We want to search further afield for projects with high roi, return on investment, or killer applications.

by | Sep 23, 2022 | Leadership

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After completing the required reading, provide a critical analysis discussion using your own professional work experience and learning from the reading. At the post graduate level you are not to provide a summary but rather provide a critical thinking assessment of the topic.
Text: Ahmed, M., Pathan, M.K. (2019) Title: Data Analytics: Concepts, techniques, and applications (1st ed). ISBN# 9781138500815 (Print),(E-Book): 9780429820908
Bartlett, R. (2013). A Practitioners Guide to Business Analytics: Using data Analysis tools to improve your organization’s decision making and strategy (1st ed). New York, McGraw-Hill. ISBN: 9780071807593.
We have arrived at week 5 of Strategic Data Analysis and Decision Making. This week we will concentrate on developing a competitive advantage using data analytics. All discussing covered in this session are excerpts from the Bartlett textbook. Let’s begin our discussion. Developing competitive advantage approaches for identifying gaps and analytics. Developing competitive advantage approaches for identifying gaps and analytics. The basic approach is to derive current needs, evaluate current capabilities, and just oppose the two. Deriving current needs is easier to envision once we have seen it done. Our approach is to follow the business flow. We can follow money, customers, products, or services. Once we inventory our needs, we can evaluate capabilities by counting the business analytics and the business quants, assessing their leadership and resources, and evaluating the approach of the decision-makers strategy. The overarching objective is to integrate business analytics into the business strategy. Larson’s and thorough and explain this thusly. If a company does not use information as a strategic asset, it will not in the strategic implementation plans have descriiptions of how the competitive advantages should be gained via the use of information. If a company does use information as a strategic asset, then next to the objectives of this strategy, it will also provide directions of how the objective should be reached via the information use. Our strategy incorporates the use of analytics to improve our performance in the marketplace. Protecting analytical property. Keeping proprietary information secret is part of our competitive advantage. We want to keep our business analytics, plan, strategy, tactics, and technology to ourselves. Back in the nineties, Fair Isaac demonstrated it’s modeling techniques worked for predicting loan risk, does help to increase sales in the short run and the long run. They were rewarded by clients who then built their own in-house analytics group and lessen their dependency on Fair Isaac. The technology was not that advanced yet. Some of the banks needed this nudge to take the next steps. Section 6.1, triage, assessing business needs. We want to assess the information needs of the corporation. One approach. Is for our business analytics leader, working in concert with senior leadership, analytic space decision-makers, expert leaders and other analytics professionals to inventory the data analysis needed to support business decisions. Many corporations already have accompany scorecard and a set of critical tracking reports. These are a good place to start because they’re close to the corporate strategy. We can usually find ways to improve these tools and thereby develop trust. We want to search further afield for projects with high ROI, return on investment, or killer applications. These applications represent the most economically impactful decisions that need to support the data analysis. Process mapping of analytics needs. One route to mapping analytics opportunities is to chart the customer flow. Figure 6.1 outlines the basic customer flow with which most industries can relate. We have embedded a number of analytics opportunities which can directly support decisions and provide business insights. The key to mapping these opportunities is to align these with corporate strategy, goal, objectives, and time phase milestones. Please stop the presentation and take a close look at the mapping process. Innovation, identifying the new killer apps. We want to be vigilant and finding and developing new killer applications that will build competitive advantage. This is the entrepreneurial aspect of the business analytics leader’s role. We apply the concept of real when worth. Is it real? Can we win? And is it worth doing? Introducing the new, the faster, the stronger is probably the most difficult undertaking for a business analytics team. Well, it is the most impactful and important aspect of innovation is that some breakthroughs do not happen suddenly. We need to, to make incremental technical advances, each of which can be a temporary economic failure until we reach the coveted final one that generates wealth. The information age should further motivate the need for innovative solutions to our business problems. In our all out effort to apply whatever means are available, we want to combine the strengths. First, knowledge of the potential data. It is important to possess a mastery of the current data, to expertise and data collection techniques. And often missed, missing component in the corporation is its expertise and how to properly collect data. We will discuss the basics about what should be provided. Some insight into the nature and value of statistic data collection. Three, business savvy and understanding of our business. We need to leverage our understanding of how the business functions and fully comprehend the context of the business problem. For competitive intelligence, part of talent planning involves competitive intelligence regarding rival analytics capabilities. This incidentally is notoriously prone to bias and embellishment. For a lack of understanding. Five, cross industry experience. Cross industry experience is particularly invaluable for hunting killer apps. Develop killer apps. This facilitates considering applications which are more common in completely different industries. The problem in getting these applications accepted still remains because industries focus on how they are different from each other. Similarly, knowledge of each other’s industry supports a fresh new view of our business and our phenomenon that encourages re-evaluation of long-term, long-held assumptions. A broad repertoire of statistical techniques, such as a range of techniques, is invaluable, though it is often lacking. Most corporations tend to overuse their most popular data analysis techniques, as mentioned in section 4.1. The two trick pony. Part of embracing this serpent DPT of statistics is to experiment with all of the statistical tools at hand. Thus making get important that we have the most of them at hand. We should consider the types of statistical problems natural to certain types of decisions. And finally, number 7, advanced training in statistics. Advanced statistical training is critical for developing