Pros and cons of using a vendor provided analytical data model in your BI implementation

The following question comes from many of our clients: what are some of the advantages and risks of implementing a vendor provided analytical logical data model at the start of any Business Intelligence, Data Warehousing or other Information Management initiatives? Some quick thoughts on pros and cons:

Pros:

  • Leverage vendor knowledge from prior experience and other customers
  • May fill in the gaps in enterprise domain knowledge
  • Best if your IT dept does not have experienced data modelers 
  • May sometimes serve as a project, initiative, solution accelerator
  • May sometimes break through a stalemate between stakeholders failing to agree on metrics, definitions

Cons

 

  • May sometimes require more customization effort, than building a model from scratch
  • May create difference of opinion arguments and potential road blocks from your own experienced data modelers
  • May reduce competitive advantage of business intelligence and analytics (since competitors may be using the same model)
  • Goes against “agile” BI principles that call for small, quick, tangible deliverables
  • Goes against top down performance management design and modeling best practices, where one does not start with a logical data model but rather
    • Defines departmental, line of business strategies  
    • Links goals and objectives needed to fulfill these strategies  
    • Defines metrics needed to measure the progress against goals and objectives  
    • Defines strategic, tactical and operational decisions that need to be made based on metrics
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BI In The Cloud? Yes, And On The Ground, Too

Slowly but surely, with lots of criticism and skepticism, the business intelligence (BI) software-as-a-service (SaaS) market is gaining ground. It's a road full of peril — at least two BI SaaS startups have failed this year — but what software market segment has not seen its share of failures? Although I do not see a stampede to replace traditional BI applications with SaaS alternatives in the near future, BI SaaS does have a few legitimate use cases even today, such as complementary BI, in coexistence with traditional BI, BI workspaces, and BI for small and some midsize businesses. 

In our latest BI SaaS research report we recommend the following structured approach to see if BI SaaS is right for you and if you are ready for BI SaaS:

  1. Map your BI requirements and IT culture to one of five BI SaaS use cases
  2. Evaluate and consider scenarios where BI SaaS may be a right or wrong fit for you
  3. Select the BI SaaS vendor that fits your business, technical, and operational requirements, including your tolerance for risk

First we identified 5 following BI SaaS use cases.

  1. Coexistence case: on-premises BI complemented with SaaS BI in enterprises
  2. SaaS-centric case in enterprises: main BI application in enterprises committed to SaaS
  3. SaaS-centric case in midmarket: main BI application in midsized businesses
  4. Elasticity case: BI for companies with strong variations in activity from season to season
  5. Power user flexibility case: BI workspaces are often considered necessary by power analysts
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Bottom Up And Top Down Approaches To Estimating Costs For A Single BI Report

How much does it cost to produce a single BI report? Just like typical answers to most other typical questions, the only real answer is “it depends”. But let’s build a few scenarios:

Scenario 1: Services only. Bottom up, ABC approach.

Assumptions.

 

  • Medium complexity report. Two data sources. 4 way join. 3 facts by 5 dimensions. Prompting, filtering, sorting ranking on most of the columns. Some conditional formatting. No data model changes.
  • Specifications and design – 2 person days. Development and testing - 1 person day. UAT – 1 person day.
  • Loaded salary for an FTE $120,000/yr or about ~$460/day.
  • Outside contractor $800/day.

Cost of 1 BI report: $1,840 if done by 2 FTEs or $2,520if done by 1 FTE (end user) and 1 outside contractor (developer). Sounds inexpensive? Wait.

 

Scenario 2. Top down. BI software and services:

Assumptions:

  • Average BI software deal per department (as per the latest BI Wave numbers) - $150,000
  • 50% of the software cost is attributable to canned reports, the rest is allocated to ad-hoc queries, and other forms of ad-hoc analysis and exploration.
  • Average cost of effort and services - $5 per every $1 spent on software (anecdotal evidence)
  • Average number of reports per small department - 100 (anecdotal evidence)
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How Much Intelligence Can You Pack Into a Tweet?

In the analytics wars, one of the quasi-metaphysical topics I try to avoid is debating the distinctions between “information,” “intelligence,” and “insight.”

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Next Best Models: The Process Agility Equation

You never know what’s coming at you next, which is why process agility is so important. Your organization must have a ready response for anything. And you must make sure that every process participant can identify, at their level, what that response might be, so they can take appropriate action.

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Self-Service Business Intelligence: Dissolving the Barriers to Creative Decision-Support Solutions

Self-service is all the rage in the world of business intelligence (BI), but it’s no fad. In fact, it’s the only way to make BI more pervasive, delivering insights into every decision—important or mundane—that drives your business. It’s the key to empowering users with actionable insights while removing many mundane BI development and maintenance tasks from IT’s crushing workload.

In mid- 2009, I published a Forrester report describing key benefits, use cases, and approaches for implementing self-service BI, under the broad heading of “mighty mashup.” Forrester customers have responded very favorably to the discussion, asking for advice on whether, when, and how they should adopt this approach. Going forward, Forrester will deepen our discussion of self-service as a best practice to be incorporated into enterprise BI Solution Center (BISC) teachings.

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Process Mining: Because Your Company’s Workflow Issues Aren’t Always Obvious

Business processes can be incredibly hard to fathom. The more complex they are, the more difficult it is to find the magic blend of tasks, roles, flows, and other factors that distinguish a well-tuned process from a miserable flop. Even the people who’ve been part of the process for years may have little clue. It’s not just that they refuse to look beyond their job-specific perspectives, for fear of jeopardizing their careers. It’s often an issue of them being too close to the problem to see it clearly, even if they try very hard.

Process analytics is all about identifying what works and doesn’t work. It’s a key focus for us here at Forrester, and I’m collaborating with one of our leading business process management (BPM) analysts, Clay Richardson, on research into this important topic. The first order of business for us is to identify the full range of enabling infrastructure and tools for tracking, exploring, and analyzing a wide range of workflows. It’s clear that this must include, at the very least, business activity monitoring (BAM) tools, which roll up key process metrics into visual business intelligence (BI)-style dashboards for operational process managers. Likewise, historical process metrics should be available to the business analysts who design and optimize workflows. And each user should have access to whatever current key performance indicators are relevant to the roles they perform within one or more processes.

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Whither Data Warehousing In The Teens?

People often use the end of a decade to say goodbye to trends that have played themselves out, or good riddance to things that have long since passed their cultural expiration dates. I like to use the beginnings of decades for that same purpose. What, we should ask ourselves, is not likely to last beyond the close of this new ten-year cycle?

In data warehousing, the most likely casualty of the Teens will be the very notion of a data warehouse. You can tell that a concept is on its last legs when its proponents spend more time on the defense, fighting definitional trench wars, than evolving it in useful new directions. Here’s a perfect case in point:a recent article by Bill Inmon, self-described “father of the data warehouse,” where he takes pains to specify what is not a data warehouse. Apparently, many of the approaches that we normally implement in our data warehousing architectures—such as subject-specific data marts, dimensional data structures, federated architectures, and real-time data integration—don’t pass muster in Inmon’s way of looking at things. Though he didn’t mention hybrid row-columnar and in-memory databases by name, one suspects that Inmon has a similarly jaundiced view of these leading-edge data warehousing technologies.

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