by Dr. Jane Fitzgerald | Dec 14, 2020 | Business, Data Science
What is Data Analytics?
Data analytics is an inclusive term in the world of data science. It is the analysis of data collected from one or different sources. Trends, patterns, and correlations emerge from the analytics, which remains valuable for business practices.
Firstly, data analytics allows data scientists to gain insights through careful examination of datasets. Management teams use these insights to make informed and strategic business decisions towards future activities. Without data analytics, all data, no matter the size and derived source, is useless.
Secondly, when companies apply data analytics to any big data collected, they can efficiently improve customer service and increase sales turnover. Companies use this strategy to boost competitive performance.
Two Components of Data Analytics
Image Credit: Medium
Processing data analytics needs two critical elements for any data initiative to succeed. These are:
Descriptive analytics
Descriptive analytics is the starting phase. It helps in describing historical and relevant trends found in the data. This analytics aims to answer the question of “what happened?”
It’s both a component and a type of analytics too.
As a component, descriptive analytics measures conventional indicators like Return on Investment (ROI). Such an indicator employed differs from industry to industry. Although descriptive analytics doesn’t make predictions or informed actionable decisions, it summarizes datasets in an expressive and meaningful way.
Advanced analytics
Here, the process uses advanced and innovative tools to extract relevant data, make appropriate predictions, and uncover trends.
Tools used in advanced analytics include, but are not limited to, machine learning and classical statistics. Machine learning tools like natural language conversion/processing, sentiment analysis, and neural networks, and more, generate new insight and info from data.
Advanced analytics focuses on the question of “What if?”
With the increasing popularity and use of large data sets, machine learning skills, and affordable computing power have made it easier to use these two data analytics techniques in different industries.
Four types of Data Analytics
Descriptive analytics
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What the analytics seeks is the answer to the question, “what happened?” Under this technique, summarizing large datasets explains findings to all stakeholders. Descriptive analytics strategies help businesses to track successful performance or failures using KPI (key performance indicators) and metrics like ROI to gauge past performance.
Diagnostic analytics
Diagnostic analytics is the technique that seeks to answer why certain things happened. The process takes information from the descriptive-analytical level and further studies to unravel the root of the problem. Any performance indicator calls for more investigation to know why they got worse or better.
Predictive analytics
The predictive analytics technique focuses on events that would happen in the future. Using historical data, machine learning, and statistical methods, the study of patterns and trends tracks the possibility of reoccurrence. The outcome will be very valuable.
Prescriptive analytics
The presence of this technique offers solutions to the problem. By employing insights gotten from descriptive analytics, we gain inspired decisions. Also, it relies on machine-learning tactics capable of finding patterns in massive data sets.
Conclusion
Data analytics is a critical aspect of data science that data scientists should understand. Its application in everyday activities is almost endless. New business opportunities arise as a result of the continuous generation of data.
by Dr. Jane Fitzgerald | Oct 27, 2020 | Artificial Intelligence, Data Science, Industrial IoT
What is IIoT and its Link to Manufacturing?
The IIoT is part of a broader framework called the Internet of Things (IoT). The IoT is a network of smart computers, devices, and objects that gather and share vast amounts of data. The data collected is sent to a central cloud-based service. Here, compiled with other data, end-users find it useful. IoT can improve the automation of homes, classrooms, stores, and many industries.
Implementing the IoT to the manufacturing industry refers to the IIoT (or Industrial Internet or Industry 4.0). It revolutionizes the production process, allowing much more data collection and storage at far higher speeds and far more effectively than before. A host of innovative companies began using smart connected devices in their factories to introduce the IIoT.
What are the Benefits of IIoT?
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For industrial organizations, the IIoT may significantly improve communication, performance, scalability, time savings, and cost savings. Because of predictive maintenance, improved safety, and other operational efficiencies, companies are already benefiting from it through cost savings. IIoT networks of intelligent devices enable manufacturing companies to break open data silos and connect their people, data, and processes from the factory floor to the executive offices. Business leaders can use IIoT data to provide a comprehensive and detailed insight into how their company is doing, which will help them make informed decisions.
Protocols
One of the issues encountered during the IIoT transition is that various edge-of-network devices have historically used different protocols to send and receive data. Many different communication protocols are currently in use, such as OPC-UA. The transfer protocol Message Queueing Telemetry Transport (MQTT) is rapidly emerging as the standard for IIoT, due to its lightweight overhead, publish/subscribe model, and bidirectional capabilities. Read more on MQTT here.
Challenges
The two biggest challenges surrounding IIoT implementation are probably interoperability and security. As technology writer Margaret Rouse observes: “Industrial IoT is a major concern about interoperability between devices and machines that use different protocols and have different architectures.” For this, Ignition is an excellent solution because it is cross-platform and based on open-source, IT-standard technologies.
Companies need to know how secure their data is. The proliferation of sensors and other intelligent, connected devices led to a parallel explosion of security vulnerabilities; this is another factor in MQTT’s growth. It’s a very stable protocol.
The Future of the IIoT
The IIoT remains one of the fundamental phenomena that today and in the future impact industrial enterprises. Industries push for the modernization of systems and equipment to comply with new regulations, maintain increasing speed and volatility, and deal with disruptive technologies. Companies that have embraced the IIoT have seen significant improvements in safety, efficiency, and profitability. This trend will continue as the technologies grow widely.
