Big data for Digital Business

Big Data & Business Analytics: This involves analyzing and systematically extract information from huge and complex data sets.

Data Fundamentals

• Database: an organised collection of data
• Data warehouse: large store of data accumulated from a wide range of sources within a company and used to guide management decisions.
• A database management system (DBMS) is a group of programs that manipulate the database and provide an interface between it and its users and other application programs

Without data and the ability to process it, an organisation could not successfully complete most business activities. Data consists of raw facts. Data must be organised in a meaningful way to transform it into useful information.

The V’s that define Big Data

• Volume: Data Size
• Variety: Different forms of data sources
• Velocity: Speed of Change
• Veracity: Uncertainty of Data
• Value: Business Value

Businesses are collecting and storing more and more Data than ever before.
However it is not only the Volume of Data that is problematic, it is also the Velocity, Variety, Veracity.

“Businesses are not yet as good at organising and understanding data as they have been at gathering it. Data has no value unless you can understand what you have, analyse it, and then act on the insights from the analysis.” Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics, (p4), 2015, Elsevier Inc.

Sources of Big Data in Business

• Documents: eMail, Power point, Word, Excel, PDF, HTML
• Data from business apps: ERP, CRM, PLM, HR
• Social media: Twitter, FB, Pinterest, LinkedIn
• Sensor Data: Process control devices, smart electric meters, packing line counters
• Media: images, audio, video, live data feeds, podcasts
• Machine log data: Call detail, event logs, business process logs, application logs
• Public Data: Local, state and federal government web sites
• Archives: Historical records of communications and transactions.

Examples of use of big data

• Retail organisations
• Advertising and marketing agencies
• Hospitals
• Consumer product companies
• Financial service organisations

Purposes of data management

Data management is driven by a variety of factors:

• to meet external regulations
• to avoid the inadvertent release of sensitive data
• to ensure that high data quality is available for key decisions

Data Governance

• Business Process Management
• Data Policies
• Data Quality
• Business Policies
• Risk Management
• Regulatory Compliance

Big Data vs Business Analytics

Business Analytics (BA) is the process of examining data sets in order to draw conclusions about the information they contain, with the aid of specialized systems and software. Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. It examines large amounts of data to uncover hidden patterns, correlations and other insights. 

Big data on the other hand is a term that describes large volume of data – both structured and unstructured – that inundates any business on a regular basis and is so large that it is difficult to process using traditional techniques.  It refers to data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.

Academic Questions on Big Data & Business Analytics

Question related to this Big Data:

Q. The healthcare industry is inundated with massive volumes of data generated each minute. With the adoption of electronic health, mobile health and wearable technologies, this is poised to increase dramatically over the next few years. This comprises of data being generated by patients in the form of reports generated by the diagnostic labs, the wearable devices an individual wear that constantly monitor his vital stats, body patches, data from medical insurance companies to name a few. Would this data be classified as Big Data? If yes, what are the characteristics of Big Data? Explain any two Big Data Techniques. 

Q. Digital music is gaining firmer ground in India. 56 percent of digital music revenue in Asia comes from music streaming. Players like Gaana, Hungama, Saavn, Wynk etc. offer users to stream music online and save songs offline with a premium subscription. They have grabbed a significant share of the audience who have given up the traditional methods of downloading music to streaming it online. Advertisers and telecom providers have also joined the bandwagon. The primary reasons for this growing popularity can be attributed to the rise in the number of digital natives, improved internet connectivity, more localized curated song lists, personalization of content, competitive pricing, huge library, availability across different platforms, simple user interface and sharing digital music with others across social platforms. How can the music industry use analytics to predict future hits, describe current trends and recommend best offerings for customers? 

Q. Retailers use analytics in a variety of ways. Specialty retailers use video analytics to study customer paths and behavior, helping them to design more effective store layouts. Big Box retailers invest in Wi-Fi networking and new mobile way-finding apps to help customers navigate through large stores or malls, getting them to desired products faster. Resorts and hotels are investing in mobile analytics to gather shopper information from their retail spaces. Mall operators are using the network to track social media and shopping patterns, and delivering this value-add information to tenants. Grocery and fastmoving goods retailers are utilizing video analytics for traffic and conversion analysis, and then using the same information to integrate workforce management and re-align staffing based on traffic trends. Specialty retailers are using social sentiment analytics to improve “voice of the customer” feedback to assess overall brand status and the launch of new products, services, or offers. Retailers can use analytics tools to measure traffic, wait times, and queue lengths, proactively anticipating resource demands across the store. For example, front-end staffing demand in grocery can be anticipated using a combination of real-time traffic counting, trip time data, and data on staff on hand. Resources are thus dynamically allocated based on real-time information, improving productivity of labor hours and improving customer satisfaction. Through presence and location-based mobility analytics, retailers pinpoint the location of opt-in shoppers when they are close to a store location. With personalized reminders or discount offers sent directly to their smartphones, consumers are more motivated to visit the store if they are nearby. Combining social and mobile analytics with loyalty information, retailers can create personalized, more relevant engagements with shoppers. For example, say that a customer enters the shoe department. Their store history shows that 60% of past purchases included a coupon. The retailer can improve the chance of another sale by sending, in real time, a special offer or communicating through Twitter about a current promotion. Such communications change the customer/store relationship from transaction-based to more value-based, creating more sustainable brand loyalty. 
(Source: Beyond Big Data: How Next-Generation Shopper Analytics and the Internet of Everything Transform the Retail Business. https://www.insight.com/content/dam/insight-web/en_US/articleimages/whitepapers/partner-whitepapers/beyond-big-data-how-next-generationshopper-analytics-and-the-internet-of-everything-transform-the-retail-business.pdf)

• a. Give an example of how an Indian retailer has used analytics to improve customer experience within the store.
• b. Give an example of a how an Indian retailer has used social and mobile analytics for better customer engagement.

Q. You are representing a startup company as head of technology. The company is a young tech company headquartered in India and intends to use Big Data to create a unique credit score for consumers using more than 8000 sources. The objective is also to derive a correlation between social media behavior and financial stability. The system is expected to generate data sets to the tune of terabytes daily and it becomes extremely difficult to process it. As an IT head of the company please suggest a suitable solution so that the above requirements can be met. You need to suggest the solution approach and key considerations, details of architectural elements and conclude by explaining how your solution will address the requirement.

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