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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!

October 2013 Archives

Last week while teaching a course at TDWI in Boston about how to achieve both Agile and Self-Service BI, I asked how many people are involved in a Big Data project or are considering one. Out of the 50 attendees, only 3 raised their hands. When exploring this further, many organizations didn’t feel they had big data challenges. And despite all of the industry hype about managing data within big data platforms, the reality is that plenty of businesses are run without large and complex data sets. If these organizations are not integrating diverse data and analyzing complex and varied data sets then the effort of big data may outweigh the benefits. Consequently, for organizations deciding whether big data will benefit them, some of the reasons organizations are looking at big data adoption include:

  • trying to understand the voice of the customer more broadly by integrating social data, location intelligence, external sources, and broader demographics data
  • looking at ways to better manage large data volumes, with big data platforms providing a less expensive way to store and manage distributed data¬†
  • the inability to get a full picture of the business due to siloed data
  • attempting to manage real-time data delivery and decipher information complexities that tax traditional BI systems

The reality for some organizations, especially SMBs, is that their struggles may not be on this level and that what they are currently using works. At the same time, as BI technologies advance, traditional BI infrastructures need to be evaluated to make sure that it is still possible to keep up with industry trends and apply relevant use cases within the confines of these technologies. Because storage is becoming less expensive and due to the fact that solutions can meet the needs of a variety of challenges, big data platforms will probably become more widely used, even for organizations without “big data” problems. The simple reason being that the open source platforms these solutions are based on can be leveraged as large data stores, for analytics, or to manage unstructured content – all of which will become more relevant as organizations place more importance on leveraging different types of information assets and BI related technologies.

So the bottom line is that the answer to the question is yes, no, and it depends…

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 October 30, 2013 3:06 PM
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A lot of the information that exists related to business intelligence and data warehousing assumes a certain level of knowledge. The technologies available are complex and require a high level of understanding and technical knowhow to identify which solution is a best fit. The reality for many SMBs, however, is that business units are becoming more essential for the decision making process and are actually driving many BI initiatives. What this means is that the way information is delivered needs to change in order to address the unique needs of business decision makers, and not always developers and technical related roles.

So, where do organizations turn when they are starting to explore the possibility of analytics but aren't sure where to start? This post will provide a starting point -- not necessarily for the specific knowledge required, but to identify the first steps and general considerations required before tackling BI. Getting started isn't always easy, but by breaking down considerations into easier chunks, organizations can get started on the road to broader and more effective analytics.

Here are 5 key considerations:

  1. Understand your top challenges and look for quick wins. This might sound intuitive but the reality is that organizations have many challenges and the severity of business pains being faced might differ based on different department perspectives and corporate roles. As a starting point, some cohesion is required to identify the top areas to start with. After all, getting solutions up and running that will be seen as valuable will help organizations justify future expansions and budget allocations.
  2. Evaluate the market place to match solutions with performance challenges. There are many solutions available and many that overlap in terms of capabilities and market positioning. Decision makers tend to make choices based on vendor marketing, previous implementations in other companies, or recommendations from friends. All of these are valid to a point, but businesses need to go further to really make sure that software selection goes beyond a high level analysis.
  3. Understand data. Big data is a term that is becoming synonymous with managing large, complex, and diverse data sets. Gaining true visibility means looking at the value proposition of information assets, how they interrelate, and where gaps in performance lie. In essence, although the front-end business applications that are based on dashboards and visualizing analytical information, the reality is that data is the key aspect of any BI initiative in relation to getting out insights.
  4. Really understand data! This means looking beyond where it comes from to identifying what is required to manage information across the organization over time. Consider data quality, new types of data, location intelligence, how to better meet the needs of customers, etc. are all areas that are actually data related.
  5. Develop a gap analysis. Understanding these areas provides a first step towards BI adoption. Matching data and business requirements to what actually exists within the companies can help organizations identify their starting points. In some cases, a technical infrastructure or parts of it might be reusable. And if not, identifying the gaps will provide a greater understanding of what needs to be managed, integrated, and the hows and whys associated with the process.

