Radha R would like to thank Krishnan Raman, a program director with the business intelligence practice at MindTree Consulting, for contributing this months column.
A decade ago not many people would have considered it worthwhile to discuss packaged business intelligence (BI) solutions. A large majority of the BI solutions, if not all, were custom implementations tailored specifically to meet the needs of the organization. A lot of things have changed in the last ten years and packaged BI solutions have shot into prominence.
Speed seems to be of primary importance in todays world and packaged BI solutions are found to satiate this need for rapid implementations. In addition, more often than not, buying a vendor-supported software package is found to have a lower total cost of ownership (TCO). Because of this, whenever there are viable buy options, the build versus buy decision is becoming an important consideration in an IT systems project. The conventional wisdom that buying has a lower TCO than building is, however, based on the presumption that the purchased solution meets business needs of today and the foreseeable future. If it doesn't, the cost to customize prebuilt software, or worse still, the costs of changing your business to fit the software can seriously impact the supposedly lower TCO.
A few other critical drivers apart from accelerated implementation and lower TCO proponents of the buy option highlight in include:
- Adaptability to changes in the environment. Bought-out applications typically have additional functionality that goes beyond meeting current needs of the business and can be leveraged to meet the emerging needs as, and when, the business grows or adopts fresh business models radically different from the existing models. For example, take the case of a consumer durable goods manufacturer adopting a direct Internet-based channel for distribution of its goods instead of the existing intermediary assisted distribution channel. Information needs from the new channel will be fundamentally different, and if a custom solution has to scale to meet the new needs, it will require some serious engineering. If a packaged application has been used that supports alternate channel options, it might be relatively easier to cater to the information needs. Thus, packaged options, if rightly chosen, provide insurance to environmental business model changes.
- Inherent standardization potential. The overarching drive to adapt to the package with minimal customization, inherently acts as a governing mechanism to drive towards standardization. This steers the organization towards standardization across the organization. Packaged BI solutions often include prepackaged business content that acts as a guiding star for the definition of critical measures and dimensions. In the absence of a dire business need, these get adopted in the organization, driving standardized metadata definitions within the organization.
- Best Practices. Most of the packaged BI solutions have industry benchmarked best practice key performance indicators (KPIs), predefined reports and dashboards addressing critical business scenarios and alignment with industry standards that help organizations align themselves to better business practices and improve process efficiencies.
Considering the compelling business proposition and the rapidly maturing packaged BI solutions, it is increasingly likely to face a situation where one has to evaluate such a solution for the BI needs. A myriad of questions will cross the mind on how to make that critical decision.
- How does one decide between the plethora of prepackaged BI solutions available?
- What are the key considerations/questions one must ask?
- What are the answers that help a decision maker identify the right prepackaged BI platform?
- What are the trade offs that can be made while picking up a prepackaged BI platform?
There are many aspects to be considered when evaluating a package, including vendor stability, customer success stories, technical standards (specifically openness of the these standards and usage of newer technology), time and effort (a commercial comparison based on TCO). However, as suggested earlier, the tipping point for a buy decision is the fact that buying has a lower TCO than building based on the assumption that the purchased solution meets business needs and is the single biggest factor.
For the purpose of this discussion, I will limit myself to the most crucial evaluation dimensions for prepackaged BI solutions. Tackling the question, How well suited is the prepackaged BI solution for my industry? I find it useful to consider the five dimensions highlighted in Figure 1.
Subject Area Coverage
A few critical questions to ask are:
- What is the coverage of analysis subject areas present in the prepackaged BI content?
- Is the data model coverage adequate?
- How relevant or complete are the analysis scenarios/reports/cubes that are present?
This evaluation is crucial and helps us appreciate the breadth of functionality that is built-in. Once you understand your required analysis areas and have clarity on your priorities, you are set to evaluate the breadth of the solution. If the breadth requirements are not met and gaps fall in areas that are critical for your business, you need not proceed evaluating any further.
A pitfall is emphasizing current information requirements too much rather than the current and future needs in the organization. It needs to be noted that a BI solution is a strategic initiative and hence it should cater to the information needs in the organization well into the future. Even if the future needs are not very clear at this point in time, it is important to do a little bit of blue sky visioning on these prior to evaluating the options.
Another common pitfall is that the subject areas get defined at a very high level of granularity. For example, a definition of analysis areas as sales analytics or logistics analytics leaves too much ambiguity when it is preferable to define it at one level deeper, such as channel analytics, POS analytics, market share analysis or promotion analysis. This ensures more clarity and brings out the strengths and weaknesses of the solution based on which trade offs can be discussed. A decision can be taken to evaluate a solution further.
