Business strategy and social business strategy

According to Wollan, Smith and Zhou (2010) a business strategy is “the direction, positioning, scope, objectives, and competitive differentiation” of the business.  However, having a social business strategy is important.  It is important and enables a business to learn from the business’ employees, customers, and partners (Li, 2010).  It is important and enables to drive a dialog (both internally and externally) and thus creates a relationship within and outside of the business (Li, 2010; Wollan et al., 2010). This relationship can lead to innovation, because of what is learned from having this relationship within and outside of the business (Li, 2010).

From an external view of a social media strategy, it is seen to engage with its customers and promote its business along with its business strategy.  Originally, marketing departments and public relations (PR) teams ran the early uses of social media, but eventually, it got too complex (Wollan et al., 2010). Today, many companies have multiple teams that use social media, and this is in line with open leadership (Li, 2010; Wollan et al., 2010).  All these departments have to deal with the following key issues related to the business strategy, and it should be contained in their social media strategy (Wollan et al., 2010):

  • “How is social media aligned to the business?”
  • “How should social media decisions are made [or prioritized]?”
  • “How do we manage social media investments?”
  • “What controls do we need in place?”
  • “How do we measure and reward?”

This social media strategy should be created not just by executives but through leaders encompassing all the departments, because of social media impacts all departments, not just marketing and PR departments (Wollan et al., 2010).

Placing an internal view of a social media strategy, it can help shape the human capital strategy, which enables the business strategy (Wollan et al., 2010).  Considering the human capital strategy helps define the needs of their employees, discovering and attracting talent, developing high-potential talent, and deploying the talent in the right place and at the right time through knowledge sharing plans (Wollan et al. 2010). Also, business employees know and even feel any changes made in the business strategy (BusinessWeek Online, 2012). Social media is one of many ways to help drive and understand employee responses to these changes.

However, engaging people/customers from a business perspective is not as simple as just starting a social media page/account and watching what happens.  Li (2010), described that there is an engagement pyramid, where watching what happens and the competitors/suppliers have the lowest level of social engagement and sharing, commenting, producing and curating content show increasing levels of engagement.  It’s in the higher levels of engagement that helps one develop a relationship via social media.  Businesses need to move past the watching what happens level of engagement and start curating content that helps customers and employees share their content (“word of mouth”), which can help drive up the sales of their products and services (Boysen, 2012).

References

Growing popularity of social media

Statement: “Emanating from the growing popularity of social media, consumers expect companies to be present on popular social media channels. As a consequence, companies can no longer maintain customer interactions solely by way of traditional channels.”

The overall statement is true. Traditional media is different from social media.  Traditional media is considered outbound via mailers, television, and cold calling, where social media is considered inbound, with SEO, social platforms (Twitter, Facebook, LinkedIn, etc.), and blogs (Boysen, 2012). Social media allows for relationship building through engagement and interaction, and can be easily measured compared to the expensive, short-term results and harder to measure traditional media (Boysen, 2012; Wollan, Smith, & Zhou, 2010). One great thing about a company’s adoption of social media is collecting, auditing and analyzing social, engagement, and influence data (Li, 2010).  This data analysis allows the company to see which products/services and which campaigns were the most effective in not only obtaining views across different social media platforms but conversion rates from views to purchasing of products/services (Boysen, 2012; Wollan et al., 2010).

Social media allows for consumers to have a more interactive way to get information from an existing company about a product/service (Boysen, 2012; Li, 2010; Wollan et al. 2010).  If consumers like or dislike the product/service enough, they would be willing for free to spread the word about the product/service through their social network. (Li, 2010). Essentially consumers are willing to call out the triumphs and disappointments of a company. Thus, social media potentially has a huge reaching or alienating of new consumers, through this concept of spreading content via word of mouth, which is a very effective way of marketing.  Word of mouth is so effective that 92% of recommendations are followed through on when they come from friends (Boysen, 2012). Social media is built on the connecting friends to each other, and companies should use that concept to their advantage, to enhance gains and mitigate losses.

Finally, a social media campaign strategy and execution is significantly more inexpensive and has a higher return on investment than traditional media (Boysen, 2012).  Companies should move forward to developing a social media strategy and should execute that strategy, to take advantage of the word of mouth phenomena that is relatively low cost compared to traditional media.  The strategy should be considered a living document because social media changes evolve with time and have dependencies to the evolution of popular social platforms (Cohen, 2011; Solis, 2010).  That is how the Red Cross of America is treating social media as they have updated their social media strategy in just a few years (Li, 2010; American Red Cross, 2012).  In 2015, TD Bank had amassed 550K Facebook likes, by having faster response time on their posts than their competitors (1.25 hours response time compared ~5 hours with their competitors), showcasing their financial education campaign (#financialeducation), and having a plan for responding to negative situations & comments (Crosman, 2015). TD Bank couldn’t accomplish this without having a strategic social media plan.

