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.



Leadership styles

Below will follow a quick discussion of some of these leadership styles:

  • Open Leadership: Has five rules, which allow for respect and empowerment of the customers and employees, to consistently build trust, nurtures curiosity and humility, holding openness accountable, and allows for forgiving failures (Li, 2010).  It is not easy to “let it go,” but to grow into new opportunities one must “let it go.”  This thought process is similar to knowledge sharing, if you share your knowledge, you will be able to “let it go” of your current tasks, such that you can focus on new and better opportunities. Open Leadership allows for one to build and nurture relationships with the customers and employees (Li, 2010).  It is customer and employee centered.
  • Situational Leadership: Is a style of leadership where the leader must continuously change their personal leadership style to meet the situation and needs of the employees/followers (Anthony, n.d.). The input of the employees/followers must come first regardless if the leader is a micro-manager, supervisor, coach, supportive, developer, or delegator. The leader would use: micro-manage if employees just need to do exactly what they are told; supervisor methods if employees are inexperienced; coaching if employees lack confidence/motivation; delegation if employees need little supervision; and developmental when the employees have high needs and little experience (Anthony, n.d.).
  • Autocratic Leadership: Is also known as authoritarian leadership, where the leader takes over everything and makes all decision with no input from the group (Cherry, 2016a). These leaders what to do it all themselves, and could maintain power through force, threats, punishment, and rewards (Community Tool Box, n.d.). This feeling is felt and creates the illusion of the classic “control freak,” “bossy,” etc. trope on the leader. But, this negative view on this style could be offset by a charismatic personality, leading to the leader being loved and respected (Community Tool Box, n.d.). This is great for when quick decisions are needed, but it comes at a cost to the followers. That is because of Cherry (2016a), stated that decisions made in this style of leadership are absolute and the followers/employees are not trusted. Autocrats create an organizational culture of fear and mistrust other people’s motives and aim to prioritize protecting themselves (Community Tool Box, n.d.).
  • Democratic Leadership: Is also known as participative leadership, where the employees/followers are a vital part of making the key decisions (Cherry, 2016b). This is the direct opposite of the Autocratic Leader.  These leaders recognize that it is the “we” that built and sustain the organization, not the “me” (Community Tool Box, n.d.). Here, participatory ideas and opinions are championed, even if the leader remains the final arbitrator (Cherry, 2016b; Community Tool Box, n.d.). Unfortunately, this style can be quite a time intensive and create a lack of “buy-in of ideas,” but this style could provide better results due to a diversity of thought. Though the diversity of thought provides a whole suite of possibilities to an organization and provides good relationships for all team members (Community Tool Box, n.d.).
  • Transactional Leadership: Is when a leader only views relationships with their team as a form or set of transactions (Community Tool Box, n.d.). Status quo is kept in this style (Raza, n.d.). Therefore, it is not uncommon to see a rewards and consequences stemming from this style (Community Tool Box, n.d.; Raza, n.d.).  This is more akin to the boss, that states “I pay your salary, you must do as I say.”
  • Transformational Leadership: Helps their team see the values and hopes that they have for them and for the organization, such as to empower them to pursue their goals (Community Tool Box, n.d.). Raza (n.d.) stated that this style leads to initiating a motivational change in an organization, team, oneself, or others. This style models the Mahatma Gandi’s overarching message of being the change you want to see in the world, even if it’s a small change in themselves or their team. This style tends to have the most engaged and empowered followers (Raza, n.d.).
  • Servant Leadership: The leader is considered a servant first to their employees/followers to allow them to grow, become healthier, wiser, freer, autonomous, and become servants themselves (Center for Servant Leadership, n.d.). The focus is on the growth of the employees/followers.  This is done by putting the needs of the team ahead of the Thus the team benefits the most from this style (Johannsen, 2014). One way to accomplish growth is a leader sharing their power to help people develop, synonymous to caring for each other (Center for Servant Leadership, n.d.). Servant leaders uplift their team (Johannsen, 2014).
  • Laissez-faire Leadership: leaders allow employees/followers make their decisions, also known as delegation leaderships (Cherry, 2016c; Raza, n.d.). There is low control over the team compared to the high control over the team in autocratic styles (Johannsen, 2014). Unfortunately, Cherry (2016c) points out that there is little guidance from leaders when it is most needed, or when there is a lack of knowledge. But, it does allow for the autonomy of the employees/followers and promotes problem-solving from them. Johannsen (2014), suggested this style for highly motived and trained team members.  However, this style is known to create low satisfaction (Raza, n.d.).

