Why Experimentation in Business Analytics is Important

Business Analytics

Business analytics is an iterative systematic exploration of institutions data with a major focus on statistical analysis. The process is used acquire the basis of decision making in a business by automating and optimizing business processes. Effective business analytics rely on data quality, experienced analysts with business and technological know-how, and dedication to data motivated decisions (Kwon 387). The three kinds of business analytics include prescriptive analytics that utilizes past performance to produce suggestions on handling similar situations in future, predictive analytics that uses data to ascertain the probability of future outcomes, and the descriptive analytics that tracks crucial performance pointers in understanding the current business state. The study is a critical thinking on current issues in business analytics.

Why experimentation perspective is essential to integrating business analytics into an organization. The key to responsive data science is experimentation because it leads to the discovery of insights leading to organizations competitive intelligence (Jourdan et al., 121). Researchers or an organization never knows the duration of results. The agility of data scientists works in small iterations while learning along the way. Experimentation makes the development of a data-driven culture by an organization easier (Kohavi 15). The advantages of gathering and analyzing entire organizations data are evident in making a decision. By accommodating everyone while making a decision reduces the amount of time used in the whole process, and provides better accurate results. Therefore, mixed with good business analytics, it holds the likelihood of giving an organization a competitive edge over opponents as well as market dominance.

Secondly, the future company’s risks are minimized. Assuming risks and managing them is an essential basic part for growth. When venturing into new markets, experimentation in business analytics is vital because it gives an overview of an endeavors outcome by examining risks encountered before acting on a prospect. It accords an organization a quick look into the future thus, leaving one with all the necessary information needed in determining the capability of a company to alleviate the risks or to take a different approach.

Thirdly, it provides an organization with a better platform for future planning (Shmueli, Galit, et al. 17). Since the analytics are customizable, through experimentation, organizations can tailor their tools to fit their needs and familiarize them towards objectives attainment. All business intelligent merchants supplies business analytics that is tailor-made. For example tools for prior planning and organization goals visualization. Hence, experimentation helps in plans implementation such as data collection, deadline setting, and styles of presenting data.

The proactive and reactive decision making is facilitated. Business plans majorly focus on the achievement of specific goals. Way before business commensuration, the prevailing market trends and clientele data are collected to set the choice of action. This puts an organization in a better position of understanding the extent of progress, forecast possible upcoming hindrances, and learn the best options of achieving the set target.

Majority of firms gains value from easy experiments. Because drawing the correct conclusions from the data generated from experiments is easier as compared to studying historical transactions (Drovandi, 385). The management must be skilled in using basic research methods by embracing the test and learn technique. Results are easy to analyze, and the data is interpreted with ease. Experimentation is extremely powerful because feedback from experiments yields immediate profits improvements.

The easiness with which organizations experiments depends on the easiness of observing outcomes. Online retailers can precisely target people with diverse actions and weigh their responses. Several firms are capable of undertaking experiments at aggregate levels thus, they are obliged to make a comparison with non-equivalent control groups. Feedback mechanism allows the observation of customer’s response to diverse treatments. On the other hand, several businesses cannot grow unless they innovate which comes through the development and implementation of ideas. Nevertheless, this carries risk with customers being reluctant to new thoughts. Therefore, testing ideas helps to understand performance on a large scale, and learning from mistakes.

Why Partnering Closely with Information Technology is Important in Building a Business Analytics Capability

Businesses and information technology have become inextricably interwoven. Today data has become an essential element upon which businesses are built as well as making decisions (Năstase, Pavel, and Dragoş 603). Strategies for finding, deducing, and data application are the foundations of business processes. To begin with, business analytics demands a focus on how organizations function in completing their personal tasks and establishing the actual firm’s ability for supplying goods and services. Thus, it helps the decision makers to discover, anticipate, envisage, work together, and handle data in a single place. With such an infrastructure, companies can take an advantage of newest digital innovations.

Secondly, it accelerates the process of making the decision. In the current world, owning the right technology plays an important role in daily decision making of businesses. Similarly, access to quality data enriches a business by steering it away from financial dangers. Getting the right things depends on systems software. Business analytics that is tailored to business needs should aid in optimizing business processes allowing innovation and competition (Schneider, Sabrina, and Patrick 1340001-6). Staying ahead o competition is important for organizations to survive. Future challenges must be tackled in the present time while current decisions have a future impact. The speed at which a firm reacts to present and upcoming challenges determines the success rate. Data analytics aids decision making and thus driving the organization future.

Thirdly, it helps in recognizing cost-effectiveness in three grouping namely, customer experience, goods creation, and company efficiency. Efficiency is the basis of better business created by information science. The highest value brought by data science to a current model is the capacity to raise efficiency. Creation of data does not always demand new collection methods. Historical data enhances interactions with the rightful customers thus, the data facilitates organization staffing with more workforce during peak moments and fewer at less demanding moments (Chakravorti, 123). Similarly, it helps in determining the website design that supports customers to their best.

