What are the best data technologies to support your data strategy goals?

Some of the best data technologies commonly used to support data strategy goals include: 1. Data Warehousing : - Snowflake - Amazon Redshift - Google BigQuery 2. Data Integration and ETL: - Apache Nifi - Talend - Informatica - Apache Spark 3. Data Storage: - Hadoop Distributed File System (HDFS) - Amazon S3 - Microsoft Azure Data Lake Storage 4. Database Management: - MongoDB - Cassandra - Redis - MySQL - PostgreSQL 5. Data Governance and Quality: - Collibra 6. Data Analytics and BI: - Tableau - Power BI - QlikView - Looker 7. Data Science and Machine Learning: - Python (with libraries like NumPy, Pandas, and scikit-learn) - R - TensorFlow

Data Storage: Think of it as your data library. Data Processing: Turning data into insights. Data Visualization: Making data easy to see. Data Security: Protecting your data from harm. Machine Learning: Teaching computers to learn. Data Integration: Making things work together smoothly. These tools help you explore data like a space adventurer!

The choice of data technologies to support your data strategy goals can depend on the specific needs of your organization, the volume of data you handle, and your overall data strategy. Here are some key data technologies and tools that are commonly used to support data strategy goals: Data Warehouses: Data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake provide a central repository for structured data, making it easier to analyze and report on. Data Lakes: For storing large volumes of structured and unstructured data, solutions like AWS S3, Azure Data Lake Storage, or Hadoop HDFS can be used to create data lakes.

Kafka (highest automation level) is by far the best data technology. It sets the data in motion. It gives the opportunity to add constraints to check data integrity, provisions for data partioning, joining data in the fly and accessibility from everywhere without much cost. IT overhead is significantly reduced for an organisation. The SQL abstraction can operate on events and form the basis of event driven communication and provides easy integration with any apps.

I use a variety of data technologies to support my data strategy goals, including programming languages, data processing frameworks, cloud computing platforms, machine learning libraries, and data visualisation tools. I choose the technologies that I use based on my specific needs and requirements, such as the types of data, the volume of data, the complexity of the tasks, my budget and my skills and resources. By using the right data technologies, I am able to efficiently collect, clean, analyse and visualise data, as well as build and deploy machine learning models. This helps me to achieve my data strategy goals and make a positive impact at Greysoft Technologies.

Snowflake is also a robust platform for various data operations. Its architecture allows for scalable compute and storage, ensuring high performance for complex queries and analytics. Furthermore, Snowflake’s Data Sharing and Data Marketplace features allow seamless collaboration and access to a plethora of datasets. Truly, it’s not just a data warehouse but a comprehensive data platform with diverse capabilities.

In the tapestry of data strategy, integration tools are the loom. They weave disparate data into a cohesive fabric, enabling a holistic view. When picking these tools, prioritize those that offer both robustness and agility. Tools that facilitate seamless ETL (Extract, Transform, Load) processes, support diverse data formats, and offer real-time integration capabilities stand out. Look for platforms with strong connectivity options, cloud and on-premise support, and the flexibility to adapt to changing data landscapes. An exceptional integration tool not only simplifies the merger of varied data but is also a silent hero in the background, ensuring data is where it needs to be, when it needs to be—accurate and actionable.

From my current experience, Here is 5 integration tools: 1. Informatica PowerCenter 2. Talend Data Integration 3. IBM InfoSphere Information Server 4. Microsoft SQL Server Integration Services (SSIS) 4. Oracle Data Integrator (ODI) 5. Apache NiFi

When I started my job as a data warehouse developer fourteen years ago, I used OWB(Oracle Warehouse Builder). throughout my career path I used ODI(Oracle Data Integrator) and SSIS(SQL Server Integration Service), too. These tools are full of features and capabilities for integration data, mapping data and transformation it. Some of complexity tasks like SCD or CDC can done easily by using these tools, on the other hand, there are many useful contents about them on the web that can use it.

Data Factory es una herramienta de integración muy completa y sobre todo intuitiva, lo cual conlleva una curva de aprendizaje corta

Tools for visualization through code such as pandas are essential for maintaining project quality and customization capacity, and also more graphical tools such as excel and power bi are great for generating more intuitive and easy-to-understand graphs for those who will observe the data.

Know your audience. While powerful, data visualization tools can lead to biased decision-making depending on how the data is presented. Some audiences tend to oversimplify and justify choices with visualization reports. Statistical + UX notions are recommended. Starting in Python/any other coding tool that allows for a combination of statistical analysis and visualization is an attractive option. You can use several libraries (e.g., seaborn is my preferred one) to provide key insights with multiple customizable graphs. Once you've ensured your data is valid, you can parse the findings into an e.g., Power BI report. This is personally my preferred tool but the options for statistics there are practically inexistent - so be aware of that.

