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Checking in on IoT

First, we lay a proper foundation of IoT, its various concepts, and then its capabilities or use cases. IoT is short for the Internet of Things. With the advent of wireless sensor technology, a lot of times we as individuals have inadvertently run into or used smart technology (IoT) from time to time. Our smartphones are considered smart due to their ability to respond wirelessly to signals via the internet. The concept of IoT is that manufacturers are increasingly able to fit in chips or micro-computers on a variety of products for numerous reasons ranging from flexibility to efficiency, more recent examples include a light switch with less than 1 MB of embedded memory that can communicate wirelessly with host IoT apps such as Alexa for actions ranging from ON or OFF to dimming functions. The ability for these gadgets to communicate with each other with the help of the wireless signal exchange whether it’s over the phone (via wifi), the internet, or via the cloud is called IoT. Now having set the foundation above, let’s discuss the reason why IoT has come to stay. IoT applications are far-reaching further emphasizing the fact that human interactions are increasingly becoming digitalized. The information economy capitals of the world have long viewed information as the new gold. Therefore, any human endeavor that eases the burden of carrying out a particular task be it in the area of organization, management, and processing will attract all the much-needed attention it deserves. The possibilities of IoT almost seem endless as we will see shortly. The more we try to break into newer frontiers of technology for example from the possibility of space mining to uncovering more dynamics of artificial intelligence (AI), the more IoT continues to create waves of impact. For the thinking man, there is no existing threshold, all it needs is further exploratory research and development over time. In the present, we can say for a fact that the future is mostly directed by those who are capable of embracing this opportunity and rechanneling it for the benefit of humanity. Companies whose success rate and efficiency are driven by the more data they acquire about their users are seeing IoT as the holy grail. There aren’t many complex calculations or permutations required to see that we are currently living out the solutions or answers by humanity’s forebears who first introduced the world to smart gadgets, smart buildings, and now smart cities etc. The wider reach of IoT includes but is not limited to; Home Automation Smart Homes are becoming more popular as recent technological manufacturers are increasingly incorporating smartness into everyday gadgets or equipment. Your entire day-to-day itinerary can be planned and scheduled using a pre-arranged routine. It begins with the push of a single button and it sets the schedule for the day. Your smart refrigerator is set to auto-initiate a response to dwindling food supplies, your car is set to notify you of upcoming maintenance activity, and your smartwatch lists all activities via a voice prompt. You head out of the house prepared and the car automatically navigates through traffic allowing you time to think about more pressing needs. You get to work and the home system automatically chimes through your mobile about an unexpected visit and upon confirmation, you grant access to the visitor. The automatic security camera powered by motion technology alerts you about what is going on at home while you are away. These and many more are just some of the features that the home automation solution presents. Below are some home automation smart devices and their areas of application Home system: a home system is a software service that serves as the central IoT command point where all devices are linked to and can be used to access all functionality. It is multifunctional, it can be controlled remotely using Wi-Fi or over the internet using its custom access point. It’s used for security purposes where all locks are controlled wirelessly or manually. It can come in different configurations from different manufacturers. Dimmers: These are chips or MCUs embedded in simple light switches that enable remote access. They are responsible for the dimming operation of the lights in the home. The lights can be set to dim after leaving the room or to dim to a level of brightness. Leak detectors: These are actuators that sense different types of leakages ranging from gas leaks to water leaks. This is synced with the home system to enable timely leak detection. Thermostats: these are smart temperature readers that are also connected to the home system. It can be used for different purposes. This includes switching on the air conditioners or heaters when it senses that the temperature set point is abnormal. It can also be used when unusually high temperatures are sensed in the case of a fire outbreak. Smart Cameras: These devices are increasingly getting smaller and more sophisticated. The smaller they are, the less conspicuous they appear. This helps deter intruders and also conveniently gather evidence when the subject matter involves some level of discretion. Motion Detectors: These are sensors that detect unwanted motion and are used extensively for perimeter guiding systems. It serves as a security watchdog. Its sensitivity can be adjusted to help capture only human movement. these and many unmentioned devices are used in the implementation of IoT in homes. When we want to achieve this for the different home units in a high-rise building, we can set all devices to communicate with each other. This is where smart buildings come into the picture. Industrial Application Industrial IoT (IIoT) refers to the application of IoT in industrial settings for the instrumentation of machinery that is geared to communicate using control sensors and devices that engage Cloud technologies. In the industrial application of IoT, we discuss about the use of these control sensors in the everyday manufacturing of goods and services and how they can be set to communicate with each other so

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Business Intelligence and IoT Implementation