References

Business Intelligence: Predictions Followup

  • Potential Opportunities:

o    Health monitoring.  Currently, smart watches are tracking our heart rate, steps, standing time, climbing stairs, siting time, heart beats, workouts, biking, sleep, etc.  But, what if we had a device that measured daily our chemicals in our blood, that is no longer as painful as pricking your finger if you are diabetic.  This, the technology could not only measure your blood chemical makeup but could send alerts to EMT and doctors if there is a dangerous imbalance of chemicals in your blood (Carter et al., 2014).  This would require a strong BI program across emergency responders, individuals, and doctors.

o    As Moore’s law of computational speed moves forward in time, the more chances are companies able to interpret real-time data and produce lead information which can drive actionable data-driven decisions. Companies can finally get answers to strategic business questions in minutes as well (Carter et al., 2014).

o    Both internal data (corporate data) and external data (competitor analysis, costumer analysis, social media, affinity and sentiment analysis), will be reported to senior leaders and executives who have the authority to make decisions on behalf of the company on a frequent basis.  These issues may show up in a dashboard, with x number of indicators/metrics as successfully implemented in a case study of a hospital (Topaloglou & Barone, 2015).

  • Potential Pitfalls:

o    Tools for threat detection, like those being piloted in New York City, could have an increased level of discrimination (Carter, Farmer, & Siegel, 2014). As big data analytics is being used to do facial recognition of photographs and live video to identify threats, it can lead to more racial profiling if the knowledge fed into the system as a priori has elements of racial profiling.  This could lead to a bias in reporting, track higher levels of a particular demographic, and the fact that past performance doesn’t indicate the future.

o    Data must be validated before it is published onto a data warehouse.  Due to the low data volatility feature of data warehouses, we need to ensure that the data we receive is correct, thus expected value thresholds must be set to capture errors before they are entered.  Wrong data in, means wrong data analysis, and wrong data-drove decisions.  An example of expected value thresholds could be that earth’s temperature cannot exceed 500K at the surface.

o    Amplified customer experience.  As BI incorporates social media to gauge what is going on in the minds of their customer, if something were to go viral that could hurt the company, it can be devastating for the company.  Essentially we are giving the customer an amplified voice.  This can be rumors of software, hardware leaks as what happens for every Apple iPhone generation/release, which can put current proprietary information into the hands of their competitors.  A nasty comment or post that gets out of control on a social media platform, to celebrity boycotts.  Though, the opportunity here lies in receiving key information on how to improve their products, identify leakers of information, and settle nasty rumors, issues, or comments.

  • Potential Threats:

o    Loss of data through hackers, which are aiming to steal someone’s identity.  Firewalls must be tighter than ever, and networks must be more secure than ever as a company goes into a centralized data warehouse.  Data warehouses are vital for BI initiatives, but if HR data is located in the warehouse, (for example to help HR identify likelihood measures of disgruntled employees to aid in their retention efforts) then if a hacker were to get a hold of that data, thousands of people information can be compromised.  This is nothing new, but this is a potential threat that must be mitigated as we proceed into BI systems.  This can not only apply to people data but company proprietary data.

o    Consumer advertisement blitz. If companies use BI to blast their customers with ads in hopes to better market to people and use item affinity analysis, to send coupons and attract more sales and higher revenues.  There is a personal example here for me:  XYZ is a clothing store, when I moved to my first house, the old owner never switched their information in their database.  But, since they were a frequent buyer and those magazines, coupons, flyers, and sales were working on the old owner of the house, they kept getting blasted with marketing ads.  When I moved in, I got a magazine every two days.  It was a waste of paper and made me less likely to shop there.  Eventually, I had enough and called customer service.  They resolved the issue, but it took six weeks after that call, for my address to be removed from their marketing and customer database.  I haven’t shopped there since.

o    Informational overload.  As companies go forward into implementing BI systems, they must meet with the entire multi-level organization to find out their data needs.  Just because we have the data, doesn’t mean we should display it.  The goal is to find the right amount of key success factors, key performance indicators, and metrics, to help out the decision makers at all different levels.  Complicating this part up can compromise the adoption of BI in the organization and will be seen as a waste of money rather than a tool that could help them in today’s competitive market.  This is such a hard line to walk on, but it is one of the biggest threats.  It was realized in the hospital case study (Topaloglou & Barone, 2015) and therefore mitigated for through extensive planning, buy-in, and documentation.

