Tag: BI

Embedded BI Changes the Strategic Role of the Business Analyst

The world of business intelligence (BI) and data analytics has existed for decades; what started as simple Business Analysis reporting in the late 1980s has evolved to today’s near real-time querying.

Departments rely on BI to make daily decisions, and at the highest-level organizations turn to BI and data analytics to make strategic business decisions that can dramatically affect a company’s bottom line and future direction.

Business intelligence is going through a transition. Its evolution combined with new capabilities provided by embedded BI empowers Business Analysts to serve a more direct, visible and strategic role in delivering analytics to organizations. As technology evolves to meet new market demands, departmental, and organizational needs, the functionality offered by BI and data analytics is dramatically evolving. This extends to change who manages BI and data analytics.

As organizations mature and become more sophisticated, business leaders realize that mining the data that already exists within the organization affords them opportunities to influence more effective strategic decision making.

With BI innovations, including new features, functions, and end-user capabilities, Business Analysts can also drive efficiencies with more timely data analysis.

BI Use Comes from Disciplines, Departments Across the Organization

Traditionally the information technology (IT) department was responsible for deploying and managing the BI solution, as well as fulfilling requests to produce reports and delivering them to end users, departmental level managers, and the executives who needed them. Once IT deployed the BI solution, the Business Analyst became the intermediary for end users and IT to develop reports and dashboards that met business objectives. Unfortunately, the process to meet user requests for new reports could take weeks.

Beyond serving as the IT and end user-intermediary, the Business Analyst historically had the pivotal role of dictating the processes and developing business systems, so the organization achieved its business goals. The role of the Business Analyst included:

  • defining business needs through detailed functional requirements
  • evaluating potential solutions to business problems
  • analyzing and evaluating business systems and user needs

Evolving Business Analyst Solutions Changing Roles

While the Business Analyst always worked with end users to understand and prioritize business goals and information needs, that role has now evolved to include understanding the analytics methodology for their organization. The Business Analyst translates critical objectives into the key performance indicators (KPIs) and metrics.

In many organizations, IT teams are resource constrained – having to do too much with too little, especially time. The evolution of BI hands more responsibilities to end users, who require greater and immediate access to real-time data.

With this shift, the Business Analyst has moved from the role of facilitator or gatekeeper working with the IT department and end users to a more strategic role. Business Analysts can now directly influence how business applications and their outputs are used by organizations to meet specific needs. In our fast-paced world, end users expect much quicker response times, and now the Business Analyst has become responsible for getting them analytics they need in real time.

Today’s Business Analyst is responsible for the following roles:

  • administrating the analytics experience for all users
  • managing data sources and access rights
  • creating and distributing reports, dashboards and data visualizations
  • performing complex analysis and implement change to improve organizational performance.

The Power of BI Today in Form and Function

Solution providers sensing this shift have responded by offering capabilities that make BI more than just a stand-alone platform. They are embedding BI capabilities in their applications to allow for self-service analytics. Available purpose-built embedded BI capabilities are becoming intuitive functions that end users access as part of their daily workflow.

An administrative graphical user interface (GUI) allows the Business Analyst to customize the BI and data analytics functions, enabling them to set up user roles and permissions. Through the GUI, the Business Analyst tailors the user’s analytics for departments and individual users.

The Business Analyst can also define data sources and blend data from multiple sources, other tasks previously handled by IT. Rather than relying on a database administrator (DBA), the GUI allows the Business Analyst to alias data fields as business-friendly terms – making analytics more approachable to the business user, thereby increasing adoption.

A powerful GUI and embedded BI functionality equips end-users with self-service capabilities to produce the reports, visualizations, and dashboards, which previously required engagement from the IT team and Business Analyst. The evolution of BI and organizational changes shift report writing and dashboard creation from the IT team to the end user.

Self-service BI empowers end-users and enables the Business Analyst to work as a true analyst, freeing them to create reports and dashboards to ensure the company follows its business model and makes effective strategic business decisions based on real-time data analysis. The Business Analyst can take learnings gleaned from analytics to better forecast and plan for the effects specific actions might have on the business. This makes the Business Analyst’s role more powerful, helping them play a pivotal role in strategic decision-making.

