BI Worst Pactice - Selecting a BI Tool Without a Specific Business Need

You might have noticed that the two business examples given in the previous section – the major airline and the telecommunications company – both had finely defined problems and solutions with clear business objectives. Their understanding of their problem helped them identify and implement an effective BI solution. On the other hand, one of the most egregious of the four worst practices in this paper is the purchase of BI software for “general purpose analysis.” In fact, the largest expenses and the smallest ROI is a result of purchasing a solution for “general purpose” BI.  In other words, a company recognizes the need for business analysis and  immediately plans a project to evaluate and purchase a BI solution for their users.

Without a specific purpose, BI rarely has impact on business. The starting point for creating a BI solution should be when you identify a project that will solve a specific problem through access  to information in a timely fashion and in the right context. “Solve a problem,” means that information will accelerate a slow running process, eliminate a bottleneck, reduce the cost of doing business, or even become a new revenue source.

When information requirements such as these are identified up front and used as the business driver behind the BI implementation, the implementation of the BI system has a much greater likelihood for success.



The Solution

The lesson here is that your motivation for pursuing and purchasing BI software, or building a data warehouse for that matter, should never be general purpose. If you want success, understand the business problem and know beforehand what can be expected if information is injected into the process.

5 razones para ser más analíticos

Parecería extraño pensar que después de 30 años que se ha manejado el concepto de inteligencia de negocio como parte de la estrategia de las tecnologías de la información en las empresas de América Latina, aún tengamos un rezago en la adopción e implementación de métodos para el uso de información en la toma de decisiones.


Las compañías están siendo llevadas al extremo por los cambios en las condiciones del mercado y los ejecutivos, tomadores de decisiones se están preguntando si los métodos actuales de análisis son los correctos, ya que gran parte de su tiempo se destina a entender el pasado a través del uso de reportes y tableros de control con indicadores financieros. Pero ¿que sucede con el presente? y más aún ¿qué pasará en el futuro? Situaciones de tiempo que están presentes en la mente de los tomadores de decisiones sin ningún tipo de respuesta concreta basada en hechos, más bien respuestas motivadas y apalancadas por la intuición de los ejecutivos.


Es por eso que a continuación se exponen cinco razones de peso para que las empresas en América Latina se conviertan en compañías analíticas.


Razón 1: Economía Local, Regional y Global


Sin lugar a dudas, el entorno económico juega un papel fundamental en la operación de las compañías. 2008 y 2009 son la experiencia más reciente, donde la crisis financiera puso en jaque a muchas economías alrededor del mundo y América Latina no fue la excepción. Por ende, ahora en el 2011 se tiene que aprovechar el momento de crecimiento económico que estamos viviendo, por lo que es muy importante incentivar en todos los niveles de la compañía el uso de datos para generar conocimiento. Los procesos analíticos no son exclusivos de los CFO, todas las áreas de la empresa deben hacer uso de estos procesos para el entendimiento del entorno y la correcta toma de decisiones

Razón 2: Transformación del Ambiente de TI


El modelo actual de TI está centrado en el producto y/o servicios transaccionales, derivando en silos de información desintegrados y en procesos monolíticos poco flexibles ante los cambios del mercado, sin embargo las compañías se han dado cuenta que con la aparición disruptiva de nuevos modelos de TI que incluyen soluciones en la nube (cloud computing), dispositivos y aplicaciones móviles, manejo de grandes volúmenes de información (big data) y soluciones de social software (socialitics), estos están siendo los transformadores del ambiente de TI y que es cuestión de tiempo para que las compañías tengan un entorno amigable a los procesos de 'Business Analytics'.

Razón 3: Patrón de Inversión en soluciones de 'Business Analytics' por parte de las Empresas en América Latina.


Cifras recientes del mercado de herramientas de inteligencia de negocios muestran que alrededor del 72% del presupuesto asignado a estas soluciones, tiene que ver con la generación de reportes o la creación de sistemas que analizan información del pasado, lo cual ha ocasionado un estancamiento importante en el camino hacia convertir a las empresas de la región, en compañías analíticas; sólo el 18% de las inversiones en soluciones de 'business analytics' se relaciona con modelos avanzados de análisis de información que tratan de simular, predecir y entender qué podría estar sucediendo en el futuro. Es decir, modelos predictivos que se adelanten a los posibles escenarios tanto favorables como negativos que las compañías puedan estar enfrentando. Este patrón de inversión es un inhibidor para lograr un nivel de madurez en el uso de información para la generación de conocimiento en todos los niveles de la organización.

