Data Science and Landscape
- [Data Science Landscape]
- Overview
Data science is a buzzword in the current market. As the market continues to change in various ways, data science is gaining popularity among businesses to understand their customers and improve profitability. Data science can even assist technologies such as AI (artificial intelligence) and ML (machine learning). As technology tools evolve, so do data science techniques. Not everyone will be a data genius or tech savvy, but other professionals will soon need a data science process.
Data science has become a very useful tool for solving problems in almost all domains. In economics, you can assess risk or predict trends. Healthcare processes the information generated to build case studies for studying certain diseases, while medical device manufacturers implement artificial intelligence to help hospital administrators improve efficiency and clinician productivity.
- Data Science Disciplines and Scaling AI/ML Initiatives
Data science encompasses a variety of disciplines -- for example, data engineering, data preparation, data mining, predictive analytics, machine learning, and data visualization, as well as statistics, mathematics, and software programming. It is mostly done by skilled data scientists but may also involve lower level data analysts.
Additionally, many organizations now rely in part on citizen data scientists, a group that may include business intelligence (BI) professionals, business analysts, data-savvy business users, data engineers, and other workers without a formal data science background.
However, while organizations understand the importance and potential impact of AI, they often struggle to move from pilot to production. According to IDC, the main challenges that organizations must address in order to scale AI initiatives are: cost (i.e. hardware accelerators and computing resources), lack of skilled personnel, lack of machine learning operational tools and techniques, lack of sufficient quantity and quality of data, trust and governance issues.
- Data Governance
Data is one of a company's most valuable assets. Data is critical to the growth and continued success of companies, especially data-driven companies. Essentially, data is an evolving legacy that companies can use to understand where they started and how they should move forward and improve.
However, how well a company manages asset quality, governance and ownership will largely determine the company's overall success. From data stewards to top management, effective data governance requires engagement and accountability across the enterprise. To ensure a successful implementation, you should understand how data governance works.
Data governance includes the people, processes, and technologies needed to manage and protect a company's data assets in order to guarantee generally understandable, correct, complete, trustworthy, secure, and discoverable company data.
- Modern Data Science Technology
Using data, algorithms, machine learning (ML) and AI techniques to discover patterns and build predictions, data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and Apply data from across a wide range of application domains. Data science is related to data mining, machine learning and big data.
The computer processing power available today, combined with the explosion in the amount of data we have available in the digital world, means that intelligent self-learning machines are now commonplace. Although, they are usually hidden behind a service or web interface, and we might not even notice them unless we know what we're looking for! But behind the scenes at Google, Facebook, Netflix, or any of the hundreds of organizations that have deployed this revolutionary technology, massive data warehouses and lightning-fast processing units process vast amounts of information to make it a reality
- [Data Science: Main Formulas for Machine Learning]
- Data Science Tools and AI-driven Innovation
Data science includes extracting value from data. It's all about understanding data and processing it to extract value from it. Data scientists are data professionals who can organize and analyze large amounts of data. Functions performed by data scientists include identifying relevant problems, collecting data from disparate data sources, organize data, transforming data into solutions, and communicating these findings to make better business decisions.
Data science tools can be of two types. One is for people with programming knowledge and the other is for business users. Tools for business users automate analysis. Python and R are the most popular languages among data scientists.
Using data science tools and solutions, you can accelerate AI-driven innovation by:
- Smart Data Structure
- The ability to run any AI model with flexible deployment
- Trustworthy and Explainable AI
- and many more.
In other words, you can operate data science models on any cloud while instilling trust in AI results. Additionally, you'll be able to manage and govern the AI lifecycle, optimize business decisions with prescriptive analytics, and accelerate time-to- value with visual modeling tools.
- Data Science and the Future of Industries
Data science in transforming industries is becoming increasingly important because it enables businesses to use the information they collect to better their operations, develop new products and services, and enhance their decision-making process.
Data science can help businesses in many ways, including:
- Developing solutions: Data science can help businesses develop solutions and optimize their day-to-day operations
- Making informed decisions: Data science can help businesses make more informed decisions based on a data-oriented approach
- Generating insights: Data science can help businesses generate insights
- Driving innovation: Data science can help businesses drive innovation
- Shaping business strategies: Data science can help businesses shape their business strategies
Data scientists are often hired to automate a company's processes and activities. However, this doesn't mean that machines will replace data scientists entirely. Instead, AI and other automation tools can help data scientists relieve work with augmentation.
- The Critical Role of Data
The future of industry is smart and driven by data. Big data refers to very large data sets that are difficult to analyze with traditional tools. It usually boils down to several kinds of data generated by machines, people, and organizations.
When the demands of data collection, processing, management, use and analysis exceed the capabilities and capabilities of available methods and software systems. These limits are usually defined by quantity, variety, speed, accuracy, etc.
Big data can create effective and challenging solutions in areas such as health, safety, government; and usher in a new era of analysis and decision-making.
AI is the most disruptive technological innovation of our lifetime. Enterprises are embracing AI applications and leveraging various data types (structured, unstructured and semi-structured) to enable integrated processes across all businesses and industries.
Everything around us is constantly generating big data. Every digital process and social media communication produces it. Systems, sensors and mobile devices transmit it.
Different industries need to be aware of the importance of the data available. Companies in the retail industry must analyze customer purchase data to predict what their customers will buy next and understand which products they are interested in.
Likewise, companies in the engineering and manufacturing industries must analyze the machine data they have available to predict which machines are likely to fail in the future.
Big data can be structured, semi-structured or unstructured. IDC estimates that 90% of big data is unstructured data. Many tools designed to analyze big data can handle unstructured data. Unstructured data generally refers to information that does not exist in traditional row-column databases. It is the opposite of structured data - data stored in fields in a database.
- The Future of Data Analytics
Big data is being obtained from multiple sources at an astonishing rate, volume and variety. To extract meaningful value from big data, you need the best processing power, analytics and skills. In most business use cases, any single source of data by itself is useless. The real value often comes from combining these streams of big data sources with each other and analyzing them to generate new insights. Organizations that can quickly extract insights from and leverage data gain an advantage.
Analyzing big data sets, so-called big data, will be a key foundation for competition, underpinning a new wave of productivity growth, innovation and consumer surplus. Leaders in every industry must grapple with the impact of big data, not just a handful of data-oriented managers. For the foreseeable future, the volume and detail of information captured by businesses continues to increase, and the rise of multimedia, social media, and the Internet of Things will drive exponential growth in data.
As more companies adopt big data analytics, more techniques will be developed to provide more accurate forecasts. It's like a chain where one factor affects another, if all factors just add up and work together to help the market, big data analytics will only grow and create more changes.
While big data is poised to grow, it is still somewhat of a raw, unstructured field. Of course, it has helped a lot of companies and it has helped the market, but there is still a need to understand how to use big data analytics more effectively
[More to come ...]