By looking at these areas, businesses can get started on the road to better business insights.

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 October 21, 2013 9:45 PM
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Although organizations are making headway at becoming more agile, there are still challenges that exist and that many organizations overlook when striving to achieve BI agility.  In some cases organizations think that by revisiting their business needs, they can simply shift their BI use without evaluating the technology and processes currently in use. In other cases, they may feel that adopting new technology will be enough, without looking at the broader implications of how to apply that technology to address business needs more broadly.

For agility to work, organizations require a broader understanding of the key areas that need consideration – infrastructure and governance. After all, the right technology and data access will help drive better business performance, but only if associated governance structures are put in place to ensure that it can be properly managed over time.

Agile BI Infrastructure and Data Access

 In recent years the data warehousing market has become more diverse with the introduction of a broader array of solutions. These include more appliances, analytical databases, cloud storage options, and more focus on in-memory processing. Adding to this the focus on big data storage and organizations have the flexibility to develop the types of solutions that best fit their current and future needs. 

Looking at agile BI specifically, however, requires the ability to deliver data that is accurate and fresh on a regular basis. This expands beyond the concepts surrounding operational BI, towards the ability to work backwards. In the past, BI was a data focused solution – the data always came first. This is no longer the case. Irrespective of how it is accessed, information needs to support business requirements and how it is stored should be looked at based on how it is needed, and not based on the technology available. This means identifying when information is required, how it will be accessed, and by who. Building a platform based on these considerations is what will help bring the organization towards more effective agility.

Understanding the Full Scope of Governance

Governance represents the people, processes, and systems that support data. This means making sure that the information being analyzed is accurate and that a level of accountability exists to deal with issues as they occur. Who is responsible, what are the processes in place to address challenges, and how information assets will be handled are all areas that apply to governance. In essence, governance reflect the people side of the agility process. But more than that, they help manage the ongoing data quality and consistent access to valid data.

Without an accurate understanding of what people need and the level of importance, the ability to develop a strong data infrastructure will be difficult. Essentially, both are required as starting points to build a strong and agile approach to BI access.

For a broader look at achieving or transitioning towards agile BI in your organization please see:

Microstrategy Webcast – 7 Steps to Achieving BI Agility*

Checklist for Achieving BI Agility*

*Requires registration

Posted October 14, 2013 4:51 PM
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A few weeks ago I attended the Tableau User Conference to get updated on their roadmap, direction, and software improvements. As always, Tableau is committed to enhancing user experience and empowering users to interact with analytics independently by:

  • developing their own interactions
  • providing easy access to data
  • enhancing data integration capabilities¬†
  • increasing data visualization
  • being committed to a self-service experience
  • enabling story boarding

All of these capabilities provide enhanced access to analytics and data insights. Additionally, they provide access to BI in a way that has opened up the market. What this means is that although Tableau is not the only solution of its kind, the way it provides access to BI fills a need within the market place. For too long, solutions have been robust, but with mobile, cloud, and data discovery concepts changing the available options for BI access points, solutions are slowly becoming easier to take advantage of. The reality, however, is that many of the solutions shifting towards this ease of access, still require a robust architecture. And although this is positive for many organizations, especially when trying to develop an analytics platform that maintains data quality and consistency over time, there are still businesses that require easy access to data. Whether for ad-hoc analyses, quick looks at performance gaps, or a way to share information with external sources, different types of BI and data visualization tools are needed within the industry.

This is one of the reasons why Tableau Software has become so popular. Organizations need a level of flexibility they didn’t have in the past. Luckily the market has finally caught up to these requirements through flexible deployment options, data storage, and self-service and data discovery based access. As time goes on these solutions will become more robust to support big data more broadly, in essence, making sure that information can be leveraged irrespective of where it comes from.¬†

Posted October 3, 2013 2:48 PM
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