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The business landscape is currently buzzing with many trends geared toward delivering the right information needed to bring about the next breakthrough or growth; from all indications, growth signifies profit. The smart business merchant and even to the least small-scale business owner wants a piece of this cake. We have seen from countless research in the 21st century, that the most pertinent reason some businesses could not sustain or remain relevant over time is due to the absence of niche-specific timely information. What separates the major players from the minor ones in a business niche is simply a case of the right information. Given the presence of other success factors, well-analyzed information could be the only thing a struggling business needs to break even, stay relevant, and overall be successful. The right information held by industrial-scale players in global commerce would remain highly confidential. A trade secret of such importance will also be inaccessible to those without privilege. As we explore the world of business and its dynamics, we see competitors in a line of business involved in what we can comfortably term ‘trade wars’. Many will ask to what end, it’s highly imperative now that we refer to the most basic human instinct that to survive one has to adapt. This and many more philosophies have become the bedrock of modern-day business. Business Intelligence on the one hand has now become the most sought-after criteria in the study and eventual profitability of businesses worldwide. Business intelligence is a technology-driven process that helps businesses to convert the available data into knowledge that is delivered to stakeholders to help them analyze and make appropriate decisions at the right time (Rohit Jonardhan). Interestingly, a lot of businesses who are still on the fence or are curious still find that the data (information) in question is elusive or think that they are unavailable. As we read on, we get to find out what exactly makes up data, how we can properly harness it, and overall get the necessary insights needed. It is also important we establish one other key body of knowledge at this point. This body of knowledge is called IoT (Internet of Things). IoT is a broad term, that has been used extensively in recent times to describe a myriad of things that can be implemented in various industries for various purposes. However, in this context, we will be talking about IoT for business intelligence or BI. The Internet of Things (IoT) refers to the network of physical objects – “things”- that are embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the Internet (Oracle). It is also defined in this context as a network that facilitates communication between devices and the cloud and between devices themselves with the sole aim of gathering generated data that will be useful in delivering business insights. Some IoT configurations may behave differently given their configuration. From foundations, IoT is categorized into three; Sensors: These are the devices responsible for gathering information. Examples include Temperature and Humidity sensors, Infrared sensors, pressure transmitters, motion devices, counters, etc. Microprocessor/Microcontrollers: These are chips responsible for the control or processing of the data generated from the sensors. Examples of this include ZigBee, Lorawa, Arduino boards, etc. Actuators: these are the devices that deliver the output based on the programmed decision of the microcontrollers. Examples include an LED light, an electric motor that drives the washing machine, the water pump, etc. In the business Landscape IoT certainly delivers a lot of automated processes bringing about innovations in the way and manner businesses were originally done. There are so many case studies and real-life scenarios that are in use. We will explore these in subsequent paragraphs. There is such a thing as an organization mining its data to see trends that could be analyzed and followed for success. These types of trends come when organizations are involved in a lot of in-house data gathering. To ensure that the data generated are relevant, organizations want to ensure that the data generated use a certain type of protocol otherwise known as data governance. Data governance is simply a process of making data secure, accurate, and available, it aims to harmonize the data generated in such a way that stakeholders from the various units are on the same page. What makes up data for Business Intelligence? From concept, data could be any set of numbers, codes, colors, characters, symbols, words, graphs, pictures, videos, etc. In recent times these sets have now included a broader range of data whereby some cases cannot immediately be identified and would be regarded as a data blob. These sets of data are also termed information. Information in this context is any set of data needed for business-related decision-making. BI data involves any data that can be used in making informed decisions regarding various aspects of a business venture. There are so many types of data that can be collected given the different aspects of a business. Before we go into what a business’s data comprises, let’s examine some real-life case studies that illustrate the need for data: Case study 1 Business XYZ had its production process stalled after it was verified that a key ingredient in the manufacturing process was low in stock and as such kept manufacturing in a precarious situation. This led to losses in manpower, energy waste, and a myriad of other losses. After investigation, stock-outs were thoroughly frowned upon from the management level, and a more comprehensive inventory management system was set up with a feedback mechanism that can be viewed on the go by production personnel. Case Study 2 A fast-moving beverage product ran out of stock in a departmental store. This dropped the sale of other consumer goods and as such led to a decrease in sales. The situation was further worsened because it happened over the weekend. Using a Business Intelligence product, fast-moving consumer goods were placed on high priority and a

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How to properly diagnose and proffer solutions to real-life security needs using IoT tools.

