BIG DATA FOR MANAGERS ACADEMIC ANALYSIS

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BIG DATA FOR MANAGERS ACADEMIC ANALYSIS – T3, 2018

WORD COUNT: 2748 (excluding references and figure captions)

Option 2:

Provide two short case studies on firms successfully utilising Big Data. Also, provide some insights into the emerging trends and how would you manage data in this dynamic environment

INTRODUCTION TO BIG DATA & CONCEPTS

“The future is going to be so personalised, you’ll know the customer as well as they know themselves.”

– Tom Ebling, Demandware CEO

Everything digital is data. Of all the data in human history, 90% has been created in the last 2 years (van Rijmenam, 2014). According to a study, the amount of data on our planet is set to reach 44 zettabytes (4.4 × 1022 bytes) by 2020 which will be ten times larger than it was in 2013 (IDC, 2014).

The continuous increase in the volume and detail of data captured by organisations, social media, Internet of Things (IoT), and multimedia has produced an overwhelming flow of data in either structured or unstructured format, called Big Data (Hashem et al., 2015; Morgan, 2014). A meta-definition can define big data at any point in time as data whose size forces us to look beyond the tried-and-true methods that are prevalent at that time (Jacobs, 2009).

Big data can be classified based on five aspects (Hashem et al., 2015) (Figure 1): (i) data sources, (ii) content format, (iii) data stores, (iv) data staging, and (v) data processing.

 

Figure 1 Big data classification (Hashem, 2015)

History

1997 – The term “big data” was first used to describe the problems about storage and capacity of such data across various interfaces (Cox & Ellsworth, 1997).

2001 – Doug Laney described the 3 “Vs” to define Big Data – Volume, Velocity and Variety (Laney, 2001).

2005 – Roger Mougalas coined the term “Big Data” referring to a large set of data that is almost impossible to manage and process using traditional business tools (van Rijmenam, 2014).

2014 – 7 “Vs” introduced by van Rijmenan to add four more descriptor to big data over the original 3 by Doug Laney – Veracity, Variability, Visualisation and Value.

The 7 V’s of Big Data

Volume – Definitions of big data volumes are relative and vary by factors, such as time and the type of data. What may be deemed big data today may not meet the threshold in the future because storage capacities will increase, allowing even bigger data sets to be captured (Gandomi & Haider, 2015). The benefit of gathering large amounts of data includes the creation of hidden information and patterns through data analysis.

Velocity –Velocity refers to the rate at which data are generated and the speed at which it should be analyzed and acted upon (Gandomi & Haider, 2015). Often big data velocity is not consistent and has periodic peaks and troughs.

Variety – Variety refers to the structural heterogeneity in a dataset (Gandomi & Haider, 2015). Technological advances allow firms to use various types of structured, semi-structured, and unstructured data collected via sensors, smartphones, or social networks. Text, images, audio, and video are examples of unstructured data, which lack the structural organization required by machines for analysis. Majority of data is now unstructured (95%), requiring different tools and analysis (Cukier, 2010).

Veracity – Accurately identified data should be analysed correctly, especially when decisions are made with no human involved. For example, customer sentiments in social media are uncertain in nature, since they entail human judgment. Yet they contain valuable information. Thus the need to deal with imprecise and uncertain data is another facet of big data, which is addressed using tools and analytics developed for management and mining of uncertain data (Gandomi & Haider, 2015).

Variability – Definition of data may be changing rapidly. This variability can be used in sentiment analyses, although the differing contexts and changes make it difficult.

Visualisation – Big Data should be comprehensible, easy to read and understand.

Value – It refers to the process of discovering huge hidden values from large datasets either as insights or decision-making tools.

Big Data Analytics and Value Creation

Big data is typically both noisy and unrepresentative. This is because big data generally accumulates, rather than being something constructed (Barns, 2015). Making it useful requires calibration with data that is both clean and representative that function as the source of truth.

The overall process of extracting insights from Big Data can be broken down into five stages (Labrinidis & Jagadish, 2012) (Figure 2). These five stages form the two main sub-processes: data management and analytics. Data management involves processes and supporting technologies to acquire and store data and to prepare and retrieve it for analysis. Analytics, on the other hand, refers to techniques used to analyze and acquire intelligence from big data.

 

Figure 2. Processes for extracting insights from Big Data (Labrinidis, 2012).

