BI Application Buy vs. Build: Is Your Data Really That Unique?

By Elliot King, Ph.D.

The buy-versus-build question has haunted the development of data warehouses and analytic applications since Bill Inmon started discussing the underlying concepts in the 1970s, and Barry Devlin and Paul Murphy published “An Architecture For A Business and Information System” in the IBM System Journal in 1988. Since then, many vendors have offered packaged applications that facilitate the development of data warehouses and analytic applications. Nevertheless, many companies still opt to build their own custom data warehouses rather than buying pre-packaged solutions. The question is, why? Read more

Determining Scope is a Top Challenge in Bringing BI Applications into Production – Research Study of 250 IT Professionals

By Elliot King, Ph.D.
Professor of Communication, Loyola University Maryland & Founder of the Digital Media Laboratory

Business intelligence infrastructures are dynamic. As companies generate new information, they need to apply that information productively to business processes. Sometimes that requires devising new kinds of analyses. Sometimes it means pushing information to new communities of users. Read more

Developing Analytic Applications is a Collaborative Process – Research Study of 250 IT Professionals

By Elliot King, Ph.D.
Professor of Communication, Loyola University Maryland & Founder of the Digital Media Laboratory

In many organizations, the IT department has the responsibility and the expertise to explore, develop, and install most major applications, as well as identify technology that can support an organization’s mission. While senior management must sign off on any significant new project, the IT group takes on the burden of such initiatives.

However, the process of identifying, developing, and implementing analytic applications is a much more collaborative process according to a research study conducted by the Lattanze Center, a nationally recognized center of excellence on issues related to business excellence and IT at Loyola University Maryland. To an overwhelming degree, IT professionals and end users work together to select and develop analytic applications.

In a survey of more than 250 IT professionals who indicated that analytic applications were used in their organizations:

  • 71% reported that selecting analytic applications was a joint decision made by IT and end users
  • 10% said that IT alone chose analytic applications
  • 5% indicated that end users alone selected analytic applications

The same pattern of collaboration held true for developing analytic applications. Among the respondents to the survey:

  • 56% said that IT professionals and end users work together to develop analytic applications
  • 24% reported that IT professionals alone created analytic applications
  • 9% indicated that consultants were responsible for developing analytic applications
  • 4% said that end users were entirely responsible for developing analytic applications

This data suggests that in many organizations end users do not have sufficient technical skill to build applications without assistance, nor are they empowered to select analytic applications entirely on their own.

On the other hand, end users do routinely collaborate on both the selection and development of analytic applications. The reason is not hard to determine. Analysis is a quintessential “ground up” activity. End users know what data they need and how they want to analyze it, while IT can identify and suggest new tools that could be potentially useful.

To a degree, the level of cooperation between end users and IT during the development process declines. While end users simply may not have the technical skills needed to assist in development, a high degree of interaction is needed to ensure the analytic tools selected, developed, and implemented can generate data and lead to the answers the end users want.

Stay tuned for part three of this series in the July/August 2011 issue of the Noetix Newsletter! We will take a look at the amount of time it takes for an organization to go from the decision to create a new analytic application, to having the application operational, including the biggest challenges found along the way.

Building a Culture of Analysis

By Elliot King, Ph.D.
Professor of Communication, Loyola University Maryland & Founder of the Digital Media Laboratory

The idea that we live in an age of information abundance has been a cliché for the last generation. With huge amounts of data being regularly generated, developing the ability to understand and apply that data for decision support, trend analysis, and predictive analytics has proven difficult.

The problem isn’t the lack of data or tools for analysis. The real challenge is three-fold:

  1. Companies must decide what they “want to know” that they “don’t know” already and identify which data and analytic tools will generate that information.
  2. They must have enough confidence in the information produced to incorporate it into decision-making and organizational processes.
  3. They must continually assess and build on the first two. Are they conducting the right kinds of analyses to improve business performance in measurable ways? Are as many people as possible making fact-based decisions that are leading to better outcomes?

Those three elements are the foundation of what could be called a culture of analysis — a culture premised on the idea that understanding the underlying patterns in past and present data will lead to improvements in the future.

