Big Data is associated to an opportunity to change the design of business models and their decision-making through assessment of huge volume of data gathered from varied sources. Many researchers anticipate that BDA (big data analytics) can facilitate management in an effective way, thereby paving a path for smarter decisions and better decisions. Nevertheless, in relation to performance measurement, the same can be regarded as analysis and procurement of information about exact attainment of plans and objectives that can impact plan realization (Chugh & Gandhi, 2013). However, currently such performance measurement systems have been encountering issues owing to enhancement in performance complexities. Some state that such complications are because of increasing competitiveness whereas some regard it as sustainability and supply chain problems (Fanning & Grant, 2013). Despite the entire scenario, it can be said that big data is a potential weapon for the organization as it leads to ample opportunities. Such exaggeration of complications has resulted in increment of amount of data to be procured, processed, and evaluated to offer meaningful information to facilitate decision-making in organizations. Big data comprises of five V’s namely variety, veracity, value, volume, and velocity. These assist in enhancing the competitive position of organizations, thereby facilitating in management and measurement of performance. When it comes to performance, the organization needs to be alert because performance management can lead to immense benefits (Fanning & Grant, 2013). In simple words, big data has become a new revolution in the management of information that can affect the way an organization conducts its business.
Organizations has always intended to attain insights from information so that they can make smarter, better, factual-based, and real-time decisions. The primary reason why the demand and growth of big data platforms and tools has enhanced can be attributed to the fact that there is a massive demand for in-depth knowledge within the organizations (Ramlukan, 2015). Further, organizations that intend to lead such change have included big data from both outside and within their enterprises that includes both unstructured and structured data, mobile and online data, machine data, etc to supplement their data and thereafter, provide an effective basis for forward-looking and historical viewpoints.
Few scholars and practitioners have gone very far and considered big data analytics (BDA) as science’s fourth paradigm or a fresh paradigm of assets encompassed with knowledge. The reason behind these assertions can be attributed to the fact that big data can assist organizations in managing and measuring performance through BDA enabled tools, infrastructure, and technologies that includes mobile devices, social media, cloud-enabled media, etc that can facilitate in sustaining competitive advantage (Ramlukan, 2015). For instance, BDA can assist in improving data-driven innovative and decision-making ways to learn, innovate, and organize, thereby facilitating in reinforcement of managing customer relationship, maximizing the operational effectiveness of organizations, improving operations risk management, and performance of the organization on a whole. Big data can significantly alter the way organizations operate and compete, as companies that contribute in and efficiently attain value from their data can pursue a competitive advantage over others (Tucker, 2017). This results in a performance gap that continues to increase as more significant data is generated. Performance measurement plays a key role in setting targets and objectives to assist the process of decision-making and performing efficiently to sustain in a risky competitive scenario. Presently, performance measurement primarily focuses on a balanced set of both non-financial (flexibility, quality time, satisfaction of consumers, etc) and financial measures to allow continuous improvement. Further, in this era, the level of complications has also enhanced because of sustainability pressures (Barney & Ray, 2015). This is the reason why a multi-dimensional metrics ser must accommodate both social and environmental measures of performance.
Broadly, such big data has become extremely significant to management of performance in various ways. Firstly, it facilitates in gaining insights about qualitative attributes like preferences of customers that can be utilized to enhance sales and marketing, thereby assisting in maximizing shareholders’ wealth and profits on a whole. Secondly, it assists in better forecasting like forecasting the spending patterns of customers in the upcoming tenure, thereby assisting in implementation of more efficient decisions. Thirdly, it permits automating of enhanced level business procedures that can result in overall organizational effectiveness and lastly, it also offers more detailed and updated measurement of performance (Junk, 2015). Big data analytics can recognize innovative opportunities in significant processes, roles, and functions. It can establish a catalyst for change and innovation and by provocating the status quo, it can assist in the creation of fresh possibilities for organizations and their people. Further, sophisticated techniques can facilitate in permitting organizations to discover primary causes, evaluate microsegments of their respective markets, transform procedures, and make effective forecasts about the events related to future.
