Blog: 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. Copyright 2014 Fri, 18 Jul 2014 20:02:09 -0700 Takeaways from MicroStrategy World MicroStrategy World 2014 in Barcelona marked a transformation in the company’s product positioning and corporate direction. From re-packaging their offerings to making their product roadmap public, MicroStrategy is looking to bring to market solutions that are easy to deploy and consume. 

MicroStrategy’s new packaging and pricing is based on taking the former 21 product offerings and packaging them as 4 products as follows:

  • Web: A browser based interface for analytics design and consumption (i.e. reports and dashboards).
  • Mobile: An interface for mobile devices to enable analytics and developed apps access on mobile devices.
  • Architect: Development and migration tools to manage application development and BI process automation.
  • Server: The infrastructure required to support multiple data source connection, scalability, administration, etc. capabilities to support governed data delivery.

Together, these solutions provide an analytics platform that supports a wide variety of uses – from traditional reporting to operational intelligence. In addition, added R integration exists to take into account robust statistical analytics to help support forecasting and predictive modelling. This means that organizations can take advantage of additional calculations and analytics available from outside of MicroStrategy’s product suite.  

The overall strategy change in MicroStrategy’s approach to the market complements their overall focus on analytics and how they are hoping to position themselves moving forward. This includes looking at solutions based on how they are delivered – through mobility and cloud availability – to broaden the way organizations store, analyze, and consume data. 


In general, analytics capabilities and enhancing what already exists is nothing new. Where MicroStrategy is making inroads is its commitment to self-service access and easier development and deployment so that users can get to the data they need in an easier way. Additionally, MicroStrategy is committed to a governed self-service approach, by leveraging their platform for centralized data storage. The goal being to make sure that business users interacting with self-service independently can trust the reliability of the data they access. Obviously this type of analytics is limited to the data being maintained within MicroStrategy, but can help organizations manage information access to ensure accurate analytics.


Over the past few years, BI vendors have been focusing on mobility as a way to get analytics out to a broader variety of users. MicroStrategy goes one step further with their app development capabilities and a focus on delivery of mobile applications. Consequently, their ease of consumption 0n mobile devices is obvious based on the fact that many customers are merging mobile apps related to customer experience with analytics insights.


Platforms in the cloud are becoming more prevalent as organizations no longer want to have to provision and manage hardware within their organizations. In addition, cloud based solutions can support infrastructure, software, and business application requirements, and in many cases do so in a way that provides quicker time to value. Since MicroStrategy first announced their cloud offerings two years ago, much of their efforts have been spent building out and solidifying their cloud platform. Moving forward, it seems like their preferential deployment model for customers. 


Security really falls outside the world of analytics and Usher is a separate product. However, it addresses the challenges that exist in a world that is rife with security breaches and identity theft. Securing the enterprise and making sure that people are who they say they are becomes more important as mobile devices store so much sensitive data. 

General Thoughts 

This time MicroStrategy World was very different than it has been in the past. As mentioned, there is an increased openness regarding communication and sharing product roadmap information. But it’s more than that. There was an admission that in order to compete strongly moving forward, their products need to become more consumable and easier to develop and to maintain. This goes beyond mobile and cloud based platforms, and towards capabilities that enable self-service, broader data blending, and managing. This was obvious with their new packaging and transparent pricing, but also in their Futures session discussing the goals of redesigning product capabilities to enable easier access for both developers and business users. 

It will be interesting to see how the new pricing and product strategy will work and whether MicroStrategy will begin to be more competitive with the likes of Qlik and Tableau.


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Understanding Audiences For Self-Service Consumption Many solutions claim to be self-service and tout the value of their offerings by saying that ease of use and high levels of interactivity exist. The reality of these solutions, however, is that there is no industry standard for self-service delivery. There are only concepts of design that are used to enable independent analytics adoption. What this means on a practical level is that the term self-service can be misleading if used without an understanding of the target audience. For instance, some self-service offerings are built with the data scientist in mind, while others are designed to enable broader analytics deployment across the organization. Although this might not seem like a big difference, both expectations and expertise differ within these groups, making their use of business analytics different.

Data analyst/scientist

Technical users expect autonomy. The ability to add data sources, create joins, and add business rules in a flexible way support the role of data scientist. The delivery of standardized dashboards or pre-defined table views only give a limited view of what analysts need to gain relevant and valuable insights. Essentially, self-service for these users is truly that – the ability to leverage data in a way that doesn’t require IT input or management to develop new business insights. 

