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Wayne Eckerson

Welcome to Wayne's World, my blog that illuminates the latest thinking about how to deliver insights from business data and celebrates out-of-the-box thinkers and doers in the business intelligence (BI), performance management and data warehousing (DW) fields. Tune in here if you want to keep abreast of the latest trends, techniques, and technologies in this dynamic industry.

About the author >

Wayne has been a thought leader in the business intelligence field since the early 1990s. He has conducted numerous research studies and is a noted speaker, blogger, and consultant. He is the author of two widely read books: Performance Dashboards: Measuring, Monitoring, and Managing Your Business (2005, 2010) and The Secrets of Analytical Leaders: Insights from Information Insiders (2012).

Wayne is currently director of BI Leadership Research, an education and research service run by TechTarget that provides objective, vendor neutral content to business intelligence (BI) professionals worldwide. Wayne’s consulting company, BI Leader Consulting, provides strategic planning, architectural reviews, internal workshops, and long-term mentoring to both user and vendor organizations. For many years, Wayne served as director of education and research at The Data Warehousing Institute (TDWI) where he oversaw the company’s content and training programs and chaired its BI Executive Summit. He can be reached by email at weckerson@techtarget.com.

Qubole is a new breed of analytic software company that runs on Hadoop in the public cloud. The ideal customer is one that already runs a lot of applications in the cloud and wants to accelerate the time it takes to make big data available to business analysts and data scientists.

Qubole's big competitor is Amazon Web Services and its Elastic MapReduce (EMR) offering, which provides the Hadoop platform as a cloud-based service. Unlike EMR, Qubole was designed from scratch to support queries and analytics. Also, the service is geared t business analysts and data scientists, not Java and other developers, so it is easier to use.

Qubole officials says its queries run faster than EMR  and offers better total cost of ownership since the service abstracts much more of the complexity of provisioning and managing an analytical data service on Hadoop  in the cloud. Qubole competes with other rising cloud-based analytics vendors, such as Altiscale, Mortar Data, and Treasure Data. 

Qubole is based in Mountain View, California. Its Web site is www.qubole.com.


Posted July 22, 2014 6:52 AM
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ScaleOut Software provides an in-memory data grid that primarily provides fast reads/writes for high-speed operational applications, such as e-commerce, reservation systems, credit card processing, equity trading, smart grids, and cable network streaming. There are a lot of competitors in the space, such as Oracle Coherence, but ScaleOut and Apache Spark differentiates itself by supporting analytics on live, operational data, among other things.

ScaleOut Software resides at the bleeding edge of the convergence between operational and analytical applications. With ScaleOut, analysts no longer have to wait until operational data is moved to an operational data store or data warehouse; they can analyze the data in place as it is changing. ScaleOut also has a product that runs on Hadoop and executes MapReduce code, but on live data at 20 times the speed of regular MapReduce. With 400 customers, ScaleOut is a company to watch in the emerging "operational intelligence" market.

See www.scaleoutsoftware.com


Posted July 19, 2014 12:45 PM
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There is no better source for the trends in the use of business intelligence (BI) tools than the BI Survey conducted annually by the German-based research house, BARC. The BI Survey, now in its 14th year, is the world's largest independent survey of BI users, with more than 2,500 survey takers around the globe.

This summer, I will help BARC's research staff evaluate the results of the BI Survey, which closes in a few weeks (late June.) The BI Survey tracks customer attitudes towards more than two dozen BI products. The published report discusses key purchasing and usage patterns, including total cost of ownership, market and customer penetration, query performance, support quality, business benefits, and technical challenges, among other things.

The report also looks at various emerging topics and technologies, such as mobility, self-service, cloud, in-memory computing, collaboration, and more. I look forward to seeing what you think of the key BI products your company uses today. So I encourage you to participate so that your views are heard and recorded.

If you would like to provide honest feedback on your company's BI tool of choice, click HERE.

To give you a taste of the type of analysis the BI Survey provides, here is a sample from last year's report. This segment focuses on mobile BI:

Mobile user interfaces are a major trend in business intelligence. Despite the media attention for mobile BI, only 16 percent of respondents use BI on a mobile device and we have seen very few success stories with mobile deployments in the field. Furthermore, even vendors with the most popular mobile solutions, don't have robust mobile usage: less than 45 percent of their customers have deployed a mobile BI solution.

