Deciphering how the newfound connective social traces of people are reframing modern eCommerce. (Aug 2015)

The Present Condition:

Many species, including people, are social organisms that leave behind social traces in nature and society (Freeman, 1995 and Baars, 1997).  The social traces people leave behind are natural occurring phenomenon and people generate these to represent their existence.  Given the right conditions for an online ecosystem people generate social traces that coalesce to form newfound connective social traces of online behaviours.  Unlike early research that saw the technology as separate from the evolutionary behaviours of people.   These newfound connective social traces of people are inseparable from their evolutionary behaviours and they create value for modern eCommerce.   Also, the impacts of these newfound connective social traces of people has been overlooked for modern eCommerce and are especially relevant in the rising big data technology and the internet of things (that is, the sensory of things in society).  In deciphering newfound connective social traces recorded in free social applications like Yelp, the traces reveal early stage decision making behaviours of conscious choices people act on while trading for necessities through eCommerce and provide merchants with summary consumer data (King, 2011).  These newfound connective social traces reveal the early stage decision making behaviours in people whenever their trading decisions are intentional (James, 1890).  The decision making trading behaviours of people represent conscious or voluntary choices (that is, psychological) they act on when using social applications (Baars, 1997).    In deciphering the newfound connective social traces for the brains neural circuitry (that is, physiological), the neural circuitry also reveals early stage decision making behaviours for people’s social motives (that is, fulfilling the need to be social) whenever they interact with social applications like Facebook.  Innately, the brain’s neural circuitry has permanent connections that stimulate behaviours people act on to be social with others, even if individuals are presently in non-social moments (Leberman, 2015).  

In line with suggestions by James (1890), Baars (1997), Lieberman (2015), some research deciphers the newfound connective social traces in order to create frictionless technology to reduce stressors that people can experience while using technology.   Stressors like technostress (that is, stressors people experience while interacting with technology) affect the productivity of people (Tams et al., 2014).  In deciphering newfound connective social traces in social applications like Twitter, stressors experienced by merchants while interacting with a financial services unreliable payments technology reveal emotional behaviours of frustration.  Also, newfound connective social traces reveal merchants were conscious of the effects of unreliable technology on income loss and productivity loss (The Sydney Morning Herald, 2015).  This form of newfound connective social traces indicates the effects of technostress flow beyond traditional boundaries of organisations’ end users.  There is evidence (Davis et al., 2011; Tams et al., 2014; Silva et al., 2014), that combining newfound connective social traces for people’s decision making behaviours with previously existing information sources, can reveal new discoveries for eCommerce, technostress and the internet of things (IoT).   Organisations unable to move beyond deciphering previously existing transactional information face problems in effectively making decisions to grow their businesses in the 21st Century.

  

The Practical Problem:    

Most organisations face a practical problem in deciphering how the newfound connective social traces of people are reframing modern eCommerce.  For instance, organisations including financial services firms have been collecting, processing and aggregating business information on their customers for years.  However, despite collecting enormous amounts of customer information, the data sets collected provide no insights to decipher correctly each stage of people’s decision making behaviours, including their early stages.  At present, the interplay between the newfound connective social traces and the early stages of people’s decision making behaviours that they act on with merchants online, is ahead in time of the decision making ability of financial services firms.   The inability of financial services firms to decipher the early stages of people’s decision making behaviours collected in social applications creates a perpetual time lag.  This time lag makes predicting demand or determining what the consequences of different actions may be for merchants unlikely (Simon, 1955). This limited use of available information within financial service firms hamper their ability to understand the decision making behaviours of people for the benefit of merchants, in the 21st Century.  There is a need for combining dimensions of the entire person’s decision making behaviours and social patterns that are reframing modern eCommerce. 

The Research Questions:

This research will attempt to decipher how the natural occurring phenomenon humans generate for the newfound connective social traces, are reframing modern eCommerce.   The research intends to explain the interplay between causal mechanisms for the early and final stages of decision making behaviours reframing eCommerce, Technostress and the IoT.   An important series of articles (Shmueli, 2010; Breiman, 2001; Breiman et al., 1984) suggest conducting statistical modelling as one way to explore and develop hypotheses, which can be tested by targeted experiments.  Whilst recognising their complexity, both explanatory and predictive modelling dimensions will combine both the newfound connective social traces and the final interactions people have with merchants at the end of the decision making process for settlements of payments of goods.  The dimensions will be used for testing the following hypotheses:

•  Are decisions associated with merchants’ expansion goals correlated to the predictability power in market characteristics modelling?
• Will the model with the most explanatory or predictive power enable both relationship managers and merchants to improve their judgement when choosing between patterns for growth markets?
•  Do these models relate to patterns or behaviour and decision making to enable increased growth/profit for merchants and relationship managers?
•  What stressors associated with unreliable banking technology affects the emotions of merchants or other people in society?
•   What newfound connective social traces of people are becoming networked into the IoT? 