In relation to managing and measuring performance, it has become insufficient for organizations to simply understand present procedures or affairs with a perspective to improve what already persists, when there is presently the ability to question if a procedure is significant to the organization, or whether there is an innovative way of redressing a problem. In simple words, the primary innovation driver within organizations is to continuously challenge present practices instead of consistently accepting the same (Barney & Ray, 2015). For instance, in the case of Tesco, the company has operations in various countries throughout the globe. Moreover, in Ireland, it developed a unique way to evaluate the temperature of its refrigerators that were placed in their stores. The company placed sensors in the refrigerators that measured the temperature every 3 seconds and thereafter, sent the data over the internet to a specific warehouse of central data (Griffin & Wright, 2015). Nonetheless, evaluation of the same information permitted the company to recognize unites that were functioning at inappropriate temperatures. Further, it discovered that various refrigerators were functioning at temperatures below the recommended ones that was costing it immense amount of energy. Gathering and evaluation of such data allowed Tesco to rectify the temperatures of such fridges. Therefore, considered that the company was expending ten million pounds every year on cooling costs, an anticipated reduction of twenty percent in such costs can be a potential saving (Vasarhelyi et. al, 2015). Further, the system also permitted the engineers to supervise the performance of refrigerators remotely. Besides, when they recognized malfunctioning of a specific unit, they could easily evaluate the issue and allow corrective actions to come forward. In relation to previous tenures, the same refrigerators were only fixed when a particular issue had been discovered manually by the store manager that would generally be the scenario when the problem had become more serious.
This is the reason why big data analytics has now been regarded as the primary game changer in facilitation of improved business effectiveness owing to its strategic potential and high operational. Such BDA can also allow organizations to manage and analyse their strategies through a data lens. In fact, it is increasingly becoming a potential aspect of organizational decision-making process. Therefore, it is now regarded as a primary differentiator betwixt low and high-performing organizations. Overall, BDA is expected to pursue immense influences within a variety of organizations (Griffin & Wright, 2015). For instance, potential retailing organizations are currently leveraging abilities of big data to enhance their customer experiences, minimize fraud, and make corrective actions just in time. In relation to organizations associated to healthcare, such BDA is also expected to minimize the operational expenses and enhance the quality of life. In contrast to this, BDA is also regarded as an enabler of business and asset process monitoring, enhanced manufacturing, supply chain visibility, and industrial automation. Overall, big data can facilitate in management and measurement of performance on the part of organizations.
Measures of performance and performance management system (PMS) has been studied from varied viewpoints throughout the years. The natural domain of the same lies within management control research. Further, its contemporary form is also associated to a balanced scorecard approach and advantage from a proliferation of measures across several disciples. In general, PMS has been utilized to facilitate implementation of strategy and enhancing overall organizational performance, thereby resulting in facilitation of decision-making and accountability on a whole (Cokins, 2013).
Despite a recognized fragmentation of research, slow progress in this segment, and doubts regarding the utilization of performance measures and PMS, big data is claimed to pursue a significant part in the use or design of performance measurement systems. IT advancements can enhance the collection, analysis, measurement, and communication of information betwixt and within organizations (Cokins, 2013). Further, innovation in IT incorporation with control of management leverages the utilization of database of enterprise systems and thereafter, offer powerful analytic abilities together with enhanced assistance for decision-making, control, and planning on a whole. In order to determine whether big data can influence changes in performance management systems, the transformation of movie rental experience can be taken into consideration. In other words, before when movies were rented from various non-reliant neighbourhood stores, the agent (rental) would apply or assert their recommendations on which specific movies customers said that they liked and a huge amount of their own idea. In the present scenario, movie rental organizations and content delivery services can use a broad array of data points so that recommendations can be generated for better effectiveness. Hence, by evaluating what was seen, when, and on what specific device (even whether such content was rewound, paused, or fast forwarded), together with all the activities on the part of users like searches on internet, scrolling, and browsing within a specific page, effective recommendations can be attained for innumerable number of customers. This illustration clearly sheds light on the impact of big data on PMS in the current scenario.
