Big data automation is gaining traction as industries start capturing more data. Know how data analysts and data scientists can take advantage of automation.
https://www.dasca.org/
IT and business leaders must increase their efforts to evolve from traditional BI tools, that focus on descriptive analysis (what happened), to advanced analytical technologies, that can answer questions like “why did it happen”, “what will happen” and “what should I do”.
"While the basic analytical technologies provide a general summary of the data, advanced analytical technologies deliver deeper knowledge of information data and granular data.” - Alexander Linden, Gartner Research Director
The reward of a smarter decision making process, based on Data Intelligence, is a powerful driver to improve overall business performance.
Wiseminer is the only and most efficient end-to-end Data Intelligence software to help you make smarter decisions and drive business results.
Contact us: info@wiseminer.com
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Data observability is a collection of technologies and activities that allows data science teams to prevent problems from becoming severe business issues.
IT and business leaders must increase their efforts to evolve from traditional BI tools, that focus on descriptive analysis (what happened), to advanced analytical technologies, that can answer questions like “why did it happen”, “what will happen” and “what should I do”.
"While the basic analytical technologies provide a general summary of the data, advanced analytical technologies deliver deeper knowledge of information data and granular data.” - Alexander Linden, Gartner Research Director
The reward of a smarter decision making process, based on Data Intelligence, is a powerful driver to improve overall business performance.
Wiseminer is the only and most efficient end-to-end Data Intelligence software to help you make smarter decisions and drive business results.
Contact us: info@wiseminer.com
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Data observability is a collection of technologies and activities that allows data science teams to prevent problems from becoming severe business issues.
Data has become one of the most valuable commodities in the world, and it can make or break a business in no time. The DataOps approach to data management is the newest and most advanced. Technology and processes in an organization can be merged with business processes through DataOps
data collection, data integration, data management, data modeling.pptxSourabhkumar729579
it contains presentation of data collection, data integration, data management, data modeling.
it is made by sourabh kumar student of MCA from central university of haryana
Unified Information Governance, Powered by Knowledge GraphVaticle
As a knowledge graph database, Grakn is ideal for storing metadata and data lineage information. Many applications, such as data discovery, data governance, and data marketplaces, depend upon metadata for management. User experiences can be enhanced by leveraging a hyper-scalable graph database like Grakn, rather than traditional graph databases. Additionally, inference-driven use cases predominantly depended on RDF Triple Stores, requiring additional plug-ins to derive the inferences. With Grakn, this can now be achieved natively.
AI IN INFORMATION TECHNOLOGY: REDEFINING OPERATIONS AND RESHAPING STRATEGIES.pdfStephenAmell4
AI has become a disruptive force within the IT industry, offering a wide array of applications and opportunities. It has gained attention for its capacity to optimize operations, foster innovation, and enhance decision-making processes. AI is making significant strides in IT, empowering organizations to streamline processes, extract valuable insights from vast data sets, and bolster cybersecurity.
This whitepaper from IBM shows how your organisation can implement a Big Data Analytics solution effectively and leverage insights that can transform your business.
Project Deliverable 4 Analytics, Interfaces, and Cloud Technolo.docxwkyra78
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Main screen
Overview | Export data | Tools | Realtime | Logout
Current month
Last
Month
Trends
c
Top selling products
Low selling products
Overview
Realtime information
Overview | Export data | Tools | Realtime | Logout
Unique ID
Activity
Result
Overview | Export data | Tools | Realtime | Logout
Reporting tools
Statistical tools
Trends
Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Introduction
Business Analytics means the practice of iterative and methodological examination of a business’s data with a special emphasis on statistic making. Business Analytics can further help businesses automate and optimize their business processes. Companies in which data plays a pivotal role, treats its data as a corporate assets and leverages it for gaining competitive advantage. A successful business analytics would typically depend on data quality, highly skillful and experienced professionals who understand the technologies, knows how to work with it and also understands the organizations processes in depth. Apart from this, the organization should have a capable infrastructure to support the operations of business analytics.