How I hope To Conduct the Research: 

This study aims to use a mixed method approach (that is, both qualitative and quantitative) to explore the research hypothesis/questions.  A mixed method approach was selected for conducting this study because the approach interweaves the best of both qualitative and quantitative methods into a lone study (Creswell, 2003 and Bryman, 2007).  Consequently, this study intends to integrate these methods into a lone study that will cover three stages.  The first stage will be quantitative, and will cover a period of one to two years.  This first phase intends to collect quantitative data to analyse the data and to identify taxonomies.   In this first phase an instrument (that is, learning algorithms) will be created based on those identified taxonomies to conduct measurements on a large sample in order to reveal how the newfound connective social traces of people are reframing modern eCommerce.  The second phase is qualitative, and will be carried out in parallel with phase one.  Qualitative data will be collected and analysed using response codes to identify advice categories.  In this second phase an instrument (that is, focused questions, transcripts) will be created from a randomised sample that will be conducted with relationship managers in order to reveal what advice categories are associated with the judgments of both merchants and relationship managers when choosing between patterns for growth in markets.   This second phase intends to identify the interchange between those advice outcomes that connect with merchant’s expansion goals or needs. The third stage for this study is an analysis, interpretation and discussion of the overall research findings.  It is intended that this third stage covers a period of one and a half years.  This third phase will explain those behaviours and capabilities for deciphering how the newfound connective social traces of people are reframing modern eCommerce. 

Newfound Connective Social Traces Are Everywhere (Mayberry, 2015)

References:

1.  Baars, B. J. 1997. In the Theater of  Consciousness. Oxford: Oxford University Press.

2. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont, CA: Wadsworth International Group. 

3.  Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by theauthor).  Statistical Science, 16(3), 199-231.  

4.  Bryman, A. (2007).  Barriers to integrating quantitative and qualitative research.  Journal of Mixed Methods Research, 1, 8-22.

5.  Creswell, J. W. (2003).  Research design: Qualitative, quantitative, and mixed methods approaches (2nd ed.). Thousand Oaks, CA: Sage Publications.

6.  Dimoka, A., Pavlou, P. A., & Davis, F. D. (2011). Research commentary-NeuroIS: the potential of cognitive neuroscience for information systems research. Information Systems Research, 22(4), 687702. 

7.  Freeman, W. J. 1995. Societies of Brains. Hillsdale NJ: Lawrence Erlbaum.

8.  James, W. 1890. The Principles of Psychology. Cambridge, MA: Harvard University Press. 

9.  King, G. (2011). Ensuring the data-rich future of the social sciences. Science, 331(6018):719–721. 

10.  Shmueli, G. (2010). To explain or to predict? Statistical Science, 289-310. 

11.  Simon, H. A. (1955). A behavioural model of rational choice. The quarterly journal of economics, 99-118.

12.  Spunt, R. P., Meyer, M. L., & Lieberman, M. D. (2015). The Default Mode of Human Brain Function Primes the Intentional Stance. Journal of cognitive neuroscience.

13.  Tams, S., Hill, K., Ortiz de Guinea, A., Thatcher, J., & Grover, V. (2014). NeuroIS—alternative or complement to existing methods? illustrating the holistic effects of neuroscience and self-reported data in the context of technostress research. Journal of the Association for Information Systems, 15(10), 723-753. 

14.  The Sydney Morning Herald. (2015). Online retailers blame lost sales on NAB payment system problems. Retrieved June 09, 2015 from http://www.smh.com.au/business/banking-andfinance/online-retailers-blame-lost-sales-on-nab-payment-system-problems-20150602-ghevpu.html

Big Data: A Playbook to pivot for success.

The idea that Big Data is about people was one of the salient themes highlighted during the panel discussion at the 2012 Big Data summit in Sydney, Australia. The reality of this Big Data phenomenon is being influenced by a trend in which the world’s ability to generate data (e.g. from humans and machines) is far exceeding our ability to analyse it. How organisations adapt to this trend in the near to long term is a critical factor enabling them to pivot for success . 

The Big Data phenomenon is a condition in which data comes in all scales and shapes encompassing millions of everyday new human interactions that are recorded and millions of old human interactions that are digitised. The phenomenon that follows has been creating massive new sources of data about human contention, collaboration and overall social behaviour. Most organisations recognise they need a resilient playbook that will enable them to pivot to find success. 

The pivot points for success in organisations can be found in how they formulate answers to questions such as:
· What data do we have?
· What data do we need or what data is valuable to the organisation?
· Is data viewed as an asset?
· What tells us that data is an asset?
· How can we extract its true value?
· How can we effectively use data as an asset to differentiate?
· How do we foster a climate for data driven discovery to flourish?

The answers to such questions need to be examined to help organisations shape the right conversations that will unleash the 4V of Big Data - ‘Value’. As shown in Figure A, digitisation is all around us and an organisation’s ability to pivot successfully depends on how they use data. The window of opportunity is rapidly narrowing and will soon leave many organisations with very little time to adapt to this Big Data phenomenon for competitive advantage. Value is much less about technology advancements and is more about how fast data experts enable their entire organisation to infer meaning from data and take action based on that meaning.

