Big Data and Business Intelligence: Driving Business Growth

“Hiding within those mounds of data is the knowledge that could change… the world.” — Atul Butte, Stanford

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There is a continuing and increasing awareness of the Fourth Industrial Revolution (4IR or Industry 4.0); its benefits, advantages, and even its challenges and negatives. Any conversation around the advancements related to the Fourth Industrial Revolution will always be a “continuing conversation.”

This is because, according to the World Economic Forum, one of the critical components of the 4IR is that technological advancements will “fundamentally alter the way we live, work, and relate to one another.” Additionally, “the transformation will be unlike anything humankind has experienced before.” And, finally, this transformation is ongoing. It has not reached its pinnacle yet.

Moreover, the scope of Industry 4.0 is so vast that it is necessary to narrow the focus of any content written on this topic. Thus, this article aims to focus on the impact of the 4IR on Big Data, Business Intelligence (BI) and ways it can drive business growth and sustainability.

Recent events in the global retail sector, such as the closing down of some of the biggest retailers have highlighted the desperate need for organizations to build sustainable growth over time.

USA statistics reported by Doug Whiteman in his article titled “These Chains Have Announced a Ton of Store Closings in 2019,” published on Moneywise.com, show that 5844 retail stores closed in 2018. In contrast, 2019 statistics increased by up to 60% with 9300 retail stores closing during the twelve months.

Structured vs unstructured data: The Third vs the Fourth Industrial Revolution

Succinctly stated, in the field of Big Data and BI, the advent of the Fourth Industrial Revolution has brought with it the capacity to store, manage, and process large volumes of raw, unstructured data in different formats, including numbers, character-based data, video, image, and audio data.

This ability to collect, store, cleanse, process, and manipulate such large volumes and such multiple types of datum are in stark contrast to the traditional data types, and their management, used during the Third Industrial Revolution. This regular and structured data is historically stored in a conventional relational database like those developed by global organizations such as Oracle and Microsoft.

And, in summary, traditional data is made up of structured data that is stored in named columns and rows as records in database tables. And it primarily consists of numeric and alphanumeric characters. As an aside, there is also a limited capacity to store large character files and binary data in data types known as CLOBS and BLOBS, respectively.

Finally, in direct opposition to Big Data or unstructured data, because traditional data presents itself as less voluminous, it can be processed and managed from a single computer, or even a centralised network server, where it can be accessed from any number of desktop or laptop computers.

Essentially, Big Data is the direct opposite of structured data. However, it must be noted that structured data still forms a fundamental component of Big Data. Big Data is structured data’s data types and formats plus unstructured data in larger volumes.

Big Data and Business Intelligence: Ways to improve growth and sustainability

According to the authors of the article, “Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies,” the process and outcome of the 4IR when used correctly have the potential to expedite the creation and transformation of sustainable business processes. Essentially, all of “our decisions, actions and even existence in the digital world generate data.

And, the collection all of this data translates into vast volumes of data that need to be stored, cleansed, and processed before being turned into useful information. This information which, when interpreted correctly, is a vital part of the consideration process when looking at ways to improve growth and sustainability.

Additionally, the statement by Atul Butte in the quotation mentioned above, namely that the information contained within the Big Data ecosystem has the potential to be a world-changer or game-changer, is an imperative that will drive sustainable business development and growth through the revision of existing business processes.

Thus, the question that must be asked and answered is how to utilize the Fourth Industrial Revolution, Big Data, and BI to drive organizational growth and sustainability. Here are a few pertinent points that, as a whole, will provide a viable answer to this question:

Big Data, Business Intelligence, and Big Data Analysis: A cohesive whole

Dong-Hui Jin and Hyun-Jung Kim posit in their article titled, “Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics,” the following three relevant points:

Firstly, corporate management must use information derived from the Business Intelligence processes as a basis for effective decision making. Because post-modern Information Technologies allow for the collection of complex and large amounts of data, it makes sense that the data collection processes derive purpose and consequence.

Secondly, there are three elements allied to the production of information from data; namely, Big Data, Business Intelligence, and Big Data Analysis (BDA). The global corporate market only recognizes the first two elements. However, data analysis is a vital part of the information-generation process.

Thirdly, the three elements, Big Data, BI, and BDA, are part of a cohesive whole. They cannot and must not be seen as independent processes. Otherwise, business runs the risk of basing executive decisions on inferior data.

BI and customer relationship management: BI leads the way

Brand marketing methodologies have moved from a brand-centric towards a consumer-centric approach. The question that the modern brand needs to answer is: “How can a brand or product enhance its target audience’s quality of life?” In other words, what can the brand do for the consumer?

Taking the time to answer this question is a core imperative of the modern brand marketing business model. And, it is vital to conduct a thorough research project, looking at information necessary to answer this question adequately; otherwise, the brand runs the risk of losing its target audience to the competition.

Also, a critical point to take note of is that technological developments related to both the Internet and eCommerce have opened up the retail environment so that both large corporations and small business can compete equally in the same retail space. Ergo, technology, including BI, has reduced the cost of marketing so that everyone can play on a leveled retail playing field.

Additionally, the twenty-first century with its enhanced ability to collect enough data from which useful information can be derived ensures that this information will provide an accurate insight into the consumer’s demands. In this manner, brand marketers can position brands and products in such a way that the brands answer the “why” question; thereby translating into the conversion of the target audience into returning customers.

Ultimately, the information-generation business process cycle; in other words, the collection of consumer data, its conversion to useful information, the repositioning of brands and products based on this information, to continually ensure that they meet their target audience’s needs is an essential part of business growth, success, and sustainability.