Big Data and how it's changed the business landscape.

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Big Data and how it has Changed the Business Landscape

Introduction

Big data is a term used to describe large volumes of data that is structured, unstructured or semi-structured, which inundates an organization on a day-today basis. Since its introduction in 2005, Big Data has been a game-changer on how businesses manage their operation. With its ability to store a significant amount of data, businesses have been able to not only sire their data but also to analyze and exploit new opportunities they can use to increase their production processes (Wolfert, Ge, Verdouw & Bogaardt, 2017). For instance, Big Data has enabled companies such as Netflix and Amazon to efficiently reevaluate potential risks in their portfolio, conceptualize the essence of data-driven marketing, and enhance customer relationships. Nonetheless, all these merits depend on whether a business is conversant with the digital world. In this perspective, the research will analyze how Big Data has changed the business landscape and how businesses use it to support their operations.

Big Data

Big Data is the process of collecting, analyzing, and storing huge amounts of data to be used for future reference or to run a defined task (Schroeder, 2016). The concept of Big Data comes into play when the traditional data management tools of companies are unable to store as well as process their huge and complex data.

Types of Big Data

There are three known types of big data: structured, unstructured, and semi-structured. Studies have proved that these three terms, despite being technically relevant at all levels of analytics, are the rudiments of Big Data (Abawajy, 2015). Considering the origin of data and how it can be presented before analysis is paramount when a business aspires to work with the quantity of Big Data. By virtue of numerous parameters associated with it, the process of extracting information has to be efficient to make the venture worthwhile.

The characterization of data not only helps businesses enhance their day-to-day operations but also learn about the insights it can produce. All generated data goes through three processes: Extract, Transform Load (ETL) prior to being analyzed. In other words, Big Data harvests information, formats it so that it can be readable by its users, and then stored for future use.

Structured Data

Data that can be collected, assessed, processed, and stored in a fixed format manner is referred to as structured data. Researchers consider it as the most effective to work with since it is very organized with proportions defined by set parameters (Schroeder, 2016). Since the introduction of digital technology, the skill-set in computer science has succeeded in developing methodologies for working with Big Data as well as derive significant value from it. Nevertheless, contemporary society is experiencing a new phase of data processing and storage whereby the size of information has grown to exponential ranges.

Since structured data is grouped intangible numbers, it is much easier for a program to track down and collect data. Structured data follows schemes, which are essential roadmaps to defined data points (Abawajy, 2015). These designs relay the location and the meaning of each datum. For instance, a payroll database will reflect an employee’s identification number, hours worked, rate of pay, compensation plan, among others. The scheme will layout each one of the dimensions for as long as there is an application that is integrated into the main system. Any individual who uses this program will realize that one does not have to work through the given data to discover the exact meaning, rather they could easily prompt it to immediately collect and analyze the generated data.

How Companies Use Structured Data

Structured data is the simplest type of data to analyze since it does not compel one to prepare or arrange the data before it is being processed. Users are only required to cleanse data and place it at its designated entry point, though there is no need for a deeper interpretation or conversion of the data before a true inquiry is performed.

One of the main advantages of using structured data is the conventional process of merging rational and enterprise data. Since relevant data dimensions are generally defined and distinct elements are in a uniform format, making all sources to be compatible will require very little preparations (Wolfert, Ge, Verdouw & Bogaardt, 2017). This reduces the cost of looking for new data and saves time when tracking down saved data.

In structured data, the ETL process stored the final product in what is referred to as a data warehouse. These facilities are highly structured and filtered to handle specific analytical processes that correlate with the reasons why the data was harvested (Liu, Mai & MacDonald, 2019). Also, the readability and qualitative nature of this classification grant compatibility with relatively all sources of information that have some notable correlation. Regardless, the amount and analysis of data are limited by the size of the information that the user collects.

Unstructured Data

Unstructured data is that which has an unfamiliar form or structure. Other than having a humongous size, unstructured data possesses multiple challenges associated with processing and getting a valuable final product. A good example of unstructured data is one of a heterogeneous source that combines, videos, images, text files, among others. In modern society, many businesses have shied off from using Big Data since they have a lot of data but they cannot generate meaningful information. This is because most of their data is in either unstructured or in raw form.