This thesis was recently played out in society on the backdrop of the 2012 American presidential election. There is no better case study than what was witnessed. The outcome from this high profile event provides more evidence that Big Data and the science of data are real. In addition, it provides a case study on the successful use and misuse of data at scale. Whilst both candidates attempted to use Big Data as a competitive advantage the playbook that was executed produced different outcomes. In the aftermath of the election lessons learnt from the winning candidate (or organisation) has outlined a playbook that enabled the candidate’s team to pivot for success to unleash the 5V of Big Data – ‘Victory’.

Figure A: This data ecosystem is on the verge of changing the way we live and work.

[1]

Signal validation for Big Data:

The presidential election affirmed that in this era of Big Data an organisation’s ability to harness data at scale as an asset is more than looking at data simply as something that just feeds into a report or dashboard that you would show to an executive once every month or quarter. The winning candidate fostered a mindset that not only looked at data as an asset in itself, but infused in its climate the science of data. As these factors shaped the operating rhythm it ultimately enabled the winning candidate’s team to continuously pivot for success. In addition, the winning candidate amassed data experts who had in-depth understanding of how to effectively use accessible technologies, efficiently collect data from a variety of sources and establish a collaborative data ecosystem or whole system with people and machines working in harmony. The winning candidate’s team of data experts understood that for systems to succeed, especially systems for data analysis, they must be consciously designed to enhance the capabilities and thinking of people, not to replace them. [2] [3] [4]


For instance, the Time article, "Inside the Secret World of the Data Crunchers Who Helped Obama Win" suggests from day one the winning candidate had a priority to attract and retain data experts (e.g. data scientist, researchers, behavioural scientist and so on) that understood the science of data and knew how to use it as a competitive advantage.

The article goes on to suggest the mindset or tone was set at the top to use data as an asset and dissolve data silos that had been stored in many different systems. The team of data experts were empowered to innovate and create as they applied the science of data to establish a collaborative data ecosystem enabling the overall team to compete at its highest potential.

The collaborative data ecosystem enabled computer simulations to run the election 66,000 times every night to predict the likelihood of the candidate’s team winning each swing state. In addition, the system’s ability to scale fostered a climate of data driven decision making that was so accurate it enabled the winning candidate’s team to not only uncover demand potential, but also to proactively pivot for success as they adapted to changes allocating resources to effectively interact with citizens across all channels. [5][6]

The opposing candidate’s team provides lessons learnt on how the misuse of data can quickly degrade its value. Although the opposing candidate’s team had a similar strategic intent, their lack of ability to apply science to data and infer meaning for execution prevented the overall team from competing at its highest potential. Their system was unable to deal with data at scale – another critical factor inhibiting the opposing candidate’s team’s ability to put Big Data and Analytics to work. [7] [8]

Among other lessons learnt (and there are many more), this disadvantage highlighted that in future the candidate with the best data and data experts who understand the science of data will have the competitive advantage.

Is this Big Data playbook to pivot for success new?

No, not really! The winning candidate’s use of data is not unlike what most Born Digital organisations have already embraced and embedded within their business models. Born digital organisations such as Google, Facebook, Amazon, Cloudera, FourSquare and many others have well established cultures that understand the value of data and how to apply the science of data to harness data as an asset. In addition, their data experts understand that behavioural and social science provides time-tested methodologies for discerning meaning in data. [9] [10] [11]

As Big Data innovations continue to introduce breakthroughs which establish a more resilient human link to the data being analysed, improvements are evident in reduced time to insight and an organisation’s ability to infer meaning from data at scale and take action based on that meaning. For instance, Cloudera Impala’s distributed query execution engine, Facebook's Corona (Under the Hood: Scheduling MapReduce jobs more efficiently with Corona), or Amazon’s recent announcement to expand its AWS footprint in a new region in Sydney, Australia are just a few of the innovations that will enable data experts to help transform their organisations into data driven powerhouses. [12] [13] [14]

Is Big Data a possible macro growth story?

The Big Data innovations are among the green shoots that have the possibility to influence the macro economic growth story. As governments look for a balance through austerity measures, interventions that focus on policy to accelerate advancements in Big Data may enable the world to emerge faster from this current economic rough patch.

There have been many recent studies that have outlined the economic opportunities that Big Data offers on the backdrop of organisations, entrepreneurs, researchers, and practitioners producing new sustainable products or services. For instance, IDC has estimated Big Data growth to be at US$258.5 million in 2011 to $1.76 billion in 2016. This is on the back of a 46.8% five-year compound annual growth rate. Gartner estimates Big Data will create 4.4 million IT jobs globally to support Big Data by 2015 and in the Asia-Pacific region Gartner suggests 960,000 new IT jobs will be created in the next three years from Big Data. [15] [16] [17]

MarketsandMarkets has estimated Hadoop will grow into a market that will rise at a combined annual growth rate (CAGR) of 54.9% over the next five years from $1.56bn to reach $13.95bn by 2017. [18]  This is not to suggest that meeting these estimates alone will spill over to stabilise global growth. However, a focus on Big Data innovations may be one of the levers that enable’s the world to tip into a much better equilibrium. This has the possibility to accelerate through public and private partnerships with the right focus.

