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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

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

Analytics projects and the associated data preparation, storage, and management require continual effort. Sometimes mid-market organizations have a good sense of what they want to achieve and how they need to get there, but overlook the value of gathering in-depth business and technical requirements before diving into software and hardware selection. Many companies I have worked with base their choices on which solution provider is making the most noise in the market, their previous experience, or what their friends are doing within their respective organizations. Although a potential good first step, some of these organizations forego any additional evaluation to identify what would fit best based on the organization’s needs. Not evaluating requirements increases risk. Organizations may end up selecting a platform that can’t meet SLAs, doesn’t scale, or simply doesn’t offer essential product capabilities. This in turn can lead to longer implementation times, greater expenditures, lack of adoption, and the inability to meet changing requirements over time, just to name a few.

However, in some cases these risks are not enough for project sponsors to take a step back and evaluate business users’ needs, what the real questions are, and what type of technology will best meet these needs in the long run. Many think it is too time consuming or feel that they are already in tune with what analytics related issues exist, not understanding that high level requirements are no substitute for understanding the challenges people face on a daily basis. Consequently, successful analytics initiatives require an in-depth understanding of the business challenges being faced to ensure that tools selected address ongoing business needs, while ensuring security and scalability.

Proper requirements gathering requires time built in to the project plan to allow business analysts and project managers the time to adequately assess existing gaps and business challenges being faced by future users and those affected by the outcome of the analytics initiative. Understanding what gaps currently exist, people’s expectations of use, how often they will be interacting with information, how not having valid and reliable data affects their roles, and what they feel could enhance their jobs are all topics that need to be addressed. Once an organization understands how business challenges being faced and data overlap, they can work to translate those requirements into technical requirements used to identify specifications for the platform required. This choice will differ within each organization, meaning that leveraging knowledge from previous roles at other companies may or may not be the best fit moving forward. Understanding in-depth business requirements helps organizations identify the best fit technology requirements and provides support for the justifications needed for additional hardware and software acquisitions.

Although more time consuming, organizations willing to understand the challenges of their business users are more likely to ensure adoption as well, and help achieve a quicker time to value as users can access what they need out of the gate and not have to make requests for requirements that were never gathered.

This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.


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Posted October 28, 2014 1:39 PM
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More solution providers are starting to integrate the concept of governed data discovery into their product offerings. After years of trying to adopt data governance initiatives as part of a larger data management framework within organizations, software vendors are integrating similar capabilities into their solutions. The reality, however, isn’t as simple. Organizations need to understand what governance within the framework of data discovery or business intelligence means to make informed decisions in relation to software selection and solution design. Sometimes organizations think that governed data access is a blanketed statement that will apply to all of their analytics use. The reality, however, can be much different.

In most cases, governed data access refers to information accessed within a managed database or set of data sources. This means that governed data access refers to data accessed within specific sources that are part of the solution provider’s offering, but that data accessed externally falls outside the parameters of data governance. Some of the challenges of this for organizations are as follows:

  • providing flexible data access to broader users while controlling data validity
  • ensuring users understand the differences between types of data being accessed
  • limiting access to governed sources
  • developing an iterative framework to manage data quality
  • providing access points and processes surrounding non-governed data sources
  • letting different types of users interact with all the data they require
  • All of these challenges also provide organizations the opportunity to identify the best way to govern their data. Without governed data access points, it becomes hard for users to trust their analytics. Without trust in data it becomes almost impossible to identify whether metrics are accurate. And in many cases, people know that they can’t trust the data they are accessing so don’t want to use their BI tools. This exists whether the tool is Excel or some advanced BI tool, unless data governance exists.

    Because more organizations are starting to quantify the benefits of their data assets, the value proposition of data governance has increased. Therefore, the bundled solutions that include governed data can help organizations achieve quicker implementation times with valid data. The only issue to consider is whether there will be data sources needed for regular analytics that will continue to reside external to the governed sources. Overall, organizations need to understand this to ensure they can evaluate solutions with an understanding of how data will be managed over time.

    This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.


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    Posted October 14, 2014 8:02 PM
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    The role of governed data discovery is becoming increasingly important as organizations manage more complex and diverse data that they want to gain insights from. Self-service BI access and broader data discovery capabilities means that BI is deployed to more users who leverage data in the way that best suits them and not according to pre-defined analytics. Being able to trust this data is essential as it is one of the main ways to guarantee information validity and correct results. Unfortunately, I have worked with several organizations using BI that continue to develop their own analytics, yet admit to knowing about inaccuracies in their data. In these cases, establishing the value of analytics becomes difficult because without trust, it becomes impossible to validate analytical outcomes.

    The goal of governed data access to support self-service and data discovery applications is to solve data related challenges and support validated data access. This access can be within a centralized data warehouse, through data virtualization, or by accessing approved data sources external to the analytics framework. With organizations being held more accountable to tie their BI initiatives to business value, the data used to develop insights driving results need to be tightly coupled with data that can be validated through governance.

