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 21^st 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 standard quantity was set to trigger stock out. This BI tool was designed in such a way that the logistics team was given a prompt each time a threshold was hit. The process led to increased sales for the departmental store and has since been implemented for the entire chain of business.

From the above case studies, we can easily deduce that data was generated, modified, and then used for a decision-making process. Other factors considered; data is the primal deciding factor for a lot of present-day business decisions. The types of data collected in a business include;

1. Consumer Data:

• Definition: Consumer data refers to information collected from interactions and transactions with customers, including demographic details, purchase history, preferences, feedback, and behavior patterns. It could be from an engagement form, customer interests specific to the product, or general services. It is used when an opinion-based decision is needed in product design or service transformation
• BI Relevance: Consumer data is crucial for understanding customer needs, preferences, and behaviors. BI tools analyze consumer data to identify trends, segment customers, personalize marketing campaigns, improve customer service, and enhance overall customer experience.

2. Analytics Data:

• Definition: Analytics data encompasses various types of data generated through the analysis of business processes, operations, and customer interactions. This includes data on key performance indicators (KPIs), trends, patterns, and correlations.
• BI Relevance: Analytics data is central to business intelligence as it provides insights into business performance, operational efficiency, and customer behavior. BI tools analyze analytics data to monitor KPIs, identify areas for improvement, make data-driven decisions, and drive business growth.

3. Inventory and Supply Chain Data:

• Definition: Inventory and supply chain data includes information related to the movement, storage, and availability of products or materials within the supply chain, including inventory levels, procurement, logistics, and distribution.
• BI Relevance: Inventory and supply chain data are critical for optimizing supply chain operations, reducing costs, and improving efficiency. BI tools analyze this data to forecast demand, manage inventory levels, identify bottlenecks, streamline logistics, and enhance overall supply chain performance.

4. Employee Data:

• Definition: Employee data comprises information related to the workforce, including demographic details, employment history, skills, performance metrics, training, and engagement.
• BI Relevance: Employee data is essential for workforce management, talent development, and organizational performance. BI tools analyze employee data to track performance, identify training needs, optimize staffing levels, improve employee engagement, and support strategic HR initiatives.

5. Perception and Digital Footprint Data:

• Definition: Perception and digital footprint data refer to information gathered from online interactions, social media, reviews, sentiment analysis, and other digital channels that reflect consumer perceptions, sentiments, and brand reputation.
• BI Relevance: Perception and digital footprint data provide insights into brand reputation, customer sentiment, and market perception. BI tools analyze this data to monitor brand mentions, track sentiment trends, manage online reputation, and identify areas for brand improvement or crisis management.

6. Product Data:

• Definition: Product data includes details about the characteristics, specifications, pricing, availability, and lifecycle of products or services offered by the organization.
• BI Relevance: Product data is vital for product management, marketing, and sales. BI tools analyze product data to track sales performance, assess product profitability, optimize pricing strategies, manage product portfolios, and identify opportunities for product innovation or expansion.

7. Market Data:

• Definition: Market data comprises information about the external market environment, including competitor activities, industry trends, consumer demographics, market size, and growth opportunities.
• BI Relevance: Market data is essential for strategic planning, market analysis, and competitive intelligence. BI tools analyze market data to identify emerging trends, assess market opportunities, evaluate competitor strengths and weaknesses, and guide strategic decision-making and market positioning.

In Summary, the implementation of business intelligence using IoT is entirely feasible. As long as there is a need for data collection IoT will continue to play a crucial role in its organization, sorting, and implementation. To implement BI there is a need for data to be collected using IoT in most cases. The impact of IoT on the BI landscape is unquestionable. From gathering data to leveraging on insights, Business intelligence (BI) and the Internet of Things (IoT) have helped businesses optimize their internal processes and gain competitive advantage in the marketplace.