Conclusion:

The science of data, the thinking, and the methods when brought together in a humanistic way are beautiful and breathtaking at the same time. The strategic intent to use Big Data and Analytics as a competitive advantage is not easy and in the aftermath of the high profile presidential election we learn that the Big Data phenomenon is real and its use can enable all organisations to pivot for success. In addition, managing data at scale to influence disruptions in markets or political systems is not isolated to America; it is within reach of every organisation, industry or sector that truly believes that data is an asset.

As organisations continue to evolve away from just looking at data as numbers on a report towards being a true asset in itself that needs to be leveraged in new ways, they too will pivot for success.

References:

[1]. Martinez, K. (2011). The NSF Perspective on HEC-FSIO (as seen from CNS). (slidefinder). NSF Directorate for Computer and Information Science and Engineering.http://www.slidefinder.net/k/keith_marzullo_director_division_computer/32919200
[2]. Harris, D. (2011). Obama seeks data scientists for election edge. GigaOM.http://gigaom.com/cloud/obama-seeks-data-scientists-for-election-edge/
[3]. Harris, D. (2012). How Obama’s tech team helped deliver the 2012 election. GigaOM.http://gigaom.com/cloud/how-obamas-tech-team-helped-deliver-the-2012-election/
[4]. Cramer, R. (2012). Messina: Obama Won On The Small Stuff ( Romney aide admits: “We thought the game would be one thing, and it ended up being another.”). BuzzFeed. http://www.buzzfeed.com/rubycramer/messina-obama-won-on-the-small-stuff-4xvn
[5]. Scherer, M. (2012). Inside the Secret World of the Data Crunchers Who Helped Obama Win. Time Magazine.http://swampland.time.com/2012/11/07/inside-the-secret-world-of-quants-and-data-crunchers-who-helped-obama-win/#ixzz2CVerEtAo
[6]. Martinez, J. (2012). Data drove Obama’s ground game. The Hill. http://thehill.com/blogs/hillicon-valley/technology/266987-data-drove-obamas-ground-game
[7]. Gallagher, S. (2012). Inside Team Romney's whale of an IT meltdown. Ars Technica. Technology Lab / Information Technology.http://arstechnica.com/information-technology/2012/11/inside-team-romneys-whale-of-an-it-meltdown/
[8]. Burns, A., & Haberman, M. (2012). Romney's fail whale: ORCA the vote-tracker left team 'flying blind'. Politico. http://www.politico.com/blogs/burns-haberman/2012/11/romneys-fail-whale-orca-the-votetracker-149098.html
[9]. Freda, A. (2012). The Promise of Big Data. Harvard School of Public Healthhttp://www.hsph.harvard.edu/news/features/files/big_data.pdf
[10]. Mayberry, T. (2012). Big Data: The Hidden Asset in Born Digital Organisations. Torrance Mayberry’s Blog. http://torrance-mayberry.blogspot.co.nz/2012/06/big-data-hidden-asset-in-born-digital.html
[11]. Shiffman, G. (2012). Big Data Analytics and the Economics of Organized Violence. The Diplomatic Courier.http://www.diplomaticourier.com/news/topics/economy/1263-big-data-analytics-and-the-economics-of-organized-violence
[12].  Robinson, H. (2012). Cloudera Impala. Paper Trail. http://the-paper-trail.org/blog/cloudera-impala/ 　
[13]. Facebook Engineering. (2012). Under the Hood: Scheduling MapReduce jobs more efficiently with Corona. Facebook Engineering Notes. https://www.facebook.com/notes/facebook-engineering/under-the-hood-scheduling-mapreduce-jobs-more-efficiently-with-corona/10151142560538920　
[14]. Amazon. (2012). New Asia Pacific (Sydney) Region in Australia - EC2, DynamoDB, S3, and Much More. Amazon Web Services Blog.http://aws.typepad.com/aws/2012/11/asia-pacific-sydney-region-open.html
[15]. Stires, C., Jimenez, D., Chung, D., Tan, D., Sehgal, V., & Rajnish, A. (2012). APEJ Big Data Technology and Services 2012–2016 Forecast and Analysis. IDC. http://www.idc.com/getdoc.jsp?containerId=AP2670106X　
[16]. Pettey, C. (2012). Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data By 2015. The Gartner Group. http://www.gartner.com/it/page.jsp?id=2207915
[17]. Barwick, H. (2012). Big data to create 960K new IT jobs in APAC by 2015: Gartner. ComputerWorld Techworld. http://www.techworld.com.au/article/441840/big_data_create_960k_new_it_jobs_apac_by_2015_gartner/　
[18]. Marketsandmarkets (2012). Hadoop & Big Data Analytics Market - Trends, Geographical Analysis & Worldwide Market Forecasts (2012 – 2017). Top Market Reports. http://www.marketsandmarkets.com/Market-Reports/hadoop-market-766.html

Big Data: The Hidden Asset in Born Digital Organisations.

This examines one of the critical factors that will enable born digital organisations to propel the trajectory of their competitive advantage. The world is changing fast and born digitals (those shaping the digitally social future) continue to innovate game changing technology based on a hidden asset helping shape the digital future. Born digital organisations have been using Big Data successfully in their businesses enabling them to change the face of technology and society.

