Analytics: Key for Transformation of Digital Enterprise

Overview 

In our digital era, businesses are overwhelmed with data, and the ways to store, process, analyze, interpret, consume it. Today, the data that we deal with is diverse. User content like blog posts, tweets, social network interactions etc. and user transactions, touchpoints are various data sources

Data Analytics addresses large, diverse, complex, longitudinal, and/or distributed datasets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future. It describes various data types and data sets covering new and unstructured data sources (NoSQL), SCADA data, M2M data, RFID data, and WSN data along with traditional (SQL RDBMS) and structured data sources.  

The advancement of software and hardware technology leads to a huge digitization of content across industries leading to a high rate of new data generation. The various types of data produced are classified as audio, video, news reports, electronic medical records, images, sensor data, blog posts, social network sites, call detail records, recording from CCTV and IPTV, cameras, etc.  

The results obtained through the processing of Data Analytics can lead to a wide range of insights and benefits, such as:

• Operational optimization.
• Actionable intelligence.
• Identification of new markets.
• Strategy for the preservation of an existing market.
• Accurate predictions.
• Supply chain planning.
• Fault and fraud detection.
• Improved decision-making.

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Role of Analytics for Digital Enterprise 

Data continues to generate and is digitally archived at increasing rates driven by customer initiatives, sensors, customer interactions, and program transactions. Making sense of this data and helping make decisions out of it, is what analytics does. Analytics has become an integral part of the digital revolution by helping organizations develop a data-driven understanding of the current market situation and their position.

Drivers of Data Analytics

Business  

Enterprises today are solving for ways to improve their marketing, enhance customer experience, improve operational efficiencies, identify fraud and waste, prevent compliance failures, and achieve other outcomes that directly affect top- and bottom-line business performance. Data analytics helps in discovering new business initiatives. This provides an opportunity to enable innovative new business models. 

Digitization

Today, for all the businesses, the digital medium has replaced physical medium as the de facto communications and delivery mechanism. The use of digital artifacts saves both time and cost as distribution supported by the vast pre-existing infrastructure of the Internet. As consumers connect to a business through their interaction with these digital substitutes, it creates an opportunity to leverage user input and other contextual data for personalization, improved customer experience, and development of optimized product features, which can be achieved through customer data analytics.

Explosion of Mobile Devices

With an increase in the use of smart phones, users expect to be able to access their information from anywhere and anytime. To provide an integrated user service, suitable for the device based on modality, mobile-user interaction needs to be analyzed. This helps improve the application and quality of a service at the same time.

Customer Experience

By enhancing the digital customer experience and leveraging the gathered data through digitization, overall customer experience improves. Digital experience analytics helps organizations to improve marketing performance and analyze customer behavior. It helps in creating visualizations and metrics with the individual elements of a digital interface. In addition, it helps in understanding customer behavior across channels, devices, and domains.

Data analysis tools help in processing and developing customer journey mapping, which includes all the touch points of a user with the organization/company over various digital channels like website, mobile application, tablet or a wearable.

Real-Time Sensor Data

The coverage of Internet and Wi-Fi networks has enabled more people and their devices to be continuously active in virtual communities. Usage of Internet-based connected sensors, Internet of Things, and Smart Internet-connected devices has resulted in a massive increase in the number of available data streams demanding the need for Data Analytics. These data streams are public and channeled directly to corporations for analysis.

Growth of Social Media

Customers today are providing feedback on product/item to enterprise, in near real-time through various channels. This helps the businesses consider customer feedback on their services, in their strategic planning. As a result, businesses are storing increasing amounts of data on customer interactions within their customer relationship management systems (CRM) and from harvesting customer reviews, complaints, and praise from social media sites.

This data is analyzed using methods like text analytics and sentiment analysis to surface the voice of the customer to provide better levels of service, increase sales, enable targeted marketing, and even create new products and services. Businesses have realized that branding activity no longer managed by internal marketing activities. In addition, enterprises and their customers are co-creating product brands and corporate reputation. For this reason, businesses are increasingly interested in incorporating publicly available datasets from social media and other external data sources.