BI’s Evolution Makes These Skills Important for the Business Analyst

As the amount of analyzable data continues to grow, current and future Business Analysts may want to consider strengthening or adding to their skillset. There is certainly no shortage in the growth of data creation, capture, management, and analysis that will require such skills as:

  • Business Acumen – Understanding the industry and its KPIs to create value for the organization
  • Application Proficiency – Mastering the organization’s business application and its BI solution
  • UX/UI Design – Knowing where users need to utilize analytics within the business application
  • Report and Dashboard Design – Understanding what data and reports are relevant to the end user and utilize their eye for storytelling and knowledge of charting to identify the most appropriate visualizations, tables, and charts to help users find insight
  • Methodology and Business Process – Understanding the processes of the organization to identify opportunities to redesign for improvement and apply analytics to improve operational performance
  • Automate Decision-Making – Analyzing and determining which reports and alerts can be scheduled and automated to move users toward additional data discovery and insight

Embedded BI Aids BUSINESS ANALYST’s Strategic Role

The demand for interactive data has helped BI to evolve from a rigid technology managed by IT to a business requirement. Today’s Business Analyst deals with BI as a business function, not an enabling technology. The continued growth of solutions that empower the end user allows the Business Analyst to further cement their role as a strategic asset for the company. Where the Business Analyst once took on the role of a project manager, embedded self-service BI empowers the Business Analyst to shift that role Business Analyst to strategic analysis.

6 Useful Mobile Analytics Apps to Gain Business Intelligence

The focus of the business world has shifted from personal computers to Smartphones. Most e-commerce businesses are now offering their mobile applications,

whereas some businesses are solely operating through mobile applications. With a staggering 6 billion Smartphone users anticipated in 2018, the m-commerce industry has a huge market for expansion. Analytics using mobile apps has become important in the Smartphone dominated market. Following are some smart business intelligence apps that can help in gaining deep business insights for better analytics.

1. RoamBI (Available for iOS, Android, Windows 8 Tablets & PC)

This application has any good spreadsheet analyzing capabilities. It can take data from various sources which includes SAP business objects, IBM Cognos, OBIEE, Microsoft reporting and Analysis services as well as Excel, Google Docs, Salesforce and more and present the data on iPad or iPhone. Roambi Pro is a hosted service for SMB’s and workgroups that creates a visualization from Excel, Google Spreadsheets, and Salesforce CRM. This application doesn’t have its own backend; it can only present data generated by other BI software.

2. QlikView on Mobile(Available for iOS, Android)

This is one of the most powerful tools for gaining business intelligence as it facilitates the creation and consumption of dynamic applications for analyzing information. QlikView provides fully interactive applications through HTML5. The app is available for iOS and android platform. It has an in-memory dynamic calculation engine which requires a server connection for real time analysis. It also has the ability to download and bookmark views for iOS which can be accessed in offline mode as well.

3. Renew Analytics application (Available for Android)

Renew Analytics app provided by Service Source has good data analysis capabilities. This app is very useful for recurring revenue business as it can track key performance drivers. It also provides role based access to key real time data sources. It provides a powerful dashboard for analysis of historical metrics and forecast.

Related Article: 10 Essential Apps for the Business Analyst

4. SalesClic (Available on Google Apps Marketplace)

If you want to have a better sales management through a mobile app, then SalesClic will be of great utility. It can easily integrate with Google Apps, Highrise, and Salesforce. It helps in fine tuning sales process by utilizing the historical data which are stored in Salesforce or any other database. It also helps in identifying opportunities and minimizing risk. It also helps in improving sales forecast.

5. Birst Mobile (Available for iOS, Android, Windows 8 Tablets & PC)

It is Software-as-a-service (Saas) business intelligence solution feature that includes an integrated ETL (extract, transform, load), data warehouse automation, enterprise reporting, ad hoc querying and dashboard. The app doesn’t require separate dashboards for different devices; a single iPad can be used to access all dashboards. The added advantage of using this application is that it takes leverage of iPad touch screen interface to swipe down, to scroll through rows in a table and use the two-fingers-spread to zoom in.