 

Resources:

Business Intelligence: Predictions

The future of …

  • Data mining:

o    Web structure mining (studying the web structure of web pages) and web usage analysis (studying the usage of web pages) will become more prominent in the future.  Victor and Rex (2016) stated that web mining differs from traditional data mining by scale (web information is much larger in number, making 10M web pages seem like it’s too small), access (web information is mostly public, whereas traditional data could be private), and structure (web pages have unstructured, and semi-structured data, whereas traditional data mining, has some explicit level of structure).  The structure of a website can contain: Page Rank, Page number, Damping factor, Number of pages, out-links, in-links, etc.  Your page is considered an authoritative piece if there are many in-links, or it can be considered a hub if it has many out-links, and this helps define page rank and structure of the website (Victor & Rex, 2016).  But, page rank is too trivial of calculation.  One day we will be able to not only know a page rank of a website, but learn its domain authority, page authority, and domain validity, which will help define how much value a particular site can bring to the person.  If Google were to adopt these measures, we could see

  • Data mining’s link to knowledge management (KM):

o    A move towards the away from KM tools and tool set to seeing knowledge as being embedded into as many processes and people as possible (Ferguson, 2016). KM relies on sharing, and as we move away from tools, processes will be setup to allow this sharing to happen.  Sharing occurs more frequently with an increase in interactive and social environments (Ferguson, 2016).  Thus, internal corporate social media platforms may become the central data warehouse, hosting all kinds of knowledge.  The issue and further research need to go into this, is how do we more people engaged on a new social media platform to eventually enable knowledge sharing. Currently, forums, YouTube, and blogs are inviting, highly inclusive environments that share knowledge, like how to solve a particular issue (evident by YouTube video tutorials).  In my opinion, these social platforms or methods of sharing, show the need for a more social, inclusive, and interactive environment needs to be for knowledge sharing to happen more organically.

o    IBM (2013), shows us a glimpse of how knowledge management from veteran police officers, crime data stored in a crime data warehouse, the power of IBM data mining, can be to identifying criminals.  Mostly criminals commit similar crimes with similar patterns and motives.  The IBM tools augment officer’s knowledge, by narrowing down a list of possible suspects of crime down to about 20 people and ranking them on how likely the suspects committed this new crime.  This has been used in Miami-Dade County, the 7th largest county in the US, and a tool like this will become more widespread with time.

  • Business Intelligence (BI) program and strategy:

o    Potential applications of BI and strategy will go into the health care industry.  Thanks to ObamaCare (not being political here), there will be more data coming in due to an increase in patients having coverage, thus more chances to integrate: hospital data, insurance data, doctor diagnosis, patient care, patient flow, research data, financial data, etc. into a data warehouse to run analytics on the data to create beneficial data-driven decisions (Yeoh, & Popovič, 2016; Topaloglou & Barone, 2015).

o    Potential applications of BI and strategy will affect supply chain management.  The Boeing Dreamliner 787, has outsourced 30% of its parts and components, and that is different to the current Boeing 747 which is only 5% outsourced (Yeoh, & Popovič, 2016).  As more and more companies increase their outsourcing percentages for their product mix, the more crucial is capturing data on fault tolerances on each of those outsourced parts to make sure they are up to regulation standards and provide sufficient reliability, utility, and warranty to the end customer.  This is where tons of money and R&D will be spent on in the next few years.

References

  • Ferguson, J. E. (2016). Inclusive perspectives or in-depth learning? A longitudinal case study of past debates and future directions in knowledge management for development. Journal of Knowledge Management, 20(1).
  • IBM (2013). Miami-Dade Police Department: New patterns offer breakthroughs for cold cases. Smarter Planet Leadership Series.  Retrieved from http://www.ibm.com/smarterplanet/global/files/us__en_us__leadership__miami_dade.pdf
  • Topaloglou, T., & Barone, D. (2015) Lessons from a Hospital Business Intelligence Implementation. Retrieved from http://www.idi.ntnu.no/~krogstie/test/ceur/paper2.pdf
  • Victor, S. P., & Rex, M. M. X. (2016). Analytical Implementation of Web Structure Mining Using Data Analysis in Educational Domain. International Journal of Applied Engineering Research, 11(4), 2552-2556.
  • Yeoh, W., & Popovič, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science and Technology, 67(1), 134-147.

Big Data Analytics: Compelling Topics

Big Data and Hadoop:

According to Gray et al. (2005), traditional data management relies on arrays and tables in order to analyze objects, which can range from financial data, galaxies, proteins, events, spectra data, 2D weather, etc., but when it comes to N-dimensional arrays there is an “impedance mismatch” between the data and the database.    Big data, can be N-dimensional, which can also vary across time, i.e. text data (Gray et al., 2005). Big data, by its name, is voluminous. Thus, given the massive amounts of data in Big Data that needs to get processed, manipulated, and calculated upon, parallel processing and programming are there to use the benefits of distributed systems to get the job done (Minelli, Chambers, & Dhiraj, 2013).  Parallel processing allows making quick work on a big data set, because rather than having one processor doing all the work, you split up the task amongst many processors.

