Turbulent environment

Table of Contents


Over the past, the development has been made in the business analytics that has provided the strategic planners with the instruments for dealing on the turbulent environment. The study aims to focus on the integration of business analytics with company performance management systems. The respondents work on the high technology firms with the strong performance that is characterized through the sophistical analytical planning process. The big data multiplies the potential for handling the engagement of the data and then shaping the enterprise strategy processes. The discussion is about handling the strategic processes through extending the managerial possibilities on the structured and the unstructured information. The increased demands are based on the business insights with the fast transformation on the competitive necessity. It comes from the low cost structure and the profitable growth along with handling the cross-value chain organizational performance. It includes the need for the proper product lifecycle with handling the offers for the focused attention. The process and the product quality is based on the continued monitoring and the attention that needs to be paid for the handling of fraud detection and the loss prevention with praise of the standard expectations.

Big Data and BI&A

The business intelligence is considered to be the set of information that is provided for the data analysis and the knowledge management. It can inform the decision makers on the areas for the response. Hence, it is in relation to the emerging trends externally and internally. The BI looks for the internal information through the business wide enterprise systems like SAP or ORACLE database systems which can be for handling the data capture systems from the organization. The analysis is defined for the business driven through the market surveys and provide the information for the expectations of the customer for the more formal analysis. The external sources might be for the direct business like the market survey which helps in providing the information for the customer expectations and the formal analysis. It is for the self-service analytical resource (Adat et al., 2018).

The big data is used by the growing organizations with the establishing of the data resources that directly impact the decisions of the management and the actions. The focus is on the business insights and the analytics which involves the marketing, sales, operations, support and the other. The team is involved with handling the actions and the plan which are fit for the business strategy. The advancement is in the marketplace along with rationalizing the technology. The options need to be realigned with handling the greater operational efficiency. The data analytics involves the faster and the smarter better decision making processes. The Foundation is for the scaled processes, with the insights, analysis and the learning company culture (Duncan et al., 2017). The big data adds the analytics dimension for the existing organizational capabilities with unleashing the diversity for the strategic re-orientation possibilities. Hence, the data organization collect, create and store the data which is completely unstructured. The big data helps in eexploring the new opportunities and the mitigation of the threats and risks. It helps in handling the data management, data mining, data cleaning. The paper will discuss about the strategic process consequences for the big data deployment for adding the views and creating the rules of strategy making process.

Connection of Information to Strategy

The management thinkers have been focusing on the corporate strategy with setting out the long term objectives. The influential research includes the views for the designing of the organization through the strategy that is for handling the organizational structure achievement (Esposito et al., 2016). The advancement of the economy, market, firms and the arrangements are mainly deployed through enabling fundamental functions for the economic activity along with the allocation of the resources with proper coordination or monitoring needs. The big data focus on handling the virtualization network with the computing of the storage and the DBMS NoSQL Standards. It involves the data science and the Hadoop ecosystems for the efficiency trust and the workload governance tools that are for working on the data ingestion and processing. The enterprise appeal for the control includes the information where:

The focus is on the exponential format with handling the forms and the customer engagement. It involves the demand driven sectors along with the supply chain optimization techniques (Gou et al., 2017).

The focus is on the consistent manufacturing process and the product quality is defined through monitoring and then working on the preventive analytics approach.

The data driven and the influenced innovation and monetization.

Decision making strategy is based on analysis of big data in real time

The enterprises appeal to the appropriate controls which include the information system to establish the price setting structures and the internal resource through coordinating and monitoring the different activities. The firms suggest that they appeal to the information system through enabling the administrative oversight for the allocation of resources and coordinating and handling performance monitoring effectively. The engagement model is for the different streams of functions with the engagement through benefit case and the understanding for the different needs and the opportunities (Iqbal et al., 2016). Hence, the assessment of the fitness for the current state in the meeting of the emerging needs. The broader contention includes the internal control of the different domain of opportunities where the organizers can search for the high returns. Here, there is a need to establish the parameters for the enterprise management with necessitating on the alignment of the broader strategic objectives with better operational activities that holds the necessary conditions along with the establishment for the formal control structures. The strategy factors includes the approach with the IT infrastructure which is influenced through the data analytics that has a major role on the customer oriented business that is for the retailing, marketing and banking. Hence, the resource factors need to be handled with the availability related to the money and human resources. With this, there are IS investments where the decisions are made for the Big Data strategy that depends on the project planning in the organization. The sponsorship levels are from the business units and the upper management which is considered to be the major factor for the strategic choice (Karta et al., 2016).