Some BI vendors are far ahead of the rest of the industry when it comes to converting their customers to mobile BI. The top three are Yellowfin (43% of customers use mobile BI), MicroStrategy (34%) and LogiAnalytics (32%). (See figure 1.)

Figure 1. BI Vendors by Percentage of Customers Deploying Mobile BI Solution

Mobile BI Leaders.jpg

South America and Asia Pacific have the highest percentage of mobile BI adoption. More stringent data security requirements in North America and Europe may be the cause of the lower-than-average. South America and Asia Pacific have wider usage of the Android mobile operating system, which is less secure than Apple iOS, which dominates North America and Europe. (See figure 2.)

Figure 2. Mobile BI Adoption by Region

Mobile BI by Country.jpg

Southern Europe is well ahead of the rest of the continent while France's adoption rate is strikingly low compared to other European regions. Our experience in the French market points to a combination of three factors that contribute to this figure. (See figure 3.)

Mobile BI is deployed across all industries. As a general rule, financial services companies have far more robust security policies than other industries and, in our experience as consultants, we have seen a few financial services firms decide against deploying mobile BI as a result of security concerns. (See figure 3.)

Figure 3. Mobile BI by Industry

Mobile BI by Industry.jpg

Initially, mobile BI was only used to view static reports and dashboards with no interactivity. Now we see that the display of interactive reports and dashboards has overtaken static reporting. Data analysis is generally more difficult to perform on a mobile device than on a desktop computer where drag and drop and drilldown is easier. But mobile apps are improving and we expect the figure for Data Analysis to increase next year. (See figure 4.)

Figure 4. Types of Usage For Mobile BI

Mobile BI Usage.jpg

Please Participate!

The BI Survey provides a shortcut to understanding the pros and cons of BI tools in actual practice. To help us prepare this valuabe report, please take the 25-minute survey now. For your time and effort, we will send you a summary of the results and get entered into a raffle for $50 gift certificates from Amazon.com. Click HERE to take the survey now!


Posted June 9, 2014 9:05 AM
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A balanced scorecard is a powerful tool for aligning an organization. It displays the metrics that represent the key drivers of long-term performance. In many ways, it's a visual representation of an organization's strategy, tailored to every department and individual.

Unfortunately, most organizations are operational in nature, not strategic. They focus on day-to-day tasks required to ship products on time and keep customers happy. While most organizations want to take a long-term view of the business, most are too busy fighting fires to focus on the big picture. And their corporate culture and funding processes undermine scorecard initiatives before the first metrics are even published.

To ensure the success of a balanced scorecard, organizations need to excel at managing change, or rather, getting an organization (and the individuals that comprise it) to change habits for addressing and solving problems. Rather than address the symptoms of issues, a scorecard requires organizations to identify the core drivers of change that lead to new levels of performance.

Whether you are creating a new scorecard or reviving an existing one, it's imperative that you build change management into your scorecard project. Otherwise, the scorecard won't gain traction and overcome operational inertia. There are three keys to ensure your balanced scorecard gets adopted and delivers lasting business value: 1) a committed CEO 2) a robust governance program and 3) a key performance indicator or KPI.

1. A Committed CEO

Since scorecards embody the strategy of an organization (or business unit or department), the primary sponsor is the top executive, or CEO for an entire organization. The CEO must desire organizational alignment and view the scorecard as a critical tool for achieving that goal. The CEO must be fully committed to the project and the scorecard methodology. And that commitment can't waver over time since it often takes months or years for the scorecard initiative to bear fruit and deliver performance improvements.

One indicator of an executive's commitment is his or her willingness to devote time to the project. Although most CEOs won't participate on a scorecard design team, they need to provide ample input upfront and feedback every step of the way. The CEO needs to ensure that the design team creates objectives and measures that align with his or her vision of the company. The CEO must also sell other senior executives about the need for the project and the validity of the methodology. The CEO must also convince these executives to spend time to participate in the project, and, most importantly, assign trusted lieutenants to serve on a scorecard design team.