Usage of Business Analysis is done for the following purposes:
· Exploration of data so as to find patterns and trends
· Identifying relationships in key data variables for forecasting. For instance next probable purchase by the customer
· Drilling down to the results to find out why a particular incident took place. This approach is done by performing statistical analysis and quantitative analysis with business analytical tools
· Predicting future results by employing predictive modeling and predictive analytics
· Testing previous decisions using A/B and Multivariate testing
· Assisting business in decision making such as figuring out the amount of discount to be given for a new customer
Post identifying of business goal, an analysis methodology needs to be selected and the data is acquired to support the analysis. This data acquisition normally involves extracting data from systems that may be spread throughout different locations an ...
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
Data pipelines are the heart and soul of data science. Are you a beginner looking to understand data pipelines? A glimpse into what they are and how they work.
Data has become one of the most valuable commodities in the world, and it can make or break a business in no time. The DataOps approach to data management is the newest and most advanced. Technology and processes in an organization can be merged with business processes through DataOps
data collection, data integration, data management, data modeling.pptxSourabhkumar729579
it contains presentation of data collection, data integration, data management, data modeling.
it is made by sourabh kumar student of MCA from central university of haryana
Unified Information Governance, Powered by Knowledge GraphVaticle
As a knowledge graph database, Grakn is ideal for storing metadata and data lineage information. Many applications, such as data discovery, data governance, and data marketplaces, depend upon metadata for management. User experiences can be enhanced by leveraging a hyper-scalable graph database like Grakn, rather than traditional graph databases. Additionally, inference-driven use cases predominantly depended on RDF Triple Stores, requiring additional plug-ins to derive the inferences. With Grakn, this can now be achieved natively.
AI IN INFORMATION TECHNOLOGY: REDEFINING OPERATIONS AND RESHAPING STRATEGIES.pdfStephenAmell4
AI has become a disruptive force within the IT industry, offering a wide array of applications and opportunities. It has gained attention for its capacity to optimize operations, foster innovation, and enhance decision-making processes. AI is making significant strides in IT, empowering organizations to streamline processes, extract valuable insights from vast data sets, and bolster cybersecurity.
This whitepaper from IBM shows how your organisation can implement a Big Data Analytics solution effectively and leverage insights that can transform your business.
Project Deliverable 4 Analytics, Interfaces, and Cloud Technolo.docxwkyra78
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Main screen
Overview | Export data | Tools | Realtime | Logout
Current month
Last
Month
Trends
c
Top selling products
Low selling products
Overview
Realtime information
Overview | Export data | Tools | Realtime | Logout
Unique ID
Activity
Result
Overview | Export data | Tools | Realtime | Logout
Reporting tools
Statistical tools
Trends
Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Introduction
Business Analytics means the practice of iterative and methodological examination of a business’s data with a special emphasis on statistic making. Business Analytics can further help businesses automate and optimize their business processes. Companies in which data plays a pivotal role, treats its data as a corporate assets and leverages it for gaining competitive advantage. A successful business analytics would typically depend on data quality, highly skillful and experienced professionals who understand the technologies, knows how to work with it and also understands the organizations processes in depth. Apart from this, the organization should have a capable infrastructure to support the operations of business analytics.
Usage of Business Analysis is done for the following purposes:
· Exploration of data so as to find patterns and trends
· Identifying relationships in key data variables for forecasting. For instance next probable purchase by the customer
· Drilling down to the results to find out why a particular incident took place. This approach is done by performing statistical analysis and quantitative analysis with business analytical tools
· Predicting future results by employing predictive modeling and predictive analytics
· Testing previous decisions using A/B and Multivariate testing
· Assisting business in decision making such as figuring out the amount of discount to be given for a new customer
Post identifying of business goal, an analysis methodology needs to be selected and the data is acquired to support the analysis. This data acquisition normally involves extracting data from systems that may be spread throughout different locations an ...