With the organizational size, there are correlation to the availability of the resources where there are limited budgets and employees who are working on the implementation of the technological edge solutions. Hence, the abilities tend to focus on the IT personnel which is considered to be the important factor for implementing the system of the higher complexity like the MapReduce or the hybrid approach. The analysis is based on how the IT personnel is focusing on the operations and the possibility to create the sufficient expertise in the organization for the training of the existent staff (Kazim et al., 2015). The contingency is through the absorptive capacity with the ability to utilize the pre-existing knowledge with the organization innovation and use of analytics to improve the business standards. The potential is for the big data where there are possibilities of the warehouse systems and the absorptive capacity that tend to influence the strategy of the big data standards as well. The contingency factor also highlights on the data privacy where the information is from the customers that is set and shared across the globe. The policy makers tend to address the problems of the privacy legislation to understand the danger of the big data collection. Hence, there are big data strategy where the privacy is related to the higher relevance and the choice is about security standards for the distributed file systems. With this, the discussion is based on the identification of performance which is considered to be important for the hybrid approaches. The argument is about the valuable options for the companies to explore about the potential for the big data analytics in an effective manner (Modi et al., 2017).


Considering the BI&A, in the different sector, it is seen that there are different competitive intelligence that is associated to handle the prediction of the competitors and gain the strategic advantage. It is for the small and the medium enterprise that is increasing the future as the software vendors tend to start on the market segments. It includes adoption of BI with the enhancement of productivity and innovation. The broader perspectives are defined for the industries like the manufacturing and the health services or the banking industry. The business intelligence strategy involves the intuitive and the user friendly approach that enables the larger users for focusing on tapping the tools. The use of the in-house transaction data is for the generation of reports and then for the business users who are interacting with the agile and the intuitive systems for analyzing the data (Somani et al., 2017). It involves the regulatory and the financial reports that are defined for the different data sets that are predicable. Hence, the BI tools have been changing with the dynamics along with the self-service business intelligence that aims at the abstracting away of the needs for the IT intervention. The self-service BI tools helps in making the internal data reports with the key to self-service BI success and the business intelligence dashboards that are for handling the pull down and the intuitive drill down points. They are for allowing the users to find and handle the transforming of the data. There is a number of threats that surround around big data which constantly lack awareness. The consumers and the company require to be highly educated on how different attacks can be an outcome to the data loss and the method in which ignore data focused attacks (Božič et al., 2019). This can just be done with the help of cloud services and also cloud-focused services the services threats as well as account focused hijacking. This is that threat towards the data storage of the company and consumer of cloud services. Generally, the cloud-focused computing technology is faced with a lot of threat consisting of the named one and many others.

In the current time, technology focused on big data is slowly gaining constant momentum with the help of challenges that have been recognized by many researchers as well as practitioners asking different questions like what are some of the challenges with big data focused researcher have putting attention in last ten years. There are challenges like encryption, authentication, detection of malware or many side channel attacks addressing researches on saving the loss of information or data. The business intelligence software and systems need to focus on the dashboard and the UIS which include the pull down menu and then allow the users to find the transformation of the data which is easy to understand. Hence, the services are based on handling the ad-hoc data engineers to run into the data security problems (Mariani et al., 2018). The business intelligence and the analytics is also for the dashboard, visualization, reporting and the data mining that handles the self-service analytics platform that can lead to the integration of the range of the data resources like the Microsoft Azure SQL Data Warehouse and the Excel. In the public focused cloud, companies have VM focused isolation along with many reconnaissance that can also be scan detecting by nature (Iqbal et al., 2016). In a concept like a cloud and data management, people lack open benchmarks for VM focused formats. There is a number of gaps that exist between the challenge’s researchers have been paying attention on as well as the issues practitioners deem to be crucial. Here, the focus is also on the cloud-focused computing infrastructure, with different number of challenges in the performance along with transfer rates and IP address space Researchers have constantly recognized this as one of the most common challenges in the present society. Legal challenges focus on big data is focused on many researchers as a challenge the present time. Quality of researcher have recognized that the high quality of big data can be assumed by things as high via other are facing certain doubts (Ong, 2016).