The Ignition IIoT system significantly increases industrial organizations’ communication, performance, scalability, time savings, and cost savings. It can unite people and processes on the plant’s floor to those on the enterprise’s level. It also enables companies to get the most value out of their system without being limited by technological and economic constraints. For these and more reasons, Ignition offers the ideal forum for bringing IIoT’s power into your company.
by Dr. Jane Fitzgerald | Oct 19, 2020 | Data Science, Information Technology
Why is Storytelling Essential in Business Intelligence?
For Business Intelligence, there is more beyond just portraying data. Expressing what data stands for is crucial since it is something that impacts every area of our lives daily.
Of course, it has become more vital for business organizations to know how to manage and effectively use larger data sources. However, obtaining the inherent benefits is also crucial, through data exploitation by way of storytelling.
Why tell stories in business?
Storytelling is the best way businesses can improve Business Intelligence (BI) visual Analytical tools such as
Image Credit: Flickr
Dashboards. Storytelling helps to interpret and explain the components (e.g., tables or graphs) of such visualization tools.
Nothing moves people better than stories. They do not only inform listeners but also inspire positive actions. Professionally constructed data stories help turn the primary data, the dispersion diagram, and the bar diagrams into compelling, actionable messages.
Like Steve Job aptly put it, “the most powerful person in the entire world is none but the storyteller.”
Data scientists and the people responsible for managing data may find their data results obvious, but what about their target audience? Businesses can close that wide gap in knowledge and employ analytical logic to guide their audience to align more with the company’s aims and objectives.
Let’s get one thing straight! Data storytelling is not the easiest of tasks. However, it is one thing business management teams cannot ignore. Businesses’ ability to tell stories with their data is now a necessary analytical skill in business best practice.
What are the requirements for effective data storytelling projects?
The basic requirements you need to make your business intelligence storytelling a successful project include tools like Microsoft Power Business Intelligence tool as a benchmark for data storytelling experience in the following order ;
Image Credit: Needpix
Flexible Transition
It is not easy to export visualizations from one given source to another to create a great BI story. The process does not only take time, but the analytical tools used for exploration and creation of data visualizations are different from those used to create BI stories. The tools’ differences also reduce the chances of embedding the annotations or metadata generated during the analysis.
For creators to export it, the storytelling setting comprises the metadata. Then analysis instantly converted under fluid and integrated processes to reduce the time and effort needed, which is what Microsoft Power BI guarantees.
Integration
BI story creators must have the right tools that help in combining all the necessary materials to produce their story. These materials include interactive visualizations, BI reports, and smart ways of indicating the story’s structure, a sequence in story presentation, highlighting capabilities, and more. Microsoft Power BI in MS PowerPoint embedded these requirements.
Focusing tools
These tools help achieve visual narrating, focusing on drawing attention towards particular visualization data, such as annotating, coloring, highlighting, and zooming (in and out). With Microsoft Power BI, you have full customization for compelling storytelling.
Interactive visualization
MS Power BI, you can achieve efficient visualization in the narrations that you shared. It also gives you full control regarding what pages can be viewed by users taking part in the report sharing process.
Reusing similar story structure
While BI data and report changes from one analysis to the other, the stories’ structure remains virtually unchanged. Therefore, users must be able to reuse the same story structure created in the same tool. As usual, MS Power BI makes provision for this.
Conclusion
With the right tools at your disposal, your business can achieve an effective BI storytelling project through the MS Power BI tool. It will make customers aware and keep your target audience engage with your business message.
by Dr. Jane Fitzgerald | Apr 2, 2020 | Artificial Intelligence, Data Science, Emerging Technology, Information Technology
Image from Forbes.com
Artificial Intelligence, AI, has been integrated into mainstream society, so much so, that most people use it almost every hour of every day without even noticing. My first experience with AI was through cyborgs in movies and I know some people will go immediately to the killer AI, Skynet, from the Terminator movie franchise. However, the reality is, AI is a more complex and diverse field than just murderous machines from the future.
Social Media
We interact with Artificial Intelligence every day, for example, social media: the main application that utilizes this technology. Facebook, Twitter, Instagram, they use AI to keep tabs on their users and target ads for any content they are advertising. Even Google’s algorithm is a form of artificial intelligence that helps us all find what we need online in an instant. Next time you use google search when you are halfway through typing, stop and take a look at the suggestions it provides. More often than not, it typically pulls like searches from around the world, but it also takes past searches and websites you have visited previously drawing conclusions for suggested web pages. A perfect example of one form of AI in our lives, and a form some of us could not live without.
AI That Moves You
A commonly missed version of AI would be that which is built into our vehicles, from giving us new routes or accurate directions to vehicles that even drive themselves. Self-driving cars are here and wildly popular; while much of that technology is used in expensive vehicles, it is making its way to the inexpensive models. For example: many newer vehicles can stop if the car in front stops unexpectedly, what a great use of AI. Not only that, but AI is also utilized to help people take different routes to and from their destinations based on traffic patterns, just as google draws conclusions for suggested web pages, AI frequently saves time via alternate routes while driving. AI is everywhere!!
AI Fears
Some people have raised concerns with the rapid rate of development of AI, the fear that they are invading our privacy. This fear is justifiable when most of our first interactions with this technology are Hollywood blockbusters with murderous AI’s that try to wipe out all humanity, but let’s remember AI is more than that. This popular movie premise has some people living off the grid to avoid all intelligence, but it is important to remember, good or bad we are making great strides and our nation is prosperous, in part, due to those strides in AI. At the end of the day, AI can scare you, it can inspire you, or it can do a bit of both, but the decision to accept AI into our life is up to us!
https://www.tech21century.com/how-ai-has-influenced-our-society/
Biometric Identification and Cybersecurity
