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Business Intelligence and IoT Implementation

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

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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