In many cases, these users support management and provide analytics that aid in decision making and daily operations. Because of this, self-service capabilities need to be flexible enough to address daily challenges that may not have been accounted for in design. Since each use case cannot be defined in advance, it might not be possible to identify how information will be needed or why. After all, static reports or views will provide limited insight that will most likely require broader analytics to understand in depth. Since data scientists understand the underlying information structure and business needs, this level of self-service is understandable.  

Business user

Business users, on the other hand, require a more guided experience. This means that self-service refers to user experience. Ease of use, guided design, governed data access to ensure accurate analytics, and pre-defined views are all aspects of this level of self-service. Too much flexibility can actually lead to invalid analytics based on incorrect inferences and joins that may be based on similar field names but that aren’t connected. Therefore, part of self-service involves providing pre-defined access while still maintaining flexibility to slice and dice and one-click analytics to give users quick results.

Organization specific

Although these two audiences are the most common, other consumers also exist. Organizations may have targeted self-service audiences that require specific capabilities or levels of interactivity. These unique requirements should be considered when audiences fall outside of the general types discussed above. Having successful self-service requires solutions that provide the level of self-service the user needs. If this isn’t provided accurately, then self-service interactivity may not be successful.

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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#SMB4Cloud Spreecast Event: The future of cloud and SMBs Last week I took part in a Spreecast event, hosted by IBM – Cloud: Reshaping The World of Business where the increasing importance and adoption of cloud computing for SMBs was discussed. John Mason, from IBM, stated that cloud computing has become a game changer for SMBs as it takes away any barriers to entry, such as the need for an IT infrastructure or venture capital due to set up costs. Laurie McCabe, from the SMB-Group, expanded on this by discussing that in the last 3 years cloud has become more important to SMBs as it has allowed them to skip on premise implementations and supports their go to market strategy more easily. McCabe mentioned three aspects of cloud adoption that are beneficial to SMBs:

  1. Cost of solutions 
  2. Ease of use
  3. Masking of complexities while providing powerful solutions

All of these aspects also reflect the increasing importance of cloud adoption within the analytics market. Organizations continually struggle with the best way to get information into the hands of decision makers, empower customers, and increase relationships with partners and suppliers. Because many SMBs do not always have the resources to build solutions like this internally, many turn to the cloud. In most cases, SMBs are already familiar with cloud offerings because they have data hosted by outside providers and are comfortable with the concept of the cloud due to a lack of internal IT infrastructure.

As the cloud becomes a more central component to SMB adoption, analytics adoption will also become more prevalent in the cloud. This is already happening as many solution providers offer cloud BI. If they don’t already, it is in their roadmap. In addition to this, app stores are becoming more prevalent due to the fact that the way people are adopting solutions are transitioning to a more self-service methodology. The Spreecast announced IBM’s new app store and many other vendors offer analytics style app stores to make adoption easier. Instead of requiring approval through an acquisitions department, a credit card can be used. All of these market transitions will eventually make it easier for SMBs to apply broader analytics applications, without the roadblocks associated with traditional deployments.

Even for organizations choosing to keep solutions in-house, the increasing prevalence of self-service applications and ease of use and deployment associated with the cloud will translate into expectations that extend to the way solutions are deployed in-house. Consequently, solutions will have to shift to meet the needs of SMBs more readily through increasing ease of use, lower costs, and ease of deployment.

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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Achieving Time to Value Looking at organization need

One of the most common concerns I hear from organizations is the ability to implement solutions quickly and gain value that can be sold as success to management. Many businesses evaluate how long a solution will take to implement along with the amount of time it takes to have it deployed and seen as a success by business users. For SMBs looking at BI offerings specifically, there has always been the challenge of weighing the benefits against the costs of deployment. Traditional solutions were seen to take many months to implement, and the level of value associated with implementations were not always easy to measure quantitatively. Luckily, the market has matured to the point that a variety of solutions exist which can be deployed in a timely fashion. But quick implementation times are only the beginning of defining value. Having any new software project up and running does not mean that its use is associated with or providing value to the people using the system. In order to truly be successful, organizations need both. 

Time to implement vs. time to value

Sometimes businesses confuse the terms time to implement and time to value. In most cases, IT will look at implementation as their main goal. Their responsibility being to develop and deliver solutions that are used by business users. Business users, on the other hand, want to take what is provided and make better decisions, address business problems, and maintain competitive edge – and do so right away. Both are important for BI and software project success.