Another process is described by the Big Data Value Creation Model (Verhoef, Kooge, & Walk, 2016) (Figure 3). This model has four elements:

Big Data Assets – Tangible or intangible resources collected over time.

Big Data Capabilities – Includes the firm’s people, systems, processes and organisation.

Big Data Analytics

Big Data Value

 

Figure 3. Big Data Value Creation Model (Verhoef et al. 2016)

Another process for big data value creation is called The Data Science Process which consists of 6 steps (Goldstein, 2017) (Figure 4):

Frame the problem: who are we helping? what do they need?

Collect raw data: what data is available? which parts are useful?

Process the data: what do the variables actually mean? what cleaning is required?

Explore the data: what patterns exist? are they significant?

Perform in-depth analysis: how can the past inform the future? to what degree?

Communicate results: why do the numbers matter? what should be done differently?

 

Figure 4. The Data Science Process (Goldstein, 2017)

Big Data in Business

Big Data is worthless in a vacuum. Its potential value is unlocked only when leveraged to drive decision-making. Research shows that companies that use data and business analytics to guide decision making are more productive and experience higher returns on equity than competitors that don’t (Brynjolfsson, Hitt, & Kim, 2011). Consumers also capture the benefits that big data generates: lower prices, a better alignment of products with needs and lifestyle improvements that range from better health to more fluid social interactions (Bughin, 2011). A GE study reports that of the companies that have increased their ability to collect and analyse Big Data, 59% were seeing a positive outcome from their Big Data investments (Columbus, 2016). The use of analytics can hugely improve the quality of business decisions and can increase decision process efficiency by as much as 25%. When executed well, it leads to higher customer and employee satisfaction (Mankins & Sherer, 2015).

Big Data offers enormous possibilities for understanding human interactions for detecting complex interactions and nonlinearities among variables (Lazer, Kennedy, King, & Vespignani, 2014). However, gathering comprehensive data involves negotiating with multiple owners who will share their data only with those they trust to keep it safe and use it impartially. We are entering a world where no company will have enough data on its own to do what can now be done.

The Big Data Business Model Maturity Index (BDBMMI) (Schmarzo, 2016)(Figure 5) provides a reference and a set of recommendations to help organisations advance from one stage to the next. This will be discussed later.

 

Figure 5. Business Model Maturity Index (Schmarzo, 2016)

RISE OF TELE-DENTISTRY AND ARTIFICIAL INTELLIGENCE IN ORTHODONTICS

Gartner (2017a) provides a roadmap to help leaders respond to the opportunities and threats affecting their businesses, take the lead in technology-enabled business innovations and help their organisations employ an improved digital business strategy.

The Gartner 2017 reports identifies some key trends (Gartner, 2017a, 2017b; Price, 2018)(Figure 6-7):

Artificial Intelligence (AI) everywhere – Due to advances in deep neural networks, AI technologies will be the most disruptive in the future.

Transparently Immersive Experiences – Human-centric trends are on the rise to create more transparency between business, people and things.

Rise of Digital Platforms – To enable data collection and analysis ecosystems for businesses.

The Gartner (2017b) report pertaining to healthcare identifies further services with the above 3 themes. Example – On the rise is precision medicine, tele-surgery and robotics. At the peak is digital care delivery, assistive robots, chatbots, AI monitoring, compliance management and the plateau comprises of technologies using sleep tracking, etc.

In dentistry, the future of its sub-speciality field of Orthodontics (meaning “orthos”-straight, “dontos”-teeth) is no longer driven by appliances that simply move teeth but by AI, which is now available for diagnosis, treatment planning, collaboration with peers, personalisation of treatment and treatment monitoring (Frey, 2017; Katyal, 2018a; Roisin, Brézulier, & Sorel, 2016).

Figure 6 Hype Cycle for Emerging Technologies, Gartner 2017

 

Figure 7 Digital Health Hype Cycle adapted from Gartner 2017 (Price, 2018).

CASE STUDY 1: DENTAL MONITORING (DM)

Orthodontists are always looking for innovations to deliver personalised treatments predictably and efficiently. DM is a cloud-based software-as-a-service monitoring solution in orthodontics allowing the patient’s smartphone to capture tooth movements (https://youtu.be/wQJ6G6lR9qI) with a calibrated cheek retractor, analysing movements in 3D and communicating them to the orthodontist (Figure 8Figure 9). It then allows the orthodontist to remotely control orthodontic treatment by communicating early with the patient, reducing treatment times, unnecessary appointments and by increasing appliance wear-times (Katyal, 2018c). The company was founded in 2013 and is based in Paris, France (Bloomberg, 2018).