6. Yellow Fin(Available for iPad)

For combining multiple data sources and querying of multiple different databases to create a single report or dashboard, Yellow Fin is a handy mobile app. The advantage of using this app on mobile is that it renders a similar view as it delivers on desktop screen. The app is available in the online as well as offline mode.

Big Data Analytics and Social Media – Are We There Yet?

Having worked in financial services, with insurance companies and a number of software companies, while being very active on social media in my free time, I noticed several interesting trends that I would like to share.

Networking with professionals in Boston and NYC areas and discussing issues facing Big Data Analytics (BDA), it seems the common conclusion is that many companies are talking about BDA, many are claiming they do it, but not many know how to do it. In fact, there are 3 big aspects of BDA:

  1. How to get the data
  2. What to do with it
  3. How to present it

Some companies are very good at collecting big data from a variety of sources and have gigantic databases or data warehouses filled with data, something I saw in the life and disability insurance industry and in the medical trials. But their product management departments have yet have to come up with good ideas for analytical processing of data they have in order to create breakthrough product offerings for the market.

The issue related to it is reporting on big data. I knew a company that had access to many statistical data sources, had developed a robust analytical engine for calculation of risks, and a SaaS product to let client companies to use it over the web, but reporting capabilities were not on par with sophistication of the calculation engine and quality and quantity of data it had. When it tried to invest in industry-standard business intelligence packages, they were not customizable enough for BDA wide range of requirements that their clients had.

Other companies I have seen or spoke to were very instrumental processing data and presenting it, and were looking hard to identify sources in order to get a variety of clean and reliable customer data.

With the rise in popularity of social media (SM) sites, about 1 billion people registered on Facebook, and big crowds using Twitter, Instagram, Pinterest, and a number of ethnic overseas sites, it is not surprising these companies have turned to acquiring data from those sites that offer it for sale.

In the case of Facebook, prior to this year, the users had a choice to opt out of having their personal data collected. Starting on 01/01/2015, the act of logging on to Facebook constitutes the acceptance of new terms for data collection. For most users it would mean that companies could now buy this personal data, information about their location and travels from their smart phones and all of their images posted and hosted on Facebook servers.. It provides companies with a wide variety of information, such as social habits and shopping and personal style preference, and even an option to use profiling of the users. Several companies have developed analytical tools that allow scanning of someone’s Facebook posts and, based on what the user posts, report on his or her personality characteristics.

In conclusion, thecompany that will be able to successfully combine all 3 aspects of BDA – how to get it, what to do with it, and how to present it – and mine social media data to successfully implement a robust BDA solution, will be golden, in my personal opinion.

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Agile Business Intelligence

Feature May8 2012  4973821An iterative methodology for fast, flexible and cost-effective Business Intelligence

Can Agile Business Intelligence finally deliver analytics and insights to the people that need it? Or is it potentially a distraction? Agile philosophy has been around for more than a decade, and it looks like BI is catching up to it, with increasing talk of Agile BI. While Agile development emphasizes technology and business teams working side by side, Agile BI puts forth the notion that business end-users should be free of the technologists altogether – with more self-service tools and ability to design interfaces and analytics to explore information without having to first go through the IT department.

Traditional approaches to BI cannot deliver solutions and reporting fast enough in this era of hyper-competitiveness. Companies spend a lot of time modeling data, and that’s precisely what IT team does very well. They collect requirements and transform those requirements into data models. But the problem is it takes too long. By the time the development is done, the requirements have changed. If the IT team didn’t foresee some of the requirements, and didn’t model them, well then, the organization really cannot analyze the condition. So we definitely need environments that run on the data itself, not from data models. Given the difficulty that many organizations have faced in delivering the BI applications their managers and executives need to understand performance and make critical business decisions, it’s not surprising that an alternative development approach is being embraced. Indeed, there is a broad and growing consensus that Agile BI’s time has come.