Achieving both means making sure that expectations from both IT and business units can be met. It also means that organizations understand what those expectations should be. The IT development effort should be driven by business need. For instance, creating customer facing dashboards and analytics to let customers gain access to their data and plan better will be different than developing self-service data discovery dashboards to increase efficiencies in marketing spend.

SMB specifics

Small and mid-sized businesses are actually driven by time to value more than their enterprise counterparts. The reality for these businesses is that they have more to lose. Resource constraints and smaller budgets mean that SMBs have to get their project choices right and make sure that they can achieve value quickly. Otherwise they risk losing money and not being able to get back on track without major trade-offs. In many cases, I’ve seen expectations about both implementation times and achieving value not met because of IT resources working on multiple projects and not being able to plan accurately. At the same time, business users lack the understanding of the development efforts required and expect that their BI project will be looked at as the main priority. 

In order to avoid these risks, project stakeholders need to work as a team, define their goals, develop realistic expectations, and make sure that the value proposition of the solution(s) can be realized based on these aspects.

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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Intelligence, Integration, and Integrity – my takeaways from Information Builders Summit 2014 This was my fourth year attending the Information Builders Summit and I generally come away feeling positive about the organization, its products and general direction. Normally new releases are announced and centre on some additional capabilities to the product suite. This year, Information Builders went a little bit further by showing a greater commitment to data discovery, broader integration with big data platforms, ESRI (location intelligence and mapping), and connectors to a wider variety of information management platforms. All of these announcements address three key market challenges:

  1. “Big data”: How to manage data complexities and develop platforms that are flexible enough to meet  both market demands and an organization’s needs. With iWay, Information Builders provides connectors for all types of platform requirements. Organizations have the flexibility to select the right data warehouse, or develop the right data integration processes to support broader analytics. SAP Hana, Teradata, Cloudera, MapR, and NoSQL were some of the new adapters announced to provide that added flexibility and the opportunity for organizations to adopt technologies that support their business challenges.
  2. Data management integrity: Leveraging iWay provides Information Builders customers with the ability to manage data and ensure quality, validity, and reliability throughout the data lifecycle. This expands beyond selecting the right adapter and making sure that data can be viewed properly. It also involves the processes required to make sure that users trust the information they are accessing it and are able to get value from their data.
  3. Data discovery and exploration: Information Builders is starting to focus more on this area to provide broader interactivity for their customers, with new solutions coming to market this year. Data discovery, coupled with information integrity, is the future of BI interactivity and access. Although a little late to the game, because customers can leverage iWay capabilities as well, they may actually have an advantage in the long-term by ensuring that what is analyzed is valid. The ability to explore data and provide self-service BI access is only effective when the data can be guaranteed to be reliable.

All of these themes provides the basis for a strong analytics framework. Leveraging data, sustaining integrity, and providing access to analytics to all those who need it are the goals of this level of integration. In addition to those general themes, I also spent time speaking with current customers to get their insights. These were the most sited reasons for selecting Information Builders:

  • Privately held company
  • iWay enables organizations to manage their data more thoroughly without having to look for additional products through a “one-suite” approach (as opposed to looking at Informatica or other data integration products to move and manage information)
  • A continued feeling of partnership with Information Builders, even long after implementation
  • Easy to install upgrades

All customers were satisfied with their solutions. Some areas to take note of are:

  • Professional services (PS) expertise with version 8.0 as it is a new product and the PS staff is still getting up to speed. If using this approach, it is important to make sure that adequate support is available with the PS team onsite
  • What solutions  to take advantage of that are not on the market yet, and what (if any) the implications of adding or switching products will be
  • The data warehouse platform to select – guidance may be required to make sure that an organization chooses the right offerings to complement their business and not simply because of brand recognition, etc.

Although I came away from Summit feeling positive about the product and customer value associated with adoption, this year it seems as if Information Builders is starting to expand their market offerings to provide additional value by integrating broader trends in adoption within their products.

