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Business Data Strategy and Management

Paper Type: Free Assignment Study Level: University / Undergraduate
Wordcount: 2431 words Published: 3rd Nov 2020

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This journal is about what we have learnt from lectures about data-driven strategies and data management. Data is the key for any organizations and organizations plan strategies using data analytics. This journal covers DVM & BMC frameworks, broad portfolio of tools & techniques used by organisations, impact of ethical & regulatory for secure data retention, evaluation of data analytics team in organisations.


In this lecture we learnt about 5 pillars of data driven strategy and its importance in today’s world.

I read an article about the upcoming “Three forces driving enterprise data strategy in 2017”. They are:

  • Security and Cloud becoming synonyms: Companies should look for approaches to protect data when unauthorized users enter the system instead of protecting data using firewalls.
  • Governance: Governance rule should be focussed at data layer for data quality
  • Smart Databases: Using No SQL databases for better performance, analysing data and decision making.


In this lecture we have studied how organisations use data analytics to make useful decisions and had seen the example of Goldcorp which used crowd sourcing for data analysis and reduced its cost of production by 600%.

I have read a research paper,”Data and Analytics – Data-Driven Business Models: A Blueprint for Innovation“ which states that Data-driven businesses have displayed 5-6% higher output and productivity compared to similar organizations who are not utilizing data-driven processes.

With the increase in the size of the data, the ability of an organization to better analyse & make use of it should increase and to become a data-driven company the quality & integrity of data should be high and below factors should be considered: –

  • Build a data warehouse.
  • Connect the most critical data sources.
  • Share data through reports, dashboards, storyboards etc.


After reading the Netflix case study, I made a SWOT analysis below-


  • Netflix has a number of strengths from Worldwide Brand Awareness (130M subscribers worldwide) to Original Content (about 700 Movies/TV Shows) to Anytime-Anywhere-Any Device.
  • It offers customers a personalised, affordable service with a large and varied movie/tv show library.


  • Region locked content
  • Time it takes to add new seasons


  • Expand on partnership with technology & content providers


  • Streaming platforms like Amazon and Hulu,
  • Distributors like Disney moving away from Netflix


In this lecture, we learned about how data analytics tools play an important role in making sense out of data. Also, we learnt about Data Value Map for Healthcare systems which helps in data acquisition, integration, analysis and delivery.

Studies from academic journal,” Decisions Through Data: Analytics in Healthcare[1] indicates that healthcare industry is experiencing an increasing rate of data. In order to extract meaningful information from data, industry must make a good use of data analytics.  The three form of data analytics like small data, predictive modelling and real-time analytics can be useful. The selection of model depends upon the uniqueness of each organization’s situation. E.g. large organisations can adopt predictive modelling whereas hospitals, practices, and healthcare systems can examine small data and perform real-time analysis. From the journal, it can be found that all the three models assist in collection, management and analysis of unstructured data to improve quality and manage cost.

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In this lecture we have seen about trending mobile payments in India. In November 2016, Indian government made a game changing move through demonetization.
People were prompted to switch to e-payment methods as there was no cash in the ATM’s or bank.
Digital banking helped in medium to large transactions and wallets became popular for day to day low scale transactions. These wallets became very popular post demonetization and people started using them widely at tea stalls, fuel stations, supermarkets, etc. Between 2016 and 2017, the growth of mobile wallet transactions grew about 2.5 times in India.

These security barriers can be overcome by providing 100% fraud protection and better encryption methodologies. In addition to this, payment method like biometric can also be used to verify payments. We have also seen the article by Ishan during the lecture and learned about the trend of cash to cashless economy in India after demonetization.


Business Model Canvas provides a framework for determining the business model of companies e.g. their value proposition, revenue structures, cost structure, client segments etc. It’s a model of how a business, its products and services work.

Firms need to continuously innovate their business models to stay competitive. We discussed in class about the kodak failure case. It failed as it didn’t innovate its business model. Earlier Kodak was a popular brand in photography industry. It was known for its features like cameras, film processing, camera films etc. After the advent of digital photography, Kodak didn’t innovate its business model and hence failed as digital photography was cheaper as compared to traditional one. They were too scared to lose revenues out of film processing and camera films.

Also, read an HBR article “4 Business Models for the Data Age” that describes four business models on the basis of data as below-

1.Cost reduction through increased data quality: Creating correct data at first time help to save a lot of efforts & extra expenses.E.g. AT&T saved more than tens of millions in a year using this approach

2.Content providers: Providing relevant data, new, add-on data to other companies.E.g. Uber.

3.Data-Driven Innovation: Using big data and advanced analytics to draw insights.

4.Data Driven in everything that Company does. Using big data analytics for decision making in organizations.E.g. Google.

Data Value map consists of Acquisition, Integration, Analysis, Delivery phase from data creator to data user. This map is a great tool at different levels. Firstly, it provides visual understanding of flow of data and their links. Secondly, it can be used by organisations in planning a data project or auditing the existing one.

Data acquisition: It deals with data collection from different sources of user actions like login, scrolling, searching, etc. About 83% of companies has faced issues with bad data quality which leads to huge cost.
Data Integration: It combines data from different sources to have Single Source of Truth (SSOT). About 80% companies lack SSOT and they get different responses for different situations therefore it is important to integrate data sources.
Data Analysis: Using data analytics tools & technique
Data Delivery: Different formats and visualizations for viewing results.


