Summary of the Case Study
The case study gives detail of how the Cleveland Clinic of Cleveland has been using business intelligence to improve their company performance over the past sixteen years.Cleveland Clinic is a nonprofit academic medical centeroperating 11 hospitals, 5 ambulatory surgery centers and 16 family health centers in United Statesof America, Canada and Abu Dhabi.In particular, the company has been using business intelligence to track their KPIs on a daily basis in order to measure the progress towards achievingtheir strategic objectives and goals. Through this effort, the company has managed to significantly improve quality of care as well as reduce labor costs and other expenses. Through this process, Cleveland Clinic has managed to successfully structure dashboards, customize dashboards, link performance and annual reviews, as well as align performance with strategic initiatives.
Analysis of the Case Study
For a period of sixteen years, Cleveland Clinic has been using business intelligence to track KPIs on a daily basis. This approach has offered substantial proof that comparison of the KPIs is essential in achieving rapid organizational changes as well as improving performances. Through this initiative, Cleveland Clinic has learnt several lessons from four major activities namely (1) structuring of CEO’s dashboards, (2) customization of dashboards, (3) linking of performance to annual reviews, and (4) alignment of performance metrics.Glass and Callahan (2014) asserted that many healthcare companies possess myriadsof helpful untapped strategic information which could be summarized in meaningful and intuitive manner to provide real time view of their actual performance.
Analysis of the case study indicates that maintaining data warehousing is beneficial to the healthcare industry. Some of the benefits of data warehousing in the healthcare industry includes the ability to free up cash flow, provide affordable powerful solutions, free up capacity of the in-house system, providequality equipment and software, provide faster connections, as well as provide solutions that enhances growth. Besides this, data warehousing is important in healthcare because it requires minimal investment in the infrastructure. In essence, this business intelligence initiative when implemented helps reduce cost while at the same time enhances quality of care. For instance, after implementation of the business intelligence in the nursing agency, Cleveland Clinic, in 2006, significantly reduced its expenses by more than $5 million. In addition, by using business intelligence system in the careful analysis of blood products, Cleveland Clinic managed to reduce the associated expenses by more $400,000 every year.
Business intelligence software comes with the advantage of improved business performance. This is depicted by the improved ability of the Cleveland Clinic to effectively recruit clinical professionals after implementing the business intelligence software for their everyday analysis and comparison of the actual performance against their initial strategic objectives. Big Data analysis actually prepares the clinical professionals for anticipated seasonal shifts in employment. In addition to effective recruitment of clinical professionals, the application of business intelligence metrics enhances performance by allowing the company to properly understand the relationship between finance and operation such as experienced by the Cleveland Clinic. This capability actually enables the company to effectively align its operational tasks with strategy goals, which is a key factor in driving performance (Mohanty, Jagadeesh & Srivatsa, 2013).
Deeper analysis of the case study indicates that the use of business intelligence metrics gave Cleveland Clinic a number of enhanced opportunities and abilities to improve their company performance. First by tracking and analyzing the performance of appointment phone line, Cleveland Clinic developed and established new goals that required all patient calls to be answered with thirty seconds, which was achieved within few months thus resulting in significant improvement of response time and overall company performance in the long run. The first step in the improvement of the response time was to identify issues of phone line appointment through dedicated monitoring and tracking.
Analysis of the case study illustrates the significance of understanding the capability of data or information visualization and dashboards. Cleveland Clinic, for instance, used the business intelligence metrics to structure dashboards for their CEOs as well as customize the dashboard view for each specified user. The modern business intelligence platforms provide performance dashboards which can be customized for specific user. The importance of dashboards depends on their ability to provide visual displays of useful information relating to performance; the required information can be arranged and consolidated in a single screen to allow easy digesting and understanding. In essence, the use of business intelligence has provided vital for improving performance of all companies.