One critical question to get an actionable insight from when it comes to Data Visualization tools is to focus on the "So What?" factor. In other words, when analyzing visualized data, don't just stop at identifying trends or patterns; instead, ask yourself, "So what does this mean for our business or objectives?" This approach encourages you to dig deeper and translate visualized data into meaningful actions or decisions, ultimately contributing significantly to informed strategies and outcomes.

In data science, open-source tools are indispensable for data visualization. If you're an R enthusiast like me, consider integrating Shiny and bs4Dash for creating interactive and dynamic dashboards. For crafting striking and versatile charts, Highcharter and Plotly are top choices. These tools empower you to build engaging data narratives without the need for costly proprietary solutions. Embracing open source not only trims expenses but also connects you to a thriving community of users and developers. Harness the capabilities of libraries such as Shiny, bs4Dash, Highcharter, and Plotly to elevate your data storytelling and effectively communicate insights.

As of November 2023, Here are 5 visualization tools: 1. Tableau. 2. Microsoft Power BI 3. QlikView 4. Google Data Studio 5. Domo

Data modeling tools are pivotal, transforming abstract data into actionable insights. They're the cartographers mapping the complex landscape of data relationships. When selecting these tools, opt for ones that harmonize sophistication with user-friendliness. The best ones offer intuitive design interfaces, support collaborative features for team synergy, and are adaptable to both traditional SQL databases and newer NoSQL systems. They should empower you to not only visualize but also to simulate how data flows and interacts, enabling preemptive tuning of your data structures for peak performance. A great data modeling tool doesn't just organize your data—it crystallizes your strategic vision, making it a tangible asset for decision-makers.

As of my knowledge, Here are 5 modeling tools: 1. Erwin Data Modeler 2. Lucidchart 3. SAP PowerDesigner 4. ER/Studio by IDERA. 5. Oracle SQL Developer Data Modeler

For robust data quality management, consider industry-leading tools such as Informatica Data Quality, Talend Data Quality, and IBM InfoSphere Information Analyzer. These tools empower organizations to ensure data accuracy, completeness, and consistency, enhancing overall data reliability. Informatica provides comprehensive data profiling and cleansing capabilities, Talend offers open-source flexibility with powerful data quality features, while IBM's InfoSphere excels in data analysis and anomaly detection. These solutions facilitate effective data modeling, validation, and documentation, aligning seamlessly with your data strategy goals and promoting a data-driven culture.

ODI(Oracle Data Integrator) tool has a powerful data modeling system that can reverse engineering the tables and represent meta data of tables such as its keys, its relations, its schemas etc. On the other hand, SSAS(SQL Server Analysis Service) is a multidimensional online analytical processing (OLAP) server and an analytics engine used for data modeling. It allows IT professionals to break up large volumes of data into more easily analyzed parts. Its tabular data model can deployed on the RAM of computers and tasks of analysis used this data model easily.

Look at the variables that have the least variation and consider extracting that dimension to improve accuracy and potentially remove background noise. Analysing the data in a matrix array using Python numpy can return eigenvectors to simplify the overall dataset.

Python Preferred for its versatility and extensive community support. R Usage: Ideal for exploratory data analysis and complex statistical modeling. TensorFlow Known for its flexibility and scalability in deep learning applications. PyTorch Favored in academia and industry for research and production. Scikit-learn Offers simple and efficient tools for data mining and analysis. Apache Spark Supports machine learning through its MLlib library, enabling fast processing of large datasets. BigML Provides a user-friendly interface for non-programmers. RapidMiner Offers a visual workflow designer, making it accessible for users of all skill levels.

In the data science field, two primary tools stand out for getting the job done. R is very effective for statistical analysis and particularly advantageous for exploratory data analysis, being the preferred tool in academic research circles, where intricate statistical methods are paramount. On the other hand, Python's versatility as a programming language is highly regarded in data science for its straightforward syntax and ease of readability. Its libraries - Pandas for data manipulation, NumPy for handling hefty data arrays, and Scikit-learn for machine learning - are the building blocks for straightforward yet powerful data analysis. It’s ideal when your project needs to scale from a few data points to enterprise-level solutions.

