The Executive 's Guide For Effective Analytics

Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.

To help sifting through and combining the right data in the right ways, businesses must develop analytical capabilities that far surpass today’s standards. The sheer volume of data impressive, that same volume can lead to confusion, or plain bad direction for decision makers. A great rule to live by in analytics is that “numbers tell THE story, ANY story can be told by the numbers.” What this means is that managers must be certain that they are using the right data set, and designing their analysis to provide an output that solves the business problem they are trying to solve, rather than just providing the answer they are hoping to provide. This is done through the deployment of analysis tools, those that are becoming more and more prevalent, and easier to use, along with building or hiring the capability into the organization. The skills needed for this work include, but are not limited to, a deep understanding of the business, and ability to communicate complex concepts in a way that is easily understood, and a mastery of statistical and analytical methods. While the first two capabilities ensure that the right data is used, and it is analyzed to yield a sound outcome, the final capabilities is often the most important, and biggest stumbling block for organizations.

In the increasingly competitive global business environment, each organization needs to take advantage of every tool, opportunity, and advantage it can to achieve the best products and services, to gain and maintain market share, and keep stakeholders happy from investors and workers to supply chain and customers. The advance of data analysis has opened up new vistas to support decision making. Decision support systems (Sauter, 2010) have emerged that process various forms of data to build outcome models. These have been adopted in every segment of society, in the private and public sector, from political campaigns and the military to corporations and nonprofits alike. As a whole, the new set of tools involving the strategic use of data is called business intelligence. Within that general framework, the term analytics refers to the statistical, quantitative use of data to produce explanatory and predictive models for fact-based decision making. (Sauter, 2010).

All great organizations share one thing in common, the use of business intelligence. Business intelligence (BI) provides tools that revolutionize the way organizations manage business and decision-making. It allows them to transform mass amounts of raw data into reliable information necessary to make important business decisions. BI delivers relevant and reliable information to those who seek it with the goal of achieving better decisions faster. An employee is independently able to navigate through a company’s data and find what he or she needs without relying on others. This means an organization no longer needs to dig through compiled webs of linked spreadsheets, analyze the data manually and mash together reports. Instead, employees can use BI systems to request the specific information that is useful for them (Hitachi Solutions Canada, 2014). BI allows managers to reach the most accurate and contemporaneous information an organization’s database cannot retrieve. The software offers applications for both data analysis and presentation of results. Applications such as data mining and decision support systems allow one to contemplate how he or she wants to analyze the data. Data mining refers to the process of searching for valuable business information in a large database, data warehouse, or data mart. Decision support systems combine models and data in an attempt to analyze semi structured and some unstructured problems with

big data is a dynamic that seemed to appear from almost nowhere. But in reality, Big Data is not new – and it is moving into mainstream and getting a lot more attention. the growth of Big Data is being enabled by inexpensive storage, a proliferation of sensor and data capture technology, increasing connections to information via the cloud and virtualised storage infrastructure, as well as innovative software and analysis tools. It is no surprise then that business analytics as a technology area is rising on the radars of CiOs and line-of-business (lOB) executives. to validate this, as part of a recent survey of 5,722 end users in the uS market, business analytics ranked in the top five It initiatives of organisations. the key drivers for business analytics adoption remained conservative or defensive. the focus on cost control, customer retention and optimising operations is likely a reflection of the continued economic uncertainty. however,

The era that we live in, in the year 2015 is considered the “big data” era. Industries all over the world are analyzing data and determining marketing and business processes to attract consumers. Data analysis which started off on a much smaller scale today can be used in much broader aspects from coupons you receive in your email, to advertisements you see when you use applications on your smart phone data also can be used to determine the frequent of a customer to a particular store or website. These are both processes of data analytics being used and conveyed in a way to attract customers or satisfy consumer needs. Data analysis focuses on finding the specific data to answer your question, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating your results to gain optimal results. Data is not a new concept in any form, the technology of today’s world makes obtaining and analyzing data easier. The recent decades have seen a fundamental change in the model of data analysis. IMB Tech Trends Report (2011) identified business analytics as one of the four major technology trends in the 2010s. According to Chen (2012) Business intelligence began its practice in business and IT communities just a few decades ago in the 1990s. In the 2000s business analytics was introduced to represent the key analytical components in business intelligence. The business intelligence and data analytics previously adopted in an

analytics beyond commerce to healthcare strategies, hiring processes, and personal life (Bayan, 2015; Germann, Lilien, & Rangaswamy 2012; Tavana, Kennedy, & Joglekar 1996). Regardless of the industry, analytics have legitimacy absent context and provide insight to the working of a structure. Organizations could, and often dp argue, that awareness of the power of analytics is enough; however, for analytics to be of the greatest support, their merit and worth is not in the capture and analyzation of data but in the application of it within a specific context. Analytics provide insight that can be

To help an organization cut costs large amounts of data is very useful. The data can be structured or unstructured but identifying patterns in it can help advertising, recognizing new business opportunities, campaigning and increasing efficiency and reducing costs. This gives the company an added competitive advantage

In the era of 2007-10 where most of the economies of the world slowed down and companies restrained themselves from making new investments there were others that were gearing up to reverse this trend. Viewing this as an opportunity lot of new companies entered into the consulting and business advisory domains. This phenomenon lead to an inception of once such company named “Analytics Quotient”. Analytics is once such field, which is not only making a mark upon the business of the companies but also changing the business trends by focusing on data utilization across functions and optimizing the available information that the company has.

Data is a valuable corporate resource; it has real, measurable value. The purpose of data is to aid evidence-based decision-making. Accurate, timely data is critical to accurate, timely decisions. Most corporate assets are carefully managed and data is no exception. Data is the foundation of our decision-making, so we must also carefully manage data to ensure that we know where it is, can rely upon its accuracy, and can obtain it when and where we need it.

The basis behind any organization is to be successful by outpacing its rivals. The key to achievement in today’s world lies how well one applies analytics to the overall functioning of business and basic leadership. Analytics should be applied across multiple aspects of business only then a firm is said to be competing on analytics. Analytics has shown a significant impact on almost every kind of industry from professional sports to entertainment, financial management, customer service, sales and many more. Businesses today understand that only by analytically using the growing volume and variety of data and converting it into actionable insights that drive faster and better decision-making, resulting in useful outcomes, greater profits, superior flexibility, and ideal operational efficiency, a business can lead the race.

Data is important to any business, but it is of no value to the manager if all the work only involves collecting and reporting data without the ability to drive business decisions. Web tools like Yahoo web analytics and Google Analytics are good at answering the “what” questions, for example, “what happened on this page today?” or “What was the best marketing channel last year in terms of site revenue?” It cannot be denied that this information is valuable; however, successful business decisions cannot come from these analytics tools alone. They cannot offer the insights that an analytics expert can by digging through the maze of data to find meaningful information. In a word, the Web analytics tool is only useful when it is used by a certified analytics person. However, some companies fall into the trap of data. They are

Being successful with analytics is about having the right mindset, the right organizational model, and the right strategy.” (Gartner, 2014)

“Information is the oil of the 21st century, and analytics is the combustion engine.” was introduced by Peter Sondergaard during Gartner Symposium/ITxpo 2011. In fact, data is like oil! It has value, but it needs to be extracted and refined to get the true value from it.

There’s an overwhelming amount of data in this industry. Organizations suffer when poor data guides key decisions. This happens when executives manage too many sales metrics and lose focus on the most important ones.