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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!

November 2013 Archives

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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Analytics have become an integral part of an organization’s infrastructure, helping to drive decision making across the organization. Increasingly, businesses are starting to leverage their information assets to help drive customer satisfaction as well. This is accomplished through customer analytics, identifying patterns in retention, satisfaction, complaints, churn, etc. Additionally, organizations are leveraging this information to better meet the needs of their current and future customers. Going one step further are the companies that offer analytics as a service to their customers to help them optimize their experience.

Data services are becoming increasingly valuable with many organizations selling and maintaining information for their customers. Delivering this effectively requires the development of customer facing analytics that provide access to information assets in a self-service manner. Doing this properly however, requires an in-depth analysis on not only the types of data required and the needed analytics, but also an assessment of what is important to the customer, how they will leverage the information provided, and the easiest way to interact with the analytics provided. Basically, providing customers with access to analytics is easier said than done!

The reality though, is that this level of customer access requires a lot of analysis to make sure that the analytics delivered provide added value to the customer and give them the competitive edge needed or the insights to aspects within their interactions that were previously unknown. Doing this properly requires reaching out to customers on a broad level to identify:

  1. what their needs are
  2. what gaps exist
  3. their business challenges
  4. what value add means to them
  5. how they currently evaluate performance success

With the goal of all of these components being used to enhance visibility into transactions and interactions.

The reality for analytics is that as organizations gain more visibility into their customer needs, they will require the ability to give their customers more information about their accounts, behaviors, efficiencies, etc. In the future, analytics may provide the value add so that when customers look for the right fit from businesses, their evaluations will also include how much visibility they get from their suppliers, service providers, and the like.

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 November 18, 2013 5:37 PM
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