 

Some of the key traditional challenges that the Agile BI addresses are as follows

Growing Demand:

Demand for information about business performance has risen dramatically. (The Information Age could just as well be called the BI Age.) BI delivery teams have a large backlog of projects from business users looking for more information to support their decisions. But it’s not just more information users want; it’s more information faster. Agile BI helps IT meet the imperatives for quantity and speed in unlocking the full value of data assets.

Flexibility:

The agile methodology is designed to adjust to changing requirements – and BI requirements change more frequently and profoundly than those for nearly all other types of software projects. In fact, in a 2011 survey of 200 businesses and IT executives conducted by Forrester, 67% of respondents said that BI requirements change at least monthly. A full 20% of respondents said their BI requirements change on a daily basis. Such changes wreak havoc on the traditional waterfall delivery cycle, yet they are inevitable during the lifetime of any BI project.

User Engagement:

The great strength of the agile methodology is that it fosters collaboration between IT and the business. While traditional approaches have struggled to place user needs at the core of the process. Agile BI is all about giving users faster access to functionality and more opportunities to provide feedback. Ultimately, user engagement equates to higher user satisfaction and adoption rates.

Manageable Scope:

Budget overruns and blown schedules can damage IT’s credibility, besides costing the company real money. Because Agile BI focuses on the delivery of smaller sets of functionality in shorter time periods, projects are driven by business defined scope and value. Project timelines and budgets can be tracked in smaller units, and users pay for the value defined. Avoiding scope creep is good news, but it’s better news that these budgets are significantly smaller and the project timelines much shorter.

Lower Costs, Higher Value:

Agile methods in BI have a strong track record in reducing project costs and shortening timelines. Further, because project budgets are aligned to high-priority deliverables and outcomes – that is, high-powered, easy-to-consume applications that users like and that meet real and urgent business needs – overall technology ROI also increases.  Conventional SDLC approaches are poorly suited for BI. Traditional waterfall methodology for SDLC calls for collecting user requirements, documenting them, transforming them into specifications, and then turning specifications over to developers, who then go through the design, build, test, implement cycle. While this approach is often successful for traditional enterprise application implementations, it is almost guaranteed not to work for the majority of BI requirements. The “build it, and they will come” mentality is directly applicable — and recommended — for BI, since only once an end user sees something in front of him, something he can touch and feel and “play with,” will the real requirement materialize.

Clearly a different approach is needed to make BI applications more flexible and able to react much faster to ever-changing business and regulatory requirements. Agile BI is first and foremost a different approach to designing and building BI applications.

The purpose of Agile BI is to: 1) get the development done faster, and 2) react more quickly to changing business requirements. Mostly Agile BI is no different than any agile development methodology that calls for incrementally delivering products versus a big-band approach; for rapid prototypes versus specifications; for reacting versus planning; and for personal interactions with business users versus documentation. The Agile BI methodology differs from other agile approaches in that it requires new and different technologies and architectures for support.

The key to driving an Agile BI project is minimizing project management overhead, reusing existing assets, and automating inefficient manual tasks. Aligning your project budgets to deliverables and outcomes generates more value – that is, high-powered, easy-to-understand, easy-to-consume business intelligence solutions that users like and that meet real business needs.

How to achieve agile development

Agile BI projects should focus on people over process. This does not mean that Agile BI is inherently opposed to thorough and careful development processes, but is guided by a minimalistic business user focused approach, to streamline development cycles. There are three key agile processes that should be adhered to ensure an Agile BI rollout:

Iterative ‘Sprint’ development cycles

  1. Systematize ongoing BI processes
  2. Implement Barely Sufficient Processes

Iterative ‘Sprint’ development cycles

In Agile development methodology, teams work in ‘sprints’ to produce bite-sized deliverables in an iterative manner. Sprints can be one to four weeks long depending on the size and complexity of the project. At the end of each sprint, the business has a working deliverable, such as a new report or dashboard, delivered to them in a production setting.