Razón 4: La Era de las Redes Sociales


El efecto de las redes sociales en las soluciones de 'business analytics' es tan importante que hoy se están planeando proyectos para incorporar en las bases de datos y en los modelos analíticos toda aquella información que se encuentra en las redes sociales, con el único objetivo de entender el sentir de los consumidores así un servicio, un producto o una compañía. Ejemplos claros de instituciones bancarias en América Latina que han invertido en social software para entender lo que sus clientes pensaban antes y después de la crisis financiera de 2008, es una muestra que las soluciones de 'business analytics' no serán las mismas después de las redes sociales.

Razón 5: Necesidades de los Ejecutivos de Negocio

Más allá del CFO, las soluciones de 'business analytics' están siendo demandadas por todas las áreas del negocio que tienen que tomar decisiones con información clara y verídica; esto hace que las necesidades de los diferentes ejecutivos de negocio tengan un peso especifico importante en la decisión de inversión en este tipo de soluciones; y en un mediano plazo tendrán la capacidad de madurar los procesos de información de las compañías.

En conclusión, las compañías deben entender que el camino hacia convertirse en empresas analíticas no tiene que basarse exclusivamente en soluciones tecnológicas; más bien tienen que usar la innovación tecnológica como catalizador de los procesos analíticos y por ende en la cultura del uso de la información. Sin olvidar que cualquier decisión empresarial que se tome tiene que pasar por analizar el pasado, entender el presente y conocer o anticipar lo que sucederá en el futuro.

Practical training, executive buy-in key to BI user Adoption

During any business intelligence (BI) deployment, organizations focus on the technical details – for example, determining the number of servers needed to support the system, choosing the data sources to be integrated, and deciding how often to refresh the data warehouse.

But in a BI deployment aimed at non-power users, addressing cultural barriers to ensure that the system is actually adopted throughout the enterprise is often just as important as mastering the technical specifications. Giving short shrift to these non-technical concerns could result in a BI manager's worst nightmare – a successfully installed, complex and expensive BI system that no one uses.

While power users, like business analysts, need little incentive to adopt BI technology, more casual BI users, such as marketing and sales associates and line-of-business managers, are a different story.

For adoption to truly take off among these users, it is important for IT and BI staff to educate them on how the technology will help them achieve personal and departmental business goals. Less important to these users are the BI system's various eye-catching features that might impress a more sophisticated user.

If a salesperson has a goal to find a certain number of new customers in a quarter, for example, the BI staff should show him or her how the new report or dashboard will help achieve that goal, rather than giving a lecture on how many different views of the data are available.

The best way to sell corporate executives, and ultimately casual users, on BI is "by realworld war-gaming and simulations, which can provoke seminal 'a-ha' moments".

"[That is] the best and maybe the only way to demonstrate the merits of real business intelligence."

At the Canadian newspaper, executives were having trouble understanding circulation reports. The data just "didn't mean anything to them,". Recognizing the importance of executive buy-in to maintain BI funding and to encourage wider adoption of the technology.

But whether it's focusing on practical training, facilitating communication, or getting executives to lead by example, the IT and BI staffs to remember that promoting end-user adoption of BI systems is an ongoing process, and not an easy one at that.

Remember: "It's not going to be done in one day or two days”, "It's hard."

Data Warehouse Strategy Considerations





Few of us would doubt that a data warehouse strategy should include the hardware/software platforms that are part of the architecture. These platforms can include multi-subject data warehouses, subject-specific data marts, master data management repositories, data warehouse appliances, enterprise information integration (EII) federated databases, operational data stores, and direct access to data in operational systems. However, when you're creating your data warehouse strategy, there are many other factors to consider as well.

Cloud Verses On-Premise Hosting

An organization's data warehousing platforms can be hosted on-premise or in the cloud. This is not an either/or decision and it is possible, and in many cases desirable, to embrace both. For example, "one-time" special analysis needs or remote field locations can use cloud-based platforms while headquarters might be supported by on-premise platforms.



Open Source Verses Proprietary Software

The choice between open source and proprietary commercial software is also not an either/or decision, and most organizations are likely to deploy both. Major decision criteria include functional requirements and vendor (or community) support as well as total cost of ownership.