Fourth, driving revenues is made easy. Information technology partnering helps in defining the correct strategy for driving incomes for your firm. Providing crucial insights such as information about the end user, determinants of their buying decisions and their locations are provided. Nowadays, the market is flooded with organizations releasing information hoping to make sales. With information technology, it enables the creation of marketing strategies resulting in increased revenues (Mithas et al., 205).

Fifth, business problems are easily spotted. Business intelligence utilizations goes further beyond a profit and loss report and gives insights into an organization’s true health status. Several intelligence applications are integrated with accounting software enabling the realization of hidden trends from the management. Similarly, it entails forecasting to avoid limitations and key decisions such as purchasing or recruiting. Data utilization highlights the problems which under normal circumstances would be missed by traditional analysis methods.

Finally, customer analysis is boosted. Client’s interaction with the business is diversified. For example, they use emails, direct interaction, and social media among others. Improved touch point numbers have led to more data from several sources. Integrating such data from several sources enhances a complete customer’s overview. Similarly, information technology through business intelligence enhances the evaluation of failure or success of strategies.

Major Managerial Impediments to Adopting Business Analytics Processes in An Organization And How To Mitigate Them

Many organizations have turned into decision making that are data-oriented in addressing complex business decisions. Huge amounts of data have been piled with fewer outlooks into customer’s behavior. Such hugeness is problematic for business managers to analyze without a business intelligence application due to high costs and complexities involved. Inadequate or lack of necessary skills both technical and analytical know-how for analytics. The prevailing labor force does not have enough qualified personnel. Even though organizations are willing to create such positions, the likelihood of filling them with suitable candidates is minimal.

The lack of a strategy is an impediment that faces many firms. When implementing business analytics processes, there is a failure of understanding the advantages and disadvantages of the solution and how it is adding value to them. Similarly, failing to devise a strategy before adopting the solution leads to confusion. Thus, any attempt to adopt and implement without strategizing is costly, hurting, frustrating, and predestined to fail. To mitigate such, the prevailing business processes should be assessed and reviewed to gather crucial requirements essential for proper roadmap laying.

Secondly, is the failure to understand how to code. Today, the managerial departments find it hard to access data at the appropriate time. Even in the event that they gain entry into the data they are in search of, they are unable to get the significant and relevant information due to the complexity and unstructured formats. Alleviating this demands the incorporation of intuitive dashboards instead of excel sheets so that the data can be more engaging, and powerful. Additionally, a business intelligence solution should allow the management to create calculations and filters without coding.

Thirdly, there lack training and execution (Viaene, Stijn, and Annabel 67). Most of the times, organizations may have a good business strategy and tools. However, they may be lacking technical skills such as designing, and maintenance. This leads to slow learning, frequent breaking, and delivery of uncertain outcomes leading to increased costs. To improve this, organizations should focus more on understanding their resource base, the reasons for having a business analytics adopted and its solution. Assets must be in line with management on the achievement that they can have by utilizing the adopted technology. Similarly, they must spend sensibly by providing continuous training to help the users understand system application.

Fourth, organizations face information privacy and security risk. An elevated value is placed on data confidentiality due to many cases surrounding the customers. Huge data may contain important business knowledge. Additionally, new challenges arise when accessing new systems. Firms harbor insecurities on transferring and saving data on a cloud-based system. Thus, in addressing these companies are forced to focus on data anonymity with an aim of protecting customer’s privacy. Elaboration of processes should take place before any actual analysis start.

Finally, it lacks commitment. Analytics programs packages are fabricated solutions that are not always hard to execute but costly. Accuracies of analytical models improve with time with the anticipated results compared with real events. Nevertheless, this appears to be a complex undertaking demanding more dedication to the solution. The executives or management does not immediately recognize the results thus losing trust. To counter this, organizations and stakeholders should be identified while realistic deadlines should be set to let the models take form (Viaene, Stijn, and Annabel 67).

Works cited

Chakravorti, Samit. “Managing organizational culture change and knowledge to enhance customer experiences: analysis and framework.” Journal of Strategic Marketing 19.02 (2011): 123-151.

Drovandi, Christopher C., et al. ”Principles of experimental design for Big Data analysis.” Statistical science: a review journal of the Institute of Mathematical Statistics 32.3 (2017): 385.

Kohavi, Ronny, et al. ”Online experimentation at Microsoft.” Data Mining Case Studies 11 (2009).

Kwon, Ohbyung, Namyeon Lee, and Bongsik Shin. ”Data quality management, data usage experience and acquisition intention of big data analytics.” International Journal of Information Management 34.3 (2014): 387-394.

Mithas, Sunil, et al. ”Information technology and firm profitability: mechanisms and empirical evidence.” Mis Quarterly (2012): 205-224.

Năstase, Pavel, and Dragoş Stoica. ”A new business dimension-business analytics.” Accounting & Management Information Systems/Contabilitate Si Informatica De Gestiune 9.4 (2011).

Schneider, Sabrina, and Patrick Spieth. ”Business model innovation: Towards an integrated future research agenda.” International Journal of Innovation Management 17.01 (2013): 1340001.

Viaene, Stijn, and Annabel Van den Bunder. ”The secrets to managing business analytics projects.” MIT Sloan Management Review 53.1 (2011): 65.

Shmueli, Galit, et al. Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons, 2017.