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?
Data management is becoming critical as organizations seek to better understand and target their customers, drive out inefficiency, and satisfy government regulations. Despite this, the maturity of data management practices at companies in China is generally poor.
I had an enlightening conversation with my colleague, senior analyst Michele Goetz, who covers all aspects of data management. She told me that in North America and Europe, data management maturity varies widely from company to company; only about 5% have mature practices and a robust data management infrastructure. Most organizations are still struggling to be agile and lack measurement, even if they already have data management platforms in place. Very few of them align adequately with their specific business or information strategy and organizational structure.
If we look at data management maturity in China, I suspect the results are even worse: that fewer than 1% of the companies are mature in terms of integrated strategy, agile execution and continuous performance measurement. Specifically:
The practice of data management is still in the early stages. Data management is not only about simply deploying technology like data warehousing or related middleware, but also means putting in place the strategy and architectural practice, including contextual services and metadata pattern modeling, to align with business focus. The current focus of Chinese enterprises for data management is mostly around data warehousing, master data management, and basic support for both end-to-end business processes and composite applications for top management decision-making. It’s still far from leveraging the valuable data in business processes and business analytics.
The 2013 New Year has begun with the removal from the global tech market outlook of one risk, that of the US economy going over the fiscal cliff. On New Year's day, the US House of Representatives followed the lead of the US Senate and passed a bill that extends existing tax rates for households with $450,000 or less in income, extends unemployment insurance benefits for 2 million Americans, and renews tax credits for child care, college tuition, and renewable energy production, as well as delaying for two months the automatic spending cuts. While it also allowed Social Security payroll taxes to rise by 2 percentage points — thereby raising the tax burden on poor and middle class people — and did not increase the federal debt ceiling or address entitlement spending, the last-minute compromise does mean that the US tech market no longer has to worry, for now, about big increases in taxes and cuts in spending pushing the US economy into recession.
While Social Business continued to evolve in 2012, 2013 will see the emergence of digital business as a new strategic theme for many firms. What's driving this shift and what does it mean for CIOs, CEOs, and chief digital officers?
The Communications Evolution
Communications continue to evolve. Consider how humans have transformed communications over the centuries: signal fires; semaphore; Morse code; the telegraph; the telephone; telex; fax; email; SMS; Facebook; and Twitter. I have no doubt that this evolution will continue in 2013 and beyond. Perhaps beyond 2013 we will eventually achieve the ability to communicate our thoughts directly — whether we’ll want to is a different question. As people the world over learn to use new social networking tools, they drop older tools that are no longer useful to them. Regardless of where you are in your personal communications evolution, the undeniable truth is that over the past decade we have significantly changed how people communicate; we are no longer dependent upon email. But social tools and 24/7 mobile access have not removed the complexity or decreased the volume of information we must process. Time remains our most precious resource and we’ll always seek ways to use it more effectively — but social tools are not necessarily the silver bullet we might think. In 2013 we need to rethink business processes to take this new communications paradigm into account.
Rowan Curran, Research Associate and TechnoPolitics producer, hosts this episode to ask me (your regular host) about The Pragmatic Definition Of Big Data. Listen (5 mins) to hear the genesis of this new definition of big data and why it is pragmatic and actionable for both business and IT professionals.
Podcast: The Pragmatic Definition Of Big Data Explained (5 mins)
In the face of rising data volume and complexity and increased need for self-service, enterprises need an effective business intelligence (BI) reference architecture to utilize BI as a key corporate asset for competitive differentiation. BI stakeholders — such as project managers, developers, data architects, enterprise architects, database administrators, and data quality specialists — may find the myriad choices and constant influx of new business requirements overwhelming. Forrester's BI reference architecture provides a framework with architectural patterns and building blocks to guide these BI stakeholders in managing BI strategy and architecture.
Enterprise information management (EIM) is complex — from a technical, organizational, and operational standpoint. But to business users, all that complexity is behind the scenes. What they need is BI, an interface to enterprise data — whether it's structured, semistructured, or unstructured. Our June 2011 Global Technology Trends Online Survey showed that BI topped even mobility — the frontrunner in recent years — as the technology most likely to provide business value over the next three years.
As John Brand and I recently wrote, business intelligence (BI) adoption drivers, technology understanding, and organizational process maturity continue to vary widely across Asia Pacific (AP). But there is one constant in this market: the regularity with which BI appears at or near the top of CIOs’ priority lists.
While the gap between global best practices and regional implementations is closing, social, cultural, economic, and underlying technology trends will continue to affect BI adoption in the region for the foreseeable future:
Social. The adoption of social computing is expanding rapidly across all AP markets, but is particularly strong in growth markets like China, Indonesia, and the Philippines. As in North America and Western Europe, this adoption is already having profound effects on how organizations identify, understand, and engage with customers and other market influencers. But the lack of significant BI investments means that organizations in these growth markets are far more likely to consider issues like sentiment analysis, predictive analytics, and near real-time data access when sourcing initial BI projects.
Recently, Forrester released a report entitled “What Drives Retention and Sales In US Banking?” that tackles this question from the consumer point of view. Using regression analysis, we uncover how these drivers vary for acquisition, retention, and cross-selling in US retail banking.
What did we find? For one thing, consumers value trustworthiness from a bank above all else for both sales and retention. This comes as no surprise to us; with so many financial institutions to choose from, consumers want to do business with a bank that they trust. This finding also supports the key theme that Harley Manning and Kerry Bodine focus on in their recent book, Outside In: Treating your customers well and providing them with a positive customer experience pays off.
The graphic below shows the drivers of retention for the US retail banking customers: The perception of trustworthiness is off the charts as a driver of retention, and offering good customer service is the second-most influential driver. What our analysis shows to not impact retention — and even shows a negative relationship with retention — is having low APR and many locations.
Every year the Center For Digital Strategies at Tuck chooses a technology topic to "provide MBA candidates and the Tuck and Darthmouth communities with insights into how changes in technology affect individuals, impact enterprises and reshape industries." This academic year the topic is "Big Data: The Information Explosion That Will Reshape Our World". I had the honor and privilege to kick off the series about big data at the Tuck School of Business at Dartmouth. I am thrilled that our future business leaders are considering how big data can help companies, communities, and government make smarter decisions and provide better customer experiences. The combination of big data and predictive analytics is already changing the world. Below is the edited video of my talk on big data predictive analytics at Tuck in Hanover, NH.