals needed to physically steal a laptop or hard disk to gain access to sensitive files? Not any longer. For each new security measure there’s a hacker or criminal somewhere focusing on breaking it. And firms rarely take security as seriously because they should. Additionally, I await with dread the very first serious terrorist attack on the data or personal computers. Consider all of the infrastructure, utilities, and vital information that depends on data and also the cloud after which consider exactly what a catastrophe it might be whether it all went lower at the same time. In the event that doesn’t provide you with nightmares, I do not understand what will
In a nutshell, big information is harmful. We want new legal frameworks, more transparency and potentially additional control over how our data may be used to allow it to be safer. But it’ll not be an inert pressure. Within the wrong hands big data might have serious effects.
Big Data Wiki
When it comes to assembling a list of key big data terms, it makes sense to identify terms that everyone needs to know — whether they are highly technical big data practitioners, or corporate executives who confine their big data interests to dashboard reports. These 20 big data terms hit the mark.
Analytics is the discipline of using software-based algorithms and statistics to uncover meaning from data.
Algorithm is a mathematical formula placed in a software program that performs an analysis on a dataset.The algorithm often consists of multiple calculation steps. Its goal is to operate on data in order to solve a particular question or problem.
Behavioral anaytics is an analytics methodology that uses data collected about users’ behavior to understand intent and predict future actions.
Big Data is data that is not system of record data, and that meets one or more of the following criteria: it comes in extremely large datasets that exceed the size of system of record datasets; it comes in from diverse sources, including but not limited to: machine-generated data, internet-generated data, computer log data, data from social media sources, or graphics and voice-based data.
Business intelligence (BI)
Business intelligence (BI) is a set of methodologies and tools that analyze, report, manage, and deliver information that is relevant to the business, and that includes dashboards and query/reporting tools similar to those found in analytics. One key difference between analytics and BI is that analytics uses statistical and mathematical data analysis that predicts future outcomes for situations. In contrast, BI analyzes historical data to provide insights and trends information.
Clickstream analytics is the analysis of users’ online activity based on the items that users click on a web page.
Data aggregation is the collection of data from multiple and diverse sources with the intention of bringing all of this data together into a common data repository for the purposes of reporting and analysis.
Data analyst is a person responsible for working with end business users to define the types of analytics reports needed in the business, and then capturing, modeling, preparing, and cleaning the required data for the purpose of developing analytics reports on this data that business users can act on.
Data analytics is the science of examining data with software-based queries and algorithms with the goal of drawing conclusions about that information for business decision making.
Data governance is a set of data management policies and practices defined to ensure that data availability, usability, quality, integrity, and security are maintained.
Data mining is an analytic process where data is “mined” or explored, with the goal of uncovering potentially meaningful data patterns or relationships.
Data repository is a central data storage area.
Data scientist is an expert in computer science, mathematics, statistics, and/or data visualization who develops complex algorithms and data models for the purpose of solving highly complex problems.
ETL (extract, transform, and load)
ETL (extract, transform, and load) enables companies to take data from one database and move it to another database. ETL is accomplished by extracting data from the database that it originally is kept in, transforming the data into a format that can be used in the database that the data is being moved to, and then loading the transformed data into the database it is being moved to. The ETL process enables companies to move data in and out of different data storage areas to create new combinations of data for analytics queries and reports.
Administered by the Apache Software Foundation, Hadoop is a batch processing software framework that enables the distributed processing of large data sets across clusters of computers.
Hana is a software/hardware in-memory computing platform from SAP designed to process high-volume transactions and real-time analytics.
Legacy system is an established computer system, application, or technology that continues to be used because of the value it provides to the enterprise.
MapReduce is a big data batch processing framework that breaks up a data analysis problem into pieces that are then mapped and distributed across multiple computers on the same network or cluster, or across a grid of disparate and possibly geographically separated systems. The data analytics performed on this data are then collected and combined into a distilled or “reduced” report.
System of record (SOR) data
System of record (SOR) datar are data that is typically found in fixed record lengths, with at least one field in the data record serving as a data key or access field. System of records data makes up company transaction files, such as orders that are entered, parts that are shipped, bills that are sent, and records of customer names and addresses.
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