TDWI The Data Warehousing Institute

CEOs and other senior executives must identify ways to improve their enterprise performance by boosting profitability, raising market share, and leapfrogging competitors. But achieving these objectives is not as simple as just looking at the numbers. What about nonfinancial measures (e.g., customer loyalty and employee satisfaction) that don't show up in financial accounting? How do you quickly and efficiently get the full 360-degree view of your business?

In order to execute their business strategy, business and IT execs need a business-focused, strategic, and pragmatic way to measure their finances and operations -- popularly referred to as business intelligence (BI). Without such measurements -- supported by enterprisewide BI deployments -- businesses can't link operational results to strategy. Organizations will also find it difficult to get a coherent view of their internal and external processes, customers, logistics, operations, and finances.

However, most firms struggle with BI strategies and programs because turning data into information is an open-ended concept. They frequently go in the wrong direction because of traditional (and often outdated) views and approaches and a focus on technology instead of business, which results in BI programs that are tactical and only project-based. What these firms need is an approach to BI that, while staying true to the importance of long-term vision and looking across silos, provides the flexibility to accommodate varying levels of resource commitment and the political, historical, and cultural obstacles that BI programs often face. Think of Forrester's new business intelligence playbook as your BI bible; it should guide your BI decisions every step of the way.

BI professionals spend a significant portion of their time trying to instill the discipline of datadriven performance management into their business partners. However, isn't there something wrong with teaching someone else to fly when you're still learning to walk? Few BI pros have a way to measure their BI performance quantitatively (46% do not measure BI performance efficiencies and 55% do not measure effectiveness). Everyone collects statistics on the database and BI application server performance, and many conduct periodic surveys to gauge business users' level of satisfaction. But how do you really know if you have a high-performing, widely used, popular BI environment? For example, you should know BI performance

• Efficiency metrics such as number of times a report is used or a number of duplicate/similar reports, etc
• Effectiveness metrics such as average number of clicks to find a report and clicks within a report to find an answer to a question and many others
• Metric attributes/dimensions such as users, roles, departments, LOBs, regions and others

Those who have implemented what Forrester calls "BI on BI" are in nirvana land reaping the following benefits such as ability to quantitatively support

• "By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions." (Source: May 2012 McKinsey Global Institute report on Big Data)
• "… trigger a talent shortage, with up to 190,000 skilled professionals needed to cope with demand in the US alone over the next five years." (Source: 2012 Deloitte report on technology trends)
• "Fewer than 25% of the survey respondents worldwide said they have the skills and resources to analyze unstructured data, such as text, voice, and sensor data." (Source: 2012 research report by IBM and the Saïd Business School at the University of Oxford)

• Work with local schools, colleges, and universities. Create a data/BI scholarship fund. Sponsor BI classes. Create BI (summer) internships for students. This can help ensure a steady stream of graduates to apply for your BI positions.
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Categories:

• Analytics
• big data
• business intelligence
• hr

We all work very hard to make our BI initiatives, programs, platforms, applications, and tools very successful. We need a break. And what better way to relax than to joke about what we do? So... as my favorite Monty Python's Flying Circus bit goes, "And now for something completely different [in BI]":

• Q: What's the ROI/cost to achieve a single version of the truth?
• A: 42 (compliments of my other favorite medium, Douglas Adams' Hitchhiker's Guide To The Galaxy).

• Q: How many BI pros does it take to screw in a light bulb?
• A: None -- it's an appliance.

• Q: How many BI pros does it take to screw in a light bulb?
• A: It depends. How do you measure/analyze/report on success?

Looking for lots more to brighten up our BI days, so please post away!

Mobile BI and cloud BI are among the top trends that we track in the industry. Our upcoming Enterprise BI Platforms Wave™ will dedicate a significant portion of vendor evaluation on these two capabilities. These capabilities are far from yes/no checkmarks. Just asking vague questions like "Can you deliver your BI functionality on mobile devices?" and "Is your BI platform available in the cloud as software-as-a-service?" will lead to incomplete vendor answers, which in turn may lead you to make the wrong vendor selections. Instead, we plan to evaluate these two critical BI platform capabilities along the following parameters:

Mobile BI

Clients often ask me to help define their job description for a business intelligence (BI) leader, executive, or manager. Here's what I typically provide:

• Our research on Agile BI organizational structures is a good starting point.
• Because that report doesn't include a deep dive into individual job descriptions or salaries, you can refer to some great external resources from TDWI and Robert Half International.

