Current and Future Trends in Analytics

Analysis of the Current and Future Trends in Analytics

The field of analytics has enabled organizations to save costs and serve clients effectively. It enables business entities to shift from relying on human beings to process and analyze data to obtaining data from various operations and mining it to gain potential prospects which allow them to save time and cut costs (Lohr, 2015). In addition, big data analytics is used by many companies to gain a competitive advantage over their rivals, since it enables analysis of market trends that enhance the creation and delivery of high-quality goods and services that satisfy the needs of clients. It also encourages innovation. The paper analyzes the current and future trends in analytics and develops a strategy that could be used to stay on top of the field.

The concept of data analytics started some time ago when analytics 1.0 came to play in the early 2000s. During the time, data regarding various activities of organizations were collected and analyzed. Companies used data warehousing to capture data and business intelligence to question and report it (Davenport, 2013). While the sets of data were limited and static, making information ready for analysis in warehouses was cumbersome. Analytics 2.0 emerged in the mid-2000s when the concept of big data was initiated. During the time, data was not got only from the internal data systems but also from external sources including the Internet, government databases, and various firms’ data stores. Many organizations began to acquire data from their clients by creating products that enabled them to get specific information from the clientele. For instance, Linkedln brought a lot of products in the market such as “Groups You May Like” and “People You May Know” among others to obtain data from its users (Davenport, 2013). It managed to get the data by first creating the needed infrastructure and employing highly competent data analysts. Therefore, big data analysis required building and implementing appropriate technologies such as NoSQL, which enabled working with data that were not structured, and “in database” analytics which aided in comparing numerals.

Currently, analytics 3.0 is in place. It enables organizations to collect and analyze great amounts of data from various sectors. The big data analytics today utilizes various kinds and extensive data (Galbraith, 2014), which was not evident in the post data analysis. The data in today’s big data analytics include maps, photos, audio, video, and text among others. Many companies use analytics 3.0 to offer intelligent products and services to clients. They incorporate big data with the Internet to come up with results that enable them to make informed decisions. Companies such as Google and Linkedln have made many strides towards success and overtaken their competitors by implementing big data. They use the information they obtain from analytics to provide clients with easier ways of decision-making and action-taking. Organizations in the conservative information business are currently putting into effect the bid data strategies of Google and Linkedln. The current trend in analytics is implementing cloud computing (Wang, Kuny, & Byrd, 2016). It is a computing platform that enables businesses to use architecture, platform, and software as a service. Cloud computing helps organizations save on costs and have enough time to concentrate on other organizational issues, as the work of maintaining information system architecture is left for services providers; firms use the services and pay for them. According to Wang et al. (2016), implementing cloud computing together with data analytics enables companies to benefit from “attractive alternative with lower cost” since storage in the cloud is cost-effective (p. 2).

The future of data analytics is evident in how big data will be utilized in various industries. Big data analytics will reduce the time of interaction between industries and clients (Davenport, 2014), by enabling real-time communication and accessibility (Galbraith, 2014). For instance, in the travel industry, Davenport (2014) indicates that big data reduced the reservation time of a client whose invitation to take part in a workshop was done when the deadline was almost complete. Immediately, the customer was granted permission to attend the conference and register for it. Moreover, she automatically received all the logistics for the conference in her scheduling application. The logistics were again automatically sent to the travel information management system preferred by the client. The system connected with the conference’s system and produced for the customer the results with various proposed activities including her preferred flight, accommodation, self-driving car, and restaurant at the conference (Davenport, 2014). Using the client’s data stored in various databases around the world, the travel management was able to know the client’s preferred temperature, music station to listen to in the car, and destination address. The customer also noted that the conference had used her information in social networks regarding her likes in the past conferences to recommend some sessions to her. Before the client left the conference when it ended, she mentioned that data regarding the sessions in which she took were already placed in her business network profile. Big data thus enable the reduction of time of interaction, because the information from various databases is readily available and delivered in real-time.

However, the future of big data analytics requires the innovation of new technologies for organizations to serve clients well and reduce interaction time. For instance, from the example above, a self-driving car is required to make the process of scheduling and reservation of the client effective. Even though self-driving vehicles are being used currently, a lot of changes have to be made to suit the needs of customers. The regulations in place forced the client to sit in the driver’s seat which made using her laptop not comfortable for her. In addition, some laws denied the customer the liberty to watch television and movies while the car was driving (Davenport, 2014). In the future, the legislation will be changed to suit the new technologies that come with big data analytics. In addition, analytics will need the development of technologies that will allow the integration of internal and external data and structured and unstructured data. Agile methods of analyzing data have been developed and are being used to analyze data and provide results more quickly. Companies also embed a model of analytics 3.0 in their various processes such as decision-making thus enhancing their speed and gains (Davenport, 2013). Some organizations implement analytics 3.0 in various services and goods to enable the top management not to forget using analytics in addition to increasing speed. Business entities also need data technology that is able to discover data that can help choose the attributes of a set of data with minimum groundwork.

With the growing necessity for the implementation of analytics in organizations, there is a need for creating the position of a chief analytics officer to help effectively manage data. There will also be a shift of power from the top management of companies to “digital decision-makers” (Galbraith, 2014, p. 3). Currently, many organizational decisions are made through a power structure where the top managers are the final decision-makers. The decisions range from those relating to marketing to those dealing with product or service development. These decisions are currently made based on insights obtained from big data. The change in power is vital for enhancing the modification required to entirely implement the capability of analytics (Galbraith, 2014). However, those who resist a shift of power may prevent many organizations from fully gaining the advantage of analytics. The magnitude of resistance to the change will depend on whether the analytics enhances or destroys competence.