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Common BI Newbie Mistakes Business Intelligence has been a top priority for many CIOs and other project sponsors for years and most organizations perform some level of analytics on a daily, if not intra-daily basis. Many organizations, however, are looking for more up to date technologies or ways of bringing their BI use to the next level. This blog tends to address a lot of how to’s and steps for success. What it rarely talks about are some of the mistakes I’ve seen organizations make that end up turning the promise of BI into a failed project. So here are 5 things that I see regularly that cause organizations’ BI projects to fail:

  1. Lack of scope: Sometimes organizations know they want to implement BI but it is more of a directive based on an article read as opposed to a direct business need. When this happens, organizations can either identify the challenges within their business or simply look to implement a tool that will replace current reporting and spreadsheet use. Although, replacing older tools and spreadsheets can be a positive step towards building up organizational efficiencies, a lack of a business-focused scope can lead to project failure. A lack in project scope generally leads to failure because of a lack of adoption, poor development, or the inability to develop metrics tied to business pains.
  2. Trying to do too much too soon: This can be considered the opposite of not having a proper scope. Sometimes organizations try to do everything at once by including everything under the sun in their BI project scope. They want to address all needs in the organization with the same solution and expect that it is possible to do so with a single project plan and implementation. The reality is that a centralized BI approach is possible, however, it needs to be planned for. This means developing an iterative project plan to take into account the different phases of data acquisition, solution development, and rollout using an iterative approach. Additionally, even if using a centralized approach to BI development, rollouts need to be incremental in nature to assume small wins before moving forward. 
  3. Being unwilling to take the time to evaluate internal requirements against the market: Software selection tends to be a challenge for many organizations as the market is flooded with solutions that sound like they meet most business needs. The reality, however, can be different. Depending on the platform used, how information will be delivered, and what type of analytics required, different business challenges may need separate solutions. The reality is that researching the market is time consuming at the best of times and requires an understanding of product roadmaps, potential implementation hurdles, and capabilities, and match that to the business requirements of the organization. Additionally, this has to happen after the business and technical requirements have been gathered, making it a long process. Many want to skip this process, but in the end, taking the necessary steps can make the difference between selecting the right solution and having to conduct a new product search after implementation. 
  4. Bad project planning: BI is a project like any other that requires a strong project plan and management. Although most organizations have some project management in place, some are not prepared for the level of involvement it requires to build a BI solution. In most cases, failure ends up occurring when organizations try to do everything at once by bundling their BI implementations with other IT projects. Whether new Website builds, updating customer applications, or developing a new service, most BI projects need to be managed independently without having to compete with many other projects at the same time. Unless enough internal developers exist to manage all of these projects simultaneously, organizations need to make sure that their BI project can be managed from start to finish, with an understanding that additional support may be required. 

These are the top 4 that I see regularly, but are not an exhaustive list. Let me know what other mistakes you’ve seen in the market – whether from the prospective of organization, consultant, or implementer. 

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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Using Embedded Analytics for Enhanced Customer Experience Organizations are constantly looking for ways to meet the needs of their customers. Better service, customized products, and price guarantees are just some ways that organizations try to ensure customer loyalty. For service and data providers, however, it is not always easy to provide added value beyond the service or data provided. The promise of more data and better visibility help ensure customer satisfaction by giving customers the tools they need to gain added insights. This gap has led to organizations providing analytics as part of their offerings to ensure that customers can access broader insights automatically without having to download data or change formats and apply their own business rules to gain deeper insight. 

Non-profit, education, and government are examples of industries that have posted demographics or other analytical data online for public consumption. When dealing with for-profit companies, most provide analytics in the guise of embedded analytics. What this means is that organizations develop applications that are embedded within their solutions that can be provided as a service to customers. This access to analytics helps provide customers with broader insights into their accounts, customers, trends, reporting needs, etc. In many cases, businesses add these additional reporting or analytics capabilities as an add-on to the services or information already provided.

For organizations considering this added level of analytics access, the first step is to understand end user requirements. What data is currently being looked at, where are their gaps in visibility, what information do customers need to give them that added advantage, and what needs to be done internally to make all of this happen. These questions represent the starting point. Luckily for organizations going this route, there are many solution providers that offer embedded BI as a key aspect of their offerings. Therefore, when organizations look at evaluating software vendors, one of the things they have to do is make sure that the capabilities they require are also provided within an embedded environment. 

Once software is selected, the process of acquisition and integration will mirror a traditional analytics implementation, with one key difference – the opportunity to monetize use. Organizations need to identify whether they will provide this new service at a premium, as pay per use, or develop some other cost model. Overall, organizations want to do more than justify their expenditures; they want to transform this new operating expense into revenue.

