Blog: Lyndsay Wise http://www.b-eye-network.com/blogs/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. Copyright 2014 Mon, 31 Mar 2014 14:47:14 -0700 http://www.movabletype.org/?v=4.261 http://blogs.law.harvard.edu/tech/rss Finding the balance between business and IT Technology projects always face unique challenges due to the fact that their success requires taking into account two viewpoints. The first being from the business point of view, identifying the business challenge and understanding how a solution will address specific needs. The second being an IT outlook and understanding the technology best fit. In some cases these will match and in other cases there will be discrepancies. The reality is that few companies will be able to avoid this disparity of views. However, if organizations can take responsibility for their portion of solution evaluation and collaborate to make the right business and technology decisions to support long term business goals, then both entities can develop a balance. 

Both business and IT need to take responsibility for their respective areas. This means that business units:

  1. develop an understanding of their business challenges and the causes of their pains
  2. evaluate the requirements based on their needs 
  3. understand the gaps between current technology use and why it doesn’t meet business needs
  4. make sure to look at must haves versus nice to haves
  5. create agreement among stakeholders who will also be using the new solution

In most cases IT supports and develops business applications meaning they require:

  1. an understanding of the business challenges being faced
  2. how these translate into features and solution capabilities and general technical requirements
  3. what the integration and storage requirements are and whether changes in infrastructure are required to support the new solution
  4. development effort and support for offerings being evaluated and the implications
  5. building up applications that meet business needs longer term

These aspects represent the first look at the responsibilities of each of these entities. The reality is that because they overlap, collaboration is required. How much and specific aspects will differ depending on the politics within the organization and the amount of collaboration that already exists. The best balance within organizations generally exist when both entities look at software projects as something that is the responsibility of both the business unit sponsoring the project and the IT department. This way both understand the value of each set of contributions. On the business side this includes all of the subject matter expertise, while the IT department develops the solution and supports the technology required to actualize the project. 

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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http://www.b-eye-network.com/blogs/wise/archives/2014/03/finding_the_balance_between_business_and_it.php http://www.b-eye-network.com/blogs/wise/archives/2014/03/finding_the_balance_between_business_and_it.php Mon, 31 Mar 2014 14:47:14 -0700
Big data for small companies The term “big data” is a bit of a misnomer. Many mid-market organizations feel that big data doesn’t apply to them based on the size and infrastructure assumptions associated with these projects. Consequently, many organizations overlook the benefits of looking at big data as a BI and data management strategy. Whether or not big data should be addressed depends on many factors. Some of these include:

  • types of data being analyzed – this means looking at whether data should/can be stored within a relational database or should be stored separately within another format
  • purpose of analytics – traditional BI environments enable certain types of analytics well, but generally don’t support operational analytics, or the ability to store and access the data variety or complexities associated with big data
  • reason for information storage – big data is broader than BI or analytics and the different applications should be explored
  • level of complexity – sometimes organizations do not have high data volumes, but do have complexities based on industry or company specific requirements that are best handled within a big data infrastructure

These represent a subset of reasons why mid-sized businesses should be looking at whether big data applies within their organizations and what needs to happen to implement a big data framework.

As with all technology projects, more considerations are required than just looking at whether the organization faces challenges that reflect the factors above or others conducive to big data environments. Organizations need to understand their current infrastructure, what they can support, what hardware or cloud based provisions need to be considered, costs of initiating a new data management project, and how all of this will directly affect business. Understanding the bigger picture and where a big data solution might fit within a broader information architecture and how it benefits the company as a whole is one of the first steps of deciding whether it’s the right step for the organization.

Overall, the important thing to remember is not to discredit the potential big data can bring to the organization because it may seem out of reach as open source technologies, internal resources, and general education can help mid-market companies move towards their big data goals. 

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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http://www.b-eye-network.com/blogs/wise/archives/2014/03/big_data_for_small_companies.php http://www.b-eye-network.com/blogs/wise/archives/2014/03/big_data_for_small_companies.php Tue, 18 Mar 2014 18:31:42 -0700
Looking at Business Intelligence Holistically Many BI implementations are based on a subset of business pains or initiatives within the organization. These projects serve one or a few departments and generally take into account a certain outlook already perpetuated within the organization. In many of these cases, analytics are predefined and even data discovery involves the analysis of pre-defined business rules. As these projects are rolled out, they begin to unravel. On the one hand, they address a specific set of requirements based on what departments have faced in the passed or are used to analyzing in the present. On the other hand, there is a general lack of ability to delve into issues more completely and address new issues as they occur or evaluate information in a different way.