By contrast, the waterfall development cycle used in traditional BI rollouts, is cemented in a regimented, sequential progression. It is inflexible to changing reporting needs and costly to make changes to. Agile BI, as a process, is about delivering functioning software regularly in short weekly or monthly timeframes – the shorter and more frequent the better (working software is the measure of BI success). Agile BI is about responding to the immediate needs of the BI user, rather than working to establish and deliver ALL potential reporting needs upfront. To establish an approach that facilitates Agile BI, reporting objectives should be readjusted regularly based on available resources and intermediate business goals to help focus attention on what is really important. Working in this manner ensures the relevancy of reporting to business goals and enables a faster, more flexible approach to changing reporting needs.

Systematize ongoing BI Processes

Agile BI development teams must automate any repetitive tasks/processes to allow more time and focus to be spent on developing and delivering end-user features. For example, whilst critical, testing the BI system manually takes up unacceptable resources each and every sprint cycle. Automated testing conducted by the users actually involved in the development of new reports, means that new changes can be quickly tested within the sprint, rather than waiting to be processed by a separate testing team. This way, accountability resides within the team.

Implement Barely Sufficient Processes

Minimizing the amount of ‘ceremony’ associated with BI development reduces the length of development cycles and allows development teams to concentrate on the work that matters. This minimalistic approach does not suggest that careful planning is unnecessary during the development process, but that formal planning and documentation should be aimed at satisfying the practical needs of the project. For example, a concept document for each sprint should focus on business user requirements and nothing more. Additional verbiage simply adds no value. Agile BI is just as much about maximizing the amount of work not done.

Summary

Successful Agile BI deployments enhance organizational flexibility and responsiveness. Increasingly, businesses and their personnel are exploiting the benefits of Agile BI, allowing them to respond with immediacy to business demands. To survive and prosper in a competitive marketplace, businesses from all industries and sectors have to be able to scan their external environment, review their internal processes and make appropriate proactive and reactive changes. Modern companies are striving to spread fact-based decision-making throughout their organizations. Agile BI solutions, with their end-user centric approach, enable organizations’ to anticipate and adapt to shifting market conditions.

References

  • Information Management Blog : Agile BI
  • Yellowfin : Agile Business Intelligence
  • Forrester report on Agile BI Out Of The Box
  • Balanced Insight: Enabling Agile Business Intelligence with Balanced Insight

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Preventing Disasters; How to Use Data to Your Advantage

The late Lew Platt, former CEO at Hewlett-Packard once stated, “If only HP knew what HP knows, we would be three times more productive.” This is a typical situation in large organizations, where far too often, disasters arise from lack of awareness. Critical information is available in the organization, but goes undetected, is not communicated or is blatantly ignored.

Take the recent mortgage meltdown, for instance. The banking industry has a wealth of data on consumers, robust credit risk models, as well as lessons learned from the past. Their analytics told them which loans were too risky according to traditional models. Yet, they decided to relax their standards, ignore the data…and the rest is history. Or, take the recent PR debacle around Southwest Airlines’ plane inspections. The FAA had inspection logs that could have told them that the planes were passing with flying colors at unprecedented rates, yet no one suggested conducting a site visit to see if the airline was actually performing those inspections. And when low-level employees reported issues to their managers, that information was not passed on. Fortunately, in that case, a tragedy was avoided.

If there is a question we should be asking in the current economic and regulatory environment, it is “Why does accountability so often fail, and what role does analytics play in preventing these disasters?” Organizations need to understand why they fail to detect early warning signs, how to filter and monitor available data to create actionable information, and how correctly applying analytics can turn data into knowledge. That knowledge can then prevent disasters and increase competitive advantage.

Why Accountability Fails

The repeated disasters that occur due mainly to failures in accountability arise for the following reasons:

  • Large, complex organizations (or environments) make it difficult to know what is happening “on the ground” and detect significant changes in the environment.
  • Very often, players in the organization (managers, employees, others) receive incentives only for presenting a positive picture and anchor on how things have worked in the past.
  • Organizations measure and monitor only past-focused, outcome measures, which only indicate a disaster once it has already occurred.
  • Many organizations lack the skills necessary to manage data, much less apply analytical techniques to make sense of that data and keep an accurate view of the current operating reality.

The Impact of Anonymity

The lack of awareness that often brings disaster stems from the anonymity that characterizes today’s organizations. A hundred years ago, most business transactions were conducted face to face. Business owners walked the shop floor. Customers who bought eggs from the village shopkeeper knew not only the shopkeeper, but also the farmer who raised the chickens. Loans where made to people the banker knew personally and regulations were made and enforced by local officials.