Finally, the CEO must be willing to spend money on initiatives and resources to effect organizational change. Many CEOs proudly display newly minted strategy maps but never fully fund the activities required to change organizational behavior. Once the scorecard fails to register performance gains, they often lose faith in the measurement system, believing it doesn't accurately represent the uniqueness of their business and processes. In contrast, successful CEOs never waver in their commitment to fact-based measurement and decision-making. They continually modify scorecard initiatives and metrics to maintain relevancy as the business changes or until the scorecard reflects the organizational performance they desire.

2. Scorecard Governance

Secondly, a scorecard needs to gain organizational altitude before it can fly on its own. In operationally focused organizations (and which aren't?), this requires several new governance structures and processes:


  1. Stakeholders. A scorecard project has to be a group effort since it will ultimately affect many people. The project must have a charter that explains its purpose, benefits, and whom it will impact and how. It needs an executive steering committee that evangelizes and funds the project and leads political interference. It needs a working committee of people who define the objectives and metrics at the heart and soul of the scorecard. And most importantly, it needs to identify stakeholders, including the executive committee, whose support and input is required. It's important to get honest feedback from stakeholders about proposed objectives, metrics, and initiatives. Stakeholders, especially those on the front-lines whose performance will be measured by the new metrics, are the only people who really understand the feasibility of proposed metrics.

  2. Strategy Management Office. Once the scorecard is designed, a strategy management office (SMO) drives the process of embedding it into the fabric of the company. A SMO consists of one or more full- or part-time people who make sure the scorecard is populated with data, used to make executive decisions, and updated to reflect changes in the business. The SMO also helps shepherd the creation of additional, cascaded scorecards, so strategy management propagates through an entire organization. Most importantly, the SMO evangelizes the scorecard methodology and facilitates the other governance tasks below.

  3. Theme Teams. In balanced scorecard parlance, "themes" are the primary strategic objectives of the organization. Typically, there are three to five themes that represent an organization's three to five year strategy. A theme team is a cross-functional group of five and eight people who are experts in the theme or vested in the topic. The theme team evaluates relevant scorecard data, interprets the results for the executive team, and makes recommendations for adding, changing, or deleting metrics and objectives from the scorecard. Theme teams ensure that subject matter experts, not just executives, are vested in the scorecard and committed to making sure it delivers relevant results.

  4. Cascaded Scorecards. An executive scorecard is just the beginning of a scorecard initiative. If leaders know and monitor business strategy, but no one else does, then the scorecard can't help overcome organizational inertia. The SMO, with backing from the CEO, works with each member of the executive team to propagate scorecards in their functional areas. Each department designs a scorecard that represents its own strategic objectives as well as drives performance of its parent organization. By cascading scorecards, organizations propagate strategy to every nook and cranny of the organization so every person knows how they contribute to the performance of the whole.

  5. Strategic Expenditures. Scorecards need their own funding, not only to support the SMO and strategic planning exercises, but also to support initiatives that drive key areas of performance measured by the scorecard. Most companies have a bevy of initiatives already and most can be mapped to scorecard objectives and metrics. But inevitably, the organization must undertake new initiatives to drive required change. While these initiatives must pass formal review to receive funding, there are often smaller initiatives or strategic exercises that are best funded from a discretionary scorecard budget.

3. Key Performance Indicator

Even with a committed CEO and strong governance structures, a scorecard won't take root unless it achieves a quick win. A quick win bridges the disparate worlds of operational and strategy management and testifies to the power of a scorecard to effect needed change.

The best way to achieve a quick win is to identify one scorecard metric above all others to serve as the focal point for the organization. The metric should be operational in nature and touch multiple functional areas and processes. Improving the performance of this metric forces employees to share information, collaborate across departmental lines, and brainstorm new processes and ways of doing business. In short, a well-designed KPI creates a ripple effect across an organization, generating widespread performance gains.

The CEO is the fuel for a KPI. The CEO must publicly evangelize the importance of the KPI, monitor its performance religiously, and call accountable executives and managers immediately when performance dips below specified levels. Since no one wants to receive potentially career-limiting calls from the CEO, a KPI forces a whirlwind of change.

John King closely monitored on-time arrivals and departures when he turned around British Airways in the early1980s. Paul O'Neill turned Alcoa from an industry laggard to a high-flier by focusing on worker safety measures. Cisco CEO John Chambers has created a unified corporate culture from 125+ acquisitions by focusing on customer satisfaction metrics.

