Look out IBM, Oracle and SAP — you’re about to lose a bit of your dominance in the master data management (MDM) market to Informatica. On January 28, 2010, Informatica announced that it acquired Siperian for $130 million (representing the largest acquisition Informatica has made to date). Siperian is a multi-domain operational MDM vendor that Forrester named as a leader in our last Forrester Wave for Customer Hubs in Q3 of 2008 (see graphic).
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
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.
Since 2007, Forrester analysts Ken Vollmer, Noel Yuhanna and I have collaborated to publish an annual review of the application, process, and data integration technology landscape. The goal of this important recurring research is to help application development, business process, data management, and enterprise architecture professionals navigate the often complex and confusing myriad of choices available to solve their organization’s integration challenges.
This year’s report focuses on ten distinct integration technologies including ESB, CIS (Comprehensive integration solutions), B2B service providers, Privacy industry exchanges, B2B gateway software, and Integration appliances on the application and process integration side, as well as ETL, CDC (change data capture), and EII (enterprise information integration) on the data integration side. In addition, we continue to look at Information-as-a-Service (IaaS) as an architectural approach to supporting data integration requirements.
A key take away from this research is our recognition that application, process and data integration can no longer remain isolated siloed competencies within an organization. Our recommendation is that organizations look to consolidate their integration strategies and resources into a shared services organization that can leverage all the strengths of these different techniques.
We hope you enjoy, and look forward to hearing your feedback.
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.
On January 4, 2010, Oracle announced its acquisition of Silver Creek Systems, a small private software company focusing on product data quality, which Oracle plans to add to its Oracle Data Integration offering. In our recent research, “It’s Time To Revisit Product Information Management”, we discussed how Forrester believes Silver Creek holds a virtual monopoly in delivering advanced product data quality capabilities, unmatched by other customer data-centric data quality vendors in the market. Due to this, many MDM, PIM and data quality software vendors, including Oracle, had relied on Silver Creek as a strategic partner to add credibility in product data quality. And as we accurately predicted in that research, Silver Creek has now been acquired which will introduce a significant challenge to these partners.