Why Companies should Maintain Big Data
Since the inception of Big Data in 2010, companies of all sizes have started reaping benefits of maintaining its technology. Many observers and authors strongly believe that Big Data is the new technology that every company should maintain in order to leapfrog into the top class competitive advantage in the business field. With the digital age, data are now woven into every sector and function of the economy globally and much of the modern economics could not take place without its use. The use of Big Data provides a company with a later pool of information which can be brought together and analyzed accordingly for the purpose of effective business decision making. According to Van (2014), the sheer volume of data mined, generated, and stored are strongly becoming economically significantly to government, business, as well as customers.
The use of Big Data is essential to every business because it has become a basis of competition and growth in every industry by reducing waste, creating value, enhancing productivity as well as increasing the overall quality of products and services offered. As explained by Davenport and Harris (2007), firms need to maintain Big Data technology because it offers them a new competitive advantage in the way of doing business. It helps leading companies to outperform their peers in the industry in the digital age. This is because the use of Big Data helps companies leverage data-driven strategies for innovation, competition and capturing of value. For instance, in healthcare, companies such as Cleveland Clinic could effectively analyze healthoutcomes of pharmaceuticals when prescribed as well as discover the risks and benefitsthat were not evident during clinical trials.
Every company shouldmaintainBig Data technology because it is essential in creating new growth opportunities. Forward thinking leaders in various businesses should aggressively start building their company’s Big Data capabilities because it sits on the middle of large information flows whereby data about buyers, suppliers, products, services, intent, as well as consumer preferences could be captured and analyzed. In addition, with the sheer scale and high frequency nature of data, the advent ofBig Data has given companies the ability to estimate metrics immediately such as consumer confidence, something which previously could only be done retrospectively thus considerably improving power ofprediction. Due to the high frequency of data, companies who maintain Big Dataare able to test theories in real time – this was previously not possible (Morabito, 2015).
Big Data enhances company’s ability to manage their data better and efficiently. The Big Data platform is design to allow data scientists to analyze, collect and sift through various forms of the available data in order to enhance how they are managed while taking some technical knowhow in defining, collecting and storing the data. In essence, Big Data is an essential business intelligence tool that let users to work on the collected data without necessarily passing through various complicated technical steps. This efficiency and ability of the Big Data has enabled companies to use data on a wide variety of formats for specific purposes. This capability of the Big Data is highly utilized in medical transcription whereby healthcare organizations are increasingly using Electronic Health Record (EHR) to transcribe, extract as well as process data within the required clinical context.
Companies that maintain Big Data benefit from improved speed, capacity as well as scalability of their cloud storage. This is essential for companies that want to use substantially large volumes of data because Big Data is able to provide storage as well as the required computational power necessary for processing data. This presents two important advantages to companies maintaining Big Data. First, it allows companies to analyze massive data sets with highlyreduced capital investment on internal data hosting and hardware purchase. Secondly, it significantly abstracts complexity of the required new skills and training thus enabling more deployment of data technology. This, too, is advantageous because it allows IT developers to build preconfigured sandbox environment which the company can use without setting configurations from the scratch.
Another reason why companies should maintain Big Data is the opportunity to effectively visualize end-user data. In essence, Big Data software requires next-level tools for data visualization which come with the advantage of presenting business intelligence data in easy to read graphs, charts, and slideshows. In addition, theseapplications are beneficial due to the vast quantities of data that are being processed and examined. Big Data offers the advantage of powerful processing engines which are designed to allow end users to quickly query as well as manipulate the provided information. Usability is another important consideration that has been enhanced by visualization of the end-user data. Through visualization, Big Data initiatives have leveraged charts, dashboards, as well as infographics thus greatly enhancing usability of the data. As explained by Raj et al (2015), companies need to maintainBig Data because, currently, leading business intelligence vendors are rapidly shifting to self-service analytic model instead of the pervious IT-driven platforms.
According to Wang, Li and Perrizo (2015), the use of Big Data allows data analysis methods and capabilities to evolve from time to time, which is essential for contemporary businesses. In the modern business environment, data is no longer taken simply as numbers in the database. Instead, Big Data uses all ranges of data formats such as texts, videos as well as audios to provide valuable insights of the business’ current situation and potential future. In essence, it provides the right tools for data analysis which are able to recognize specific patterns which are based on the predefined criteria. Big Data goes beyond the simple data analysis using natural language into providing sentimental analysis, text mining, name entry recognition as well as clinical language efforts among other capabilities.