An example that is becoming more prevalent these days is an organization’s need to develop customer-facing applications and create competitive advantage through better customer experience. A holistic approach to BI and data access is a key way to achieve this.  For instance, developing successful customer-facing applications means understanding the needs of the customer, how they interact with the tools they have access to, why they call in for support, how important of a customer they are, what their account looks like, what services they use, etc. Only by consolidating a wide variety of data sources can an organization really gain insight into their customer base. Information such as accounts receivable, customer support, transaction history, application use, and the like should be consolidated to provide account managers, call centre reps, and anyone with access to the customer insights into how to better service their customer or where to draw the line.

Obviously, customer-facing applications is just one area that requires a broader outlook when looking at the information required to provide better products and services. Whether sales, marketing, supply chain, or industry focused, organizations require the ability to analyze data that extends beyond sales pipelines, sales performance, or marketing. With mobile access, cloud based computing, and big data stores all becoming a reality and providing more flexibility in what and how information is accessed, the fact remains that unless all types of important data are easily accessible, BI remains limited.

The concept of holistic BI right now is still just a concept to many organizations, and that is ok. Not all traditional BI infrastructures can adequately handle new data types or complexities with ease. Consequently, many organizations are looking at how to restructure their outlooks and BI infrastructures to address newer challenges that apply a more holistic approach to data management and analytics. Looking at holistic BI as an iterative approach and developing a game plan on how to achieve broader data access and better customer insights is the first step to taking advantage of the full benefits BI and supporting technology now have to offer organizations.

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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http://www.b-eye-network.com/blogs/wise/archives/2014/02/looking_at_business_intelligence_holistically.php http://www.b-eye-network.com/blogs/wise/archives/2014/02/looking_at_business_intelligence_holistically.php Fri, 28 Feb 2014 17:32:29 -0700
Looking at the value proposition of leveraging information assets Sometimes SMBs get stuck trying to identify the value proposition of investing in more robust reporting or analytics solutions to take their businesses to the next level. After all, running a business is stressful enough. Taking time away from daily operations to analyze business processes and take an accounting of what is working, what isn’t, and how to use data for better information efficiencies can seem like a waste of time. The reality, however, is that because data is what drives organization success, analytics are becoming harder to overlook. Where Excel used to be enough, the fact is the time it takes to create the right spreadsheets and recreate the process on a regular basis could be spent more effectively making business decisions and planning competitive strategies. Organizations need more standardized ways of managing their customers, supplier relationships, products, and services. This requires developing a holistic approach to information access points so that employees and decision makers do not have to spend time trying to find data instead of interacting with it. 

For some SMBs, the actual roadblock was due to the software available (time to value, pricing, licensing, etc.) and not a dread of spreadsheet use and broader information management. Luckily the availability of a wide variety of solutions that can be deployed various ways helps organizations get closer to this goal. This means that although the initial requirements identification is needed, organizations can develop solutions that meet their needs and that automate the generation of regularly required information and insights. The level of detail and complexity is left up to the organization, but taking this step gives employees an automated approach to business insight.

Irrespective of why analytics are starting to become more important to SMBs, technology can now match business needs and help support information insights without breaking the bank. Overall, understanding customer buying habits, supply chain inadequacies, or market opportunities cannot be achieved effectively through traditional spreadsheet use. At the same time, evaluating what is working and what needs improvement requires looking at information assets holistically to identify how things can run more smoothly and what opportunities can be identified.   

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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http://www.b-eye-network.com/blogs/wise/archives/2014/02/looking_at_the_value_proposition_of_leveraging_inf.php http://www.b-eye-network.com/blogs/wise/archives/2014/02/looking_at_the_value_proposition_of_leveraging_inf.php Wed, 12 Feb 2014 00:19:14 -0700
Getting real value from BI investments Many organizations struggle with their data. Whether those without any structured analytics or data management practices, as well as businesses with mature BI infrastructures, there are always gaps to visibility. Data volumes are on the rise, companies expand their business models or provide new services, and the way in which data can be managed and processed is constantly evolving. Consequently, the roles of business intelligence and information management also need to reflect these changes. Organizations need agile practices to ensure that they are collecting, managing, and analyzing data that can be acted upon. In the past, technology could not keep up and the promise of BI was not all it was cracked up to be. 

Now things are different. Database technologies, “big data” storage, in-memory analytics, and the ability to leverage multiple types of data expand the value proposition of what business intelligence has to offer. The challenge becomes understanding the options that are available and making sure that the right choices are made within organizations that not only reflect current needs, but that can also support future needs. Looking at scalability, user licensing, business pains, levels of use and interactivity, and information architecture are some of the starting points when making sure that solutions support broader organizational needs.