How businesses can use Unstructured Data

The most difficult part of examining unstructured data is instructing an application to comprehend the information it is extracting. In most cases, it requires one to convert it into some structured form (Perrons & Jensen, 2015). Ostensibly, the decoding process is relatively hard and its specifics vary from format to format. All the same, scholars have suggested alternative methods such as converting hierarchies by using taxonomy, natural language processing, and text parsing. From a wider perspective, the entire process involves the use of complex algorithms to blend the processes of collecting, interpreting, and understanding functions.

Businesses that have used these methods have realized that the contextual facet is what makes unstructured data pervasive. The stated models have the capability of merging internal information with external perspectives to generate meaningful ideas (Hasan, Popp & Oláh, 2020). Some of the companies that can effectively use these applications are those that depend on machine learning. These applications enable businesses to teach their AI systems on how to interpret data. Moreover, these applications can improve the models that businesses are using as it aids the AI system to teach itself on how to improve as well as discover new patterns.

As compared to structured data whose storage medium is known as data warehouses, unstructured data is stored in data lakes (Abawajy, 2015). These mediums have the capability of preserving data in its raw form as well as all other information it contains.

Semi-structured Data

Semi-structured data contain elements of both the structured and unstructured data. In most cases, the Semi-structured Data is presumed to be unstructured data with attached metadata. This can represent inherent data collected over a certain period such as email address, device ID stamp, location, time, or that which is semantically attached to the original data after processing (Perrons & Jensen, 2015). For example, assuming that one takes a picture of his or her favorite pet using a phone. The application will automatically log the exact time the picture was taken, the device ID, and the GPS data during the time of capture. When storing this data in web services that handle Big Data such as iCloud, the subject’s account info automatically becomes attached to the initial file. Another example is when one sends an email, the IP address of the device used, email address f the sender and reviver, time sent as well as other featured are linked to the original content of the email. In both cases, the actual content, which is the features that make up the email and the pixels that generate the clarity of the photo, is unstructured but some elements allow the data to be grouped owing to the alignment of certain characteristics.

How Companies use Semi-Structured Data

Semi-structured data bridges the gap between structured and unstructured data in that by using the correct data set, it becomes a good source of information. This type of data can inform machine learning and AI training by correlating patterns to metadata. The only difference in this type of data as compared to structured and unstructured data is that it does not have sets of schemes (Abawajy, 2015). This can be either advantageous or disadvantageous to the business that is using this type of data. It can be rather challenging when one anticipates working with it as it requires a significant amount of effort t help the application learn the meaning of each data point. However, this signifies that the restrictions in structured data do not exist.

Information regarding semi-structured datasets can be arranged through the creation of metadata; however, they are not bound to operate using its features. Data generated from the original content, which is common in all unstructured data, can be grouped further with the proposed metadata for a deeper understanding that can create demographic information.

Markup languages used in applications such as the XML allow collected data to be defined by its original contents rather than depending on a set scheme to generate the specific description. The relational model is created out o the original data rather than depending on one that has been filled into a pre-configured form (Hasan, Popp & Oláh, 2020). Businesses that use this application can organize data into a tree structure deriving attributes as well as decorations from semantic tags, potential metadata, and individual nodes.

Fundamentals of Big Data

The first fundamental concept of Big Data is to focus on the end results; therefore, whenever undertaking a huge data analytics project or task, businesses should always figure out first what the goal is and what is an objective of the project (Schroeder, 2016). Where possible these final goals should be quantified with metrics (Borne). Also, the knowledge of our end goal is mostly the key stimulus that assists in the selection of the required ingredients of the project.

The second fundamental concept of Big Data is to know the data being collected and analyzed. With the knowledge of our data, businesses can figure the best data for a particular project and the best features to select. This is also called data profiling (Dedić & Stanier, 2016). This process involves analyzing the aspects of the data such as; aggregate values, min/max values, scale factors, missing values, interdependencies, indices, and more. If working with labeled data, it becomes important to identify which data is the predicted variable or class label.