Companies can take several steps to create a culture of analysis:

  • Assess how deeply the idea and practice of analysis has penetrated the organization to date. What analytic tools does the company have? How effectively are they used and by whom? Who utilizes the output of the analysis and how does it help them do their jobs?
  • Ask difficult questions. What information would be helpful to know that is not currently available? Does the company have the resources to develop that information from the data available? Does the company need to cast the information-gathering net wider to include unstructured data from outside databases or from third-party sources?
  • Create a strategy for broadening analytic capabilities. Can elements of the analytic process be automated to accelerate time to insight? Can the way data is visualized and promoted be improved? Can the use of analysis throughout the organization be broadened?
  • Set expectations. What kind of data and analysis must be generated before a decision will be made? How long will decision-makers wait to access that information? If people know that they are expected to justify their actions with data, they will.

Analysis is an iterative exercise. As more data is produced, companies are under great pressure to make better use of it. Developing a culture of analysis is critical in that effort.

Customization Plays Key Role in Analytic Application Development – Research Study of 250 IT Professionals

By Elliot King, Ph.D.
Professor of Communication, Loyola University Maryland & Founder of the Digital Media Laboratory

The production and retention of data within organizations has grown on an exponential curve for at least the past decade. All that data jamming up companies’ storage infrastructures can be virtually useless if people can’t extract actionable information from it. The amount of data a company collects is not nearly as important as what it does with that data. Read more

Establishing the Business Need for New Analytical Applications

By Elliot King, Ph.D.
Professor of Communication, Loyola University Maryland & Founder of the Digital Media Laboratory

Building a culture of analysis is an ongoing process for most organizations. As companies generate more data, the need to access and analyze data to measure and monitor performance and identify trends is no longer the sole domain of business analysts and senior executives. Line managers and customer-facing personnel can also take advantage of better data access and an opportunity to understand that data more fully.

However, developing and deploying analytic applications can be time-consuming and expensive. Custom-built analytic applications often take months to build, demanding a significant investment of money and resources. In many cases, companies don’t build these applications from scratch but customize packaged applications to a greater or lesser degree. (See post, Customization Plays Key Role in Analytic Application Development).

How should a company determine where to put resources to expand the number of analytical tools available to their employees? The wrong way to go about that decision is to try to oil the squeaky wheel. In every organization, there are business groups that voice their needs with more urgency than others. Responding to them is easy but not always the right choice.

The right approach is to establish the business need and business case for the proposed application, which requires answering four questions:

  • What is the current pain point? In other words, what is the business group not able to do that better access to data and analysis would allow it to do? Can the HR department produce accurate employee counts on a regular basis?
  • What business goal would be achieved by implementing the new application? Can the business benefit of having an accurate head count be quantified in some way?
  • Can you access the data you need to make the analysis in a cost-effective way?
  • How long would it take to create and deploy the application needed?

As requests for new kinds of data percolate up (and down), it is important to formulate a methodology to prioritize those requests. By asking the right questions and collecting the right information, the right answers will become clear.

Noetix Featured in IDC Analyst Connection Report

Business Intelligence Solutions – Now More than Ever

Tech industry analyst firm IDC recently invited Noetix to participate in an Analyst Connection trend report on business intelligence. Noetix posed a series of questions to Dan Vesset, program vice president of IDC’s Business Analytics research, on behalf of Noetix’s customers. An excerpt from the report follows. To read the full report, click here.

Q. How does the economy affect the way companies think about and invest in business intelligence (BI) and analytics solutions?

A. Lack of visibility has been one of the key effects of the recession and the current slow economic recovery. Managers have been required to react to a growing number of unknowable factors. This requirement drives the need for flexibility and agility at the operational and tactical decision-making level. As a result, decision makers at all levels of the organization are seeking support from tools that enable rapid access to the freshest, most relevant, and actionable information. In addition, there is a growing body of evidence that BI and analytics solutions play a significant role in creating competitive advantage. In combination, these factors contribute to a continued investment in BI and analytics technology and processes across organizations of all sizes.

Forrester’s thoughts on data governance and business process

This morning, I read a great blog post by Rob Karel of Forrester. He talked about the need to increase focus on business process to build momentum for data governance. I couldn’t agree more. Most data governance programs consider data AND business process AND organization; it’s the only way to provide the proper level of context to defining how data is used across the enterprise. Read more