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The future of analytics also demands the automation of the existing processes (Davenport, 2014). Most of the current models of organizations were developed many years ago this may not be compatible with the current trends in data analytics. Davenport (2014) provided an example of a call center of an airline in the United States which required many improvements and upgrading to be useful in forecasting the behavior of clients by converting the voice of customers into texts, analyzing the information, and providing the results in a manner that can help managers make decisions regarding the future actions of clients. Big data also require that many processing functions that worked independently should be brought together, thus many functions of data such as processing, analytics, reporting, protection, and exploration among others will be done in a single big data podium thus removing the need for complex coding and specific expertise to bring systems together.

Developing a Strategy

Various strategies can be used to implement analytics and stay on top of the competition. First, the strategy of implementing data analytics and cloud computing will ensure that organizations overcome their rivals. Since analytics involves an analysis of great volumes of data in real-time, cloud computing provides a flexibility advantage. Companies are able to scale their bandwidths up or down depending on the needs and amount of data to be analyzed. The level of suppleness brought about by cloud computing can provide companies a competitive advantage (Wang et al., 2016). Considering that data stored can be very instrumental to organizations by providing insights when analyzed, its recovery is important when a disaster occurs. Implementation of cloud computing services together with analytics will enable business entities to back up and recover their data cheaply and in a timely manner. This technology also enables companies or their departments to access, work on, and share any data they have and any time they like (Wang et al., 2016).

In this regard, business entities can encourage teamwork which is essential for decision making and innovation. Cloud computing also enhances document control. Prior to the implementation of this technology, organizations had to transfer files from one system to another and then back for other workers to work on them. However, the advent of cloud computing has allowed storing files in one central place and companies can work on the files when they want; they only need to retrieve the files from the cloud and work on them. Since analytics analyzes large volumes of data obtained from various server locations, the sending of files back and forth might not have necessitated its implementation (Wang et al., 2016). Cloud computing will, therefore, enable companies to work on data stored in the cloud and produce results that provide insights in real-time. The last importance of cloud computing in analytics is the enhanced security of data. This technology enables companies to access and work on their data even when their internal computer architectures are destroyed or stolen.

However, the cloud computing strategy has two shortcomings. First, since cloud service providers serve many clients, they may be overburdened with the increasing workload thus making some business’ services to be down; this would suspend the operations and processes of the organizations and negatively affect data analytics which provides information in real-time. Second, cloud computing introduces privacy and confidentiality concerns. Since companies may be forced to provide cloud providers with access to important data, there is a likelihood that the data can be accessed by unauthorized persons for selfish gains or just because of ignorance.

The second strategy is to hire chief analytics officers (CAO) and data analytics professionals to ensure that the insights obtained are interpreted properly and implemented effectively (Galbraith, 2014). Since many companies are changing big data into a strategic asset, hiring a chief analytic officer will enable them to advance analytics more strategically. These professionals enable a better organization of data. The expert will be able to identify future opportunities from analytics insights and advice the management accordingly. In this regard, the CAOs will help unearth insights that can add value to various decision-making processes in companies. These specialists will help their organizations to implement a compact data infrastructure (Galbraith, 2014), tools and technologies that complement the infrastructure, and hire experienced human resources which include database professionals, programmers, statisticians, and business analysts. The CAOs have a wide knowledge of the flow of information, appreciate its content, and know the manner in which the information is linked throughout organizations. These experts mediate between businesses and information technology. They will be able to align information system strategy with information technology governance to ensure that the workings of information technology are geared towards enhancing the outcomes of businesses. The CAOs will also align their companies’ architecture and analytics to their information architecture. They will work together with a team of experts in such fields as business intelligence, prescriptive analytics, big data analysis, and predictive analytics (Galbraith, 2014).

However, the position of CAO faces one disadvantage, since it is an emerging post in many companies; there is the risk of role overlapping. Analytics has been practiced for some time, especially by statisticians and actuarial scientists. Therefore, the role of CAO can overlap with those of other data analysts in various departments of an organization. Each CAO will be responsible for analyzing big data and interpreting the insights to management for decision-making. In this case, some managers may feel threatened that their jobs or powers are being taken by the new specialist and resist or fail to implement this position in their organizations (Galbraith, 2014). Nonetheless, the role of CAO is critical for effective decision-making and the implementation of big data analytics infrastructure. Hiring such a professional is thus the best strategy that would work for any company that would like to implement big data analytics.

Conclusion

In conclusion, big data analytics is an important technology to implement, since it lowers costs, enhances sound decision-making, and provides insightful information in real-time. It began with analytics 1.0 which supported structured data; however, analysts spent a lot of their time organizing the data. Analytics 2.0 emerged in the mid-2000s and allowed analysis of both internal and external data; however, organizations had to build appropriate technologies such as NoSQL and “in database” analytics for it to work well. Currently, companies are implementing analytics 3.0 to be able to analyze various formats of big data such as maps, photos, audio, video, and text among others. In addition, analytics 3.0 enables business entities to offer intelligent products and services to their clients. Companies should thus implement strategies such as could computing and hiring a CAO to be able to read more from big data analytics. Cloud computing will help with reliability, security, privacy, flexibility, and recovery issues, while CAO helps with ensuring that the right analytics infrastructure is put in place, qualified and relevant employees are recruited, and sound decisions are implemented.