From my experience, Here are 5 tools: 1. Microsoft visual studio 2. VS code 3. Jupyter Notebooks 4. Apache Spark 5. TensorFlow and Keras

the best data science tools vary based on project specifics, organizational structure, and team skills. Data Processing & Integration: Apache Hadoop for distributed data storage/processing, Apache Spark for fast big data handling and etc. Data Exploration & Analysis: Jupyter Notebooks for interactive exploration, Pandas and NumPy for data manipulation, and R or Python for statistical analysis. Machine Learning & Analytics: Libraries like Scikit-Learn, TensorFlow, PyTorch for machine learning

There are various Data science modules or tools available in today's world. Each and every module process and derive insights from the data. * Programming: Python, R * Data Processing: NumPy, Pandas, Apache Spark * Data Exploring: Matplotlib, Seaborn, Plotly * Model Building: Scikit-learn, (Advanced- Keras, PyTorch, TensorFlow) * Data Visualization: Tableau, Microsoft PowerBI, AWS-QuickSight, Google- Looker, Qlik Sense * Tools: Anaconda Distribution- Jupyter Notebook, Google - Colab, Databricks Notebooks and other IDEs supports to do the Data Science tasks There are many such modules/tools/frameworks available in data science space but Knowing about What, When and Why about Data will make you stand out of the crowd as Good Data Scientist.

Since Data Governance focus on different views of data managements, such as: Data Architecture, Data Quality, Meta-data, Master Data, Data Integration and Interoperability ,Data security, Data Storage and Operation etc. I believe Data Governance is a standard that has some disciplines. Therefore, I think there is not any tool that support all aspects of data governance. But depend on requirements we can used some different tool for various purposes. For example, based on research there is a questions sheets that measured metadata issues or master data issues in organization and displays the results in the form of radar charts, these charts argue the amount of accurate of metadata or master data, so will help to improved them.

While there are variety of tools that can help with data governance, none are foolproof. Instead of a tools-first approach, I recommend the following: 1. Identify all data governance needs. 2. For each need, identify edge cases that might be difficult to detect, either for an automated tool or an informed human user. 3. Given your needs and their potential edge cases, identify the best solution. Maybe that will be an automated tool—or maybe you realize you can easily build your own custom process at minimal cost. Maybe a combination of the two will be most effective. 4. Regardless of your solution, continue to audit your data regularly to a) ensure your tool is working as expected and b) to identify new edge cases.

In the manufacturing industry, the implementation of data governance tools like Collibra plays a pivotal role in maintaining product quality and compliance. For instance, consider a scenario where a company produces precision components for aerospace applications. By using data governance tools, the manufacturer establishes and enforces data governance rules for product specifications, ensuring accurate cataloging, tracking of changes in design data lineage, and adherence to stringent quality standards. This meticulous approach facilitates compliance with aviation industry regulations.

Use AI to continuously monitor and ensure compliance with changing regulations, reducing the risk of non-compliance and associated penalties. Implement tools that not only catalog data but also provide rich metadata management, enabling better understanding and utilization of data assets. Integrate governance tools with AI and ML models to predict potential data quality issues before they impact the system. Develop dynamic access controls that adapt to context, user behavior, and sensitivity of the data, ensuring that data governance policies adjust to real-time scenarios. Employ tools that provide visual maps of data lineage, making it easier for stakeholders to understand how data transforms and flows across systems.

For robust data governance, leverage leading data quality tools like Informatica Data Quality, Talend Data Quality, and Trifacta. These tools empower organizations to ensure data integrity, enforce standards, and enhance overall data quality. With features like data profiling, cleansing, and monitoring, they enable businesses to maintain secure, compliant, and reliable data ecosystems. Choose the tool that aligns with your specific data governance needs, facilitating efficient data cataloging, lineage tracking, and adherence to data quality standards.

Data virtualization platforms like Denodo and Cisco Data Virtualization provide a unified view of data from multiple sources without physically integrating them. They allow for real-time data access and abstraction, simplifying data integration and improving agility.

Another critical area is the use of ML & AI platforms, which vary in application from business to business. Some deploy them in customer engagement tools like chatbots, while others leverage them for in-depth data analysis and making groundbreaking decisions. These technologies are not just trends; they are becoming fundamental components of effective data strategies.

In order to make a data strategy efficient and effective you really need the right tech for your business. But you there is an other piece of the puzzle missing: establishing a data culture and involving the right stakeholders from the beginning to fulfill their needs. Adding the business context on top of these technologies and having data consumers are crucial steps to put data in a centric position. You may have the best technologies and the best data implemented but you need something else to go from gut feeling to data consuming for decision making at a higher level.

In the realm of blending technical expertise, analytical prowess, and business insight, data scientists and the field of data science stand out as pivotal. Their deep understanding of business objectives from the ground up is invaluable. They are not just instrumental in setting comprehensive business goals but also serve as the cornerstone for essential functions like data collection, Exploratory Data Analysis (EDA), and crafting models utilizing machine learning. Their diverse responsibilities are fundamental to driving data-driven decision-making in any organization.

Having all these data technologies does not ensure that your data strategy is going to be successful. More important than the technologies is having a great data leader within the organization. This leader can ensure the data strategy is implemented effectively from both a business and technical standpoint.