Big Data is at the center of the modern industrial revolution. It has gained application in manufacturing, automation, research, supporting business decisions, among others. Big data refers to large and complex data sets that cannot be captured or analyzedwithstandard end-user applications. The term "big"does notnecessarilyrefer to physical storage; it impliesthat the datasets may be complex and unstructured, rendering them unusable without further processing (Vasakis, Petrakis, &Kopanakis, 2018). The data comes in several formats, including text, graphic, and audio.
One of the areas where big data has become essential is in business analytics, which is the process of making data-driven decisions. AsVassakis, Petrakis, &Kopanakis(2018) assert,"without high-quality data providing the right information on the right things at the right time, designing, monitoring and evaluating effective policies becomes almost impossible" (p. 3). Firms use big data analytics to audit their processes and increase organizational efficiency.All kinds of businesses, from small firms to multinationals, need to leverage big data to remain competitive. The global big data market is projected to expand at a compound annual growth rate of 10.6 percent between 2020 and 2025(Markets and Markets, n.d.). The growth will increase the big data market size from $138.9 billion in 2020 to $229.4 billion in 2020(Markets and Markets, n.d.). Most of this growth will be concentrated in Australia, North America, and Asia-Pacific.
Big data is readily available; most data that supports business decisions is drawn from the web. However, firms are facing challenges in extracting, cleaning the data, and processing it. Also, since big data involves large datasets, small companies may not have the necessaryinfrastructure to store and process such data. Traditional data applications cannot handle big data due to its heterogeneous format and massive volume. Also, though the small and medium-sized enterprises (SMEs) may have enough physical storage, their physical infrastructure may not be able to support real-time processing (Urbinati, Chiaroni, Chiesa, &Frattini, 2019). The projected big data market growth and the challenges faced by SMEs in the adoption of big data by SMEs present a niche to be exploited. The idea is to start a data analysis company called New World Data Solutions. The company will provide data mining, cleaning, and analysis solutions for small and medium-sized firms. New World Data Solutions will also provide a platform for data storage and real-time data analysis.
The principal activities of the New World Data company will be data mining and analysis. The company will take over the data management functions of the client's company. The company will develop data scrapping software to obtain consumer data from the web and make it readily usable. If the client does not have the capacity to analyze the data, New World Data will analyze the data and provide the client with business insights. Clients can also request for real-time data processing and analysis. For such clients, the company will provide data processing applications that can be accessed on the cloud. The company will also be involved in the provision of post-analysis services such as error collections.
The value proposition informed the kind of service the business would provide. Big data is an in-demand service since it streamlines business operations and increases the accuracy of demand prediction analysis. Small and medium-sized enterprises collect different forms of transactional data. Besides, there is a lot of data available on the internet that can be used to make evidence-based business decisions. Big data adoption among small businesses in Australia is still low. The first value created will be encouraging SMEs to leverage big data analytics to increase internal efficiency and make better business predictions.
The high price of big data analytics services is one of the reasons behind the slow uptake of big data analytics among SMEs (Coleman et al., 2016). New World Data Solutions will aim to provide the services at a lower cost since its services will be specifically tailored for SMEs. Though price is the most significant determinant of the demand for goods and services, the quality of service provided often has a bigger influence on the demandfor software products (Bevan, 1999). Customers can opt to buy more expensive software services at a higher price, provided that the services are reliable. With said, providing quality services that meet the clients' business needs will be the company's operating principle.
Seeing as big data is a relatively new field, New World Data will not be a success without the input of critical partners in the technology industry. The partners will include providers of cloud storage services and data analysis learning institutions. Since mostSMEs' technology infrastructure cannot handle big data applications, the datasets and the data processing applications will be stored in the cloud. In the short term, New World Data will not acquire cloud storage infrastructure; it will strike key partnerships with established cloud services providers. Also, Australia is experiencing a shortage of data analysts: acquiring quality talent from the general labor market will be challenging. New World Data will partner with data scientist trainers to identify talent and equip employees with the necessary data analysis skills.