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On account of the broad nature of the topic, we will narrow our discussion to one Use Case for the sake of clarity and comprehension. To begin any process of problem solving we begin by defining it. Problem-solving is a systematic process that involves steps. Before delving into the problem-solving process let’s examine the reason or reasons why a problem is prone to recurrence; Unidentified and Underlying issue: this is akin to having a short-term solution bias. Solutions aimed at providing immediate relief may not be sustainable in the long term. Inadequate Root Cause Analysis: when a problem is not properly defined or identified and a solution is proffered, no matter the well-meaning intentions, research has shown that there is a high chance of the problem recurring. Insufficient Implementation: due to the unavailability of the right tools, expertise, and resources, lasting solutions become easily far-fetched. Lack of monitoring and follow-up: without a preventive maintenance culture of effective feedback and scheduled time-based monitoring, the chances of a problem resurfacing are usually very high. Complexity: some problems are complex and will require more ongoing adjustments. This is because some of the problems are part of a larger one and hence completely solving it requires more effort over time using updates. Given the above, we immediately see why there is always a need to correctly diagnose and proffer a solution in any given situation because the consequences are mostly dire. Homeowners, property managers facility administrators, and a host of other key responsibilities are bequeathed with this situation most of the time. The following are the best practices when it comes to proper diagnoses of real-life security or comfortability needs Identify the problem: to understand a problem based on the needs of the moment, we start by gathering the relevant information. Also, we determine the extent or boundaries of the problem this is called the scope. We also differentiate between symptoms of the problem and its root cause. Analyze the problem: After gathering the required data, we can now analyze the problem to understand its context and implications. There are different techniques used in carrying out analysis. Some of these techniques are already popular and have grown in their use due to adoption. There are 5 Whys, Fishbone Diagram, RCA or Root Cause Analysis Generate Potential Solution: when analysis has been concluded, it’s now time to generate potential solutions. We also consider several factors such as the resources available, the potential impact of the solution, and the long- and short-term issues that will be encountered going forward. Evaluate and Select Solution: All solutions are thrown in the mix using a chart or a visual queue such as a decision tree Develop an Action Plan: this comes in after a vote of confidence is passed at the solutions stage. All factors are considered long-term and short-term simultaneously. Implement the Solution. Here we begin gathering the resources required and then we can now start the implementation by putting the action plan into practice. Evaluate the Results: From here we gather the necessary feedback needed to ensure the procedure or solution worked. If there exists an issue that was not considered or was an oversight, it is immediately worked on accordingly with the most critical concerns addressed first. Let’s look at a real-life case study to see how the problem is defined and the solution proffered: Case Study A shop was burgled over the night and was only discovered in the morning after the incident. Goods worth millions were carted away at the expense of the shop owner. Fortunately, there was insurance cover which will cover some costs and possibly get the business up and running in no time. but let’s take a break here, things could have played out differently if some factors were in place. Problem Definition: this is a case of a security breach. From the information gathered, there was no surveillance footage, it happened at night when visibility was low, Security personnel on duty were possibly distracted, there was no alarm system in place and hence no system breach triggered. Previous records showed that it was not a first-time occurrence. In this case, the symptoms were financial loss and property damage. The root cause was identified to be a skeletal or minimal security apparatus that was inefficient around the clock. In consideration, the shop owner could either file a legal action against the security company and wait for security beef up or activate personal security. For several reasons, the latter and not the former is the best course of action to take because we were informed by the case study that an insurance process was involved. To prevent such incidents in the future, a robust IoT-based security system can be implemented in the shop. Here is a more comprehensive solution: IoT-based Security System for the Shop Smart Surveillance Cameras: 24/7 Monitoring: Install high-definition, night-vision cameras both inside and outside the shop to monitor activities around the clock. Real-time Alerts: Cameras equipped with motion detection and AI can send real-time alerts to the shop owner’s smartphone and security personnel if unusual activity is detected during closed hours. Cloud Storage: Store footage securely in the cloud for easy access and review, ensuring evidence is available if needed. Smart Alarm System: Intrusion Detection: Install sensors on doors and windows that trigger alarms if forced entry is detected. Silent Alarms: Set up silent alarms that alert the shop owner and authorities immediately without tipping off the intruders. Integration with Surveillance: Link the alarm system with surveillance cameras to start recording and send alerts when the alarm is triggered. Access Control System: Smart Locks: Use IoT-enabled smart locks that can be controlled remotely and log entry and exit times. Biometric Access: Implement biometric or RFID access controls to limit and monitor who can enter the shop, especially after hours. Remote Access Management: Allow the shop owner to grant or revoke access remotely, ensuring only authorized personnel can enter. Environmental Sensors: Glass Break Sensors: Install sensors that detect