Born digital organisations like Google, Facebook, Twitter, Amazon, FourSquare, and many others, have embodied and embedded social sciences into core business models ensuring they are better positioned to thrive irrespective of economic uncertainty. They understand that social sciences play a significant role in disrupting markets in a digitally social world. It is this hidden asset that will become increasingly important as it gives them a solid foundation to ethically harness social data to help advance the human condition.

On the backdrop of the world becoming increasingly interconnected, a phenomenon inherent in this Big Data era is the vast amount of social data that is digitally produced as humans go about their daily lives. The circulation of social data throughout the wider social-ecosystem will virtually have no limit as technology innovations introduce all kinds of everyday things in society that can be connected and context aware.

For instance, Vint Cerf, Chief Internet Evangelist at Google, suggests that virtually everything human beings own in society will have its own internet address and, as the momentum continues, "home appliances, keys, wine cellars, the dog's collar - everything" has the possibility to be affected[1]. The significance of the phenomenon has also influenced traditional organisations. Ford recently opened a Big Data lab in the Silicon Valley highlighting what they now envisage is an integral part of the automotive future [2].

Researchers and entrepreneurs around the world have been developing systems that will optimise digital interactions. Researchers at the University of Virginia have demonstrated the commercial possibilities that can be achieved through sensors to track typical objects for household environments. This has the possibility to provide household environments with an enhanced ability to keep track of the locations of everyday household objects such as keys, remotes, kids toys and so on. Households empowered with access to such capabilities will find it much easier to keep track of all type of physical objects in their daily lives [3][4][5].

There is also greater availability of autonomous sensors used to monitor everything from weather to human digestive tracts. This produces a diversity of social data unseen until now and it has the ability to usher in the digitally social world.

Organisations that apply social sciences will be better positioned to understand what this new data is telling them in order to shape the digital future and develop products and services that can help change the lives of people for the better. Most born digital organisations already have a head start and most have successfully infused social sciences, imagination, creativity and, of course, computational thinking within their organisations. Big Data, combined with the hidden asset of social sciences, is enabling them to quickly introduce innovations to compute their way to a socially and ecologically sustainable future.

Born digitals will break away from much of the competition as they have recognised the importance of moving beyond the fields of hard science and business to truly combine the fields of social sciences into their working practices. They have established the necessary bench strength to compete in this new era. For instance, researchers at Twitter, Facebook and Google have been applying social sciences to shape the digital future through actively creating new knowledge for the field. This will become an increasingly valuable asset as digital interactions will require an understanding of human behaviours [6][7][8][9].

This advantage will not only help them to discover new types of research data about human behaviour, but it will also enable them to adopt feedback learnings into their core business models to disrupt markets [10]. Infusing and applying social sciences within their business models enables them to investigate human and social dynamics at all levels of analysis, including cognition, decision making, behaviour, groups, organisations, societies, and the digital world.

The many discussions I've had with information technology researchers and analysts about the role social data plays in shaping the digital future, prompted me to reflect on a past experience I had as a technologist in the 90's.

At that time social science researchers, practitioners, and policy makers recognised and seized upon an opportunity that enabled many with a technologist background to apply computational thinking to develop applications and instruments for the human services field (Sociology, Psychology, Social Work, Psychiatry).

A critical aspect of technology innovations back then focussed on the ability to provide the human services field with richer information on human related behaviours, perceptions, intentions, desires, concerns, and beliefs. An aim was to ensure insights were gleaned from social data to help empower children, families and communities. Although in the early days my experience in social science research was concentrated in the US, collecting, storing, and analysing this data was critical to understanding the mechanisms by which people express themselves worldwide and in diverse situations.

I had the privilege of applying computational thinking to help produce Intervention Services-Activity Based Costing (IS-ABC) as an instrument. It was an automated system of costing intervention services or programs in the human services field. Among other things, it enabled policy makers to evaluate the relative performance of programmes and interventions. In addition, it provided the foundation for Outcome Based Decision Making (ODBM) to ensure optimal and sustainable delivery of services [11].

Although, much of the focus in the Silicon Valley at the time was on technology innovations for business, I was fortunate to experience first hand how the ethical use of social data can help to change outcomes in society.

The collaborative process with the researchers in the field was an important factor that ensured the instruments I helped develop kept people at the centre of service. For instance, time spent with Metis Associates http://www.metisassoc.com/index.html  and the Annie E. Casey Foundation Family-To-Family Initiative http://www.aecf.org/MajorInitiatives/Family%20to%20Family.aspx also helped me to understand the value of social data when there is an ethical intent to use it to advance the human condition. Our analysis method (IS-ABC) was accepted and presented in 1997 at Portland State University Regional Research Institute for human services. http://www.rri.pdx.edu/

There are still many unanswered questions about Big Data and how the social sciences field will be used to help shape the digital future. The issues regarding personal privacy and how people will have the peace of mind that their information is not being used unfairly is still a work in progress. As born digital organisations contribute to social science research through their innovations they will have an advantage  – they will better understand how the rise of Big Data will influence changes as the world becomes more digitally social.

Now back to those discussions with information technology researchers and analysts – it was my experiences in the social sciences field that gave me a solid foundation to combine computational thinking and the social sciences to encode and decode real world social data signals. Moreover, the experience inspired me to innovate applications and instruments in collaboration with social science researchers, practitioners, and policy makers, placing at the epicentre of our innovations the purpose to advance the human condition.

