The contemporary corporate realm is a composite of various fields and expertise which encompasses unique fields of study. Separately, these studies do not make it seem that they can be in any way part of business functionalities. However, the advancements in corporate undertaking and policies now require a combination of different areas in order to be effective and sufficient. Data science has become an integral part of modern corporate norms. With logistics and analytical aspects becoming crucial, data science plays a vital role in establishing a solid foundation, especially with regard to financing.

First and foremost, defining ‘Data Science’ in this context is imperative. Simply put, ‘Data Science’ refers to the analysis of large amounts of data and information, using modern tools and methodologies to discover previously unknown patterns, extract crucial data, and make business choices. In order to create prediction models, data scientists employ complicated machine learning algorithms for the best results. The data being used for analysis might come from a variety of sources and be presented in various ways. ‘Data Science’ entails applying advanced analytics techniques and scientific principles to determine and extract valuable information. It is a way of digitizing aspects of corporate processes vital in this day and time.

Speaking with the current business trends, data science is vital in almost all elements of corporate operations and initiatives. For example, it gives information on clients that enables businesses to design more efficient promotional campaigns and focused advertising in order to enhance product sales. It assists in financial risk management, detecting fraudulent transactions, and preventing equipment malfunctions in manufacturing and other industrial facilities. It aids in the prevention of cyber threats and other security concerns in IT systems.

Data science projects may improve operational supply chain management, product inventories, distribution networks, and customer support. From a more intrinsic level, they point to enhanced efficiency and cost savings. Data science also helps businesses develop strategies and plans based on an in-depth examination of consumer behaviour, industry trends, and competition. Without it, organizations risk missing out on opportunities and making poor judgments.

It has been mentioned before that data science is especially crucial for financing within corporate sectors. Before getting into that discussion, let’s explore how it works in any given sector requiring extensive analysis. Usually, there are five stages of data analysis, each containing its own task line. The stages are:

1. Capture – This stage focuses on tasks like Data collection, data entry, signal reception, and data extraction are all steps in the data collection process. This step also entails collecting both organized and unstructured data.
2. Maintenance – This stage includes Data Warehousing, Data Purging, Data Construction, Data Processing, and Data Architecture are all aspects of data management. This stage further entails collecting raw data and converting it into a usable format.
3. Process – Predictive analytics, clustering/classification, data modelling, and data summarization are all examples of data processing techniques. Data scientists assess the generated data for patterns, trends, and flaws to determine its usefulness in predictive analysis.
4. Analysis – Introspective, Predictive Analysis, logistics, Text Mining, and Qualitative Analysis are all examples of exploratory/confirmatory analysis. This is the core of the entire operation that is called ‘Data Science’, which entails doing various analyses of the data.
5. Communication – Data reports, visual analytics, business intelligence, and decision-making are all aspects of data management. Analysts present the studies in clearly legible forms like infographics, graphs, and reports in this last stage.

Putting all the mentioned stages and cycles of the data science work cycle into perspective, it becomes clear how it can boost productivity in corporate sectors and increase decision-making efficiency. Corporate financing is the heart of all corporate operations. However, in most cases, the financing sectors are still being utilized with obsolete means, which leads to errors and flaws in decision-making and business structuring.

For the greater good, this situation in corporate financing has to change and evolve. Across sectors, a company’s Finance staff should offer light on what’s occurring now with sales and other financial metrics while also forecasting what the future may bring. The same must also be done for the rest of the organizations.

Until recently, meeting these expectations would have been impossible. Excel-based forecasting necessitates herculean efforts to gather data and produce results before the end of each quarter. However, for most finance teams, offering daily insights remained a pipe dream. In the end, only the past is disclosed, not the future. In the corporate realm, actionable insights based on existing data may play an essential role in corporate financing.

Saying that corporate financing has not improved or digitized to some extent will be wrong. Some Software-as-a-Service or SaaS tools are available that assist in data forecasting and simulation of different situations. However, the problem with these SaaS tools is that it becomes difficult to quantify what has changed between projections, constraining analysis and forward-looking insights, and said data could not be shared with the rest of the company, preventing a shared knowledge across business departments and with important stakeholders.

This is where data science and data scientists can become the saviour by analyzing the data at hand and opening up real-time feeds of operational data to various opportunities without being subjected to limitations. Embedding data scientists for this particular job description is pertinent in this respect. It is the data scientist’s job to provide actionable insights. This is done by identifying the data-analytics issues that provide

< the most potential for the organization,

< determining the correct data sets and variables,

< Obtaining vast amounts of organized and unstructured data from many sources and devising and applying models and algorithms to mine the stores of big data.

However, like most professional elements, data science also comes with a few challenges. Due to the obvious complex character of the analytics involved, data science is complicated. The massive volumes of data that are generally evaluated add to the complexity and lengthen the time it takes to finish tasks. Furthermore, data scientists usually work with pools of large data that may contain a mix of structured, unstructured, and semi-structured data, confounding the analytics process even further.

One such difficult task and challenge is removing bias from data sets and analytics systems. This encompasses both problems with the underlying data and problems that data scientists unwittingly add into algorithms and prediction models. If such biases are not discovered and corrected, they can distort analytics results, resulting in incorrect conclusions and poor business decisions.

However, with properly trained professionals and a well-built and well-oriented team of data scientists and analysts, such challenges can be overcome. If executed properly, matters of finances with the help of big data can become a lot easier, and the overall structure based on such finances can be solidified. It is worth mentioning that, in Bangladesh, data science has started to take off, but it is yet to reach its potential, especially in corporate financing. Ensuring a large-scale reach in known sectors and corporate businesses will certainly amplify their ultimate result.

Author – Shiddhartho Zaman