In this lecture we learned the importance of data security on wireless networks because if data is not encrypted properly anyone can gather data packets over network and listen to it. The encryption allows only the authenticate user with password to view the data. The various encryption methodologies like WEP, WPA, WPA2.

WEP (Wired Equivalent Privacy) was the first encryption methodology. However, there were some vulnerabilities in this methodology, it generated first bytes of output keystreams as non-random. Thus, it was easier to hack the entire WEP key from collecting few packets. The best methodology is WPA2(Wi-fi Protected Access) on enterprise network because of its ease of administration. Nowadays WPA is used over WEP as it is more powerful and secure.

We have seen the case study of TJ Maxx which has suffered from security breach issue due to weak encryption technique causing unauthorized users to access sensitive customer data. The best solution for this is strengthening network security by securing both resting data and moving data.


In this lecture, we have started working in group and each one of us had picked one case study and we started reading and analysing the case study individually. Towards the second half of the lecture we started discussing the ideas and details about every case study. Finally, our group decided to select Netflix because of below reasons-

      Netflix is an excellent example of data-driven business model

      Used their customer data from the beginning and designed an innovative recommendation algorithm

      Impressed that Netflix were forward thinking and took a risk and bet on future technologies such as bandwidth

      Moved away from tradition model of renting movies from brick and mortar stores and brought them online streaming via your PC


General Data Protection Regulation (GDPR) is the new legislation of EU took effect from 25th May,2018. We live in a data driven world and every personal detail is collected and stored by companies for providing services. With the GDPR compliance, organisations are obliged not only to gather information in a legal manner but also to protect the data from misuse or exploitation. Thus, ensuring a respect for individual’s data. Every organisation within EU or outside which offers services/products in will fall under GDPR compliance.

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We also saw that some websites got unavailable after its implementation, for example US news websites like The Los Angeles Times.
We discussed various GDPR case studies, I chose the case study of “Credit unions transmitting personal data via unsecured e-mails”. The case study was about credit unions sending users confidential information over emails directly after registration. Since it was a weaker security method, the issue was addressed and a more secure approach was implemented in the system. They also changed their email to more secure one.


Master Data Management (MDM) gives a golden origin of truth that is used collaboratively for supporting transactional and analytical operations of an organization. MDM is the key component of any data-driven organization. Customer, Product, and Supplier are some of the key business entities of master data. It collects, cleans and augments master data and synchronizes with all the applications and analytical tools. MDM suspects data quality issue at its source on the operational side of the business. For example, when a new customer is created in an application, MDM verified all the business rules to check if it is not a duplicate record. This is useful in reporting, operational efficiency and data-driven decision making. Introducing MDM to DW improves data integrity and closing the loop with transaction systems

Data Warehouse (DW) systems are used for forecasting, predicting and analytics using multidimensional historical data

MDM and DW complement each other and gives quality data to the business but MDM has extra benefits like-

  • Latency: There is no delay in MDM as it is real time while DW has atleast 1 day delay.
  • Feedback: MDM’s operation application ensures that the data is corrected initially unlike DW which don’t provide corrected data to original applications.


People use Dropbox, Google Drive, Microsoft Azure, etc. to store their data on cloud because they are cheap way of backing up personal data, easy to share data between multiple devices/users.

Unfortunately, traditional cloud storage solutions provide no guarantee about the confidentiality of the data that the users store in the cloud. While most of these services guarantee that the data is encrypted in transit to protect against a network eavesdropper, no mechanism prevents the service provider itself from accessing the users’ data. All of these factors contributed towards the need of ECS which stores user’s data in an encrypted format that even service provider cannot access it.

SpiderOak 2 is the most popular ECS services providing end-to-end encrypted cloud storage. SpiderOak used the branding tagline Zero-Knowledge initially which is now replaced by No-Knowledge. Illustrating that SpiderOak themselves have no way of accessing the content of the encrypted users’ storage.


This module helped me to develop the understanding of business data strategy and management framework which helps in complete exploration of data in a consistent & secure manner for any organizations.


Brownlow, J., Zaki, M., Neely, A. and Urmetzer, F. (2015). Data and Analytics – Data-Driven Business Models: A Blueprint for Innovation.Cambridgeservicealliance.eng.cam.ac.uk. Available at: https://cambridgeservicealliance.eng.cam.ac.uk/resources/Downloads/Monthly%20Papers/2015FebruaryPaperTheDDBMInnovationBlueprint.pdf

Krupa, K. (2017). Three forces driving enterprise data strategy in 2017. Kmworld.com. Available at: http://www.kmworld.com/Articles/Editorial/ViewPoints/Three-forces-driving-enterprise- data-strategy-in-2017-116988.aspx [Accessed 3 Dec. 2018].

Redman, T. (2015). 4 Business Models for the Data Age.Harvard Business Review. Available at: https://hbr.org/2015/05/4-business-models-for-the-data-age

Wills, M. (2014). Decisions Through Data: Analytics in Healthcare : Journal of Healthcare Management.LWW. Available at: https://journals.lww.com/jhmonline/Abstract/2014/07000/Decisions_Through_Data__Analyti cs_in_Healthcare.5.aspx


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