References:

[1] Bort, J. (2012).A Whole New Version Of The Internet Is About To Be Switched On.
Business Insider.
http://www.businessinsider.com/ipv6-new-internet-switched-on-2012-6

[2] King, R. (2012). Ford Opens Silicon Valley Lab to Mine Big Data. The Wall Street Journal.
http://blogs.wsj.com/drivers-seat/2012/06/20/ford-opens-silicon-valley-lab-to-mine-big-data/

[3] Nirjon, S and Stankovic, A., J. (2012). Kinsight: Localizing and Tracking Household Objects using Depth-Camera Sensors. Department of Computer Science University of Virginia.
http://www.cs.virginia.edu/~stankovic/psfiles/DCOSS2012.pdf

[4] Living Labs. (2012). Farglory LeftBank Smart Urban in New Taipei City
http://www.youtube.com/user/livinglabsglobal?feature=watch

[5] Esser, B., Schnorr, J. M., Swager, T. M. (2012). Selective Detection of Ethylene Gas Using Carbon Nanotube-based Devices: Utility in Determination of Fruit Ripeness. Angew. Chem. Int. Ed., 51: 5752–5756. doi: 10.1002/anie.201201042.

[6] Markoff, J. (2012). Troves of Personal Data, Forbidden to Researchers. The New York Times.
http://www.nytimes.com/2012/05/22/science/big-data-troves-stay-forbidden-to-social-scientists.html?_r=3&smid=tw-nytimesscience&seid=auto

[7] Lin, J. and Mishne, G. (2012). A Study of "Churn" in Tweets and Real-Time Search Queries (Extended Version). Social and Information Networks (cs.SI), Cornell University.
http://arxiv.org/abs/1205.6855 

[8] Simonite, T. (2012). What Facebook Knows. Technology Review, MIT.
http://www.technologyreview.com/featured-story/428150/what-facebook-knows/

[9] King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science 331, 719.

[10] Cha, E. A. (2012). ‘Big data’ from social media, elsewhere online redefines trend-watching.
The Washington Post.
http://www.washingtonpost.com/business/economy/big-data-from-social-media-elsewhere-online-take-trend-watching-to-new-level/2012/06/06/gJQArWWpJV_story.html

[11] Alford, K., Mayberry, T., Woodard, L. J. (1997). IS-ABC : Intervention services-activity based
costing. Library of Congress, Copyright 1997, TXu000778283.
http://cocatalog.loc.gov/cgi-bin/Pwebrecon.cgi?Search_Arg=Intervention+Services&Search_Code=TALL&PID=5HdIf9_6UDchwoTxAYmI6uzGe&SEQ=20120518171328&CNT=25&HIST=1

Harnessing Social Data: Understanding IT's Role‎

CIOInsight:  Westpac saw a huge opportunity to use social data as a lens into both the business’ future and the relationship between brand and customer. But, selling this kind of “out there” vision to stakeholders can be more difficult than executing on it.

Read More: http://www.cioinsight.com/c/a/Business-Intelligence/Harnessing-Social-Data-Understanding-ITs-Role-145382/

The Trend of Big Data in Asia-Pacific

The IDC Asian Financial Services Congress 2012 (AFSC) brought together thought leaders from the financial services industry across Asia-Pacific. The event enabled more than 500 attendees to network, learn and share best practices to address unique challenges in the 21st Century.

The rise of Big Data was a recurring theme recognised to have major impacts on future business. As a trend, Big Data was discussed in depth at the congress. There was an overwhelming consensus that Big Data and analytics can no longer be left at the fringes. It was clear that organisations had an urgent need to quickly bring it in from the fringes and create the internal mindshare to monetise the true value of Big Data. The region’s financial services organisations made it clear they plan to put Big Data to work http://www.ap.afscongress.com/2012/.

This was a critical factor that everyone understood to rest solely within an organisation’s control. Their success and competitive advantage will ultimately depend on their ability to grow and make data centric capability a core competency.

Many organisations had a very strong reliance on third parties for creating new knowledge and innovating in the data space. The market shift to a data intensive society has caught them on the back foot. Additionally, businesses across a wide range of industries are not aware of the speed at which data centric innovations and digitisation are occurring at born digital organisations disrupting markets (e.g. Twitter, Cloudera, Facebook, Google, FourSquare, LinkedIn, Bitly and many others.).

The old business models that outsourced data centric knowledge and innovations worked well before the global financial crisis (GFC), but they are not sustainable anymore. As the trend of Big Data continues to gain momentum it will expose organisations’ weaknesses and threaten future growth as they will be slow to adapt to change continuing the loss of competitiveness. In essence they will have failed to recognise that Big Data is a by-product of the digital age and, given enough time, it will encompass the entire future world of business. This too was a common theme that reverberated at the World Economic Forum 2012 in Davos Switzerland. In a report it suggests Big Data is a new class of economic asset, like currency or gold http://www.weforum.org/reports/big-data-big-impact-new-possibilities-international-development .

