Competitive Analysis

Conducting a competitive analysis on one facet of such large companies seems to be almost an injustice. When researching competitors for IBM’s Watson, three seemed to be more prevalent than the rest: 1. Google's BigQuery, 2. SAS, and 3. SAP’s HANA. Below we will discuss the costs, value, and risks associated with each in brief detail.

BIGQUERY

BigQuery, developed by Google, is unique in the sense that it has no infrastructure and does not require an administrator. Data is analyzed in real-time or through upload of say, Google Cloud or Google Cloud Datastore. There is a pay-as-you-go cost structure in place, which lends itself to being more of a lost cost analytics service (BigQuery, 2015).  As depicted in the Tier Pricing chart on page 13, the first terabyte of data that is processed through BigQuery each month is free. Beyond this, each terabyte can be paid for individually there after. This ensures that a company is not wasting money for unused storage or queries (BigQuery, 2015.). 

As far as risks are concerned, security is an issue as well as the likelihood that at one point or another, the cloud provider will go down. Google has faced this issue on more than one occurrence during BigQuery’s short life span. In one case, BiqQuery users did not have access to their data for over two hour. On May 17th, Google engineers claimed, “We understand the high level of reliability that is demanded and expected of a service like BigQuery and apologize for the disruption. We are taking immediate actions to ensure we minimize the risk of this issue repeating itself” (Google Platform, 2015). Like any new product, bumps along the road can be expected. We are, however, sensitive to IVK’s stockholders heightened anxieties following the June attack. For this reason, it was easy for us to cross this competitor off the list of suitable analytics services. 

SAS

According to the SAS website, SAS provides data management, advanced analytics, cross-functional solutions, industry specific solutions, and business intelligence (SAS, 2015). The cost of SAS comes in at about $9k per year, double the projected cost of Watson Analytics. There is individual pricing as well as free trial offerings for interested customers. A one-year license to an individual comes to a staggering $5k. Though many other similar products can be found for much less or free, SAS is considered to be the industry standard. In fact, SAS is the largest privately held Software Company in the world. As discussed throughout the course, sometimes a strong reputation comes with a hefty price.  A risk identified by SAS CEO himself, James Goodnight, is as follows: 

“And while many companies are waking up to the benefits of big data — and analytics technologies which use sophisticated algorithms to comb through that data to help discover efficiencies and identify new trends — making those tools easy to use for everyone in an organization, not just the IT staff, remains a challenge for the industry. Just as many employees had to be trained how to use a PC in the early days of the computer revolution, big data is facing a similar learning curve within many organizations” (Hartley, 2014). 


The issue Hartley is addressing here that there is an over all shortage of college graduates who “get this stuff.” As of late, the company has begun to work with various universities around the world to help tech students how to analyze large amounts of data. A skillset that Goodnight believes must be taught. Without it, how can these analytics technologies be created at all? 

HANA

HANA was released in June of this year as an in-memory, column-oriented, relational database management system developed and marketed by SAP SE. The application allows for the processing of massive amounts of real-time data in a very short period of time. HANA processes he data stored as opposed to reading it from a disk (Tolentino, 2012). According to the website, HANA allows for monitoring and optimization of telecommunications network, supply chain and retail optimization, fraud detection and security, forecasting and profitability reporting, energy use optimization and monitoring, and more (Appleby, 2014). The CEO explained HANA in a conference earlier this year: "It's all about hyper-processing power with in-memory computing, just multiply yesterday's capabilities up to 100,000 times over, so that you can ask questions about your margins, your manufacturing mix, marketing, and get answers and insights unbelievably faster. What took three days to do before, takes three minutes now. That's 4.6 billion records analyzed in less than two seconds, with data stored in RAM accessed instantly through the cloud” (Amazon, n.d.). Research collected also showed that 34% of users helped them optimize costs and 24% found that it increased innovation in the finance department (Appleby, 2014).

                                            

As far as risks are concerned, security (or lack there of) presents itself once again. Further, users are struggling to see the business case. A ZDNet article detailing SAP’s current “big data race against time” highlights the time sensitivity of it all (Kelly, 2013).  Of those surveyed, 52% of respondents say that they aren’t planning to use with 52% blaming expenses and 37% are left unable to see the benefit of HANA at all. To avoid falling behind, they need a clearer strategy and to put their running shoes on. Currently, their customer base is heavily European and to avoid falling behind there need to be acquisitions on the horizon.

Though the specific pricing has not been released, it is estimated that customers can be up and running for under $300,000 (Grimes, 2015). See Chart B for a pricing a user example. Pricing level can reach a staggering $2 million or more. The cost of HANA comparatively is considered to be a premium but according to ZDNet is “not out of whack” (Kelly, 2013).

To learn more, visit here.

WATSON

Watson was developed in IBM's DeepQA project and named after IBM's first CEO Thomas J. Watson. In short, according to IBM's website, Watson “is built to the same learning process that we (humans) have through the power of condition. What drives the process is common cognitive framework that humans use to inform their decisions: Observe, Interpret, Evaluate, and Decide"(IBM, n.d.). Unlike its competitors, Watson  has the ability to “learn” (Asay, 2014). The company believes that Watson has a strong future as a cloud service and as a technology that will change lives (Ante, 2014). In fact, one of Watsons greatest strengths and differentials is in the health sector. In 2013, IBM released that Watson's very first commercial application would be decisions in lung cancer treatment.  Currently utilized by Memorial Sloan Kettering Cancer center, IBM’s former business chief claims that 90% of oncology nurses who utilize Watson follow its advice. Their most recent collaboration is with Citigroup Inc. to create a version of Watson that can recommend financial products to their customers (Ante, 2014).

In the risk department, research shows that although Watson knows “almost everything there is to know”, it isn’t doing so well in the generating revenue department (Asay, 2014). Unlike its competitors detailed above, Watson is great for answering structured questions but struggles whenever it comes to messy data. An investigation conducted by the Wall Street Journal delved into reports and interviews of Watson’s first customers. The consensus being that Watson is having trouble solving real-life problems. Because of this, IBM’s engineers are forced to “master the technicalities of a customers business and translate those requirements into software” (Ante, 2014). Beyond this, IBM is still struggling to figure out how Watson can create a reliable revenue stream.  Because Watson’s competitors already have, the clock is ticking.

IBM has invested more than $1 billion to create a company division that employs over 2,000 people. It is predicated that Watson will generate $10 billion in annual revenue within the next 10 years (Ante, 2014). A recent deal with M.D. Anderson, another cancer treatment hospital, was priced at around $15 million. It is estimated that the hardware Watson uses is around $3 million and to put this into perspective, a hospital would spend that much on an MRI machine (Merian, 2011).

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