Challenges of Maintain Big Data
Despite its numerous encouraging benefits and opportunities, companies are experiencing some challenges in maintaining Big Data technology. Despite thesechallenges, the Big Data technology is steadily maturing and it has reached a point where more organizations are prepared adopt it as a core component in information management. As companies and enterprises try to maintainBig Data, they face a number of challenges that requires serious interventions and measures.
The first challenge of Big Data relates to how companies manage data. According to Davenport (2014), most companies that are maintainingBig Data are struggling with their ability to capture, store and run analyses due to thehuge amount of data that are produced by their businesses. This is a major challenge experienced even with data received from external sources. In essence, the amount of data that is produced by the business and received from external sources actually outstrips their internal infrastructural storage. Another problem associated with data management is that in practical sense it is impossible to collect some of the most valuable data such as positive and negative historical responses.This is further complicated because majority of data in the modern warehouses are associated with significant quality issues.
Data interpretability and complexity is another challenge which is holding back companies that want to maintainBig Data. If data warehouses attempt to account for thousands of different ways of consumer behaviors, then they will yield enormous amount of syntax and codes which might be very difficult to manage. In addition, if this data is captured in in legacy system then their true interpretation can get lost if the company poorly captured the required information. Data management is associated with the challenge over uncertainty of data landscape.One problem associated with this is the use of varied innovative frameworks for data management – this is the disruptive facet of Big Data. The disruptive facet ofBig Data is that the designs of frameworks for data management are intended to support both analytical and operational processing.
According to Acharjya et al. (2015), another challenge that most companies are experiencing is the existence of talent gap in the Big Data Analytics. With the existence talent gap, it is apparently difficult to pursue high-tech media and analystbecause the Big Data is bombarded with content which eventually tout the value of Big Data Analytics. This happens because of the correspondence reliance on myriads of different techniques which often get disruptive. These technologies provide new tools with alternative data layouts which eventually decrease the in-memory analytics, storage footprint, broad Hadoop ecosystem, as well as NoSQL data management frameworks. In addition, a growing number of application developersare compromising the Hadoop ecosystem. Even though Big Data technology is being promoted, in reality, the wealth of skills in the market is not sufficient to maintain it effectively.
Another great challenge of Big Data is the synchronization across the data sources.In essence, copies of data which are being migrated from different schedules, sources, and rates often get out of synchronization when being configured into the Big Data platform. In addition, the sequences of data transformations, extractions, and migrations in the conventional data warehouses provide situations which increase the risks of data becoming unsynchronized.Another issue with synchronizationis that with the Big Data, data management becomes very difficult due to the level of governance and speed at which updates are expected to be made.
Acharjya, D. P., Dehuri, S., & Sanyal, S. (2015). Computational intelligence for big data analysis: Frontier advances and applications.London: Springer.
Davenport, T. H. (2014). Big data @ work: Dispelling the myths, uncovering the opportunities.Boston Massachusetts: Harvard Business Review Press.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning.Boston, Mass: Harvard Business School Press.
Glass, R., & Callahan, S. (2014). The big data-driven business: How to use big data to win customers, beat competitors, and boost profits.Hoboken: Wiley.
Mohanty, S., Jagadeesh, M., & Srivatsa, H. (2013). Big data imperatives: Enterprise big data warehouse, BI implementations and analytics. New York: Apress.
Morabito, V. (2015). Big data and analytics: Strategic and organizational impacts.Cham: Springer.
Raj, P., Raman, A. C., Nagaraj, D., & Duggirala, S. (2015). High-performance big-data analytics: Computing systems and approaches.New York: Springer International Publishing.
Van, R. M. (2014). Think bigger: Developing a successful big data strategy for your business.New York: American Management Association.