It should be noted that a KPI doesn't give a CEO or organization license to ignore the other metrics in a scorecard. Rather, a KPI gives credibility to the remaining metrics on the scorecard and teaches the organization how think and act strategically. Indeed, once an organization optimizes the performance of one KPI, it should elevate the next most important metric on the scorecard to KPI status. Meanwhile, the former KPI takes its rightful place in the scorecard or is revised to highlight new areas for improvement.

Summary. A balanced scorecard is a powerful agent of organizational change. But since organizations (and the individuals that comprise them) resist change at all costs, it's imperative that a scorecard project supports a vigorous change management strategy of its own. The key elements of a scorecard change management strategy are a committed executive, a robust governance program, and a key performance indicator wielded by a change-hungry CEO.


Wayne Eckerson is principal consultant of Eckerson Group, a business-technology consultancy that helps organizations turn data into insight and action. He regularly helps organizations design or revitalize dashboard and scorecard projects.


Posted May 13, 2014 12:24 PM
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Hadoop advocates know they've struck gold. They've got new technology that promises to transform the way organizations capture, access, analyze, and act on information. Market watchers estimate the potential revenue from big data software and systems to be in the tens of billions of dollars. So, it's not surprising that Hadoop advocates are eager to discard the old to make way for the new.

But in their haste, some Hadoop advocates have plied a lot of misinformation about so-called "traditional" systems, especially the data warehouse. They seem to think that by bashing the data warehouse, they'll accelerate the pace at which people adopt Hadoop and the "data lake". (See "Big Data Part I: Beware of the Alligators in the Data Lake"). This is a counterproductive strategy for a couple of reasons.

Evolution, Not Revolution. First, the data warehouse will be an integral part of the analytical ecosystem for many years to come. It will take many years (decades?) for a majority of companies to convert their data and analytics architecture to a data lake powered by Hadoop, if they do at all. Organizations simply have too much time, money, resources, and skills tied up with existing systems and applications to throw them away and start anew. The mantra of big data is evolution, not revolution. (To learn about these countervailing strategies, see "The Battle for the Future of Hadoop.")

Slippery Slope. Second, Hadoop is at the beginning of its journey, and while things look bright and rosy now, this new architecture will inevitably encounter dark times and failures, just like all new technologies. Thus, it's unwise for Hadoop advocates to take potshots at a mature technology, like the data warehouse, which has been refined in the crucible of thousands of real-world implementations. Just because there are data warehousing failures doesn't mean the technology is bankrupt or that a majority of organizations are eager to cast their data processing destiny to a new, untested platform whose deficiencies have yet to emerge.

Too Much to Bear. Many data warehousing deficiencies stem from the fact that the data warehouse has been asked it to shoulder a bigger load than it was designed to handle. A data warehouse is best used to deliver answers to known questions: it allows users to monitor performance along predefined metrics and drill down and across related dimensions to gain additional context about a situation. It isn't optimized to support unfettered exploration and discovery or to store and provide access to non-relational data.

But, since the data warehouse has been the only analytical game in town for the past 20 years, organizations have tried to shoehorn into it many workloads that it's not suited to handle. These failures aren't a blemish against the data warehouse as much as evidence of a lack of imagination about how best to solve various types of data processing problems. Fortunately, we now have other ways to capture, store, access, and analyze data. So, we can finally offload some of these workloads from our overburdened data warehouses and give them space to do what they do best--populate reports and dashboards with clean, integrated, and certified data.

A Process, Not a Technology. A final reason that Hadoop proponents shouldn't disparage the data warehouse is because the data warehouse is ultimately a process, not a technology. A data warehouse reunites an organization in electronic form (i.e. data) so that it can function as a single entity, not a conglomeration of loosely coupled fiefdoms. In this sense, the data warehouse will never go away.

The truth is that companies can implement a data warehouse with a variety of technologies and tools, including a data lake. Some are better than others, and none is sufficient in and of itself. But that is not the point: a data warehouse is really an abstraction, a logical representation of clean, vetted data that executives can use to make decisions. Without a data warehouse, executives run blind, making critical decisions with inaccurate data or no data at all.

So, despite what some critics say, the data warehouse is here to stay. It will remain a prominent fixture in analytical environments for many years to come.


Posted April 10, 2014 2:41 PM
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