Considering the changing strategy of BI and Analytics’, there are software platforms that are working on the functioning of the systems with handling the valuable business processes. It involves the high value recommendations with the decision makers focusing on the efficient, and the accurate information standards. The focus is also on the infrastructure of big data, where there have been issues in the performance, transfer rates, address of IP space and the restricted capacity of big data. The high-quality researchers have recognized that the quality of big data is taken at a higher level. There are a number of people in the community that have confidence in the emergence and utility of new data storage process. A BI&A strategy is essentially a roadmap to help your business measure business performance, identify competitive advantages, and tune into your customers, products and suppliers. Ultimately, it gives your organization a goal and direction (Rose et al., 2017).


The concept of big data services has the accountability to save its consumers and the company from the above threats as well as challenges. This can assist the cloud-focused services towards the consumers. For instances, the cloud-focused services can make the sure right amount of security to consumer’s data. Many consumers further prefer to utilise a personal computer since there is a fear that data can be exposed to many people however if big data services give the right kind of security consumer can prefer utilising it. The consumer, as well as the company, need constant assurance of security for people to trust the cloud-focused computing. As per the suggestion, every consumer makes sure that data safety or security by keeping a number of secrets of different account details along with passwords. Other than this, the cloud services must enable ample amount of supply of network focused coverage. This will empower all consumer to have the ability to gain access over big data services worldwide (Huang et al., 2017). The cloud services must help education to eliminate illiteracy among people who can be found across the world. The help towards education will raise the number of cloud consumer. As per the research, cloud-focused services can improve the overall quality of many services. In case, when there is an improvement in the service, the researchers can gain the attention of more consumers and companies. The focus is on providing the business strategy with the involvement of the business departments. It focuses on the handling of the different project details with prioritizing the projects and building the roadmap (Kowalczyk, 2017). The implementation for the BI&A is related to handle the increased factors of the communities that are for the surveying and the information technology. The identified business analysis includes the forms and the processes that are defined for the business processes for the deep analytical skills. BIA Strategy is for the top-down approach to understand the business information requirements. The BI&A strategy focus on defining the problems and identifying the forms for the different requirements. It works on handling the procedures with the solutions that are for adding value. The strategy involves the adoption of solutions which could lead to the failure of adoption and so there are issues with the roadmap and devising of the Data Management Strategy.

Some of the strategy includes:

RDBMS: This is the strategy to deal with the big data through data warehousing architecture. It involves the internal and the external sources, with selection, aggregation and loading into the data warehouse. There are different business tools for the analysis and the access of the data (Venter et al., 2017). The volume and the velocity of the data is processed through handling the contemporary companies who revert on the parallelized RDBMS for handling the larger amount of data. The data is stored in the different machines and so it is partitioned in the nodes. Here, the capability is to focus on the variety and the velocity of the data that includes the optimization of the data analysis. The instances are defined for the processing of the unstructured input data with handling the problems that are intensified through velocity and the data might be loaded in the frequent manner.

Map Reduce and DFS: The referenced strategy is based on the Hadoop architecture where the strategy is able to handle the introduction of the new systems where there are data file systems to handle the processes. The analysis is based on the file systems and the streaming of the data is done through handling small programs which are necessary for the execution of the strategies. Here, the programs are injected into the distributed processing framework with the map and reduce instances that have to be handled on the nodes (Abot et al., 2019).