    Achieving this on a systematic level requires developing a strategy and taking data governance seriously. This requires involving the proper stakeholders, defining the processes required, and managing compliance over time. Additionally, the analytics infrastructure needs to support this initiative by providing the framework to manage data quality over time and provide steps to identify issues and support the organization as they try to fix them. Certain solution providers now focus more extensively on providing these capabilities as a part of broader offerings to help organizations overcome their data challenges. As organizations expand their data use and look at broader data sets to leverage as part of their analytics, the importance of data governance increases. Essentially, it is becoming impossible for organizations to ignore the role data governance plays within any BI, Big Data, or Information Management initiative.

    Organizations need to ensure the validity and reliability of their data. The only way to do this is to ensure that data governance is an intrinsic part of any data related initiative. More detail on how to do this can be found at: Understanding The Role Of Data Governance
    Additionally, here is a Webinar link developed with MicroStrategy that also shows a vendor’s stance on Governed Data Discovery and the importance of integrating a data governance framework within broader BI and analytics solutions: Understanding The Role Of Data Governance

    Posted October 1, 2014 4:10 PM
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    BI implementations are becoming more commonplace as organizations realize they cannot overlook the management of and access to their data in order to facilitate better decision-making. What this means for many is the re-evaluation of resources, skill sets, and project planning to ensure that the proper resources exist to support BI and analytics development. Unfortunately, many businesses overlook the fact that IT development expertise does not equal BI savvy. There are SMBs without the IT resources to facilitate a project but there are also companies with an IT department and developers on-hand, but without BI development experience or skills. The reason why this is an important consideration is because in order for solutions to be effective, they need to be designed right – and right includes understanding data, analytics, and design in a way that promotes BI best practices. In general, two types of organizations exist, and identifying and leveraging the right skill sets are equally important for both.

    Organizations that don’t care

    Businesses may feel that they don’t have the bandwidth to hire new people or to train existing staff. The problem is that risks increase as developers, already over allocated in many cases, struggle to get a solution up and running that they don’t understand. Although solutions will be developed and analytics access granted, there may be inconsistencies in performance, leading to the inability to access the data required when needed or to required functionality not being leveraged efficiently. Either way, organizations need to understand that investing in a BI initiative may require budget set aside for skill set development or outsourcing services.

    Organizations willing to invest

    Other organizations are willing to invest to make sure that their BI solutions are developed properly. In many of these cases, vendor professional services or outside consultants are used to develop the initial solutions to get BI up and running, or alternatively, to enhance what already exists. For these organizations it still becomes important to ensure that support exists on an ongoing basis or a transfer of skill sets occurs so that BI can be properly maintained moving forward.

    The reality for organizations is that BI success requires specific skills and knowledge, and are not within the realm of a generalist to do effectively. Although many organizations attempt to go it alone, the reality is that businesses require an in-depth understanding of the technology and tools used in order to develop successful BI applications.

    This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.


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    Posted September 24, 2014 3:16 PM
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    This is a question I’ve been asking myself for a while. The data infrastructure exists to support Big Data, operational data streams, data quality practices, and the list goes on. Best practices exist for organizations to follow to achieve a strong information management framework and tie data to business processes enabling decision makers the ability to take actions on the insights they’ve gleaned. A variety of solutions exist in the market place providing BI access to any type of user and that are geared towards a strong IT infrastructure or small business with little to no internal IT support. Additionally, organizations understand the value their data brings to the table. Yet, many companies still struggle with silos of data, lack of visibility, the inability to consolidate information assets and develop the essential correlations between the data they need to drive strategic business value.

     Despite all of these facts, the answers are still elusive to me. Sometimes I think that nowadays project sponsors think data management should be easier than it is and cut corners to ensure quick implementation times without weighing the facts surrounding how this will affect time to value. I have seen it many times with organizations that don’t conduct in depth requirements gathering or identify how business and technical requirements are developed to work cohesively together. I have also seen organizations select products based on marketing hype and end up with a subset of the capabilities they require. Within SMBs, there is also a mistake whereby organizations don’t take into account the expertise they require to develop a strong BI initiative and either do not want to invest in the right skill set or feel that the resources currently available can be used without the proper training.

    All of these areas contribute to the confusion, but so does the market itself. There is very little that is available in the form of a series of best practices or guide that can be used on a broader level to guide organizations through the transition from traditional BI infrastructures and other traditional models towards agile solutions that help support organizations in this transition. After all, the complexities of data integration haven’t gone away despite the promise of automated processes, more APIs, and easier to use solutions. Luckily, as the market matures, businesses can start to benchmark against those with successful implementations. At the same time, it seems like an integrated approach to data management is still needed across the board to really help organizations support broader data management. Solutions are still piecemeal and full stacks are not always accessible.

    Hopefully as the market continues to mature and as more organizations move towards an agile approach to their data management, there will be more success with data management as a whole. But this still requires better planning, knowledge of IT infrastructure options, and an understanding of the value data can bring to the organization if leveraged well.

    This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.


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    Posted September 19, 2014 1:32 PM
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