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Lyndsay Wise

Hi and welcome to my blog! I look forward to bringing you weekly posts about what is happening in the world of BI, CDI and marketing performance management.

About the author >

Lyndsay is the President and Founder of WiseAnalytics, an independent analyst firm specializing in business intelligence, master data management and unstructured data. For†more than†seven years, she has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Lyndsay conducts regular research studies, consults, writes articles and speaks about improving the value of business intelligence within organizations. She can be reached at lwise@wiseanalytics.com.

Editor's Note: More articles and resources†are available in Lyndsay's BeyeNETWORK†Expert†Channel. Be sure to visit today!

May 2014 Archives

Business Intelligence has been a top priority for many CIOs and other project sponsors for years and most organizations perform some level of analytics on a daily, if not intra-daily basis. Many organizations, however, are looking for more up to date technologies or ways of bringing their BI use to the next level. This blog tends to address a lot of how to’s and steps for success. What it rarely talks about are some of the mistakes I’ve seen organizations make that end up turning the promise of BI into a failed project. So here are 5 things that I see regularly that cause organizations’ BI projects to fail:

  1. Lack of scope: Sometimes organizations know they want to implement BI but it is more of a directive based on an article read as opposed to a direct business need. When this happens, organizations can either identify the challenges within their business or simply look to implement a tool that will replace current reporting and spreadsheet use. Although, replacing older tools and spreadsheets can be a positive step towards building up organizational efficiencies, a lack of a business-focused scope can lead to project failure. A lack in project scope generally leads to failure because of a lack of adoption, poor development, or the inability to develop metrics tied to business pains.
  2. Trying to do too much too soon: This can be considered the opposite of not having a proper scope. Sometimes organizations try to do everything at once by including everything under the sun in their BI project scope. They want to address all needs in the organization with the same solution and expect that it is possible to do so with a single project plan and implementation. The reality is that a centralized BI approach is possible, however, it needs to be planned for. This means developing an iterative project plan to take into account the different phases of data acquisition, solution development, and rollout using an iterative approach. Additionally, even if using a centralized approach to BI development, rollouts need to be incremental in nature to assume small wins before moving forward. 
  3. Being unwilling to take the time to evaluate internal requirements against the market: Software selection tends to be a challenge for many organizations as the market is flooded with solutions that sound like they meet most business needs. The reality, however, can be different. Depending on the platform used, how information will be delivered, and what type of analytics required, different business challenges may need separate solutions. The reality is that researching the market is time consuming at the best of times and requires an understanding of product roadmaps, potential implementation hurdles, and capabilities, and match that to the business requirements of the organization. Additionally, this has to happen after the business and technical requirements have been gathered, making it a long process. Many want to skip this process, but in the end, taking the necessary steps can make the difference between selecting the right solution and having to conduct a new product search after implementation. 
  4. Bad project planning: BI is a project like any other that requires a strong project plan and management. Although most organizations have some project management in place, some are not prepared for the level of involvement it requires to build a BI solution. In most cases, failure ends up occurring when organizations try to do everything at once by bundling their BI implementations with other IT projects. Whether new Website builds, updating customer applications, or developing a new service, most BI projects need to be managed independently without having to compete with many other projects at the same time. Unless enough internal developers exist to manage all of these projects simultaneously, organizations need to make sure that their BI project can be managed from start to finish, with an understanding that additional support may be required. 

These are the top 4 that I see regularly, but are not an exhaustive list. Let me know what other mistakes you’ve seen in the market – whether from the prospective of organization, consultant, or implementer.¬†

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I‚Äôve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions.

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Posted May 27, 2014 6:06 PM
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Organizations are constantly looking for ways to meet the needs of their customers. Better service, customized products, and price guarantees are just some ways that organizations try to ensure customer loyalty. For service and data providers, however, it is not always easy to provide added value beyond the service or data provided. The promise of more data and better visibility help ensure customer satisfaction by giving customers the tools they need to gain added insights. This gap has led to organizations providing analytics as part of their offerings to ensure that customers can access broader insights automatically without having to download data or change formats and apply their own business rules to gain deeper insight. 

Non-profit, education, and government are examples of industries that have posted demographics or other analytical data online for public consumption. When dealing with for-profit companies, most provide analytics in the guise of embedded analytics. What this means is that organizations develop applications that are embedded within their solutions that can be provided as a service to customers. This access to analytics helps provide customers with broader insights into their accounts, customers, trends, reporting needs, etc. In many cases, businesses add these additional reporting or analytics capabilities as an add-on to the services or information already provided.

For organizations considering this added level of analytics access, the first step is to understand end user requirements. What data is currently being looked at, where are their gaps in visibility, what information do customers need to give them that added advantage, and what needs to be done internally to make all of this happen. These questions represent the starting point. Luckily for organizations going this route, there are many solution providers that offer embedded BI as a key aspect of their offerings. Therefore, when organizations look at evaluating software vendors, one of the things they have to do is make sure that the capabilities they require are also provided within an embedded environment. 

Once software is selected, the process of acquisition and integration will mirror a traditional analytics implementation, with one key difference – the opportunity to monetize use. Organizations need to identify whether they will provide this new service at a premium, as pay per use, or develop some other cost model. Overall, organizations want to do more than justify their expenditures; they want to transform this new operating expense into revenue.

The ability to provide analytics to others as part of a new or added value service is one of the reasons more and more organizations are looking towards embedded analytics. Aside from added revenue, though, the reality is that as customer expectations grow and analytics become the norm, more and more businesses will start to take advantage of embedded analytics to be able to provide their customers with greater information visibility. Currently providing a level of competitive edge will no longer be the case when customers start to expect this level of insight into their data.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I‚Äôve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions.

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Posted May 22, 2014 12:31 PM
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