Data Delivery Vehicles

Although one of the early goals in the design of a data warehouse architecture was to ensure the delivery of the right information to the right people at the right time, it should now be extended to include "regardless of where the recipient happens to be." We live in a mobile society and the ability to run an analysis and access information should not be limited to desktop PCs or laptops (or, for that matter, printed reports!).

Many organizations have standards relating to approved smartphones, tablets, and laptop computers, but most users do not wish to carry two devices -- one for personal use and one for work. Some users will try to connect their own personal devices to their organization's network, and you must educate them on the possible risks. Better yet, accommodate their needs with appropriate solutions.

In general, an organization needs to protect its data (and the privacy of its constituents). Access from mobile devices that can be easily lost or stolen makes that task more difficult. Techniques such as encryption and data wiping (after a specified number of password failures or when a device is reported missing) must be a part of the design, not a feature rushed into place following a major (and frequently costly) data breach. With that in mind, organizations might consider providing -- or subsidizing the cost of -- user-owned mobile devices under the condition that the organization can install appropriate software to remotely disable the device in the event it is lost or stolen.



Data Sources and Data Consistency

One of the major advantages of data warehousing is the ability to extract data from multiple heterogeneous operational systems and third-party data providers and transform it to match organizational data standards including formats, value lists, and units of measure when loaded into the data warehouse. Furthermore, it is desirable to load data into an enterprise data warehouse first and then distribute subsets to other data warehouse platforms. However, complete consistency is often unlikely because the data warehouse environment may include independent data mart platforms and/or allow analyses directly against operational systems whose data elements don't conform to organization standards that were established long after the application was initially deployed.


It is, therefore, extremely important that users understand the limitations of the data on individual data warehouse platforms. At a minimum, use a metadata repository (a master metadata manager?) that describes what data resides where along with any associated limitations. Furthermore, the same queries run against two platforms should produce consistent results, although they might have dramatically different response times. For example, if the data under analysis resides in both an enterprise data warehouse and a data warehouse appliance, the results should be the same (although the appliance might generate them faster).



Unstructured Data

With the growing recognition of the value of data contained in social media sources, an organization's data warehouse strategy should include the ability to accommodate unstructured data.



Data Security

Some data is simply too sensitive or too private to allow unrestricted access to everyone in your organization. For example, someone analyzing salary trends might only be allowed to see yearly average salary values by job code rather than individual salaries. Delivery of the right information to the right people does not imply delivery to everyone. Protective measures should be established to ensure that access is restricted as appropriate.

Although many factors influence the creation of an organization's data warehouse strategy, one of the most important characteristics of a good data warehouse strategy is its ability to respond to the changing needs of the organization. Make sure your data warehousing strategy is flexible enough to accommodate changes.

 

10 Mistakes to Avoid in a Business Intelligence Delivery (Part 2/ 5)

Mistake #3: Assuming service provider companies own everything about the successful delivery of the project.

This is yet another critical factor for a successful BI delivery. A service provider who has signed a contract to put the BI project into production definitely has ownership on the delivery. That being said, the delivery cannot be a success without active participation from the client and end users having in each stage and phase of the entire lifecycle. Service providers are specialized consultants who can give you options and best practices, much like a professional home decorator consultant. Because it's your home, you will have to give the consultant your input and exact specifications. If this does not happen, then the decorator will decorate the home according to his assumptions of your likes and dislikes, which you may or may not approve of. And if this happens at the last minute, then not only will you end up paying for the work that has already been done, but you will also invest more time and money on rework. Without active and adequate client involvement at every phase, no BI delivery can ever have an assured success.

 
 

Mistake #4: Bringing in a solution architect halfway into the project and assuming that he/she is going to magically fulfill all the deficiencies.

This is the most common scenario one sees in most of the BI projects. When things are not happening the way they should, the management thinks the immediate remedy is to get a solution architect. What one has to understand is that a solution architect cannot just walk in and wield a magic wand to set things right. The  solution architect s experience will determine how soon he/she can start delivering the value-adds. Also, the time at which you bring in the solution architect is a driving factor for success. Often when it comes to BI architecture, business users are from Mars and IT people are from Venus. To get them to a common platform is in itself a premium skill for a solution architect.

