BIG DATA ANALYTICS

INTRODUCTION

Vodafone Analytics is a system put up to give intuition to a company and abstract value from data. Various problems in the company are solved by evaluating big data through telecommunication records based on user location tracking and behaviours, and is delivered by working with other insights and visualisation partners like Citi logik and Carto. Vodafone Analytics provides an easy and graphic way to learn where and why events occur, as well as forecasting future projections, resulting in informed decision-making and increased competence for businesses of all scopes, industries, and sectors. Vodafone Analytics service makes use of Big Data which can be applied to generate business and tactic decisions based on insights obtained from billions of phone users.

APPLICATIONS OF USING BIG DATA ANALYSIS IN THE COMPANY’S DECISION MAKING

Data Contextualization

 Operating a business or an organization entails more than just the goods and services you provide to your target market. It is important to do surveys while using data analysis to guarantee that you are making evidence-based conclusions, implementing appropriate strategies, and meeting your objectives. One of the most essential principles used by Vodafone in this regard is data contextualization. The organization is able to quickly evaluate huge data with the support of data contextualization; otherwise, it can be tough to turn complex records into something relevant. Data contextualization works by combining interrelated information with data to make it more valuable, digestible, and interpretable. These data are utilized to detect patterns, trends, and correlations, authorizing them to stand out against a context. By doing so, the corporation is capable of extracting more value from the data that has been collected. Given the importance of data analytics to any business or organization, Vodafone executives collaborate with large analytics firms to guarantee that suitable methodologies and tools are used to assemble reliable information. Compilation for data analysis is considerably easier to use and grasp with their assistance. With this data connection, the company offers insight to users, leading to the improved data interpretation and allowing the company to make more informed decisions.

Performance Management

Interpretation of big data in an enterprise database through pre-defined queries and multivariate analysis is part of performance management. This research relies on transactional data, such as years of client purchase behaviour and inventory status and volatility. Managers can ask queries like, "What are the most profitable client sectors?" and obtain concrete answers that can be utilized to make short-term management decisions and long-term strategies. Today's Vodafone analytics technologies include a dashboard feature. The user, who is typically a supervisor or analyst, can select which queries to run, sort and prioritize the analysis results by particular parameters (e.g., location), and drill down/up on the data. Managers can easily examine trends thanks to a variety of reports and infographics. A significant advantage for Vodafone report developers is the ability to engage with several parts of business data, including human resources, advertising, revenue, customer service, and production data, and gain numerous viewpoints on how the company is performing. The great news is that Vodafone's business intelligence tools' capability and convenience of use have considerably improved over the past few years. The company’s business intelligence is well-designed and deployed; these technologies provide leaders in various departments in the company a window into a massive volume of business transactions that help with their daily decision-making.

Data Exploration

Data exploration is strongly reliant on analytics to explore and discover answers to questions that administrators may not have thought of. In the company Data exploration technique makes use of predictive modelling tools to forecast user behaviour derived from past business transactions and preferences. Cluster analysis are used to classify clients into subgroups based on similar attributes that may have gone unnoticed by analysts. Managers then execute targeted actions such as modifying marketing messages, improving service, and upselling to each unique group once these subgroups are identified (Kaffash, et al., 2021. P 107868). Another typical use for this method is predicting which set of users will "drop out." With this knowledge, Vodafone's executives can design proactive measures to retain this customer subgroup and reduce the churn rate. The rise of powerful statistical/analytical approaches in the company have resulted in quick, direct outcomes for data-exploration in the company which has led to enhanced quick and direct decision making.

Social Analytics

Social analytics measure the massive amount of non-transactional data  that is available nowadays. Much of this information is accessible on social media platforms, such as Facebook chats and Instagram reviews. In Vodafone communication company Social analytics department are be divided into three basic categories: awareness, engagement, and outreach. Awareness looks at how the public reacts towards a certain ad campaign by looking at the number of video views, likes and followers that the video has attracted. Engagement measures the level of activity and engagement between platform members generated by the ad campaign. Vodafone Group Plc uses modern, mobile apps and platforms such as Facebook which provide corporations with location-based data that is used to gauge brand recognition and engagement, including the rate and number of visits, by organic active users.