and for recognizing business problems. They can be solved using statistics. Section 6 to evaluating analytics prowess. The white glove treatment, leading and organizing. The most important ingredient for implementing change is a strong, confident advocating leadership. The typical concerns around having enough analytics professionals and efficient structure and adequate leadership. And inefficient location. Most likely with some centralization. Large corporations, we likely want to incorporate enterprise-wide advocate, a business analytics later, a handful of expert leaders and an enterprise-wide analytics group and a plethora of analytic space decision-makers. It is important to have expert leaders oversee and provide visible leadership. Statistical qualification, statistical diagnosis and Statistical Review, and the three building blocks of data collection, data software and data management are all important for business leaders that run a data-driven organization. Progress in articulating analytics, we want to evaluate the degree in which our corporations make solid analytic space decisions, the effectiveness of our decision-making, and the extent of our analytics driven culture. We can identify where mistakes are occurring and how well we are functioning within each of our four acts described in the analytic space. Decision-making process, we need to think through how we make decisions and how we incorporate data analysis and decision-making. These are the hard questions. As leaders, it is important to evaluate the acceptance of analytics in the corporation. This includes measuring how well the corporation converts analytics into industry knowledge. We may want to train the staff in a manner similar to Six Sigma training. Evaluating decision-making capabilities. Business leaders need to identify the key decision-makers in each area. Then consider their analytics sophistication and training needs. We want to understand their analytics usage, their comfort level, and their humility and accepting new contradictory information and their analytics knowledge. We want to help decision-makers understand statistical diagnosis and better wheeled analytics. We can identify the types of analysts and analysis that correspond to poor decision-making. We might expect our corporations best decision-making performance to occur for business problems. Evaluating technical coverage. As corporations expand their team of analytics professionals, they grow their capabilities. There’s a great deal of synergism that is difficult to capture. We need to think about how to keep the practitioners at optimal productivity. We want to ensure that we are applying the full depth and breadth of our statistical capabilities. Executing best statistical practice. We want to evaluate our ability to execute best statistical practice. When involving business analytics problems. Evaluation is important to confirm that business analytics leaders are an expert leader is overseeing the needed statistical qualifications, diagnose, gnosis, and review so that our capabilities will meet the needs of our current and future business. Problem inventories. Constructing effective building blocks. We want to evaluate three central building blocks that support analytics. Data collection, data software, and data management. Are all important for business analytics leaders or an expert leader to evaluate the extent to which three building blocks are going to meet current and future statistical needs. Leaders want to know how much value is lost in one going without data that we can collect to compensating for insufficient software. And 3, struggling with data or computer support. That is not customer centric. It is not unusual for inefficiencies to consume 20 to 60 percent of productivity. Section 6.3, innovation and change from a producer on the edge. Innovation tends to come in two flavors. One, sudden and unexpected and to plan yet doggedly obtained. Innovation is built new corporations and revive old ones. It enables and establish cooperation to shed its old scan and adapt. We want to employ analytics to promote innovation, and we want to innovate and analytics as well. Analytics thrives in a change base environment. Such an environment requires humility, the courage to embrace the new and the wisdom to foster freedom, to create. Emphasis on speed. Speed is the thing. We prefer a team that has a relative speed advantage over other virtues. Importantly, speed creates time that can in turn be spent developing bigger advantages, such as greater speed. Many decisions must be made quickly. And so reaction time can be critical. We want a team, they can react quickly and can anticipate the need. Facts prior for decision-making. This does not mean that we need to sprint back and forth from our desk all day. Simply put, we need to think about how we make available visible analytics based decisions faster. The primary solution to obtain greater speed is a improved infrastructure. We want a trained organize team with the software and hardware infrastructure to mass-produce, react quickly and anticipate as much as possible. Continual improvement. Integral to our strategy is to create competitive advantage by continually improving our ability to make analytics based decisions. Building this competitive advantage involves long-term investment and capabilities for making decisions faster, better, and less expensive. We want to integrate continually into every project. We have already discuss a few of the important topics for support of continual improvement. That is leadership specialization delegation and incentives analytic. delegation and incentives, analytic based decision-making, and a corporate culture and organization and leadership. As mentioned in Chapter 3, there can be a fifth act in the analytic space decision-making process, which is to prepare for future business problems by learning from the present one at hand. The fifth act is covered by statistical review and there are no new causes of death. The focus should be on the quality of the solution for the business problem and the analytics based. Decision. Leaders need to review the entire contexts including the broader business needs. Timeliness, client expectation, accuracy, reliability, and cost. Best statistical practice. Leaders must review how well we solve this problem within the constraints provided and as feedback to provide the infrastructure. Another point to make here is that some problems require incremental gains before business leaders can build an economically powerful supermodel or business strategy. They may need to go through several generations of mediocre ones. Worst case, we might need to fail in a very public manner. The fall forward references our discussion this week contains excerpts from the Bartlett’s course textbook. I encourage you to read each assigned weekly chapter to fully benefit and completely gain knowledge of our topics discussed this week. As always, we are here at Liberty University to train champions for Christ. I pray you are blessed by our brief discussion today.

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