 

Figure 8. DM doctor dashboard view showing treatment tracking (Katyal, 2018c)

 

Figure 9. DM analysis graph of tooth movement in 3-D

DM’s engineering team has spent the last 4 years developing the DM proprietary technology to make remote monitoring a reality. The company has invested millions of dollars gathering and tagging clinically relevant data to enable the development of highly accurate artificial intelligence algorithms. Today its technology can detect over 170 different clinical situations from images/videos taken by patients using their smartphones. In just a few years, DM has validated nearly 2 million clinical records from patients and the number is growing exponentially (DM, 2018).

DM has also pioneered an AI-driven “Go-Live” program which semi-automates doctor decisions for treatment issues taking into account individual doctor preferences. For example, if oral hygiene declines during orthodontic treatment, DM can be automated to send videos to patients on techniques to improve their brushing techniques and plaque control.

DM collects more than just orthodontic data. It collects emails, demographics of the patient, geo-spatial variables, doctor preferences and treatment tracking data with various appliances used. DM today lies in between Data Monetisation and Metamorphosis stages as graded by the BDBMMI (Schmarzo, 2016). The Metamorphosis stage leverages the organisation’s cumulative insights, data and analytics to create net new components of the business strategy – new business models, new consumption models and new corporate goals. New consumers may extend to monitoring skin for acne, or patient’s sleep-breathing with self-quantified sensors/apps. The applications are immense for this technology.

CASE STUDY 2: BEST ORTHODONTIC SEMINARS & SESSIONS (BOSS) – VIRTUAL ORTHODONTIC CONSULTANCY (BOSS CLUB)

BOSS (www.breakthroughwithboss.com) is a private Australian-based small business growing exponentially in the dental education industry, primarily to help dentists increase their confidence, competence and case acceptance with simple-moderate orthodontic treatments. Since its inception in 2016, BOSS’s operations have discovered (via structured customer feedback) that besides educational courses and qualifications, dentists would like access to a virtual orthodontic consultant for everyday decision-making and to provide relevant education using the problem-based learning approach in their own environment (Alrahlah, 2016). To create a new opportunity in the existing market, BOSS has introduced The BOSS Club (Katyal, 2018b)- a virtual consultancy to help diagnose, treatment plan and monitor orthodontic treatments with specialist orthodontist expertise. It has been released as a pilot in 2018 following Lean Startup principles (Ries, 2011). BOSS will be co-partnering with DM to provide this service comprehensively. Eventually, an App-based platform for access to the expertise can be created for automating some of the repeated actions.

Given the soaring popularity of smartphones, retailers like BOSS will soon have to deal with hundreds of thousands of streaming data sources that demand real-time analytics (Gandomi & Haider, 2015).

BOSS is currently in the 1st Monitoring stage of BDBMMI (Schmarzo, 2016) and the Data Assets stage in Verhoef (2017) model. It is yet to incorporate Big Data capabilities, analytics and value creation.

To increase its competitive advantage and step up to the next few stages BOSS has to do the following actions internally, before data monetisation or business metamorphosis is possible externally.

Change in Organisational culture – Create an Information-centric organisations to take advantage of Big Data by encouraging employees to collect data at every point of customer contact and other areas.

Identify key business decisions – BOSS is an industry sponsor for Ducere’s MBA students in 2019, to help gain market and business strategy insights. Big Data incorporation, analysis and integrations will also be a reason-for-service.

Build Customer Profiles – Australians are now spending more than half a day per week (12.5 hours) on Facebook alone, up four hours from last year (Sensis, 2016). Social media is one of the only digital avenues where businesses can have a two-way communication with customers, giving them the opportunity to receive feedback, monitor sentiment and build a relatable brand personality.

Historical detailed operational and transactional data at the most granular level from website, feedback forms, digital marketing, accounting, etc.

Internal unstructured data about engagements and conversations (consumer comments, surveys, notes, email conversations, etc.) with individual clients, advisers or traders.