Finally, outreach division quantifies the degree to which material is shared with other people throughout social media sites. Factors such as the number of retweets on Twitter and shared likes on Facebook is used to calculate reach in a certain ad campaign. Vodafone Group Plc uses Social metrics because they tell administrators about the effectiveness of their external and internal social digital initiatives and activities. Marketing efforts incorporating contests and promotions, for example, on Instagram can are evaluated based on the number of consumer ideas submitted and the public comments linked to those proposals. Managers can pivot and make clear decisions if the metrics show unsatisfactory results. For example, low Instagram involvement may indicate that more intriguing and interactive content is required. Vodafone Group Plc analysts frequently collect online traffic and business analytics, as well as social metrics, to assess their company impact, and then look for correlations. In the case of viral videos, the analysts analyse whether there is traffic to the firm's website after seeing the YouTube films adverts, followed by ultimate service purchases.

Descriptive Analytics

Descriptive analytics is the most common and important form of analytics that corporations employ Descriptive analytics can be used in all areas of the organization to monitor operational performance and trends. Key performance indicators (KPIs) such as year-on-year percentage growth of sales, revenue per consumer and the minimum duration taken by customers  to pay their bills are some of the descriptive analytics (Munawar, et al., 2020. P 4). Descriptive analytics results can be found in financial statements, various reports, displays, and demonstrations. Descriptive analytics results are found in financial statements, various reports, dashboards, and presentations.

 Vodafone Group Plc compiles massive volumes of data, but frequently it is not possible to grasp the meaning of the data without some evaluation. Analysing hundreds of individual purchase transactions for the most recent quarter, does not reveal the estimated rate the customers spent or whether the overall revenue was higher or lower than in earlier quarters. The very first step in evaluation of such raw data is descriptive analytics. It frequently employs fundamental mathematical processes to provide statistical results — for example the average revenue per customer — in order to have an improved understanding of the company's present state of affairs. After identifying trends, company's analysts utilize different sorts of analytics to dig deeper into the reasons and implications.

Why Is Descriptive Analytics Technique is Important to Vodafone Group Plc

Descriptive analytics enables anyone within the organization to make more informed decisions that will guide the organization in the right path. It uncovers trends in raw data that would otherwise be concealed, allowing management to know at a glimpse how well the cooperation is operating and where modifications may be required. Descriptive analytics also assists organizations in sharing information within the departments and to individuals outside of the organization. Before investing into a business, prospective creditors, for example, may be interested in looking at sales, profitability, working capital, and debt data.

BENEFITS OF USING BIG DATA ANALYSIS FOR THE COMPANY

Boost sales and retain customer loyalty

 The goal of Big Data is to collect and analyse huge amounts of client data.. Customers' digital footprints show a great deal regarding their tastes, wants, purchase behaviour, and much more.  Creation of personalized products and services to meet their specific needs of distinct customer segments is enabled by the consumer data. The higher the company's personalisation ratio, the more consumers the company will entice. This, obviously, will result in a large boost in sales. Personalization of product/service quality also have a favourable impact on client loyalty. Customers will return to you if you provide high-quality products at reasonable pricing, as well as tailored features/discounts.

Improve efficiency

 Big Data tools have the ability to considerably improve effectiveness and production. Big Data technologies in the company have collected huge volumes of u consumer data used through engaging with clients and gaining their valuable contribution. The data is then inspected and evaluated to identify important trends hidden within it , allowing the company to create customised products/services. Big Data Analytics in the company is important as it is used to uncover and evaluate the most recent industrial trends, allowing you to keep one step ahead of your competitors. Another benefit of Big Data in the company technologies is their ability to power normal procedures and tasks. This helps to frees up important time for human staff, which they can then dedicate time to jobs requiring cognitive abilities. 

Control and monitor online reputation

Online reputation management. As organizations shift to the internet arena, it is becoming progressively more important for Vodafone Group Plc to assess, regulate, and improve their online status. After all, what most of its clients say about the company on various internet and social media platforms might impact how potential purchasers see the corporation. There are a plethora of Big Data technologies that are expressly built for sentiment analysis in the company. These technologies assist administrators in navigating the large web realm to discover and comprehend what consumers are saying about your products/services and brand. Only after you comprehend client complaints can you can attempt to enhance your services, which will ultimately boost your online reputation.

To summarize, Big Data has evolved as an extremely effective tool for the company, regardless of its size. The most important advantage of Big Data in the company is that it offers the company with new opportunities. Better-quality working competence, amplified consumer contentment, a push for innovations, and profit intensification are just a few of the many advantages of Big Data. Despite the demonstrated benefits of Big Data that we have seen thus far, it still has a plethora of untapped potential that needs to be explored.