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
Data pipelines are the heart and soul of data science. Are you a beginner looking to understand data pipelines? A glimpse into what they are and how they work.
Big data automation is gaining traction as industries start capturing more data. Know how data analysts and data scientists can take advantage of automation.
Big data jobs are taking the highest rankings in the job market. Learn how you can excel in big data job roles as analysts, scientists, or engineers here.
An efficient data science team is crucial for deriving value from the humongous data a business collect. Learn how the data science team can help in this regard.
Data science skills are necessary for entrepreneurs today, irrespective of their job title. Know why data science skills are important for entrepreneurs.
Data Scientists mainly use tools like SQL and Pandas to perform tasks like exploring data sets, understanding their structure, content, and relationships.
Augmented analytics essentially takes all but the first and last part of the general BI Workflow & delivers increasingly relevant business insights.
Data literacy is now a sought-after ability for many workers. To begin, leaders must be aware of data literacy and develop a common language for learning.
The job of a citizen data scientist is relevant and important; however, a lot of what goes into a successful citizen data scientist project is still unprecedented in the data science community.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
A powerful data-driven narrative opens up new perspectives and concepts within the minds of those who read it by strategically utilizing narrative, data analysis, data visualization, and storytelling techniques.
Suit yourself with the prestigious senior data scientist certification to carry a massive workload. With our exciting exposure to big data science & world-class credentialing, we can help you to prepare for the role of accomplished big data professional. Explore plenty of opportunities in big data sciences.
https://www.dasca.org/data-science-certifications/senior-data-scientist
The Data Science Council of America brings you the powerful new program Senior Big Data Analyst certification known as SBDA for professionals who are eager to thrive even more. Know what it takes to be a senior big data analyst. Explore the bright career possibilities in big data arena. Accomplish your big data dreams with our esteemed DASCA credentials now! Know more about the prestigious certification on data analytics.
The Data Science Council of America brings you the powerful new program Associate Big Data Analyst certification known as ABDA for professionals who are eager to thrive even more. Know what it takes to be an associate big data analyst. Explore the bright career possibilities in big data arena. Accomplish your big data dreams only with our esteemed DASCA credentials now! Know more about how to learn data analytics online.
The senior big data engineer certificate helps senior big data professionals get into a better working space. While big data software and applications are widely used in every organization today, there is an increasing demand for senior big data engineers as well. SBDE, by honing skills in the field makes sure you get ahead in the big data marketplace!
https://www.dasca.org/data-science-certifications/senior-big-data-engineer
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Why Big Data Automation is Important for Your Business.pdf
1. Why Big Data Automation is Important for
Your Business
Businesses receive humongous data on a daily basis. To harness valuable
insights from it, it is necessary to analyze them. Automating the process can
lead to massive benefits for businesses such as reduced cost, improved
competence, self-service modules, and increased scalability.
Every business collects data from various sources such as the Internet of
Things (IoT), websites, social media, and mobile. Capturing massive amounts
of data is easier, but the data can become effective for a business only when it
is managed well.
Though big data can enable organizations to accelerate management
decisions in a better way, a comprehensive strategic plan is essential to
radically transform an organization. The overload of information, its storage
costs, and uncertainty on how to use add to the confusion.
The solution lies in automation. Big data and its automation can make internal
processes efficient and decision-making easier. Before going into details, let’s
analyze the situation and understand the challenges.
Big data capturing and storage:
Challenges faced by an organization
The approach to capture and storage of big data and its management
considered by an organization can significantly, affect the entire organization.
When capturing accurate real data, most organizations face the following laid
challenges.
Human error : As the data becomes larger and disparate, there is every
chance of an error while handling it manually. The time taken to do the job
would go to waste and also, the resulting data cannot possibly be fully trusted.
All the employees in an organization may not be well-versed in data as the
data science professionals are, and there may occur a mismatch in the data
sourcing and storing processes. One of the reasons is that the data is
unstructured and comes from documents, text files, audio, videos, and other
sources.
