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Big data is an essential topic of analysis as it shapes the future of most businesses, the method of business situation, and how companies market products to their customers. For instance, the complex algorithm of the Amazon business improves to show potential customers other products related to the one bought or the one they are willing to accept (Atal & Mike, 2018). It is the choice of the customers to give a look at the product or to ignore it. Looking at the product improves the knowledge of the companies on what they should advertise to the potential customers regardless of whether they look at it but never purchased it.
Big data have advantages of volume, velocity, and the variety of products. Regardless of big data having these advantages, human vision, insight, and interpretation are vital for big data utilization to full potential. If the department of research in the company interpreted this information as false, it could result in failure. However, based on clear evidence, data-driven decisions are the best decisions since it requires the data scientists to interpret vast amounts of data to use their knowledge in the best possible way. Big data is giving hope to the future of many businesses, especially in the management of businesses. The article “big data” that help create a revolution, and of which we are currently in the middle of the process.
The most critical issues for this reading.
The evolution process. This article discusses how significant data evolution has changed the operation’s routine in many companies. With the rapid increase in the power of computation, advancements to the use of smartphones, and reduced storage cost, organizations have therefore gained access to affordable and easy varied sources of significant amounts of data (“Big data: The management revolution – Harvard business review – Analytics Week,” 2014). Businesses that can effectively put the data into practice gain significant benefits over the others. The authors compare the big data approach to the analytical methods that have previously been used, and they concluded that big data is compelling and can handle large data volumes at almost real-time speeds. Furthermore, big data has also enabled the users to process different data types, including the location information and images. The effective use of big data is a better quicker and easier method to help in decision-making.
The growth of big data is termed as the management evolution. When data was scarce, then most of the organizations were made by the highest-paid person’s opinion. However, most of these personal decisions were subjective since they were based on personal intuition and predictions. The current transformation has helped the decision-makers to make well-informed and better decisions. With the rise of prominent data experts in this domain, they have become the most crucial individuals in the decision-making process. The correlation between the data-driven approaches and the improved performance is direct and evident as the data-driven companies have shown to enjoy higher profits and productivity than the competitors.
The article also looks at the advantages and disadvantages of transforming the analytics approach to the data-driven approach. With the decrease in computation elements’ cost, there is always an increase in the amount of data that can be accessed within a given time. Among the many challenges that a company should overcome when making the transformational shift and after the transformation is a wrong interpretation of data and the failure to address privacy issues. Nevertheless, with the rapid technological changes, change is the only constant, and the companies which fail to shift into data-driven companies while the overtaken by the ones which shift.
Though the data-driven approach is bound to be successful, a company must overcome some hurdles whenever it needs to transition to a data-driven company. First, the companies must set clear goals, and they must also be capable of questioning right. Secondly, the company needs to have experts in handling unstructured data since the organization should know how to translate the data to possible challenges or objects that the company could take.
The final section of this article looks at the challenges of making the best use out of big data. The first challenge is a leadership (Andrew McAfee & Erik Brynjolfsson, 2012). Big data need to be combined with the human vision of the knowledge about the market and the ability to walk the other through the journey of big data. The second challenge is talent management, where a company must have a data scientist who is a rare type of person who can make sense out of the big data. The person must possess the soft and hard skills to manipulate data while still making sense of businesses and management terms.
Technology is the third challenge, which is a requirement in handling big data. With the new changes in technology IT, professionals are a requirement in mastering the data skills. The fourth challenge is decision making as the perspective generated from the data need to be in the same place as the decision-makers and must be understood by them. This requires that the organization should be practical and flexible across all the boundaries of work. The last challenge is company culture. Moving to a data-driven organization needs moving the intuitions to be more data-driven and adopting that as organizational culture. This shows the willingness to change and an awake of new perceptions to capitalize on them.
Lessons learned for this reading.
Information security is likely to be revolutionized by big data. With the use of big data, Executives can measure and as well manage data with increased accuracy than has happened ever before. They can make better predictions with big data ideas and come up with smarter decisions. Managers can target the use of interventions that are more effective in areas that are previously intuition and gut dominated instead of the domination of rigor and data. There is a big difference between analytic and big data, which is brought about by volume, variety, and velocity.
The data accessible today by using the internet is by far much more than what was stored twenty years ago. Nearly it is information that is time that makes a company more agile than the competitors. Such knowledge comes from webs, social networks, sensors, and other sources that are unstructured. However, difficulties in management are real, as senior managers require to learn how to question right and adopt the approach of making decisions based on the evidence.
The second lesson is why encouraging data security professionals to pursue data visualization and statistics training may be critical. Additionally, organizations should have hired scientists capable of finding patterns from big data sets and translating them into business information (Andrew McAfee & Erik Brynjolfsson, 2012). The Department of Information and Technology has to integrate all sources of data both internal and external.
As significant data philosophies and tools continue to spread worldwide, there will be changes in ideas about experience, management, and expertise in the business. Smart people need to see what this is as it is an evolution of power that comes with the challenge of interpreting the big data; thus, this revolution may be an enormous hands-on to leadership while others may be hands-off leadership.
The third lesson learned from this is the difference between best practices and performance, Familiarity with the story of Amazon masks the company’s power. It is expected that companies that are digitally born to attain significant accomplishments that the other executives could dream of in generations.
Besides that fact, big data can transform traditional businesses into big famous companies. As online business is known to competing on how well the data analyst interprets and understands the data (Andrew McAfee & Erik Brynjolfsson, 2012). The revolution of big data is much powerful than traditional analytics; thus, we can easily manage and precisely measure for more wise decisions and better predictions. Companies can target can also use it as a tool to target areas that have earlier been dominated by intuition.
Essential Practices for this Reading
With the immense data knowledge and what it contains, it is essential to make the best out of the bug data. There are three crucial best practices learned from the big data revolution. Before you get the company to the big data, one must first perform analysis and understand its requirements and goals. Business users must understand the company’s projects before getting into the study of big data to maximize profits.
Best practices are from years of testing and result measurement, which gives organizations the best foundation to build on. However, today, the application of big data is a short-circuiting and new methodology that was used in the past in deriving new methods; this means that there is still something to learn from the success and mistakes of others in defining what may work best for the company and what may not.
The second practice from the reading is identifying what is missing. Once data that is required for a particular project s obtained, then one needs to identify the additional information that might be necessary for the specific project. For example, suppose a company needs to big lavage data. In that case, the organization needs to understand the employees’ workouts that come with information such as login and log out, email reports, and medical reports.
As with business intelligence, it is crucial to have a clearer understanding of the requirements of data management and establish a strategy that is well defined before venturing far into rage path of big data analytics. Analysis of big data is broad, and companies from all sectors are flooding with new ideologies on their data analysis method; therefore, without a clear understanding of what the company needs, the company may drown for taking the wrong option.
The last practice is continuous data analysis. One must be aware of what information is available in the company and what is being done to the data. The company should have continued and periodical checkers on their data’s health to avoid missing any critical hidden signals in the data. Before implementing new technology in the organization, it is good to have formulated how to obtain the best out of practice.
What is the best today may not be the best tomorrow? As time progresses, data analysts find out some core proven techniques applicable in analysis and can withstand the time test. With new skills, new products, and new terms, analytics may seem unfamiliar, but the right and tried best practices to data management hold up to this discipline still emerging.