Open leadership differs because it is not fully a democratic leadership nor laissez-faire leadership, but has qualities of it, due to its centering itself against other customers and employers. It is similar to the situational leadership because open leadership must be met based on the situation the organization is faced with at that time.  If the organization cannot be transparency and authenticity, then it must meet its situation and shouldn’t practice open leadership.  Open leadership doesn’t try to grow the customers and employers and the “Let it go” nature of open leadership is the worst nightmare of an autocratic leaders.


Leadership and Social Media

The frequency, use, and depth of engagement on social media will increase the popularity of social media increases. Thus it is important to be able to define what it is (Wollan, Smith, & Zhou, 2010).  However, the definition of social media would change with time because social media is dependent on the technology and platforms that enable and facilitates a social connection (Cohen, 2011; Solis, 2010). The social connection from social media shifts content creation and delivery from a “one-to-many” model to a “many-to-many” model (Solis, 2010; Wollan et al., 2010). This social connection exists between content hosted online versus the consumers of the content (Cohen, 2011). Wollan et al. (2010) defined that social media is highly assessable and scalable.  Thus, social media allows for democratizing content/information and influence (Solis, 2010; Wollan et al., 2010). In the end, social media allows for swift content creation and dissemination by the content creators, whether it is from organization or a single/group people (Wollan et al., 2010).

From a business perspective, social media has become a customer relationship management tool between the business and its customers (Wollan et al., 2010). There is a different type of leadership style needed for companies that use social media to deliver products and services. Li (2010), explains a case study where the Red Cross wanted to control social media right after it saw the negative impacts from the Hurricane Katrina response.  However, over time (not over-night) the Red Cross realized it was better to engage in an open dialog with its participants over social media, which paid off because they were able to raise $10M for Haiti earthquake relief in three days in 2010.  This could only be done when the social media strategy handbook was published online to allow for not only the corporate but the local Red Cross chapters to begin their usage of social media (Li, 2010; American Red Cross, 2012). They had to let go of controlling their image, word for word, but allow their chapters to do so.

This “Let it go” style is the main type of leadership style that Li (2010) proposes in Open Leadership for businesses to succeed in their use of Social Media for its future success. Social media is driving a leadership style that is more democratic (Stupid stuff for dummies, 2011), due to social media’s democratizing properties.  This is because in this world, people vote with how they spend their dollars, social media allows for a company to engage with its customers through exhibiting (not all but) greater transparency and authenticity (Li, 2010). Open Leadership is not about controlling technology but establishing a plan or relationship that is wanted with the social media platform, to maintain a democratic leadership style that grows a corporation successfully.


Compelling topics

Hadoop, XML and Spark

Hadoop is predominately known for its Hadoop Distributed File System (HDFS) where the data is distributed across multiple systems and its code for running MapReduce tasks (Rathbone, 2013). MapReduce has two queries, one that maps the input data into a final format and split across a group of computer nodes, while the second query reduces the data in each node so that when combining all the nodes it can provide the answer sought (Eini, 2010).

XML documents represent a whole data file, which contains markups, elements, and nodes (Lublinsky, Smith, & Yakubovich,, 2013; Myer, 2005):

  • XML markups are tags that helps describe the data start and end points as well as the data properties/attributes, which are encapsulated by < and a >
  • XML elements are data values, encapsulated by an opening <tag> and a closing </tag>
  • XML nodes are part of the hierarchical structure of a document that contains a data element and its tags

Unfortunately, the syntax and tags are redundant, which can consume huge amounts of bytes, and slow down processing speeds (Hiroshi, 2007)

Five questions must be asked before designing an XML data document (Font, 2010):

  1. Will this document be part of a solution?
  2. Will this document have design standards that must be followed?
  3. What part may change over time?
  4. To what extent is human readability or machine readability important?
  5. Will there be a massive amount of data? Does file size matter?