CHALLENGES OF USING BIG DATA ANALYSIS IN THE COMPANY

 In this modern digital age, the organization generates a massive amount of data every hour. The capacity of data produced every hour makes it hard to keep, consolidate, custom, and assess. As previously stated, the amount of data generated by Vodafone Group Plc is growing at a rate of 30 to 70% per year. Some challenges faced by using big data analysis in the company include:

Acute Shortage of Professionals Who Understand Big Data Analysis

Data analysis is supposed to make use of immense amount of data collected per hour. With the increasing volume of information, the economy has developed a massive plea for big data researchers and big data experts. Because the role of a data analyst is diverse, it is important for company to select a data analyst with diverse talents. A major key issue that organization has is scarcity of experts that understand Big Data analysis. In comparison to the massive quantity of data generated, there is a severe shortage of data experts in the company.

Data Storage and Quality

The company is swiftly expanding. The amount of data generated nurtures in conjunction with the implausible growth of the company and.  The organization is finding it challenging to store this massive bulk of data. Widespread data storage facilities, such as data warehouses, are regularly used to assemble and store immense quantities of unstructured and structured data in their native setup. The major problem emerges when a data warehouse attempts to mix unstructured and inconsistent data from contradicting sources and make mistakes. Data quality issues are usually caused by missing data, inconsistent data, logic conflicts, and duplicate data.

SOCIAL, LEGAL AND ETHICAL DILEMMAS ASSOCIATED WITH BIG DATA ANALYSIS IN THE COMPANY

 The legal issues concerning big data are still unexplored territory, and IT leaders of the company must be cautious in understanding and mitigating risks they may be looking to take on before they get too deep in. The ownership of data is a sensible place to begin when assessing data security and customer confidentiality. Big data analytic programs will make identifying data ownership difficult, which will rely on the type of data, how it was developed, how it was acquired, where the data came from, and whether it is linked to a human or a computer or device.

Data ownership

Consumers are most worried about privacy when it comes to big data. However, increased data accessibility and quantity are presumably not the issue; the issue will be inadequate or wrong contexts, erroneous interpretations, and dangerous or invasive acts. Legal analysis is a key component of identifying who has ownership rights to certain data, but the context is also significant. The primary source of data, and hence who owns the data, will be the point of contention in the organization when using client-generated data. Furthermore, who owns the data determines who gets to access it and how – or whether – the data is used. Vodafone Group Plc, as a result, will need to closely follow regulatory changes in the big data space and properly assess their risk exposure.

Consumer privacy

Privacy legislation distinguishes between public and private data. Private data is information that is reserved to be totally private. Public information are which have been wholly given to the public or partly published to a restricted audience under certain conditions. Since the line separating public and reserved data is not always evident in the actual world, it causes legal issues in the publication of big data. To understand and control legal issues, the company must be able to interpret the legal difference into a real-world distinction between what is public and what is private.  The company should be capable to determining who created the data, i.e., who was the original owner of the data. Identifying who may have a claim to data ownership rights will assist the organization in determining who it may need to consult to confirm whether it has a right to privacy.

Fair treatment of stakeholders and integrity of big data

The dependability of Big Data and the ability of algorithms to provide meaningful results in the company are hotly debated topics. Unlike out-dated statistical approaches, which depend on samples that are selected to be a descriptive of the entire population being studied, the new datasets formed by Big Data may not be statistically precise, resulting in inaccurate outcomes. As a result, the company has supplemented use of veracity, which relates to the reliability and authenticity of information. When executing numerous tactics, the organization encountered this type of problem. Significant concerns may develop when the authenticity of a dataset cannot be ensured. Some employees in the organisation may be given more exposure by mistake and hence be favoured or discriminated against at the cost of those who are less prominent.  Employees in the company vary in their accessibility to and capacity to utilise new information and technologies in the company. Similar differences, sometimes referred to as the "digital gap," may result in inequities in opportunities and results.

CONCLUSION

 Big data is with a type of novelty that is both constructive and empowering while also restricting and oppressive. We can now make more accurate forecasts and potentially better decisions in dealing with social unrest, as well as commercial marketing and sales, thanks to the rising power of Big Data approaches and technologies. At the same time, one cannot ignore the ethical, legal, and social issues and dilemmas that it has caused in the organization, experts, and society as a whole. More decisive actions can be done by the administrative body to checkmate the use of big data analytics in the organizational levels to confront various big data challenges and implement the right judgments.