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
I recently came across a trade-press article with the headline “Mining the Cloud.” The cynic in me immediately issued a silent scoff: How is that different from “crawling the Web”? Are we just mapping old wine to shinier new bottles? Or is there something different here?
But, seeing as how I too like to proliferate discussions of mining this or that information type, I was willing to cut the reporter some slack. The article was from Redmond Developer, and concerns “Project Dallas” under Microsoft’s Azure cloud initiative. Essentially, “Project Dallas” (still in beta) supports discovery, manipulation, visualization, and analysis of data retrieved from multiple public, commercial, and private data sources via the Azure cloud. “Dallas” allows enterprises to provide users (via REST, Excel PowerPivot, and/or Visual Basic applications) with online access to aggregated feeds via Azure, which essentially operates as an online information marketplace. Also, “Dallas” allows customers to have Azure host their data for them, or simply continue to host it on their own premises while the cloud service connects securely to it.
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.
Price-performance is everything in data warehousing (DW), and it’s become the leading battleground for competitive differentiation.
As I noted in a blog post last month, the price of a fully configured DW appliance solution has dropped by an order of magnitude over the past 2-3 years, and it’s likely to continue declining. In 2010, many DW vendors will lower the price of their basic appliance products to less than $20,000 per usable terabyte (TB), which constitutes the new industry threshold pioneered by Oracle, Netezza, and other leading DW vendors.
But that’s just a metric of price, not price-performance. Ideally, each DW appliance vendor should be able to provide you with a metric that tells you exactly how much performance “bang” you’re getting for all those bucks. In a perfect world, all vendors would use the same price-performance metric and you would be able to compare their solutions side by side.
But, as I noted a year ago in another blog post, truly comparable cross-vendor DW benchmarks have never existed and are unlikely to emerge in today’s intensively competitive arena. No two DW vendors provide performance numbers that are based on the same configurations, workloads, and benchmark metrics. And considering how sensitive these performance claims are to so many variables in the vendors’ solutions and in customers’ production environments, it can be quite difficult to verify every vendor performance claim in your specific environment.
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.
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.
Social networks have always been with us, of course, but now they’ve gained concrete reality in the online fabric of modern life.
Social network analysis has, in a real sense, been with us almost as long as we’ve been doing predictive analytics. Customer churn analysis is the killer app for predictive analytics, and it is inherently social. It’s long been known that individual customers don’t always churn themselves—i.e., decide to renew and/or bolt to the competition—in isolation. As they run the continual calculus called loyalty in their heads and hearts, they’re receiving fresh feeds of opinion from their friends and families, following the leads of peers and influencers, and keeping their fingers to the cultural breeze. You could also make a strong case for social networking—i.e., individual behaviors spurred, shaped, and encouraged within communities—as a key independent variable driving cross-sell, up-sell, fraud, and other phenomena for which we’ve long built predictive models.
The other day, a Forrester client was asking me for educated guesses on how fast the average enterprise data warehouse (EDW) is likely to grow over the next several years, and as I was working through the analysis, I couldn’t avoid the conclusion that social network analysis—for predictive and other uses—will be an important growth driver (though not the entire story). I’d like to lay out my key points.