BUSINESS ANALYTICS LESSON 1 Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information. According to a study by MicroStrategy, companies worldwide are using data to: •Improve efficiency and productivity (64 percent) •Achieve more effective decision-making (56 percent) •Drive better financial performance (51 percent) The research also shows that 65 percent of global enterprises plan to increase analytics spending. In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace. “Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics, in a previous interview. “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.” Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means. Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions. Business Analytics may be defined as refining past or present business data using modern technologies. They are used to build sophisticated models for driving future growth. A general Business Analytics process may include Data Collection, Data Mining, Sequence Identification, Text Mining, Forecasting, Predictive Analytics, Optimization, and Data Visualization. Every business today produces a considerable amount of data in a specific way. Business Analytics now are leveraging the benefits of statistical methods and technologies to analyze their past data. This is used to uncover new insights to help them make a strategic decision for the future. Business Intelligence, a subset of the Business Analytics field, plays an essential role in utilizing various tools and techniques such as machine learning and artificial intelligence technologies to predict and implement insights into daily operations. Thus, Business Analytics brings together fields of business management, and computing to get actionable insights. These values and inputs are then used to remodel business procedures to generate more efficiency and build a productive system. After going through What is Business Analytics, let us understand more about its evolution. Evolution of Business Analytics Technologies have been used as a measure to improve business efficiency since the beginning. Automation has played a considerable role in managing and performing multiple tasks for large organizations. The unprecedented rise of the internet and information technology has further boosted the performance of businesses. With advancement today, we have Business Analytics tools that utilize past and present data to give businesses the right direction for their future. As we now have a stronghold on What is Business Analytics, let us next look into the types of business analytics techniques. Types of Business Analytics Techniques Business analytics techniques can be segmented in the following four ways: 1.Descriptive Analytics: This technique describes the past or present situation of the organization's activities. 2.Diagnostic Analytics: This technique discovers factors or reasons for past or current performance. 3.Predictive Analytics: This technique predicts figures and results using a combination of business analytics tools. 4.Prescriptive Analytics: This technique recommends specific solutions for businesses to drive their growth forward. A complete business analytics life cycle starts from raw data received from the devices or services, then collecting data in an unstructured type, then processing and analyzing data to draw actionable insights. These are then integrated into business procedures to deliver better outcomes for the future. Business Analytics Applications Business Analytics is now systematically integrated across several applications in the field of supply chain management, customer relationship management, financial management, human resources, manufacturing, and even build smart strategies for sports too. Importance of Business Analytics •Business analytics can transform raw data into more valuable inputs to leverage this information in decision making. •With Business Analytics tools, we can have a more profound understanding of primary and secondary data emerging from their activities. This helps businesses refine their procedures further and be more productive. •To stay competitive, companies need to be ahead of their peers and have all the latest toolsets to assist their decision making in improving efficiency as well as generating more profits. The Scope of Business Analytics Business Analytics has been applied to a wide variety of applications. Descriptive analytics is thoroughly used by businesses to understand the market position in the current scenarios. Meanwhile, predictive and prescriptive analytics are used to find more reliable measures for businesses to propel their growth in a competitive environment. In the last decade, business analytics is among the leading career choices for professionals with high earning potential and assisting businesses to drive growth with actionable inputs. We have understood well about what Business Analytics is, let us next understand its benefits. The Benefits of Business Analytics To club in one phrase: Business Analytics brings actionable insights for businesses. However, here are the main benefits of Business Analytics: 1.Improve operational efficiency through their daily activities. 2.Assist businesses to understand their customers more precisely. 3.Business uses data visualization to offer projections for future outcomes. 4.These insights help in decision making and planning for the future. 5.Business analytics measures performance and drives growth. 