The primary reason behind the use of performance measurement systems and big data analytics can be attributed to the fact that both are equivalent in nature in the sense that both support action-taking and decision-making on a whole. This is a major evidence of an interconnection betwixt both these aspects. On one hand, while the PMS supports decision-making to offer appropriate and meaningful data through a series of affairs like evaluation and interpretation of data from the past actions so that future performance can be influenced. On the other hand, the purposes and goals of big data analytics are also similar in nature (Wamba et. al, 2017). Further, the same is applicable to all the professionals who undertake the job of performing such evaluation. Nonetheless, analytics offering meaningful or significant information to the users for decision-making is a primary example or evidence of the fact that big data can influence PMS’s. Further, big data can also broaden the horizons of such systems of performance measurement, thereby empowering the analytics to process huge amounts of both unstructured and structured data.
There are evidences of a Brazilian multinational cosmetics company that highlight the use of big data in relation to performance measurement systems. It had been observed that such company also applied big data analytics (BDA) on its sales. Even though the utilization of BDA in measurement of performance was prohibited to sales department in such company, yet the evidences clearly prove the aforesaid. There were two different analytics used by the company namely predictive and prescriptive. The prescriptive evaluations enriched clustering assessments and drill down. Further, the users of performance measurement were also capable to examine more complicated hypothesis and enhance their discovery of knowledge through application of fresh prescriptive analytical methods. In addition, the utilization of predictive tools also allowed the establishment of sales strategies of products in the event of market niches, thereby exploring the capabilities of sales representatives (Krahel & Titeraa, 2015). They also utilized such models to improve and control their sales. The changes reflected in the PMS of such company necessitated few investments on data infrastructure and skills of employees. Moreover, the sales department of such company established a different area to tackle big data analytics and big data. Nevertheless, a group of skilled employees was being hired by the company to operate this are that corroborated with the introduction of a data scientist. Further, additional investments were also required in order to build data marts (Krahel & Titeraa, 2015). In relation to this, it must be noted that many organizations only possess transactional databases but as per the evidence of such study, it was reflected that change in perceptions towards performance was required to undertake new evaluations. The data scientists helped the related parties and assisted the decision maker by providing them relevant information. In this scenario, it is significant noting that such variation is vital to return on investment on hiring professionals and data infrastructure. This highlights the significance of financial performance measures to measure performance of sales. The study signified that return on investment on sales was the most vital performance measure while the causal interconnection of such ROI with non-financial measure was also clear in nature (Wamba et. al, 2017).
Overall, Big data can be considered as an alternative to the failures or disgraces of traditional performance measurement systems. This is because such systems do not operate effectively when it comes to accuracy and quality in addition delivery and cost. Furthermore, such big data plays a vital role in influencing PMS and performance measures like financial measures (ROI, etc) can be improved through the adoption of such tool.
Conclusion
The segment of big data analytics has been evolving rapidly and companies cannot ignore its benefits. However, they must also observe at the way they utilize data so that it can be ensured whether they are controlling the fresh opportunities in alignment with management of new risks. Since, organizational information is incomplete, historical, etc in nature, a predictive and statistical modelling is needed that can enrich these with external information. Big data is the primary player behind this requirement as traditional approaches and systems are inflexible and slow and often fails to handle such huge complications and volume of data. Overall, big data has immense potentiality as it can create value for various sectors through the process of decision-making. The process of decision-making helps in balancing the organization and leads to further opportunities. Furthermore, the same can be effectively used when it comes to management and measurement of performance. Moreover, the interconnection between performance measurement and big data deserves more attention because the same can play a key role in creating enhanced value for the organizations, and can lead to better sustenance in such competitive and complicated environment. Overall, it can be commented that big data is a boost to the overall organization as it leads to the enhancement of the work culture and strengthens the value
References
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