Category Archives: Business Intelligence

Strategic Analysis – Blackberry

Recently I created a strategic analysis of Blackberry – struggling Canadian mobile developer and service provider. Here is some information that I collected that might help in bringing the organization back to profitability.

Strategic Analysis – BlackBerry

by Andre Kaminski


Executive Summary


The target audience of this document is BlackBerry’s senior management. The objective is to assess current competitive position of the organization and to provide recommendations for the future.

BlackBerry, founded in 1984, was once leader in wireless innovation that revolutionized the term “Smartphone”. After period of exceptional growth in revenues from $85M (2000) to $20B (2008), for last three years it is in constant decline with revenues of $11B in 2013. The Net Income during the same period dropped from $3B profit (2011) to $0.6B loss (2013) (see figure 1).

During this analysis, there were several tools used. Five Forces and Environmental Analysis were used to examine the current industry structure. Both of the tools give different perspectives on the competitors and market segments, allowing to predict future movements. SWOT and VRIN tools were used to analyze current capabilities and competitive positions of the organization. Other tools like Competitive Lifecycle and Capability Analysis diagrams were not found suitable due to nature of the business and the purpose of this document.

Industry Analysis

The current situation of smartphone industry could be described as ‘Red Ocean’ (see figure 2), with high level of fierce competition. Although there are high entry barriers, there is already large number of competitors that saturated the market (according to IDC report – over 150 vendors). The industry is in early maturity phase, with well-established competitors with strong brands and excess capacity. In November 2013, IDC found that smartphone shipments grew 40%, while average pricing declined by 12%. Over last few years, more and more organizations allow employees to bring their own devices to work (BYOD) which changed the market landscape. There is no more clear differentiation between enterprise and consumer market segments, but rather an overlap. Another strategic group with fierce competition is middleware area – software that connects disparate mobile applications, programs and systems (Fig. 5).

In order to keep the costs down, many competitors buy standard components provided often by the same suppliers, which is increasing companies’ vulnerability to any changes in the relationships or new alliances. Most of the vendors are able to relatively quickly imitate new features, and currently there is little differentiation among offered devices. Apple’s introduction of iPhone in 2007 was a good example, currently almost all vendors offer the touch screens, including BlackBerry. There are high risks of substitution as voice and data services could be used on several types of devices like tablets, smartphones or laptops. Due to continuing difficult economic situation, end users are sensitive to pricing, leading to high cross price elasticity. Since handsets are sold through resellers who add data services on top of voice, relatively small number of resellers in given geographic market segments provides them with high bargaining power when negotiating pricing. Another important aspect in the industry is the need of complementing the hardware devices with software offering. Without large number of applications, it is almost impossible to sell products to consumer segment. This led to consolidation of operating systems – Google’s Android and Windows Phone OS are used on several vendors’ devices across the market space, with only two used on proprietary handsets – Apple’s iOS and BlackBerry’s QNX. Ability to attract good software development companies is directly linked to number of sold devices, which currently unfavorably positions BlackBerry (see figure 3 and figure 4).

The industry is very dynamic, with constant changes – clearly an example of Schumpeterian rents, where timing and adoption are critical to success. Blackberry lost its dominant position when it failed to recognize threat from Apple’s iPhone in 2007. Inability to quickly respond, with several misses of deadlines for delivery of new generation of devices by as much as 18 months (model Z10), only deepened the crisis for Blackberry. Key factors that will drive the industry over next few years will be related to security and privacy protection (driven by NSA scandal), data consuming and social networking shifting to mobile devices due to changing demographics, and feature rich operational systems, supporting large number of applications (figure 6).

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Big Data, Data Warehousing and Data Mining

Michael Koploy from Software Advice posed recently a question about plain definitions of some basic Business Intelligence concepts – Big Data, Data Warehousing and Data Mining. Although question seems to be quite simple, it is mind provoking due to changes that BI is experiencing to during last year or two. New developments in this area force us to look again at these concepts. Here is my view on these 3 topics:

Big Data

Simple definition:

The concept of the big data is not new, although it gained popularity during recent years. It describes all the data available to organizations and that includes structured and unstructured data. It is characterized by its large volume, variety and velocity, which makes it challenging to analyze. Until recently organizations tended to limit amount of information by putting breaks and structure through governance and architecture. Too much information was considered bad thing, due to limited capacity of systems and capabilities to process this information.

How it is changing:

The old saying – ‘garbage in – garbage out’ is not true anymore. Organizations realized that among the garbage there might be lot of valuable information that could be monetized. This could be done directly or indirectly and used not only to generate revenues but also to gain competitive advantage. The value of information might not be correctly estimated at the time of its creation or during its initial intended use. Value is often defined by its context – to paraphrase – “the value is in the eye of the beholder”, and it is also time variant. Traditional BI was dealing primarily with the structured data, as it was easier to work with and get results quickly. The rest was mostly ignored or treated as necessary evil. The problem however is that unstructured data constitutes around 80 to 85% of data within the organization, or floating over there in the web, and it could be in one or another way related to the business. Social networks like Facebook, Twitter, blogs, discussions, memos, emails and so on are equal sources of potentially useful information. The winners from losers are separated by ability to see the value where others do not, and ability to use it.


Data Warehousing

Simple definition

Traditionally data warehousing is a process of consolidating and aggregating information from various sources within the organization, and used for historical analysis and reporting. The outputs from the analysis are used for operational, tactical or strategic planning. Before the data could be used for these purposes however it has to go through process of cleanup, standardization, normalization, integration and so on. Once stored in Data Warehouse it could be aggregated, and correlated to find answers to typical business questions.

How it is changing

Once data is in Data Warehouse it becomes relatively non-volatile, time variant, representing subject oriented historical value of data. Here is the problem in the new world – the process of standardization and structuring of the data often strips the most valuable part – intrinsic relationships between data, that might not be visible at the time when the structuring rules are established. Usually Data Warehouses are created with specific goals, and these goals might be changing relatively quickly. Adjusting Data Warehouse to fit these new goals might be as painful as turning a large ship in narrow fiord. In the light of Big Data, the whole concept will have to be reevaluated.


Data Mining

Simple Definition

In short it is discovery of true meaning of data from large datasets that integrates structured and unstructured data. These datasets might come from data warehouses or from any other data sources. Data mining helps to answer specific business questions that might be unique and might not have predefined processing paths.

How it is changing

Data mining is building on available data and thus closely related to the above discussed two terms. Since these terms are changing, so it is the data mining concept. The organizations need to employ innovative techniques like statistical tools, semantic analysis, neural networks, artificial intelligence and so on, to extract information from combination of both structured and unstructured data in order to gain knowledge. This single step is what separates ‘wheat from the chaff’, winners from losers – it is the ‘holy grail’ of Business Intelligence.