The reality for most organization is that there will be the realization that BI needs to be an ever changing, constantly evolving process. Additionally, until organizations start to look at BI as an essential part of daily business processes and as an extension of daily operations, its use internally will be limited. Business intelligence and analytics can no longer be developed to monitor specific metrics without being able to provide action items. Identifying performance gaps, or issues with the supply chain, or lack of sales is a great start. But unless there is also the ability to look at cause and effect, and identify what needs to be done and the tools required to get there, then full BI value can’t be realized.

Although still a pipe dream to many, as technology continues to catch up and applications can be developed to support the convergence between operations, business process, and analytics organizations will continue to get closer to the realization that technology can be used to drive information value. 

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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http://www.b-eye-network.com/blogs/wise/archives/2014/01/getting_real_value_from_bi_investments.php http://www.b-eye-network.com/blogs/wise/archives/2014/01/getting_real_value_from_bi_investments.php Thu, 30 Jan 2014 15:22:35 -0700
Considering Embedded BI 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.

 

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http://www.b-eye-network.com/blogs/wise/archives/2014/01/considering_embedded_bi.php http://www.b-eye-network.com/blogs/wise/archives/2014/01/considering_embedded_bi.php Tue, 21 Jan 2014 15:22:51 -0700
Self-service BI is about more than interactivity 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’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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http://www.b-eye-network.com/blogs/wise/archives/2014/01/self-service_bi_is_about_more_than_interactivity.php http://www.b-eye-network.com/blogs/wise/archives/2014/01/self-service_bi_is_about_more_than_interactivity.php Mon, 13 Jan 2014 15:06:10 -0700
Is BI still pie in the sky for small and mid-sized businesses?  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’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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http://www.b-eye-network.com/blogs/wise/archives/2013/12/is_bi_still_pie_in_the_sky_for_small_and_mid-sized.php http://www.b-eye-network.com/blogs/wise/archives/2013/12/is_bi_still_pie_in_the_sky_for_small_and_mid-sized.php Mon, 30 Dec 2013 15:16:47 -0700
The Transition From CRM to Customer Intelligence 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’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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http://www.b-eye-network.com/blogs/wise/archives/2013/12/the_transition_from_crm_to_customer_intelligence.php http://www.b-eye-network.com/blogs/wise/archives/2013/12/the_transition_from_crm_to_customer_intelligence.php Mon, 16 Dec 2013 15:27:28 -0700
Planning For The Future: Understanding Scalability Requirements 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’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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http://www.b-eye-network.com/blogs/wise/archives/2013/11/planning_for_the_future_understanding_scalability.php http://www.b-eye-network.com/blogs/wise/archives/2013/11/planning_for_the_future_understanding_scalability.php Fri, 29 Nov 2013 03:53:29 -0700
Leveraging Customer Facing Intelligence 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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http://www.b-eye-network.com/blogs/wise/archives/2013/11/leveraging_customer_facing_intelligence.php http://www.b-eye-network.com/blogs/wise/archives/2013/11/leveraging_customer_facing_intelligence.php Mon, 18 Nov 2013 17:37:18 -0700
Do SMBs really need big data? 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’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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http://www.b-eye-network.com/blogs/wise/archives/2013/10/do_smbs_really_need_big_data.php http://www.b-eye-network.com/blogs/wise/archives/2013/10/do_smbs_really_need_big_data.php Wed, 30 Oct 2013 15:06:21 -0700
5 considerations for newbies to BI 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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http://www.b-eye-network.com/blogs/wise/archives/2013/10/5_considerations_for_newbies_to_bi.php http://www.b-eye-network.com/blogs/wise/archives/2013/10/5_considerations_for_newbies_to_bi.php Mon, 21 Oct 2013 21:45:01 -0700
The realities of BI agility – understanding infrastructure and governance requirements 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

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http://www.b-eye-network.com/blogs/wise/archives/2013/10/the_realities_of_bi_agility_understanding_infrastr.php http://www.b-eye-network.com/blogs/wise/archives/2013/10/the_realities_of_bi_agility_understanding_infrastr.php Mon, 14 Oct 2013 16:51:01 -0700
Tableau Customer Conference and shifting BI needs 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. 

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http://www.b-eye-network.com/blogs/wise/archives/2013/10/tableau_customer_conference_and_shifting_bi_needs.php http://www.b-eye-network.com/blogs/wise/archives/2013/10/tableau_customer_conference_and_shifting_bi_needs.php Thu, 03 Oct 2013 14:48:42 -0700