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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!

January 2014 Archives

Many organizations struggle with their data. Whether those without any structured analytics or data management practices, as well as businesses with mature BI infrastructures, there are always gaps to visibility. Data volumes are on the rise, companies expand their business models or provide new services, and the way in which data can be managed and processed is constantly evolving. Consequently, the roles of business intelligence and information management also need to reflect these changes. Organizations need agile practices to ensure that they are collecting, managing, and analyzing data that can be acted upon. In the past, technology could not keep up and the promise of BI was not all it was cracked up to be. 

Now things are different. Database technologies, “big data” storage, in-memory analytics, and the ability to leverage multiple types of data expand the value proposition of what business intelligence has to offer. The challenge becomes understanding the options that are available and making sure that the right choices are made within organizations that not only reflect current needs, but that can also support future needs. Looking at scalability, user licensing, business pains, levels of use and interactivity, and information architecture are some of the starting points when making sure that solutions support broader organizational needs.

The reality for most organization is that there will be the realization that BI needs to be an ever changing, constantly evolving process. Additionally, until organizations start to look at BI as an essential part of daily business processes and as an extension of daily operations, its use internally will be limited. Business intelligence and analytics can no longer be developed to monitor specific metrics without being able to provide action items. Identifying performance gaps, or issues with the supply chain, or lack of sales is a great start. But unless there is also the ability to look at cause and effect, and identify what needs to be done and the tools required to get there, then full BI value can’t be realized.

Although still a pipe dream to many, as technology continues to catch up and applications can be developed to support the convergence between operations, business process, and analytics organizations will continue to get closer to the realization that technology can be used to drive information value. 

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’ve 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 January 30, 2014 3:22 PM
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The way in which organizations apply analytics is in constant flux. Technology and integration advancements make access to analytics and BI applications much more flexible, leading to greater adoption of embedded analytics. Companies want to embed their analytics within their day-to-day applications to make analytics access more seamless within their daily operations. One of the reasons behind this is the ability to grant access to more people without being limited by BI expertise. Additionally, companies want to empower their employees to act upon issues as they occur, instead of having to rely on accessing multiple applications and searching for answers.

For organizations transitioning towards embedded analytics, there are a number of considerations required, some of which were addressed in a recent Webinar titled 7 Considerations of Embedded Analytics in conjunction with Pentaho.  These considerations (which include looking more broadly at data integration, understanding potential big data challenges, and ensuring closed-loop processes to make information actionable) provide general guidelines as well as some of the technical requirements for embedded BI adoption. Many businesses adopt this type of analytical approach as a way to deliver BI access to a broader array of business users without requiring high levels of training to go with it.  More accessibility and easy access to data translate into more effective decision making overall.

On the other side of the argument, organizations need to realize that when they choose an embedded approach, they may be limiting data access to specific data sources and not creating a broader approach to decision-making across the organization. Embedded BI is most intuitive when limiting analytics views and interactions to the questions being addressed within the transactional/operational applications being used. This means that two types of BI may be required – the first being a more holistic approach to information challenges within the organization, and the second requiring more targeted analytics addressed through embedded analytics.

Overall, there are definite benefits to leveraging an embedded BI approach to analytics. At the same time, organizations need to realize that considerations for embedded BI adoption require looking at the analytical needs of the organization more broadly. Users adopting embedded analytics might also need access to additional data sources and other ways of interacting with BI meaning that although embedded analytics can provide added value, it may not be the only analytics use required for more effective decision making.


Posted January 21, 2014 3:22 PM
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Organizations constantly struggle with their data. Integrating, managing, and verifying data sources are continuous exercises required for businesses looking at ways to increase their competitive advantage and understand what is occurring within their organization’s daily operations. Historically, the benefits of business intelligence and data warehousing have focused on this aspect – making information accessible and managing it within a series of strict guidelines. For instance, developing specific data models to understand how table fields are related or looking at the development of business rules and how they are to be applied. The recent advancements in technology and shift towards a more “social” approach to information access and interactivity has shifted the way in which organizations are accessing and interacting with information assets. Due to this change, expectations have also shifted. It is no longer good enough to have information available if only a subset of employees can access that data and make sense of its value proposition. Not only do these employees need to access data assets, they need to be able to interact with it and drive business decisions that benefit the company as a whole.

Essentially, this is the promise of self-service BI. Self-service applications should be easy enough to use that they appear intuitive to business users while maintaining the integrity of the data and managing business rules on the back end of the application. Call it a tall order, which it is, but luckily for businesses applying newer offerings, vendors are becoming more efficient at making sure that these two aspects fit together to allow business users the option of accessing more advanced analytics without requiring statistical skill sets. 

Organizations require flexible solutions that meet the needs of a variety of business and technical skill sets without limiting the types of information available – in essence creating a true self-service environment. Doing this effectively does require looking at the data as well (the whole process is circular in nature because we always come back to the data). To develop a true self-service solution, organizations also need to consider information access points and be able to look at data holistically. Since a variety of sources are required to get a true sense of what is happening within the organization, developing a self-service BI approach means taking these considerations into account and looking at self-service in a way that includes more than experience and delves into the value proposition broader information access provides.

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’ve 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 January 13, 2014 3:06 PM
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