The primary resource in this business will be tech talent. In the short term, the company will need about 20 employees with data mining and analysis skills. These skillsinclude web scrapping, correlation analysis, and knowledge of data analysis programming platforms such as Python and R. New World Data will also need physical equipment such as computers and furniture.
New World Data may have excellent services, but the company will not gain any benefit if the services do not reach the consumers. The company plans to use various distribution models to take its products to SMEs that need them. New World Data will popularize its services through influencers and paid targeted advertisements. Regarding distribution, the company plans to leverage both an in-house approach and cloud distribution. On-premises distribution entails visiting the customer's premises to analyze their data and processes. In cloud distribution, thecompany will place data, the analysis softwares, and results in the cloud where the clients can access them.
Customer satisfaction boosts cash flow in a business and increases customer loyalty (Morgan &Rego, 2006). Customer satisfaction is also key to maintaining the trust of crucial partners (Morgan &Rego, 2006). To reap such benefits, New World Data aims to provide excellent customer services to its clients. This will be achieved through the provision of quality services, after-sale services, and round-the-clock availability on calls and live chat. The business's customer segments are SMEs looking for cheap and quality big data mining and analysis services.
The main costs of starting and running the business are related to the hiring and training of data analysts. Buying physical infrastructure will also constitute substantial costs. Secondary costs will arise from marketing to promote the company's services. Selling the company's data mining and analysis services will be the company's primary source of revenue. The revenue will help the business cover operational costs and turn in a profit.
New World Data services will be tailored to meet the needs of SMEs, which make up more than 90 percent of Australian businesses. This provides a potentially broad client base for its services. The business will also gain from the availability of data. Web users and businesses create many quintillions of data every day, so there is enough data available to support business decisions (Marr, 2018). Finally, though many businesses are aware that they need to adopt big data services to gain a competitive edge, the uptake of the services by SMEs in Australia is still low. That implies that New World Data services will be easy to sell.
The business assumes that SMEs in the target market will be interested in integrating big data in their processes once the adoption barriers are reduced; this exposes it to the risk of an unresponsive market. Finding talented data scientists and maintaining them will be a challenge for the business. Given the current shortage of data scientists, the company risks being outpriced by big firms in the acquisition and maintenance of top talent. Finally, SMEs may not be comfortable with third party data storage due to privacy risks (Kalan&Unalir, 2016)
Despite the risks and universal problems that face startups, the projected big data market growth, and the value that big data services create for the customers justifies the investment. The company will benefit from the rising big data awareness among SMEs. Though the market growth projection covers up to 2025, the immense benefits of big data imply that the service will be in demand in the long term. These factors make New World Data Solutions a feasible investment.
Bevan, N. (1999). Quality in use: Meeting user needs for quality. Journal of systems and software, 49(1), 89-96.
Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort‐Martorell, X., & Reis, M. S. (2016). How can SMEs benefit from big data? Challenges and a path forward. Quality and Reliability Engineering International, 32(6), 2151-2164.
Kalan, R. S., &Ünalir, M. O. (2016, October).Leveraging big data technology for small and medium-sized enterprises (SMEs).In 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 1-6).IEEE.
Marr, B. (2018). How much data do we create every day? The mind-blowing stats everyone should read. In Forbes.
Morgan, N. A., &Rego, L. L. (2006).The value of different customer satisfaction and loyalty metrics in predicting business performance.Marketing Science, 25(5), 426-439.
Urbinati, A., Chiaroni, D., Chiesa, V., &Frattini, F. (2019). The Role of Business Model Design in the Diffusion of Innovations: An Analysis of a Sample of Unicorn-Tech Companies. International Journal of Innovation and Technology Management, 16(01), 1950011.
Vassakis, K., Petrakis, E., &Kopanakis, I. (2018). Big data analytics: Applications, prospects, and challenges. In Mobile big data (pp. 3-20).Springer, Cham.
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