A critical factor highlighted in my presentation at the congress was the importance of staying on trend in this new era. Organisations at the congress understand how significant it is that they figure out creative ways to make use of Big Data. Sustaining competitive advantage will come down to organisations’ ability to monetise Big Data as it has become a critical ingredient for the successful execution of future business models.

For instance, American Express is harnessing social data fom Twitter in creative and disruptive ways. The thinking behind their recent business extension into the twittersphere provides a nice vantage point into how organisations can monetise Big Data and put it to work for growth. They’ve converged Big Data on-premises and off-premises to extend their digital presence across social web channels. This is enabling their organisation to connect with social customers and merchants in relevant ways http://venturebeat.com/2012/03/06/amex-tweet-savings/ .

The various parts of an organisation’s business operations (e.g. marketing, sales, human resources, product development, contact centre and finance) must become tightly integrated across multi-channel touch points to ensure that when decisions are made or interactions happen, all parts of an organisation work together to cohesively and adaptively respond to change.

According to Peter Drucker “a time of turbulence is a dangerous time, but its greatest danger is the temptation to deny reality” (Drucker, 1980). Organisations at the congress recognised they must leave past practices behind and expand upon or create data centric capability that is aligned with the new realities of Big Data. This transformation in thinking will call for new mental models and approaches that will enable organisations to sustain future competiveness. Organisations need to build their business ecosystem for what the world needs today and tomorrow.

The bottom line - organisations need to stay on trend in this new era. They must work through what the Big Data trends mean to future business to determine how best to charter a sustainable course underpinned by a holistic Big Data Strategy and Data Governance. It’s clear the region is looking to further expand its use of data centric capability. This aspiration is further reflected in the recent IDC Big Data Technology and Services Market Forecast that suggests the market “represents a compound annual growth rate (CAGR) of 40% or about 7 times that of the overall information and communications technology (ICT) market” http://www.idc.com/getdoc.jsp?containerId=prUS23355112 .

This explosion of data is creating a winner takes all market. Given enough time the entire world will head towards digitisation and organisations that are slow to adapt will continue to face extreme headwinds as they struggle to move Big Data from the fringes and build mindshare to monetise it.

Big Data & the Seven P’s: To Advance an Organisation’s Growth Agenda

The data intensive society in which we live, work and play has rapidly emerged as a force influencing radical changes in the competitive landscape. The business ecosystem has become increasingly digital and globally interconnected. As a consequence organisations face challenging headwinds as cycles of change that are much faster create greater uncertainty. Organisations must innovate to grow, creating new business models in this era of “Big Data”. Adaptation is critical because markets react to reality and not expectations.

In this new norm the amount of data generated in society is staggering. Its complexity and the pace at which it is generated are also continuing to accelerate. Among other things, these factors have emerged testing the future competitiveness of organisations. An organisation’s resilience will be challenged and their ability to withstand the headwinds will depend on their ability to capitalise on Big Data.

Organisations that find creative and innovative ways to grow holistically will continue to break away from the pack. As long as they continue with a quickened responsiveness and innovation cycle that keep pace with this new norm – the data intensive society – they will have the advantage over their competitors.

This has left most organisations flat footed as yesterday’s business as usual is not aligned with today’s data intensive reality, and this is a disadvantage. As organisations struggle to understand how data can open up new possibilities in this data intensive society, they remain vulnerable to the sensitive ups and downs of faster change cycles. At the World Economic Forum (WEF) 2012 this theme was recognised as another major challenge that organisations must address http://forumblog.org/2012/01/davos-2012-decoding-the-data-deluge/

Most organisations in attendance agreed the way forward to address such a challenge was to reinvent business models to ensure they align with the market and societal expectations. In addition, most organisations agreed the long-term outlook would remain a challenge and uncertain until transformations occur to shape new models. Although this may be the case for most organisations, others will need to consider reformations to align with the new reality of Big Data. Successful reformations undertaken will enable organisations to effectively compete. This aspiration and its execution will depend on an organisation’s ability to make sense of data and how it gets integrated at the core of its business. An ability to harness Big Data to improve productivity and customer experience are a few examples of business outcomes they can achieve.

The reformation that fosters a greater level of data awareness and data imagination will have more potential to move an organisation’s thinking beyond yesterday’s business as usual mind sets. Reformations must also ensure organisations establish the right climate and culture to create the conditions to innovate for growth. For instance, organisations should focus reformation initiatives to reduce the amount of cognitive effort involved in decision making for both customers and employees.

In this new data intensive society, data is described as the digital air, the oxygen that binds together the rhythm of society and the carbon dioxide that it exhales (Boyd and Crawford, 2011). Organisations that focus on data centric capability as being core to future competitiveness will be better positioned to harness data intensive possibilities as society breathes this digital air.

The vantage point in Figure A – the Seven P’s shows the different but interconnected dimensions of Big Data. It can be used to create data awareness to tap into data imagination that explores new business models and opportunities. This is critical for organisations that are competing in the digital economy. The digital economy will continue to reward organisations that recognise the significance of the radical shift that has emerged and can unleash data-centric capability to bring forth new value.

In a series of posts I will examine the Seven P’s and their influence on an organisation’s ability to unleash value in this new data intensive society. As the emergence of this data intensive society stakes out the new competitive landscapes, methods of business will eventually need to adapt.