Securing Data : Securing the datasets is again a daunting task for
companies. Often, the companies get involved more in understanding, storing,
2. and analyzing data sets that data security-related aspects fall behind, which is
not a smart move.
For this, the companies must involve cybersecurity professionals and
implement steps such as data encryption, data segregation, endpoint security,
real-time security monitoring, and the use of big data security tools.
Integrating data : An organization collects data from a variety of sources
such as websites, social media pages, customer logs, reports, ERP
applications, and emails. The data is often present in different formats such as
images, simple files, or relational databases. Combining all this data is a
daunting task and companies must use data tools to make the job easier.
They need to think differently to put big data to the best use.
Complexity in IoT applications : As IoT applications are deployed at every
stage in an organizational ecosystem such as sensors, edge services, and
gateways, it is exponentially increasing the IT complexity and lessening user
satisfaction.
To overcome this human error, privacy, security, and IT concerns, automation
stands as the best-recommended solution. Automation helps to integrate data
seamlessly across systems while improving data accuracy and completeness.
Automation can enable organizations to innovate business while managing big
data.
Big data automation : The ‘what’ and
‘why’ for an organization
The automation of Big Data Analytics improves data science to a greater
extent. Being a self-service model, it helps business owners to leverage big
data by making it more accessible and cost-effective. It facilitates data
scientists to dedicate more toward core competencies by saving time that gets
consumed in data analysis tasks.
Several leading organizations have opted for automation and realized its true
benefits. The implementation of the right technology can reduce the whole big
data process to a few weeks. Some of the benefits include:
Reduced operational costs
Improved operational efficiency
Increased scalability of technologies
3. Improved self-service modules
Automation reduces the time involved in predictive analytics. It takes a few
hours of work for which humans take a few months to decode predictive
algorithms.
Automation facilitates the access to traditional Business Intelligence and
Cognitive Computing Analytics while reducing costs. Further, the self-service
modules get support from Data Lakes and data preparation platforms.
Moving forward, let’s understand when and how to proceed with automation.
Big data automation: The ‘when’ and
‘how’ for an organization
As a simple rule, the tasks that are rule-based, repetitive, and form a part of
the stable business process are fit for automation. To mention a few, they
include:
Creation of dashboard and reports : Automation can stream, process, and
aggregate data easily and make it more presentable to understand even by
non-tech staff.
Data maintenance : Automation simplifies the task by tuning the data
warehouse. Organizations can take advantage of several tools that facilitate
automation.
Data preparation tasks : KNIME platform can label data, train and validate
models, and iterate processes related to optimization. [KNIME-Konstanz
Information Miner is an open source data analytics, reporting, and integration
platform].
Data validation process : Automation of data validation helps to detect typos,
flag and assign missing values; streamline data modeling processes, and
transform data.
Data monitoring : An intelligent system that has access to ingestion and
replication of data can monitor available bandwidth, engineering, and delivery
calendars, all in real-time.
4. Automation of big data is helpful for both data analysts and data scientists.
Let’s see how to automate big data. An organization must follow this process
to ensure maximum benefits.
Defining objectives : It is essential to involve cross-functional team members
such as marketing, operations, and human resources. The organization must
have clear goals and expectations for the automation process.
Determine metrics : Codify your objective and ensure whether they are met
by measuring the performance and utility. It also acts as a reference point for
future projects or plans to extend your automated system(s).
Select automation tools : Select automation tools such as Python’s NumPy,
SciPy, and Pandas packages. These packages make it easier to move code
and processes and improve collaboration between humans.
Conclusion:
Automation improves data science. Big data automation enables businessmen
to eliminate complexities in businesses. It helps data analysts and data
scientists to dedicate their time toward value-added activities for their
organization.
If you are one of the professionals who want to carve a path in data science,
then data science certifications can help you climb up your career ladder
faster.
5. If data science is your forte, then big data analytics is your playfield. Learn big
data analytics to expand the scope of automation in your organization.