The more complex an organization becomes, the less transparency there is, and the more difficult it becomes to make good decisions. Consumers and producers don’t know one another. Decision makers and implementers don’t have direct lines of communication. By the time information reaches a decision-maker at the top, it is usually highly filtered, and often inaccurate. The information and implications have been spun so as not to upset management or cast dispersion on employees, and therefore fail to present the reality of the situation.

These conditions not only impair the organization’s ability to understand what is currently going on, but also remove any ability to detect change in the environment. Outside information can effectively be closed out in extreme examples. The U.S. automakers in the 1970s, who looked out the executive suite window into their parking lot and saw only U.S.-made cars, determined that Japan was not a threat. Meanwhile, dealers in California had significant early signals in their sales numbers that Japan was indeed a threat to the U.S. auto industry.

Incentives for Bad Behavior

An even more insidious problem is that disasters often arise because organizations have actually encouraged behaviors that lead to them. The filtering of information cited above is actually a very mild form of this. Employees and managers are rewarded for highlighting what they’ve done well, so why would they ever identify something that is going wrong on their watch?

We tend to blame those who bring bad news, whether they deserve it or not. Consider any major whistle-blower of the past. The amount of scrutiny, negative media attention and damage to their career is enough to dissuade most people from taking a stance. And yet those same people brought to light, and often prevented, significant disasters in the making.

So many organizations reward those who bring in good short-term results, prove out the organization’s current business model and don’t ruffle too many feathers. In return, we get exotic financial instruments in an attempt to make quarterly revenue, low standards on food or workplace safety and fudging on project and financial status reports. The contrarian voices pointing out the impending disaster go unheard and unheeded, and changes come too late to matter.

Driving While Watching the Rear View Mirror

The vast majority of the data that organizations look at represent outcomes that are past-focused. The traditional financial statements show the outcomes of business activities (revenues, expenses, assets, liabilities, etc.) while nothing in those statements measures the underlying activity that produces those outcomes. Hence, nothing gives any indication of the current health of the organization.

Kaplan and Norton sought to remedy this with their Balanced Score Card approach. By focusing on the drivers of those outcomes, the organization should be able to monitor leading indicators to ensure the continued health of the enterprise. Relatively few organizations have fully adopted such an approach, and even those few have struggled to implement it fully. Too often, managers do not fully understand how to impact the metrics on the scorecard. And as time moves on, the scorecard can fail to keep up with changing realities, suggesting relationships between activity and outcome that no longer exist.

Numeracy?

“Numeracy” is the ability to reason with numbers. John Allen Paulos, Professor of Mathematics at Temple University, made this concept famous with his book Innumeracy, in which he bemoans how little skill our society has in dealing with mathematics, given how dependent upon it we have become. Organizations today struggle to maintain a workforce that has the skills to manage the data their operations generate. Once the data have been wrangled, the analytical reasoning skills required to make sense of that data are lacking.

Analytics provides powerful tools for dealing with massive quantities of data, and more importantly, for understanding how important relationships in our operating environment may be changing. But without a strongly numerate workforce, organizations cannot apply these techniques on their own and have a very limited ability to interpret the output of such techniques. A lack of good intuition and reasoning with numbers means that many warning signals go undetected.

What Drives Organizational Outcomes?

Organizations that want to prevent disasters and increase competitive advantage first need to define what constitutes critical information – in other words, what really matters to the organization. Prior assumptions have no place in that determination. Let’s say, for example, a company is proposing to increase its customer repeat rate by increasing satisfaction with its service. But does that relationship between customer repeat rate and satisfaction with the service really exist? And to what degree? Amazon.com, for example, does not simply assume that a person who buys a popular fiction book will want to see a list of other popular fiction books. Rather, it analyzes customer behavior. Thus, someone who is ordering Eat, Pray, Love might see an Italian cookbook, a Yoga DVD and a travel guide for Bali as recommendations because other people who bought that fiction book also bought those other items.