Cloud Computing 

Cloud computing plays an essential role in data analytics. In many scenarios, it acts as a data source, providing real-time streams, analytical services, and device transaction hub.  Businesses have an opportunity to leverage highly scalable, on-demand IT resources for storage and processing provided by cloud environments in order to build-out scalable Big Data solutions that can carry out large-scale processing tasks.

The ability of a cloud to dynamically scale, based on load allows for the creation of resilient analytic environments that maximize the efficient utilization of ICT resources. Cloud computing can provide three essential ingredients required for a Big Data solution: external datasets, scalable processing capabilities, and vast amounts of storage.

Cyber Security

A Big Data security strategy should be aligned with enterprise practices and policies already established, avoid duplicate implementations, and manage centrally across the environments.

Enterprise security management seeks to centralize access, authorize resources, and govern through comprehensive audit practices. Adding a diversity of Big Data technologies, data sources, and uses adds requirements to these practices.

Cybersecurity has become stronger using machine learning and AI in the recent past. Providing great digital experience inherently implies for an organization to provide easier, faster, and safer digital transactions. Automatically detecting fraud or illicit transactions, along with continuous security provision from any cyberattacks, can be possible with the help of analytics and AI/ML.

Keeping the process smoother for a user, providing security using new techniques like face recognition based online transactions helps in building a great digital experience

Advanced Analytical Capability

Technological advancement in data collection, storage, analytics, and visualization allows enterprises to increase the amount of data they generate and produce actionable intelligence to support real-time decision making. It helps organizations foresee key events and take appropriate and timely actions.

Benefits of Using Analytics 

The following are the outcomes and recommendations on the usage of Big Data Analytics, 

• Provide insights into why and how a current business' ventures are performing (Descriptive and Causal Analyses).
• Design of Better Projects by being more customer-centric.
• Determine likely future scenarios and recommend best courses of action (Predictive and Prescriptive Analyses).
• Gauge customer sentiment and understand their perceptions of and attitudes towards enterprise products, policies(Customer Analytics).
• Provide a system of dashboards and decision boards that enable administrators to monitor and implement enterprise programs effectively.
• Improve collaboration among various stakeholders.
• Provide a tool for research in Data Sciences and statistical analysis 
• Enhance customer satisfaction through participation in decision-making
• Formulate policies that effectively leverage the needs of customers.
• Enhance the transparency of public institutions through feedback and social audits.
• IncreasedtTrust between organizations and customers to allow the free flow of information.
• Real-time fraud monitoring can be done by integrating large amounts of diverse, structured, and unstructured, high-velocity data (Fraud analytics).
• Real-time location information to provide more accurate traffic and drive-time information by analyzing the commute patterns, drive times to and from work.

Summary

Data Analytics allows an enterprise to convert raw data into visual graphs and reports, map patterns for making better decisions by taking action based on patterns revealed by analyzing large volumes of related and unrelated, structured and unstructured data.

Data growth is the outcome of the deployment of billions of intelligent sensors and devices that are transmitting data (popularly called the Internet of Things) and by other sources of semi-structured and structured data.

Structured data is usually stored in legacy systems or data warehouses of an enterprise. Unstructured data includes social media, email, videos, images, etc. Data Gathering is performed on an ongoing basis, analyzed, and then provides direction to the business regarding appropriate actions to take, thus providing value. 

Analytics helped multiple organizations build various disruptive technologies and applications by helping them understand the need of the market.

Acknowledgments

The authors would like to thank David Kenner, Raju Alluri of Global Enterprise Architecture Group of Wipro Technologies for giving the required time and support in many ways in bringing this article as part of Architecture Practice efforts. 

Further Reading

• What Data Analysis Tools Should I Learn to Start a Career as a Data Analyst?
• A Kafka Tutorial for Everyone, no Matter Your Stage in Development.
• A Complete Guide To Math And Statistics For Data Science.