Finally, here's a description of a senior BI architect/manager/executive that I've used in the past. I would love to hear from everyone on what's missing, what can be added, changed, etc.

Job title: Director/manager/VP of business intelligence

Reports to: Chief operations officer, chief strategy officer, chief information officer, chief financial officer (more popular in the past than the present) or some of the newer titles such as chief customer officer, chief data officer, or chief analytics officer.

Job description: The director/manager/VP of BI has primary responsibility for setting the strategy and vision and for managing the day-to-day tactical operations of the BI teams. He/she will be responsible for all strategic, tactical, operational, financial, human, and technical resource managerial responsibilities associated with the following BI and BI-related functional areas:

* < 10 : 1
* 10-24 : 1
* 25-49 : 1
* 50-74 : 1
* 75-99 : 1
* 100-199 : 1
* 200 - 499: 1
* > 500 : 1

BI is used to build, report, and analyze business performance metrics and indicators. What about measuring the performance of BI itself? How do you know if you have a high-performing, widely used BI environment? Is your opinion based on qualitative "pulse checks" or is it based on quantitative metrics? BI practitioners who preach to their business counterparts to run their business by the numbers need to eat their own dog food: run their BI environment, platforms, and apps by the numbers. For example, do you know:

• How many reports and queries do end users create by themselves versus how many IT creates? That's a great efficiency metric.
• How many clicks within a dashboard does it take to find an answer to a question? That'/s another great efficiency metric.
• How long does each user stay within each report? Do they just run and print the reports, or export the data to Excel, or do they really slice, dice, and analyze the information? That's a good example of how effective your BI environment is.
• Do you see any patterns in BI usage? User by user, department by department, or line of business by line of business?
• How many reports, queries, and other objects are being used, how many are shelfware (not being used)? How often are people using the ones that are being used?

I find surprisingly little research out there on this subject, so we plan to publish a report on it. With that in mind, I'd like to solicit your input. If you have some experience, specific use cases, and best practices, please share them with me. For example:

It's that time of year again, when everyone starts asking for the BI predictions for next year. Good news: We did a pretty good job on the last year's predictions, and so there're only a few reasons to update them. Therefore this year we'll do the predictions in the following manner: base 2013's predictions mostly on our one-year-old 2012 ones and then use the latest results from Forrester's Forrsights surveys on BI and big data (as well as other Forrester research from the past year) to confirm or disprove the 2012 predictions and whether they still apply to 2013. If there's room to add new ones (stay tuned), we'll do so. So here we go:

#1 (From 2012 prediction #1). The best tool for each BI job trumps IT standards. BI has traditionally been ruled by overinsistence on enterprisewide standards and a single version of the truth. These will continue to be important, but they won't be the Holy Grail. A purely standards-based approach to addressing most current business requirements is neither flexible nor agile enough to react and adapt to ever-changing information requirements. In 2012 (and now in 2013), expect IT to start embracing agile BI tools, such as ones based on flexible in-memory models, in addition to enterprise-grade BI tools and standards. For information workers who need information anytime and anywhere, agility concerns will trump standards.

I often see two ends of the extreme when I talk to clients who are trying to deal with data confidence challenges. One group typically sees it as a problem that IT has to address, while business users continue to use spreadsheets and other home-grown apps for BI. At the other end of the extreme, there's a strong, take-no-prisoners, top-down mandate for using only enterprise BI apps. In this case, a CEO may impose a rule that says that you can't walk into my office, ask me to make a decision, ask for a budget, etc., based on anything other than data coming from an enterprise BI application. This may sound great, but it's not often very practical; the world is not that simple, and there are many shades of grey in between these two extremes. No large, global, heterogeneous, multi-business- and product-line enterprise can ever hope to clean up all of its data - it's always a continuous journey. The key is knowing what data sources feed your BI applications and how confident you are about the accuracy of data coming from each source.

For example, here's one approach that I often see work very well. In this approach, IT assigns a data confidence index (an extra column attached to each transactional record in your data warehouse, data mart, etc.) during ETL processes. It may look something like this: