We use cookies and other similar technologies (Cookies) to enhance your experience and to provide you with relevant content and ads. By using our website, you are agreeing to the use of Cookies. You can change your settings at any time. Cookie Policy.


Blog: Lyndsay Wise Subscribe to this blog's RSS feed!

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!

October 2014 Archives

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.

 

website statistics


Posted October 28, 2014 1:39 PM
Permalink | No Comments |

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.

     

    website statistics


    Posted October 14, 2014 8:02 PM
    Permalink | No Comments |

    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
    Permalink | No Comments |