In brief the Seven P’s are:

Possibilities: opening the imagination of an organisation towards using Big Data for disruptive competition to meet business goals and deliver new value.

Purpose: the establishment of data centric capability that harnesses innovation and creativity to ethically advance the human condition.

Process: the implementation of data centric techniques that harnesses data in an ethical manner so all units of a business can act in real time to generate value that others have not imagined.

People: utilising the knowledge and skills of data centric capability as a catalyst for innovation by exploiting data as a raw material to add value.

Profit: capitalising on transformative data to achieve organisational goals and profit motivations.

Policy: the formulation of policies for the security and privacy of data collection and use while keeping pace with advancing technology innovations (Tene and Jules, 2012) http://www.stanfordlawreview.org/online/privacy-paradox/big-data.

Productivity: enabling real time cognitive decision making while reducing effort for both employees and customers. The reduction in time to make decisions creates value, freeing up the entire organisation to focus on driving results to meet goals.

An organisation’s ability to sustain benefit realisation through the Seven P’s will depend on execution and embedding them into business models. In the data intensive society, the era of ‘Big Data’ has emerged giving disruptive potential to those organisations with the vision to imagine where data fits at the core. An organisation’s ability to embrace big data possibilities for the purpose of improving productivity and customer experience will create strong signs of certainty.

The transformative use of data is a factor that will ensure organisations not only advance their growth agenda but it will improve productivity with the right climate and culture.

Organisations are struggling to understand how data can open up new possibilities. In the meantime they remain vulnerable to the sensitive ups and downs of faster change cycles. As organisations look forward to the future in the aftermath of the WEF 2012 the Seven P’s combined with the right reformations undertaken is a pathway to enable organisations to address many themes outlined at Davos and compete in the new era.

More to come on each of the Seven Ps in future posts to this blog.

Big Data - Skating with the puck

In this era of big data, organisations need to review their existing technology-enabled customer-centric business strategies. They need to rethink their approach to gain the next big competitive advantage. To succeed, organisations need to creatively harness their data-centric capabilities to positively impact real-time business operations and customer experience.

Organisations that are skating with the puck are focusing on how to leverage their customer-centric technology to consistently deliver the “next best offer” across multiple channels. They are looking to improve the customer experience while boosting employee productivity. Providing a customer with a tailored next best offer is one of the most powerful ways to cross-sell and up-sell to improve the customer lifetime value. However, for a multi-product company with countless possibilities finding that right next best offer can be a challenge. Getting the next best offer wrong decreases the value of the direct interactions with a customer and their affinity with the organisation.

In addition offering too many options decreases productivity and can turn customers off. Hick’s Law suggests the time it takes to make a decision increases as the number of alternatives increases. Hence, the number of alternative products or services and channels an organisation offers are factors which influence the time taken for the frontline staff or customers to make a decision.

Powerful data-centric technologies, such as master data management (MDM) and complex event processing (CEP), when used together, can help organisations better predict what that next best offer should be in real-time. The organisations that adopt this technology have the opportunity to detect, learn, communicate and act consistently across all channels to improve the customer experience and employee productivity.

Customers have become digitally oriented and their preferred channel is digital. Organisations that are circling the ice with their reactive approach to next best offer may find themselves at a significant disadvantage when engaging with customers across multiple channels, particularly if they fail to find creative ways to drive value through digital channels.

An organisation’s ability to create one positive customer experience is an anthesis of the true value of harnessing data intensive possibilities in this business ecosystem. Whether the customer is interacting on the social web or across traditional channels, technology like Informatica’s MDM and CEP can instantaneously suggest a tailored next best product or service. This data-centric capability not only improves the immediate customer experience, but the customer’s lifetime value, while boosting employee effectiveness and productivity. In addition, organisations who use data more broadly, mastered, to encompass the social graph and interest graph are enabling business agility. Mining historical transactions for patterns, trends, and predictors (“big data”) provides a strong indicator of future behaviour leading to appropriately tailored next best offers.

Another critical component of this approach is a feedback loop. Frontline staff at customer touchpoints provide feedback about how the customer responded to the technology-enabled “next best offer”. The offers taken up and those that weren’t through online channels can be evaluated in order to continue to improve “next best offer” possibilities, sharpening an organisation’s ability to better meet their customers’ needs.

The digital future of an organisation’s customer-centric strategy hinges on their ability to move away from the reactive business model of the past to an instant, intelligent business that better leverages its data to impact results. The first tangible outcome of this new proactive business model is to improve the customer experience and boost employee productivity by suggesting tailored next best offers. This empowers the frontline staff and customers to make smarter decisions faster across multiple channels.

The next best offers are event driven, reducing wasted effort in making offers to customers who are not ready to convert. An event can be as simple as a website activity or interacting with a frontline employee.

It is the real time suggestion of a tailored next best product or service that enables organisations to gain a more balanced, integrated approach to the customer experience. As an accelerator of their customer-centric strategy into the digital future, this advantage is a critical factor for organisations operating and competing in this new data intensive society.

Read more about CEP: http://blogs.informatica.com/perspectives/index.php/author/chris-carlson/