The third concept of Big Data is its correlation with science. In other terms is important for firms to realize that this is an experiment involving data combinations, data selections, combinations of a , accuracy measures, logarithm, success metrics, algorithms, and much more (Schroeder, 2016). When trying to solve a problem, all of these items should undergo testing for applicability as well as validity. Using the past learned experience we can identify which combination of features, data, and algorithms are best suited to satisfy the particular needs.

The fourth fundamental concept of Big Data is the level of accuracy (Perrons & Jensen, 2015). It’s normal to presume that good data is normally distributed and is a perfectly clean data. The fact is that most data are no accurate. Therefore businesses should always seek to find the best in our data.

How Big Data has the Business Landscape

Data is the most valuable component of any business in modern times. Firms can operate much more efficiently after analyzing the collected data and formulating business decisions on the same basis. In other words, Big Data Analytics is the modern way of realizing profits. Researchers have established that Big Data Analytics is relatively objective as compared to the non-conventional methods and organizations can make precise business decisions using the acquired knowledge (Liu, Mai & MacDonald, 2019). There were times when businesses could only interact with their clients directly in stores. Also, companies could not comprehend what commodity customers wanted on large scale. Now, everything has changed with the introduction of Big Data Analytics in that businesses can engage with customers using online platforms regardless of geographical location and know what their wants and needs. The use of these mainstream platforms has also changed the business landscape in a variety of ways.

Customer Retention through Marketing

Customers are the only ones who determine the existence of a certain business. Therefore, attracting more potential customers and even retaining them is vital for any business. With Big Data Analytics, businesses can achieve this factor through observing the trend of their customers hence specialize in providing them with what they desire the most (Perrons & Jensen, 2015). The more the information that a business has regarding its customer, the more precise they observe customer patterns and trends. This will guarantee that the business will deliver the desired goods or services. In the end, customers will be happy and will strive to become loyal to the business.

Creating Market Campaigns

The most effective way of selling products to potential customers is through marketing. However, not all marketing strategies can guarantee businesses of acquiring a big customer base. Companies that use traditional methods of marketing tend to lose customers than those which use conventional methods (Hasan, Popp & Oláh, 2020). With Big Data Analytics, businesses can examine the customer base as well as comprehend what the masses desire. This information aids businesses to focus their marketing strategies in a way that will guarantee customers for ultimate satisfaction.  By using social media platforms, businesses can monitor market rend and anticipate customer behavior in the same market. As a result, these businesses will venture into establishing successful marketing campaigns.

Risk Management

Businesses can never sustain themselves if they lack a successful risk management plan. Even so, how can big businesses function if they are unable to detect risks in real-time and implement strategies that would mitigate or bar them from emerging in the future? With Big Data Analytics big companies can collect and examine internal data stored in their archives hence enable them to develop short and long-term risk management plans (Dedić & Stanier, 2016). Using this methodology, businesses can make out future risks and their effects hence establish better strategic business decisions that will guarantee them a sustainable future.

Supply Chain Management

An effective supply chain system is one that has a steady creation of products and ends by handing over the finished products to the customers. In big companies, supporting an efficient supply chain system can be cumbersome considering the number of people and the number of products and amounts that are moving from the manufacturing point to the consumption point (Schroeder, 2016). Companies can ease this process by using Big Data Analytics to examine the products that are in their warehouse inventories as well as their retailer detail. The tool will enable these organizations to determine their production needs after establishing a pattern of distribution. As a result, these companies will make fewer errors hence making their supply chain management run smoothly.

Product Creation

The stiff competition in the business market compels organizations to not only create products but create good quality products. Many companies have failed to produce recommendable quality products since they do not understand what their clients want before creating the defined product (Dedić & Stanier, 2016). With Big Data Analytics, a business can use previous data on the responses they got from customer feedback to ascertain the specifics attached to the products. This information enables businesses to compete favorably in the market since they know where to improve in the production process.

Conclusion

In conclusion, Big Data has been a common component of managing data for many businesses in the current generation. The upsurge in its usage has been predominantly associated with its ability to change the business models that support business operations such as the creation of products, customer retention, creation of personalized marketing campaigns, and supply chain management. With these merits, it is vital for organizations that use traditional data management tools to consider assimilating Big Data systems.