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:
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
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
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:
Map your BI requirements and IT culture to one of five BI SaaS use cases
Evaluate and consider scenarios where BI SaaS may be a right or wrong fit for you
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.
Coexistence case: on-premises BI complemented with SaaS BI in enterprises
SaaS-centric case in enterprises: main BI application in enterprises committed to SaaS
SaaS-centric case in midmarket: main BI application in midsized businesses
Elasticity case: BI for companies with strong variations in activity from season to season
Power user flexibility case: BI workspaces are often considered necessary by power analysts
Our latest BI maturity survey results are in. We used exactly the same questions from our online BI maturity self assessment tool to survey over 200 Forrester clients. Now you can compare your own BI maturity level against your peers by using data from the survey.
In the self assessment tool and in the survey we ask over 30 questions in the following 6 categories
Data and technology
Our clients rated themselves on the scale of 1 to 5 (5, if they strongly agree with our statement or 1, if they strongly disagree). Here are the overall results. Keep in mind that these results do not evaluate BI maturity accross ALL business, but rather in businesses that are already pretty far ahead in their BI implementations (they are Forrester clients, they read our research reports, they talk to our research analysts):
I get many questions about the usage, pervasiveness, and adaption of mobile BI applications. What's a mobile BI application? Beyond a simple delivery of alerts, URLs, or actual reports via email - functionality that has existed for years - here are a few newer approaches to deliver BI on a mobile device:
The no brainer. In theory any mobile device equipped with a browser can access web based, thin client, HTML only BI applications. Yes, these BI apps will be mostly static, not interactive reports and dashboards. Navigation (scrolling, zooming, etc) will be quite awkward. But, this approach indeed requires no additional effort to deploy.
Customization. The next step up is to render each (or all) reports and dashboards to a format suitable to any mobile device in terms of screen size, usage of screen real estate, and mobile device specific navigation instrumentation. A variation of this approach is to create device specific navigation controls (thumb wheel or thumb button for Blackberries, up/down/left/right arrows for Palms, gestural manipulation for iPhone, etc). This obviously requires more development effort, but still no additional software.
I'd like to drill into some more details on my BI SaaS blog from September 2009. A key critical point to "what differentiates one BI SaaS vendor from another" discussion is what really constitutes multi-tenant architecture. Here are some initiall thoughts to stimulate the discussion:
DBMS. There's got to be back end, DBMS architecture that allows for one of the following:
Automatically generate a separate DBMS instance for each client
Use same DBMS instance for multiple clients, but automatically generate a set of unique tables for each client
Use same DBMS instance and tables for multiple clients, but automatically assign unique keys to to each client so that they can only update and retrieve their own rows
Application. Similar functionality has to exist in the application tier:
Automatically connect to the appropriate, client specific DBMS instance, or
Automatically use views that only point to client specific tables, or
Append "where" clause to each SQL statement to only retrieve client specific rows
You don’t need to be a scientist to boost your business with applied mathematics
On 22/9/09 SPSS Inc. announced a new certification process to confirm an individual’s expertise with some of their statistical solutions. “Look at this”, I thought “sophisticated software still requires experts to unfold the value they can provide”. Being a physicist by background, I like it how applied mathematics can improve business. However, not everyone sees beauty in algorithms or is interested in statistics.