The ability to provide analytics to others as part of a new or added value service is one of the reasons more and more organizations are looking towards embedded analytics. Aside from added revenue, though, the reality is that as customer expectations grow and analytics become the norm, more and more businesses will start to take advantage of embedded analytics to be able to provide their customers with greater information visibility. Currently providing a level of competitive edge will no longer be the case when customers start to expect this level of insight into their data.

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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Understanding the BI Landscape The business intelligence market is diverse. Many organizations try to decipher which solution will work best in their environment but find it challenging because of the number of vendors and similar marketing campaigns. Although there is no easy way to simplify the BI landscape, decision makers can break out potential solutions based on their needs to identify which offerings are on target. It should be noted that some vendors fall into more than one category, while others are niche vendors that do one thing well. In the latter case, an organization may have to develop a piecemeal approach to make sure that all business and technical needs are addressed. 

The following are overviews of product categories based on business challenges being faced within organizations. Technical infrastructure and data management are also taken into account because any successful BI offering also requires the management of data assets:

  1. Reporting – organizations need reports and many still use standardized reporting solutions. Some BI vendors have reporting modules or specialize in reporting, while others are focused specifically on visualizations. 
  2. Dashboards – represent a big trend as they provide data representations and metrics in a visually interactive way. Many organizations like dashboards due to their highly interactive nature. Vendors offer varying levels of analytics within their dashboards. Some only visualize the data meaning that it needs to be stored with associated business rules separately, while others also offer analytical capabilities.
  3. Operational intelligence – some organizations require near real-time access to information. In these cases, operational intelligence becomes important so that relevant metrics can be updated on a regular basis with the flexibility to reflect the needs of the business. This means that different use cases will have different latency requirements.
  4. Advanced analytics – this is a broader category and could be divided further but for the purposes of this overview, it can remain a single category. A variety of analytics exist. Some organizations have complexities that require the management of many business rules and statistical algorithms while others want predictive and what if? capabilities. Certain solution providers provide targeted analytics while others are considered generalists – meaning they offer general capabilities but not many niche analytics.
  5. Data Warehousing – although some businesses are moving away from traditional data warehousing, the reality is that companies require a way to consolidate, centralize, and manage their analytical infrastructure. In some cases, a data warehouse is the most effective way. Even if it’s not, every organization evaluating BI should look at their data to identify how it needs to be managed.
  6. Data integration – helps transition data from one source to another. Within BI applications, it also serves as a transformation engine, can help provide quality control, and work towards providing a cohesive view of information assets both internal and external to the organization.

Some or all of these areas may apply when looking at advancing a BI initiative. The reality is that selecting the right solution means first getting the category right – selecting a reporting solution when advanced analytics are required will only lead to failure. Organizations need to be prepared to identify what their business and technical requirements are and how the market can best serve them – otherwise the risks for failure are too high.

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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Looking at the Cloud – SMB adoption is not new Mid-market organizations have always been familiar with service-based (hosted) offerings. Whether Salesforce, or ERP, Accounting, etc. types of applications, these organizations are used to hosted applications that store data off-site. What this means is that the new push to the cloud, isn’t necessarily so new for all organizations. Many are starting to take advantage of what the cloud has to offer, while other businesses are simply continuing or expanding their cloud deployments. The two things that have changed are the number of solutions available and the understanding of those offerings.

More awareness about the cloud

Although there is more awareness about cloud offerings, not all organizations want to have their data hosted or outside their firewall. In addition, some organizations misinterpret the concept of cloud-based offerings by not understanding the difference between where the data resides and whether it is just hosted or if services are attached. For instance, I have been told by more than one organization that they do not use any cloud offerings, only to find out through more analysis that a number of applications are cloud-based but not hosted as a service – in essence creating a differentiation between the level of control the customer has over the data they use.

With SMBs specifically, this makes a big difference. Many organizations want solutions with lower overall TCO and quicker times to implement, but still want to control their access to data and the overall use of the systems they deploy. Consequently, for these businesses, the cloud represents two levels of use:

  1. the hosting of and access to data off-site within a cloud environment
  2. the ability to manage that data or the applications the way the company desires, or alternatively, to be provided access to the data as a service through a hosted offering

Basically, a lot of the hype around cloud adoption and expanded offerings, simply means that SMBs have more choice when identifying the best way to leverage technology and find value through their data assets.


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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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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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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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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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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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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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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