6.Discover hidden trends, generate leads, and scale business in the right direction. Difference Between Business Intelligence and Business Analytics Business Intelligence(BI) uses the past and present to identify trends and patterns in the organizational procedures, while Business Analytics determines the reasons and factors that led to present situations. Business Intelligence focuses mainly on descriptive analysis, while Business Analytics deals with predictive analysis. BI tools are part of Business Analytics that helps understand the Business Analytics process better. A Career in Business Analytics The role of Business Analytics professionals may change accordingly to meet organizational goals and objectives. Several individual profiles are closely associated with business analytics when dealing with data. In this competitive age, business analytics has revolutionized the procedures to discover intelligent insights and gain more profits using their existing methods only. Business Analytics Techniques also help organizations personalize customers with more optimized services and even include their feedback to create more profitable products. Large organizations today are now competing to stay top in the markets by utilizing practical business analytics tools. Several business analytics tools are available in the market that offers specific solutions to match requirements. Professionals might need business analytics skills, like understanding and expertise of statistics or SQL to manage them. The age of analytics: Competing in a data-driven world Big data’s potential just keeps growing. Taking full advantage means companies must incorporate analytics into their strategic vision and use it to make better, faster decisions. Is big data all hype? To the contrary: earlier research may have given only a partial view of the ultimate impact. A new report from the McKinsey Global Institute (MGI), The age of analytics: Competing in a data-driven world, suggests that the range of applications and opportunities has grown and will continue to expand. Given rapid technological advances, the question for companies now is how to integrate new capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries. A 2011 MGI report highlighted the transformational potential of big data. Five years later, we remain convinced that this potential has not been oversold. In fact, the convergence of several technology trends is accelerating progress. The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, virtual-reality applications, and billions of mobile phones. Data-storage capacity has increased, while its cost has plummeted. Data scientists now have unprecedented computing power at their disposal, and they are devising algorithms that are ever more sophisticated. Earlier, we estimated the potential for big data and analytics to create value in five specific domains. Revisiting them today shows uneven progress and a great deal of that value still on the table (exhibit). The greatest advances have occurred in location-based services and in US retail, both areas with competitors that are digital natives. In contrast, manufacturing, the EU public sector, and healthcare have captured less than 30 percent of the potential value we highlighted five years ago. And new opportunities have arisen since 2011, further widening the gap between the leaders and laggards. Leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most situation in some markets. The leading firms have remarkably deep analytical talent taking on various problems—and they are actively looking for ways to enter other industries. These companies can take advantage of their scale and data insights to add new business lines, and those expansions are increasingly blurring traditional sector boundaries. Where digital natives were built for analytics, legacy companies have to do the hard work of overhauling or changing existing systems. Adapting to an era of data-driven decision making is not always a simple proposition. Some companies have invested heavily in technology but have not yet changed their organizations so they can make the most of these investments. Many are struggling to develop the talent, business processes, and organizational muscle to capture real value from analytics. The first challenge is incorporating data and analytics into a core strategic vision. The next step is developing the right business processes and building capabilities, including both data infrastructure and talent. It is not enough simply to layer powerful technology systems on top of existing business operations. All these aspects of transformation need to come together to realize the full potential of data and analytics. The challenges incumbents face in pulling this off are precisely why much of the value we highlighted in 2011 is still unclaimed. The urgency for incumbents is growing, since leaders are staking out large advantages, and hesitating increases the risk of being disrupted. Disruption is already happening, and it takes multiple forms. Introducing new types of data sets (“orthogonal data”) can confer a competitive advantage, for instance, while massive integration capabilities can break through organizational silos, enabling new insights and models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets. Granular data can be used to personalize products and services—including, most intriguingly, healthcare. New analytical techniques can fuel discovery and innovation. Above all, businesses no longer have to go on gut instinct; they can use data and analytics to make faster decisions and more accurate forecasts supported by a mountain of evidence. The next generation of tools could unleash even bigger changes. New machinelearning and deep-learning capabilities have an enormous variety of applications that stretch into many sectors of the economy. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories. These technologies could generate productivity gains and an improved quality of life, but they carry the risk of causing job losses and dislocations. Previous MGI research found that 45 percent of work activities could be automated using current technologies; some 80 percent of that is attributable to existing machine-learning capabilities. Breakthroughs in natural-language processing could expand that impact. Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass—and as machines gain unprecedented capabilities to solve problems and understand language. Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.