Hybrid Approach: The big data strategies are for handling the traditional RDBMS which includes the potential that have to focus on the benefits and approaches. In MapReduce technology, there are extension of the relational components that leads to the efficient processing of the larger volumes of the structured and the unstructured data. It is considered to be the dominant hybrid which includes the integration of the MapReduce Capabilities in the engines to improve the processing abilities for any of the unstructured data.


The paper highlights and discusses about the approaches where there are data strategies which needs to be discussed along with handling the contingency strategic choices. It involves the big data strategy choices where the illustration is about handling the limitations related to the subjective interpretation and the preferences (Shirazi et al., 2019). The scientific research is based on the practices where the objectivity focus on the technological aspects and then dealing with the organizational and the societal aspects. The focus is on handling the analytical abilities and focusing on the production sector where the RDBMS strategy tends to benefit the most.


Abot, I., & Ariyachandra, T. (2019). Digital Transformation and Geospatial Analytics. In Proceedings of the Conference on Information Systems Applied Research ISSN (Vol. 2167, p. 1508).

Adat, V., & Gupta, B. B. (2018). Security in Internet of Things: issues, challenges, taxonomy, and architecture. Telecommunication Systems67(3), 423-441.

Božič, K., & Dimovski, V. (2019). Business intelligence and analytics use, innovation ambidexterity, and firm performance: A dynamic capabilities perspective. The Journal of Strategic Information Systems28(4), 101578.

Duncan, R.A.K. and Whittington, M., 2017, February. Creating an Immutable Database for Secure Cloud Audit Trail and System Logging. In Eighth International Conference on Big data, GRIDs, and Virtualization, 19 February 2017-23 February 2017, Athens, Greece.

Esposito, C., Castiglione, A., Martini, B. and Choo, K.K.R., 2016. Cloud manufacturing: security, privacy, and forensic concerns. IEEE Big data3(4), pp.16-22.

Gou, Z., Yamaguchi, S. and Gupta, B.B., 2017. Analysis of various security issues and challenges in big data environment: a survey. In Identity Theft: Breakthroughs in Research and Practice (pp. 221-247). IGI Global.

Huang, S., Hu, N., Li, G., Hou, J., & Pan, T. (2017, April). The production management system of mining group based on business intelligence. In 2017 Second International Conference on Information Systems Engineering (ICISE) (pp. 69-73). IEEE.

Iqbal, S., Kiah, M.L.M., Dhaghighi, B., Hussain, M., Khan, S., Khan, M.K. and Choo, K.K.R., (2016). On cloud security attacks: A taxonomy and intrusion detection and prevention as a service. Journal of Network and Computer Applications74, pp.98-120.

Karta, Y. and Avraham, I., Zimperium Inc, (2016). Detection of threats to networks, based on geographic location. U.S. Patent 9,503,463.

Kazim, M. and Zhu, S.Y., (2015). A survey on top security threats in big data.

Kowalczyk, M. (2017). Study D: An Ambidextrous Perspective on Business Intelligence and Analytics Support in Decision Processes. In The Support of Decision Processes with Business Intelligence and Analytics (pp. 73-98). Springer Vieweg, Wiesbaden.

Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management.

Modi, C.N. and Acha, K., (2017). Virtualization layer security challenges and intrusion detection/prevention systems in big data: a comprehensive review. the Journal of Supercomputing73(3), pp.1192-1234.

Ong, V. K. (2016). Business intelligence and big data analytics for higher education: Cases from UK Higher Education Institutions. Information Engineering Express2(1), 65-75.

Rose, J., & Lennerholt, C. (2017). Low cost text mining as a strategy for qualitative researchers. Electronic Journal of Business Research Methods15(1), 2-16.

Shirazi, F., & Mohammadi, M. (2019). A big data analytics model for customer churn prediction in the retiree segment. International Journal of Information Management48, 238-253.

Somani, G., Gaur, M.S., Sanghi, D., Conti, M. and Buyya, R., (2017). DDoS attacks in big data: Issues, taxonomy, and future directions. Computer Communications107, pp.30-48.

Venter, C., & Goede, R. (2017). The use of critical systems heuristics to surface and reconcile users’ conflicting visions for a business intelligence system. Systemic Practice and Action Research30(4), 407-432.