Every BI delivery must have a solution architect with expertise and wide skills in DW and BI. This is vital, as they bring a wealth of knowledge from reference architectures and similar implementations with them. This ready-recon eventually reduces the cost and time to implement technology solutions.

The best time to start their involvement is from the analysis stage itself. If not then, do it at least before you spend a large amount of time exploring technology options and assessing the appropriate solutions. Carefully set expectations of the value a solution architect would deliver. If you decide to engage a solution architect when your data model is near completion and expect the architect to do magic to make a performance-tuned and efficient design, it might be overexpecting, as things would have already crossed certain stages involving a good amount of time and cost. At that stage, again the issue resolution becomes more political than technical.

Worst Practice #1: Assuming the Average Business User Has the Know-How or Time to Use BI Tools

Too Much for Too Few
BI report design, ad hoc query, and OLAP analysis tools have hundreds, if not thousands, of features. Although the user interface is often simple, complexity is introduced from the data side. Even a simple data warehouse has hundreds of columns of data, and it’s not uncommon for more complex systems to have thousands of columns. When an end user is faced with a blank canvas, thousands of columns of data, and hundreds of accessible features, complexity is automatic. “Where do I begin?” is often the first question, shortly followed by “I don't have time for this,” or “I give up.”

The user skill pyramid is a widely discussed and generally agreed upon description of the end users in most organizations. The simple version of the pyramid shown below demonstrates that 90 percent of the users within most organizations fit into the class of users known as non-technical business users, which means that only 10 percent of users are advanced enough to use a BI tool.


What may not be obvious from the pyramid is that most executives and managers, often the primary strategic decision-makers, are in the lower portion of the pyramid – that is the non-technical users.


It’s a Matter of Time
In some instances executives and managers are technical enough to use a BI tool, but they don’t have the time to work with a BI tool and navigate a data warehouse to produce the information they need. Most people need a faster, easier way to get the information they need than that provided by a BI tool.


BI Go-To Guys and Multiple Versions of the Truth
In some cases, moderately successful deployments of BI tools are found in individual departments. Usually that means that each department has identified and relies on a handful of advanced users who become the tool experts, or the “BI go-to guys.” These users employ the BI tool on the behalf of others, and create and distribute information for their department. In these cases, another issue is brought to the surface – the inconsistency of the answers generated by more than one
advanced user, also known as multiple versions of the truth.

Multiple versions of the truth result when two or more people apply different query methods and functions, and arrive at different conclusions. The challenge is that it’s difficult to know which, if any, conclusion is correct.

The tool-based efforts of advanced BI users do not go through the same rigorous quality testing of an IT department. Their work within a tool is typically not auditable. When this occurs, the validity of the information system, the BI tool, and the data warehouse are all brought into question. Valid or not, many companies have more confidence in operational reports generated, and tested by IT professionals. Many become skeptical of pure ad hoc information created with a BI tool because of the potential for variations and inconsistencies.

The Solution
Organizations need BI solutions that are easy to use for the entire user population, especially those in the bottom portion of the usability pyramid. In addition, they need a solution that mitigates multiple versions of the truth by providing access to a common source of enterprise information and standardized report generation methods. A BI platform is the answer to all of these requirements.

A BI platform leverages BI tools along with other technologies, including databases, data integration, and portals to provide an end-to-end solution for a defined business problem or set of business problems that can be termed a BI application. While BI platforms are implemented by IT professionals, their end result, the BI application, is designed for business users.

Organizations have been led to believe that BI platforms are too complex for their needs. This couldn’t be further from the truth. When you consider the data integration, warehousing, and end-user training costs associated with BI tools, a BI application built on a BI platform has about the same time to market as a BI tool. And end users embrace easy-to-use BI applications as part of their day-to-day routine, which is arguably the most critical success factor of any application.
This is why BI platforms have far greater success than BI tools.

The fact is that most non-technical business users can and will access information through BI applications, which are much simpler to use than BI tools. BI applications leverage reporting technology, Web browsers, and e-mail to make information more accessible to these business users in a comfortable, easy-to-use environment.

For example, today’s parameter-driven BI applications provide users a simple Web interface to navigate to the report they want, much the same way they would find an item on eBay or a book on Amazon. BI applications allow users to easily customize the report by selecting options from pull-down menus the same way they would fill in their address and select their home state or a shipping option from a drop-down list.