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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.

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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!

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‚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 January 13, 2014 3:06 PM
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¬†The end of the year marks the time when both people and organizations take stock. We analyze trends, predict what’s in store for next year, and try to see whether we’re keeping pace with the broader market place. In some organizations planning activities take place to identify the best course for technology adoption and/or optimization for the upcoming year. When I speak with smaller organizations, however, many still struggle with the concepts surrounding BI, how to make the most of the data they have, and deciphering the market to make the right solution choices.¬†

Although there is a lot of information available about technologies related to analytics and data warehousing, the reality is that much is still targeted towards the technical user – one who understands the complexities of a variety of technologies and how the pieces fit together. For business users and potential project sponsors the technical jargon doesn’t help address the challenges that exist. This means more education is required that prepares people to make the right strategic choices for their organizations.¬†

BI concepts

This requires an understanding of how the pieces fit together and how to take advantage of database technology, data integration, and analysis to create effective BI tools to address business challenges. This also requires making sure that a solution will be flexible enough to interact with analytics in the way that is required to answer questions on the fly and address business challenges as they occur.

Optimization of technology

This involves identifying what exists, what is working, where there are gaps, and what is needed. Understanding this on both a technology (infrastructure) and business level will help organizations select solutions to help put the pieces together.

Software selection

This means making sure that there is an understanding that not all technology is created equally and that solutions are optimized for different business challenges. It becomes important to evaluate the market to identify which types of technologies will fit best (i.e. cloud, appliance, etc.) and what capabilities are required. 

Even though this is the case within some organizations, this does not mean that SMBs are not adopting BI – quite the contrary actually. SMBs want to take advantage of what the market has to offer. The challenge is getting access to the information that is most relevant and sifting through technologies to understand which one best meets the needs of the organization. The reality, however, is that many SMBs are still struggling with this. In addition to making the most of spreadsheets, trying to understand how to best apply analytics, and looking at the market more broadly, SMBs have great adoption challenges in the sense that the market is still stacked towards the enterprise. Although easy to implement and less cost prohibitive options exist, the question is – do SMBs who need access to these solutions really know where to look?

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 December 30, 2013 3:16 PM
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The customer has always been and should be the focal point of the organization. In the past this took on the guise of CRM applications to manage transactions and customer touch points for support, service, etc. This expanded to Customer Experience Management to take the view of and access to the customer one step further. Customer Intelligence does the same thing, but through increased data visibility. Organizations strive to understand their customers better in an increasingly competitive world. Online access and a global economy have given consumers (both business and otherwise) the upper hand. A person or business no longer has to purchase goods due to proximity. They can buy products and services based on the value add of knowing they are getting the best value for their dollar – whether due to price competitiveness or better levels of service. The reality for organizations trying to compete is that the only way to distinguish oneself from the competition is to provide added value by making sure that customers are more than satisfied with the products and services they purchase.

For organizations struggling with how to actualize this goal of providing value add, the reality is that the answer exists within data that is already available. Some of this data includes:

  1. Competitor data – what is available in reviews and online about competitor products, customer satisfaction, and the like.
  2. Social media related data – identifying what people think and general perceptions, Facebook likes, Twitter rants, etc.
  3. Internal customer information within CRM, AR, AP, and other corporate applications – this includes household, account, transaction history, and other general customer information.
  4. Sales and marketing data to identify what is working at the product and product visibility level.
  5. Sentiment analysis to understand issues, retention, satisfaction, etc. This combines analytics with internal customer information.

All of this data consolidated provides the access point to Customer Intelligence. It is quite difficult to develop core competencies and to manage them without understanding the whole customer landscape and wider data points than those within the organization.

The bottom line: to transform the organization into one where brand recognition goes beyond a product towards providing the value add to make customers stay requires a holistic approach to analytics that takes into account internal, external, structured, and semi/un-structured data sources. This means looking at analytics differently and understanding how leveraging the right data for analytics can actually be translated into better relationships with customers and the ability to tie in data access with business value creation.

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 December 16, 2013 3:27 PM
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When organizations embark on any analytics, data warehousing, BI, or broader software project, much of the focus remains on how to meet current goals and challenges. Requirements gathering looks at current data requirements and business rules in order to support development for solutions that will be supported on the premise of current data volumes, number of end users, data sources, etc. And although many of these solutions are successful, the reality is that they are only successful in as much as they will also be able to support future requirements. 

When evaluating software, platforms, new analytics, or BI expansion, the following considerations need to be addressed in order to ensure that a solution can scale:

  1.  Type of platform: The type of platform selected will determine the range of expansion available as well as the restrictions that exist in terms of licensing, new data sources, storage, latency, etc.
  2. Number of data sources: Over time any BI initiative will expand simply due to the amount of data being stored. Keeping historical data and adding additional years worth of data naturally expands the storage required. The number of data sources also need to be taken into account. Additional data sources translates into more data integration, new business rules, and additional resources.
  3. Number of users/departments: Although solutions generally start off addressing a few issues, the more successful BI projects are, the more likely they will expand into other areas of the organization. Consequently, IT departments need to take expanded use into account so that any licensing and development requirements will be evaluated to make sure they meet these needs.
  4. Types of users: Different roles within the organization will interact with BI differently. Coupling this with market trends such as self-service and data discovery requires solutions that have built-in capabilities enabling flexible interaction and easy expansion for new development.
  5. Integration: In some cases data integration requires the bulk of the development effort. Expanding BI and analytics use potentially leads to new integration considerations. Although not always possible to think of everything in advance, understanding how broader solutions integrate with each other can lead to less hassles down the road.

This 5 considerations are a subset of many and just scratch the surface when looking at scalability. All of these areas look at internal aspects, and do not take into account the solutions being used which have their own criteria to evaluate when identifying how they scale. Even though it isn’t always easy to know what future projects will entail, the reality is that the more forward looking an organization is, the more likely less rework will be required in the future.

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 November 29, 2013 3:53 AM
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