External unstructured data about individual clients, advisers or traders publicly-available activities (social media postings, college donations, job promotions).

Deploy Right-time Analytics. Create a “Right time” analytics capabilities that can monitor individuals’ behaviors (across individuals’ transactions, engagements, events, activities, etc.) to flag behavioural changes or insights that might be worthy of analysis (Schmarzo, 2016).

Build Big Data Networks – There are paid open public and free datasets in the marketplace that can be accessed by BOSS such as Australia-wide dental industry surveys/reports.

Employ a Big Data Artisan (Saragoza, 2013). Big data analysts are predicted to be scarce in the future (Brown, Chui, & Manyika, 2011). This will be a priority for 2019.

Big Data requires big security measures – Most common way is to encrypt data. A crisis plan should also be in place, if data gets hacked. BOSS Club has created a users platform via BOSS website, with 2-step authentication and communication of sensitive material via a private Slack channel.

Transparency – Advise the clients why the data is being collected and their risk by declaring this information online (Golbeck, 2013).

Big Data is about combining different datasets collected at various moments from different devices and using them for business insights. BOSS can use this data in real-time and help customers at the point of purchase or service via its website platform. E.g. – Employ chatbots.

Further data collection with cloud computing. Cloud services have become a powerful architecture to perform complex large-scale computing tasks and span a range of IT functions from storage and computation to database and application services (Hashem et al., 2015). Cloud service models typically consist of PaaS, SaaS, and IaaS (Figure 10).

PaaS (Platform-as-a-Service), such as Salesforce.com can provide great speed and flexibility to the entire sales process. Salesforce incorporation is underway for BOSS for cloud marketing, customer relationship management and sales reporting.

SaaS (Software-as-a-Service), such as Slack, Talent LMS, Mailchimp refers to applications operating on a remote cloud infrastructure that can be accessed through the Internet. SaaS has been used by BOSS for:

short-term projects that require collaboration – Slack, Google Docs, Dropbox.

applications that aren’t in-demand very often – Xero accounting.

applications that need both web and mobile access –BOSS provides its e-learning and test materials via Talent LMS, activated by a 2-step authentication login from its website. Mailchimp is used by BOSS for email marketing and marketing automations per view/click.

marketing analytics via social media platforms – Facebook, LinkedIn and Twitter.

IaaS (Infrastructure-as-a-Service), such as Amazon’s EC2 refers to hardware equipment operating on a cloud. BOSS currently does not use any IaaS.

 

Figure 10. Cloud computing & workflow. Retreived from http://www.mechanosphere.com/Media/Images/DigitalEcosystems/Fig_small.jpg

CONCLUSION

AI, machine learning and innovation will change mankind. One thing we cannot do is slow down innovation because man in nature is creative and will continue to drive machine learning (Sharma, 2018).

Big Data Value Creation model will be the biggest transformation that would help BOSS attain a strong competitive advantage and long-term sustainability in the dental education sector. DM, AI, self-quantification and rise in smartphones have paved the way for BOSS to utilise such networks, customise it to its operations and redefine continuing professional development for dentists, including clinical coaching globally. With Big Data insights and value creation, BOSS could deliver an innovative education model for the dentists of tomorrow.

References

Alrahlah, A. (2016). How effective the problem-based learning (PBL) in dental education. A critical review. The Saudi dental journal, 28(4), 155-161. doi:10.1016/j.sdentj.2016.08.003

Barns, M. (2015). Big Data’s Big Impact on the Future of Advertising. Retreived from https://www.recode.net/2015/6/22/11563780/big-datas-big-impact-on-the-future-of-advertising.

Bloomberg. (2018). Company Overview of Dental Monitoring SAS. Retreived from https://www.bloomberg.com/research//stocks/private/snapshot.asp?privcapId=558980413.

Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’?. Retreived from https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/are-you-ready-for-the-era-of-big-data.

Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? Retreived from https://ssrn.com/abstract=1819486

Bughin, J. (2011). The Web’s €100 billion surplus. Retreived from https://www.mckinsey.com/industries/media-and-entertainment/our-insights/the-webs–and-8364100-billion-surplus.

Columbus, L. (2016). GE Global Innovation Barometer 2016: 61% of Execs Using Big Data & Analytics To Improve Decisions. Retreived from https://www.forbes.com/sites/louiscolumbus/2016/01/20/ge-global-innovation-barometer-2016-61-of-execs-using-big-data-analytics-to-improve-decisions/ – 6986891211d0.