Once the breadth of the solution is established, we can proceed to evaluate the depth available in the solution, which we evaluate by looking under the hood of the solution at analysis perspectives and the industry fitment.
Analysis Perspectives (or Analysis Dimensions)
The first step in evaluating the details of the prepackaged content in a solution is to take a deeper look at the analysis perspectives or the dimensions of analysis present as a part of the solution. We need to ensure that all the critical dimensions, such as customer, product and channel, required for the analysis subject areas have been identified. Once we have assured ourselves that the dimensions are present, we need to evaluate deeper to understand the attributes that are present to support the alternate drill paths for analysis. In my personal experience, this detailed evaluation is mandatory for the critical dimensions.
An area that needs attention is industry specific demands that are on the dimensions. For example, in a retail industry, key dimensions should address the need to manage item code definitions and global trade identification number (GTIN), while in procurement it could be in terms of the need to store UNSPC classification.
Once we are clear that the dimensions of analysis are being catered to, the next area to focus on is evaluating the data model on the standard measures and the industry benchmark KPIs that it caters to. For example, if you are in the supply chain industry, SAP BW has data models physically instantiated in operational data store (ODS) objects and infocubes. These accelerate the time taken to deliver a solution and provide many out-of-the-box best practice KPIs as a part of the solution. A key driver in adopting a package is the best practices that it brings to the fold by using it. Hence, it is important to evaluate what best practices are encapsulated as a part of the data model.
In most of the industries, there are certain nuances that need to be taken care of and it is imperative to ensure that these are addressed as a part of the prepackaged content. For example, in the corporate banking industry, tracking interrelationships between legal entities that deal with the bank is an important prerequisite to understanding the risk exposure associated with that legal entity. Towards this, the data model must support tracking of the same legal entities playing multiple roles in different transactions (guarantor, borrower and approved user) and interrelationships between them to correctly identify the risk exposure.
An inherent flaw in many of the standard evaluations of prepackaged content that gets done is that the emphasis remains fairly internal in nature (i.e., usage of data generated internally in the organization being put to use for extracting meaningful information).These studies tend to overlook fitment of prepackaged content to accommodate external data feeds or syndicated content. For example, AC Nielesen feeds or POS feeds are important for an effective analysis of sales performance, promotion performance and price performance in the consumer products goods industry. In a typical scenario, this garners a sizable chunk of the BI budgets.In fact, our experience indicates, implementing a solution for this area is much more challenging than internal systems as the data is outside of your control and can have varying levels of data quality.
Having analyzed the breadth and depth of the solution and made sure that it meets your requirements, the other aspect that you should assess is how easily the solution scales to meet the future demands from the application. A couple of areas that we evaluate relate to flexibility/scalability and the industry standards adoption.
BI and data warehousing solutions are strategic investments and the solutions are expected to scale easily to meet the emerging needs of the organization. There are many changes that cannot be forecasted. In custom developed solutions, most environmental changes mean getting back to the drawing board and rearchitecting the solution. Prepackaged BI is often seen as an insurance against these changes as these solutions are expected to have the sufficient width to cater to additional requirements.
While it is not simple to identify and evaluate these border scenarios that will help us assess the flexibility and scalability, it is important that we make an earnest attempt to identify many potential scenarios, if not most of them, to make a reasonable judgment on the scalability and flexibility. For example, if you are in the food and beverages industry and you are currently in a self-owned restaurant model, if there are chances that you might utilize a franchised channel or a direct channel for expansion, then it is important that you evaluate these upfront. You should also explore the possibility of potential mergers or acquisitions in the future and how easy it would be to integrate the new organizations and move forward without any radical changes.
Industry Standards Adoption
Last but not least, a way of ensuring that the prepackaged business content is reasonably sound and is scalable is to look at how well industry standards have been adopted as a part of the solution. ACCORD for insurance, ARTS for retail and
SCOR for supply chain are some of the standards that are present or emerging and are expected to grow in the future. Alignment with these provides a slightly improved reassurance on the completeness of the data model and also on the future scalability of the solution.
There is a perceptible trend towards an increase in the adoption of packaged BI solutions. With this, more and more organizations will start evaluating these solutions to replace or augment existing solutions. While evaluating prepackaged BI solutions, the most crucial thing to do is to evaluate how well suited the prepackaged BI solution is for your industry. Solutions are still maturing. I suggest a cautious approach to the adoption of packaged BI solutions.
Draw out your parameters in the five-point framework in Figure 1, get an objective evaluation and give a score towards each of those. This is sure to see you through your final decision.
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