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Building In-Demand Business Intelligence from Factual to End Product

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To build an in-demand business intelligence (BI) product we must first do justice to its proper definition by concept, areas of use, why is it important, and overall, how they can be implemented even to the most minute scale. Business Intelligence (BI) is the concept of delivering fact-checked solutions to business issues using technology. BI comprises the strategies and technologies used by enterprises for the data analysis and management of business information. BI tools are used to handle structured, unstructured, and semi-structured data sometimes in large amounts (big data) to help in their processing, analysis and finally developing insights. Although many aspects of BI can be automated as we will see shortly, there is however a need for human inputs to reach a consensus while implementing the insights from the reports generated by a BI tool. Why is BI so important to organizations? Insights from BI tools help organizations tell a better story about their well-being or general activity. This goes a step further in shaping their decisions and helping them increase revenue amidst a host of other benefits. Without BI organizations cannot use factual data for insights, they tend to rely on experience or historical data which at best may not give a holistic view of the issues. Apart from driving revenue, BI tools can help an organization optimize its internal business process by ensuring that resources are mapped out in the correct proportions when needed. Processes that do not seem to flow into one another properly due to some unknown bottlenecks are easily greyed out. Businesses can easily spot problems that are not usually visible because with a BI tool management has a bird’s eye view of the entire line of business no matter how large an organization is. This is because a lot of the performance indices or data have already been captured in real-time or near real-time. This accounts for a quick assessment and affords the least cognizant some in-depth knowledge of the situation. It increases operational productivity and efficiency when all factors necessary for success are considered simultaneously. Businesses whose products are underperforming can easily use BI to engage sales and marketing to get an insight into how consumers perceive the products and find out areas that need to be further explored in terms of emerging businesses through market trends. Areas of use Business intelligence can virtually be used in almost all forms of business. As long as a business leaves a footprint daily and needs to drive profit or find out ways it needs to improve as explained above then it needs BI. It can be found in different industries as can be seen below Manufacturing: Business intelligence is used to gather data about the machinery, workforce, Inventory, supply chain, target KPIs, and organizational milestones. From these sets of data maintenance schedules are made, the workforce is well managed, and the raw materials needed for production and maintenance activity are well stocked. A lot of activities happening on the shop floor can be monitored using some IIoT (Industrial Internet of Things) enabled devices that in turn send the data captured to an on-premises data warehouse or data cloud. These insights can be derived or arrived at using expertise in BI tools. Hospitality: The hospitality industry is comprised of travel and tours, hotels, leisure, and recreation. The hotel industry alone operates large sets of data ranging from housekeeping, distribution channels, and customer behavior to direct customer experience. BI tools exist to help in capturing these types of data. This can be used to drive insights into the need to increase the workforce, make adjustments to accommodate customer preferences, and explore other areas of emerging business. Aviation: some processes may appear complex in the aviation industry such as the number of passengers available for a flight viz a viz delayed flight and subsequent rescheduling. BI tools can be used to harness the power of data by ensuring the reasons for delayed flights are captured and analyzed over several similar cases. A lot of data can be captured using the sensors aboard an airline for scheduling maintenance, data available from the front desk can be used for flight scheduling based on historical data, and data available from community inputs can be used for crew scheduling based on crew availability. All these are used by a BI tool to drive insights extensively. Agriculture: BI tools can be used to track crop performance using weather forecasts and this in turn helps farmers plan for additional needs such as labor, and fertilizer interventions. Using modern sensor technology large sets of data can be generated from the field, farm steads, animal husbandry, etc. These kinds of data collected by farming organizations are virtually impossible using conventional methods. Adopting BI systems makes these herculean tasks much more easier and decisions that are mostly based on forecasts are reached quickly. Food and Retail: the food industry is densely populated and equally rewarding for the majority of its players. Major key players in this industry are already harnessing business intelligence data and stats in untapped niches and are using the same to make informed decisions. Restaurants can take advantage of the BI tools in managing inventory for effective procurement strategy, and streamline services to meet a line of business by gathering specific data. Large retail chains can make use of BI tools in planning for the workforce, gathering information on the best-performing goods based on geographical area so that proper reallocations can be made. Oil and gas: this industry will not be termed a latecomer to the BI space as most of its critical decision-making is entrenched on lots of available data such as more technically oriented data such as seismic, mud density, flow rate, temperatures, etc. Other necessary data usually collected in this industry include geographical data, workforce, machinery or assets, operations monitoring, vendor operations, etc. The aforementioned are some of the data required by a very robust BI tool to give the forecasts of events necessary in making key decisions in this industry.

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