The steps to decide what matters are:

  1. Decide what the organization wants to accomplish.
  2. Identify the activities (customer behaviors and management techniques) that appear to produce that outcome.
  3. Test and retest those relationships, collecting data from operations to measure the link between activity and outcome.

Once an organization has identified what constitutes its key activities, how can it find the information it needs to monitor them?

Find the points in the value chain where the key actions have to occur to deliver the intended outcomes.

  1. Collect critical information at, or as close to, those points as possible. The closer an organization can get to the key points of value delivery, the more accurate the information it can collect.
  2. Continuously look for the most direct and unfiltered route to obtain the richest, most consistent information on each key point of the value chain.
  3. Keep testing each assumption by asking the question, “What surprising event could I see early enough to take corrective action?”

Stop Trying to Prove Yourself Right

Several traditional ways of doing business blind organizations to warning signs of potential disasters. First among these is looking for data that confirms that all is well. Although extremely counterintuitive, it is critical to look for evidence that things are not all right. Ask the question, “if something were going to cause failure, what would it be and how can it be measured?” If it can be measured, then it can be corrected early and failure can be avoided. Rather than indicating what has gone right in the past, these measures contain warnings of what could go wrong in the future.

To see the early warning signs, follow this process:

  1. Ask what assumptions are being made in the process of executing strategy to deliver value. For example, if the goal is to increase the efficiency of inspections, is there an assumption that inspectors will become more efficient while still adhering to the same high quality standards? Or, in a call center, is there an assumption that reps can decrease call handle time and still provide superior service?
  2. These assumptions are alert points where failure might occur. Don’t wait for the final outcome, but track, measure and monitor each assumption to make sure it is playing out successfully. This process is well known to project managers. They don’t just design Work Breakdown Structures and Critical Paths and then wait around for the end date to see if the project was successful. As soon as a task begins to exceed its scope, the impact is assessed all the way down the line.
  3. Keep testing each assumption by asking the question, “What surprising event could I see early enough to take corrective action?”

Organizations that do this well are not operating with a negative, doom-and-gloom perspective. Rather, they want their positive outcomes so badly that they look for data that might be telling them something is going wrong so they can correct it before it is too late. They are willing to “Fail Fast” and “Fail Forward,” keeping the failure small to ensure large successes.

People Power the Process

Creating knowledge from data to prevent disasters depends on both technology and human skill. Computers are powerful tools that can help collect, store, aggregate, summarize and process data, but the human brain is needed to analyze the data and turn it into actionable information. It’s this human factor where the biggest gap exists in most organizations. Finding people who can perform the required analysis is becoming increasingly difficult. A spreadsheet is just a pile of data until someone applies critical thinking, adding subjective experience and industry knowledge to derive insights into what the numbers really mean.

Organizations must invest in developing these skills in their workforce. Here’s how:

  1. Provide employees with the training, job assignment, education and mentoring opportunities needed to develop their analytical skills, industry expertise and decision-making acumen.
  2. Subject decision-making to evidence-based approaches, providing feedback to improve future decisions.
  3. Ensure employees have the tools they need to manage the volumes of data they are expected to digest and act upon.

Blame Is Not an Option

In his book The Fifth Discipline, Peter Senge said that a “learning organization” depends on a blame-free culture. In other words, when a problem arises, people need to refocus from laying blame or escaping blame and start fixing the problem.

In today’s data-rich world, preventing disasters large and small requires monitoring and filtering through the large volumes of information that stream into organizations every day to find early warning signs of imminent failure. Intellectually, just about everyone will agree that it makes sense to look for what could go wrong. Emotionally, however, it’s another matter. It is both counterintuitive and intimidating to ask managers to search out constantly how the organization is failing. Establishing a blame-free culture is the final frontier to create a new awareness and encourage people to test assumptions, make better use of analytics and communicate information without fear.


Charles Caldwell is Practice Lead, Analytics, with Management Concepts. Headquartered in Vienna, VA, and founded in 1973, Management Concepts is a global provider of training, consulting and publications in leadership and management development. For further information, visit www.managementconcepts.com or call 703 790-9595.