Hadoop’s Distributed File System (HFDS), breaks up big data into smaller blocks (IBM, n.d.), which can be aggregated like a set of Legos throughout a distributed database system. Data blocks are distributed across multiple servers. Hadoop is Java-based and pulls on the data that is stored on their distributed servers, to map key items/objects, and reduces the data to the query at hand (MapReduce function). Hadoop is built to deal with big data stored in the cloud.

Cloud Computing:

Clouds come in three different privacy flavors: Public (all customers and companies share the all same resources), Private (only one group of clients or company can use a particular cloud resources), and Hybrid (some aspects of the cloud are public while others are private depending on the data sensitivity.  Cloud technology encompasses Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).  These types of cloud differ in what the company managers on what is managed by the cloud provider (Lau, 2011).  Cloud differs from the conventional data centers where the company managed it all: application, data, O/S, virtualization, servers, storage, and networking.  Cloud is replacing the conventional data center because infrastructure costs are high.  For a company to be spending that much money on a conventional data center that will get outdated in 18 months (Moore’s law of technology), it’s just a constant sink in money.  Thus, outsourcing the data center infrastructure is the first step of company’s movement into the cloud.

Key Components to Success:

You need to have the buy-in of the leaders and employees when it comes to using big data analytics for predictive, prescriptive or descriptive purposes.  When it came to buy-in, Lt. Palmer had to nurture top-down support as well as buy-in from the bottom-up (ranks).  It was much harder to get buy-in from more experienced detectives, who feel that the introduction of tools like analytics, is a way to tell them to give up their long-standing practices and even replace them.  So, Lt. Palmer had sold Blue PALMS as “What’s worked best for us is proving [the value of Blue PALMS] one case at a time, and stressing that it’s a tool, that it’s a compliment to their skills and experience, not a substitute”.  Lt. Palmer got buy-in from a senior and well-respected officer, by helping him solve a case.  The senior officer had a suspect in mind, and after feeding in the data, the tool was able to predict 20 people that could have done it in an order of most likely.  The suspect was on the top five, and when apprehended, the suspect confessed.  Doing, this case by case has built the trust amongst veteran officers and thus eventually got their buy in.

Applications of Big Data Analytics:

A result of Big Data Analytics is online profiling.  Online profiling is using a person’s online identity to collect information about them, their behaviors, their interactions, their tastes, etc. to drive a targeted advertising (McNurlin et al., 2008).  Profiling has its roots in third party cookies and profiling has now evolved to include 40 different variables that are collected from the consumer (Pophal, 2014).  Online profiling allows for marketers to send personalized and “perfect” advertisements to the consumer, instantly.

Moving from online profiling to studying social media, He, Zha, and Li (2013) stated their theory, that with higher positive customer engagement, customers can become brand advocates, which increases their brand loyalty and push referrals to their friends, and approximately 1/3 people followed a friend’s referral if done through social media. This insight came through analyzing the social media data from Pizza Hut, Dominos and Papa Johns, as they aim to control more of the market share to increase their revenue.  But, is this aiding in protecting people’s privacy when we analyze their social media content when they interact with a company?

HIPAA described how we should conduct de-identification of 18 identifiers/variables that would help protect people from ethical issues that could arise from big data.   HIPAA legislation is not standardized for all big data applications/cases; it is good practice. However, HIPAA legislation is mostly concerned with the health care industry, listing those 18 identifiers that have to be de-identified: Names, Geographic data, Dates, Telephone Numbers, VIN, Fax, Device ID and serial numbers, emails addresses, URLs, SSN, IP address, Medical Record Numbers, Biometric ID (fingerprints, iris scans, voice prints, etc), full face photos, health plan beneficiary numbers, account numbers, any other unique ID number (characteristic, codes, etc), and certifications/license numbers (HHS, n.d.).  We must be aware that HIPAA compliance is more a feature of the data collector and data owner than the cloud provider.

HIPAA arose from the human genome project 25 years ago, where they were trying to sequence its first 3B base pair of the human genome over a 13 year period (Green, Watson, & Collins, 2015).  This 3B base pair is about 100 GB uncompressed and by 2011, 13 quadrillion bases were sequenced (O’Driscoll et al., 2013). Studying genomic data comes with a whole host of ethical issues.  Some of those were addressed by the HIPPA legislation while other issues are left unresolved today.

One of the ethical issues that arose were mentioned in McEwen et al. (2013), for people who have submitted their genomic data 25 years ago can that data be used today in other studies? What about if it was used to help the participants of 25 years ago to take preventative measures for adverse health conditions?  However, ethical issues extend beyond privacy and compliance.  McEwen et al. (2013) warn that data has been collected for 25 years, and what if data from 20 years ago provides data that a participant can suffer an adverse health condition that could be preventable.  What is the duty of the researchers today to that participant?

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