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:

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Big Data Analytics: Pizza Industry

Pizza, pizza! A competitive analysis was completed on Dominos, Pizza Hut, and Papa Johns.  Competitive analysis is gathering external data that is available freely, i.e. social media like Twitter tweets and Facebook posts.  That is what He, Zha, and Li (2013) studied, approximately 307 total tweets (266 from Dominos, 24 from Papa John, 17 from Pizza Hut) and 135 wall post (63 from Dominos, 37 from Papa Johns, 35 from Pizza Hut), for the month October 2011(He et al, 2013).  It should be noted that these are the big three pizza chain controlling 23% of the total market share (7.6% from Dominos, 4.23% from Papa Johns, 11.65% from Pizza Hut)(He et al., 2013) (He et al., 2013). Posts and tweets contain text data, videos, and pictures.  All the data collected was text-based data and collected manually, and SPSS Clementine tool was used to discover themes in their text (He et al., 2013).

He et al. (2013), found that Domino’s Pizza was using social media to engage their customers the most.  Domino’s Pizza did the most to reply to as many tweets and posts.  The types of posts in all three companies varied from the promotion to marketing to polling (i.e. “What is your favorite topping?”), facts about pizza, Halloween-themed posts, baseball themed posts, etc. (He et al., 2013).  Results from the text mining of all three companies: Ordering and delivery was key (customers shared the experience and feelings about their experience), Pizza Quality (taste & quality), Feedback on customers’ purchase decisions, Casual socialization posts (i.e. Happy Halloween, Happy Friday), and Marketing tweets (posts on current deals, promotions and advertisement) (He et al, 2013).  Besides text mining, there was also content analysis on each of their sites (367 pictures & 67 videos from Dominos, 196 pictures & 40 videos from Papa Johns, and 106 pictures and 42 videos from Pizza Hut), which showed that the big three were trying to drive customer engagement (He et al., 2013).

He et al. (2013) lists the 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.  Thus, evaluating the structure and unstructured data provided to an organization about their own product and theirs of their competitors, they could use it to help increase their customer services, driving improvements in their own products, and driving more customers to their products (He et al., 2013).  Key lessons from this study, which would help any organization gain an advantage in the market are to (1) Constantly monitor your social media and those of your competitors, (2) Establish a benchmark of how many posts, likes, shares, etc. between you and your competitors, (3) Mine the conversational data for content and context, and (4) analyze the impact of your social media footprint to your own business (when prices rise or fall what is the response, etc.) (He et al, 2013).

Resources:

  • He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472.