Cox, M., & Ellsworth, D. (1997). Managing big data for scientific visualization. Paper presented at the ACM Siggraph.

Cukier, K. (2010). Data, data everywhere: A special report on managing information: Economist Newspaper.

DM. (2018). Straumann Becomes a Strategic Investor in Dental Monitoring. Retreived from https://www.prnewswire.com/news-releases/straumann-becomes-a-strategic-investor-in-dental-monitoring-679479743.html.

Frey, S. (2017). A Quick Look at Dental Monitoring. Retreived from https://theorthocosmos.com/quick-look-dental-monitoring/.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. doi:https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Gartner. (2017a). Gartner Identifies Three Megatrends That Will Drive Digital Business Into the Next Decade. Retreived from https://www.gartner.com/en/newsroom/press-releases/2017-08-15-gartner-identifies-three-megatrends-that-will-drive-digital-business-into-the-next-decade

.

Gartner. (2017b). Hype Cycle for Consumer Engagement With Healthcare and Wellness. Retreived from https://www.gartner.com/doc/3765263/hype-cycle-consumer-engagement-healthcare.

Golbeck, J. (Producer). (2013). Your social media “likes” expose more than you think. Retreived from https://www.ted.com/talks/jennifer_golbeck_the_curly_fry_conundrum_why_social_media_likes_say_more_than_you_might_think?language=en – t-579929.

Goldstein, A. J. (2017). Data Science Deconstructed. Retreived from https://ajgoldstein.com/2017/11/12/deconstructing-data-science/.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115. doi:10.1016/j.is.2014.07.006

IDC. (2014). International Data Corporation Digital Universe Study. http://www.emc.com/leadership/digital-universe/index.html.

Jacobs, A. (2009). The pathologies of big data. Retreived from https://queue.acm.org/detail.cfm?id=1563874.

Katyal, V. (2018a). Artificial Intelligence (AI) driven customised orthodontics is the future. Retreived from https://www.linkedin.com/pulse/artificial-intelligence-ai-driven-customised-future/.

Katyal, V. (Producer). (2018b). BOSS Club launch announcement. Retreived from https://youtu.be/2S-zwnK5W3g.

Katyal, V. (2018c). Future of Orthodontics is Artificial Intelligence(AI). Australasian Dentist(74), 54-56.

Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proc. VLDB Endow., 5(12), 2032-2033. doi:10.14778/2367502.2367572

Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science, 343(14th March), 1203-1205.

Mankins, M. C., & Sherer, L. (2015). Creating Value through Advanced Analytics. Retreived from https://www.bain.com/insights/creating-value-through-advanced-analytics.

Morgan, J. (2014). A Simple Explanation Of ‘The Internet Of Things’. Retreived from https://www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can-understand/ – 5c07ef271d09.

Price, L. (2018). The Digital Health Hype Cycle 2017. Retreived from https://www.healthcare.digital/single-post/2017/09/17/The-Digital-Health-Hype-Cycle-2017.

Ries, E. (2011). The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. USA: Crown Publishing Group.

Roisin, L.-C., Brézulier, D., & Sorel, O. (2016). Remotely-controlled orthodontics: fundamentals and description of the Dental Monitoring system. Journal of Dentofacial Anomalies and Orthodontics, 19(4), 408.

Saragoza, A. (2013). Why Data Artisans Are The New Data Scientists. Retreived from https://www.fastcompany.com/3020211/why-data-artisans-are-the-new-data-scientists.

Schmarzo, B. (2016). Big Data Business Model Maturity Index Guide. Retreived from https://infocus.dellemc.com/william_schmarzo/big-data-business-model-maturity-index-guide/.

Sensis. (2016). How Australian people and businesses are using social media. Retreived from www.sensis.com.au/socialmediareport. Retrieved from

Sharma, R. (Producer). (2018). Big data as a competitive advantage. Ducere Learning Materials. Retreived from https://learning.ducere.edu.au/course/view.php?id=1697.

van Rijmenam, M. (2014). Think bigger: Developing a successful big data strategy for your business: Amacom.

Verhoef, P. C., Kooge, E., & Walk, N. (2016). Creating Value with Big Data Analytics: Making Smarter Marketing Decisions. Routledge, .