It is evident that the world has seen an exponential growth of machine-related data due to the rapid advancement of technology and internet penetration. Data is the central component of digital transformation. The impact of digital transformation can be seen across countries, businesses, and even governmental organizations. All organizations generate data in various forms and the abundant data at hand has led to the discovery of unidentified areas of business processes. Artificial Intelligence, Data Science, and Big data continue to garner attention due to their advanced use-cases and unmatched power.
However, not all organizations have switched to a data-driven model. Reports suggest that 99.5% of collected data never gets used or analyzed. The major reason for the inability of businesses in adopting data science is due to lacking knowledge and quality data. Here, we look into some of the key points on how businesses can strive towards becoming a data-driven business.
Explore and frame the business problem
The first step in the journey to building a data-driven business is to explore the problem. It involves finding the areas of business that possess the greatest potential for improvement with the help of valuable insights from data.
For instance, customer purchase data plays a major role in understanding the preferences and buying patterns of the customers in the retail industry. Whereas, for the healthcare industry, the patient’s historical record data can help predict and diagnose an illness faster.
For the retail industry, switching to data for insights might be from a profit-perspective, while for the healthcare industry, it can be a life-saving tool. The impact of using data varies for every business. However, the opportunity for improving business processes using data science is huge for every industry. Formulating questions related to data is another way of discovering new use-cases of data science for a business.
Prepare a data pipeline
Data is the underlying factor for every process related to data science. According to the nature of the business, the data can be operational, customer, business performance, or transactional. Enabling the system to be able to work with different sets of data simultaneously requires an effective data flow infrastructure, which is known as the data pipeline. Essentially, a data pipeline contains the channels and processes to efficiently store, extract, transform, and load data.
Data can be of various types: structured, semi-structured, and unstructured. The analysis is of no use if the quality of data is not optimal and consists of problems such as missing data, corrupted, or duplicated data. Hence, proper knowledge of the data pipeline and its processing is required to be able to collect data from multiple sources and use the data to its full potential. An early-stage data auditing can help businesses discover new data sources and regulate the existing data collection pipeline.
Develop a processing pipeline
Setting up a data science process requires extensive work with data. Businesses often choose to hire a consultant or a data science team to implement these necessary processes for loading and transforming the data so as to build scalable data science solutions.
A typical data science process consists of the following:
Data processing: The collected data needs to be cleaned and formatted to be able to generate relevant information from data.
Exploratory Data Analysis: Various characteristics of data are explored to find patterns, trends, and features for analysis.
Data modeling: The data is modeled on the basis of mathematical, statistical, and machine learning algorithms to generate a model that predicts results based on the pattern of data.
Final Result: The generated insights are visualized and compiled in the form of reports and findings or compiled in the form of a software.
Implement the results
An effective mechanism is required to maximize the value of the data science solution. There are numerous use cases of data science for business such as customer acquisition and retention, operation optimization, transform and improve business process, and risk management. These metrics should be the fundamental foundation behind setting up the data science process.
Transform into a data-driven business
Creating a data-driven business requires a long-term vision for capitalizing the data sources and analytics to deliver concrete business values over a period of time. The key factor should be to identify the key business elements and processes that can be automated using the data. Additionally, growing a data-driven business requires continuous exploration of supplementary ways to use the data available and finding new sources of data to be utilized in the coming days.
With the ample amount of data available, businesses are slowly understanding the importance of data as a valuable resource. Data science is a critical tool for small and large companies alike irrespective of the size of the data. A data science process helps in solving diverse business challenges faced by various kinds of businesses.
You might also want to read our article on ‘How can businesses use data science.’
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