All XML data documents should be versioned, and key stakeholders should be involved in the XML data design process (Font, 2010).  XML is a machine and human readable data format (Smith, 2012). With a goal of using XML for MapReduce, we need to assume that we need to map and reduce huge files (Eini, 2010; Smith 2012). Unfortunately, XML doesn’t include sync markers in the data format and therefore MapReduce doesn’t support XML (Smith, 2012). However, Smith (2012) and Rohit (2013) used the XmlInputFormat class from mahout to work with XML input data into HBase. Smith (2012), stated that the Mahout’s code needs to know the exact sequence of XML start and end tags that will be searched for and Elements with attributes are hard for Mahout’s XML library to detect and parse.

Apache Spark started from a working group inside and outside of UC Berkley, in search of an open-sourced, multi-pass algorithm batch processing model of MapReduce (Zaharia et al., 2012). Spark is faster than Hadoop in iterative operations by 25x-40x for really small datasets, 3x-5x for relatively large datasets, but Spark is more memory intensive, and speed advantage disappears when available memory goes down to zero with really large datasets (Gu & Li, 2013).  Apache Spark, on their website, boasts that they can run programs 100X faster than Hadoop’s MapReduce in Memory (Spark, n.d.). Spark outperforms Hadoop by 10x on iterative machine learning jobs (Gu & Li, 2013). Also, Spark runs 10x faster than Hadoop on disk memory (Spark, n.d.). Gu and Li (2013), recommend that if speed to the solution is not an issue, but memory is, then Spark shouldn’t be prioritized over Hadoop; however, if speed to the solution is critical and the job is iterative Spark should be prioritized.

Data visualization

Big data can be defined as any set of data that has high velocity, volume, and variety, also known as the 3Vs (Davenport & Dyche, 2013; Fox & Do, 2013; Podesta, Pritzker, Moniz, Holdren, & Zients, 2014).  What is considered to be big data can change with respect to time.  What is considered as big data in 2002 is not considered big data in 2016 due to advancements made in technology over time (Fox & Do, 2013).  Then there is Data-in-motion, which can be defined as a part of data velocity, which deals with the speed of data coming in from multiple sources as well as the speed of data traveling between systems (Katal, Wazid, & Goudar, 2013). Essentially data-in-motion can encompass data streaming, data transfer, or real-time data. However, there are challenges and issues that have to be addressed to conducting real-time analysis on data streams (Katal et al., 2013; Tsinoremas et al., n.d.).

It is not enough to analyze the relevant data for data-driven decisions but also selecting relevant visualizations of that data to enable those data-driven decision (eInfochips, n.d.). There are many types of ways to visualize the data to highlight key facts through style and succinctly: tables and rankings, bar charts, line graphs, pie charts, stacked bar charts, tree maps, choropleth maps, cartograms, pinpoint maps, or proportional symbol maps (CHCF, 2014).  The above visualization plots, charts, maps and graphs could be part of an animated, static, and Interactive Visualizations and would it be a standalone image, dashboards, scorecards, or infographics (CHCF, 2014; eInfochips, n.d.).

Artificial Intelligence (AI)

Artificial Intelligence (AI) is an embedded technology, based off of the current infrastructure (i.e. supercomputers), big data, and machine learning algorithms (Cyranoski, 2015; Power, 2015). AI can provide tremendous value since it builds thousands of models and correlations automatically in one week, which use to take a few quantitative data scientist years to do (Dewey, 2013; Power, 2015).  Unfortunately, the rules created by AI out of 50K variables lack substantive human meaning, or the “Why” behind it, thus making it hard to interpret the results (Power, 2015).

“Machines can excel at frequent high-volume tasks. Humans can tackle novel situations.” said by Anthony Goldbloom. Thus, the fundamental question that decision makers need to ask, is how the decision is reduced to frequent high volume task and how much of it is reduced to novel situations (Goldbloom, 2016).  Therefore, if the ratio is skewed on the high volume tasks then AI could be a candidate to replace decision makers, if the ratio is evenly split then AI could augment and assist decision makers, and if the ratio is skewed on novel situations, then AI wouldn’t help decision makers.  They novel situations is equivalent to our tough challenges today (McAfee, 2013).  Finally, Meetoo (2016), warned that it doesn’t matter how intelligent or strategic a job could be, if there is enough data on that job to create accurate rules it can be automated as well; because machine learning can run millions of simulations against itself to generate huge volumes of data to learn from.