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 Tue, 23 Dec 2014 15:56:38 -0700 http://www.movabletype.org/?v=4.261 http://blogs.law.harvard.edu/tech/rss Market Diversity Creating Better Access for Midmarket Organizations As I start some preparatory work for 2015 initiatives, I have noticed that many analytics vendors are starting to shift their views of SMBs and the needs of midmarket companies in general. When solution providers first started developing their market strategy and developing analytics related solutions for SMBs, their products took similar shape. They were less expensive, less robust, with a smaller number of relevant capabilities. The general premise was that because an organization was smaller and didn’t have the same budget and IT resources as their enterprise counterparts, their business challenges were somehow “less” as well. The reality, however, has always been something slightly different. Although midmarket organizations may not have the same resources as their enterprise counterparts, the reality is that business challenges are the same for organizations irrespective of size.

Within the past six months, I have seen this reflected in many vendor go-to-market strategies, with flexibility existing to account for varying budgetary allocations, but products reflecting the same robust capabilities irrespective of size. The reality is that with cloud, mobile, and self-service paving the way to broader access to information, and storage costs lowering, analytics access is becoming more of a reality irrespective of company size.

The implications for midsized organizations looking at augmenting their analytics implementations or evaluating BI offerings are great. There is no longer the need to settle for a solution with a subset of capabilities or be limited by the number of data sources, number of users, etc. And in many cases, vendors are able to understand the broader needs of SMBs by developing offerings that can meet those needs without taking away functionality that was previously relegated to enterprise applications. Obviously, I am simplifying all of the factors that are contributing to this shift in the market. However, the benefits to SMBs are the same – organizations can now take advantage of offerings that may have been out of their range before. With cloud offerings and data storage in the cloud becoming more commonplace, midsized businesses can also develop more robust data infrastructures without having to worry about hardware and on-site implementation costs. All of these factors create an environment where solutions are finally able to adequately meet the needs of small and midsized businesses looking to leverage data for analytical insight and to meet their strategic goals.

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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http://www.b-eye-network.com/blogs/wise/archives/2014/12/market_diversity_creating_better_access_for_midmar.php http://www.b-eye-network.com/blogs/wise/archives/2014/12/market_diversity_creating_better_access_for_midmar.php Tue, 23 Dec 2014 15:56:38 -0700
The rise of embedded Business Intelligence (BI) As BI use matures within the organization, the way in which it is being applied is also changing. Organizations are looking for more strategic ways to deliver analytical insight and make sure that the right people have direct access to the information they need. Part of this includes the increasing adoption of embedding analytics within operations. At the same time, organizations are taking advantage of embedded BI to provide customer facing analytics and added services by leveraging the data they collect. Consequently, the way in which data is delivered is evolving. Broader industry trends, such as self-service and data discovery, support this shift as well because the technology available is easily embeddable and analytics are easier to interact with.

These factors, as well as others, are slowly transforming the way BI is being adopted within organizations. Analytics no longer needs to be a separate application but can be accessed within operational systems to help business users gain the visibility they require. Businesses want to have a cohesive view of information. The ability to embed analytics within an application gives them a new way of deploying analytics without limiting who can access data. Better access to data in general, also helps sell its value overall, providing organizations with the basis to justify budgetary allocations to manage their data more effectively.

The reality is that successfully adopting embedded BI within operations requires this added level of data management to ensure that information being accessed is valid and reliable. Otherwise, organizations are stuck with the same information related challenges that exist through the use of spreadsheets.

The bottom line for organizations is that the added focus of software providers on better BI accessibility and increasing governed data access provides a more holistic framework to data access. Being able to embed analytics within daily operations or as part of a customer facing application while taking advantage of the broader capabilities BI vendors now have to offer, also supports better competitive advantage by leveraging integrated technologies. Therefore, when organizations evaluate the benefits of embedding BI within their organizations it is also important to identify how broader BI and data access can benefit end users by making information easier to access and more reliable.

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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http://www.b-eye-network.com/blogs/wise/archives/2014/12/the_rise_of_embedded_business_intelligence_bi.php http://www.b-eye-network.com/blogs/wise/archives/2014/12/the_rise_of_embedded_business_intelligence_bi.php Sun, 21 Dec 2014 01:14:24 -0700
The rise of embedded BI As BI use matures within the organization, the way in which it is being applied is also changing. Organizations are looking for more strategic ways to deliver analytical insight and make sure that the right people have direct access to the information they need. Part of this includes the increasing adoption of embedding analytics within operations. At the same time, organizations are taking advantage of embedded BI to provide customer facing analytics and added services by leveraging the data they collect. Consequently, the way in which data is delivered is evolving. Broader industry trends, such as self-service and data discovery, support this shift as well because the technology available is easily embeddable and analytics are easier to interact with.

These factors, as well as others, are slowly transforming the way BI is being adopted within organizations. Analytics no longer needs to be a separate application but can be accessed within operational systems to help business users gain the visibility they require. Businesses want to have a cohesive view of information. The ability to embed analytics within an application gives them a new way of deploying analytics without limiting who can access data. Better access to data in general, also helps sell its value overall, providing organizations with the basis to justify budgetary allocations to manage their data more effectively.

The reality is that successfully adopting embedded BI within operations requires this added level of data management to ensure that information being accessed is valid and reliable. Otherwise, organizations are stuck with the same information related challenges that exist through the use of spreadsheets.

The bottom line for organizations is that the added focus of software providers on better BI accessibility and increasing governed data access provides a more holistic framework to data access. Being able to embed analytics within daily operations or as part of a customer facing application while taking advantage of the broader capabilities BI vendors now have to offer, also supports better competitive advantage by leveraging integrated technologies. Therefore, when organizations evaluate the benefits of embedding BI within their organizations it is also important to identify how broader BI and data access can benefit end users by making information easier to access and more reliable.

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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http://www.b-eye-network.com/blogs/wise/archives/2014/12/the_rise_of_embedded_bi.php http://www.b-eye-network.com/blogs/wise/archives/2014/12/the_rise_of_embedded_bi.php Sun, 21 Dec 2014 01:14:24 -0700
Driving Technology Projects The Right Way Sometimes people get stuck in the weeds when evaluating technology projects by focusing on key features and product capabilities and not on solving business challenges. Although gathering both business and technical requirements are essential to any successful technology project, projects should start from a business perspective and based on a business pain being experienced. A common one might be lack of visibility into what is happening across the organization. Or not meeting yearly targets but missing some key information to find out why. In some cases, organizations launch initiatives based on business challenges being experienced, while in others, BI or analytics is an initiative on its own. Although there are potential positive outcomes, there are also greater risks of failure due to scope creep or the inability to design solutions that help business people gain insight into what they require for better decision making.

To implement a solution successfully, there needs to be a balance between both – the need to address business challenges on the one hand, and the development of a supportive infrastructure on the other hand. Sometimes, however, there are time crunches or unrealistic time constraints placed on the selection process. The outcome tends to be technical resources looking at which solutions are the best technical fit and then fitting that to how solutions should be developed for end users. This differs from a traditional evaluation whereby organizations take the time to engage their stakeholders and ensure that business needs and daily processes are taken into account. Although time consuming, this phase helps with the software selection process, development of solutions that meet the needs of business users, metrics identification, and potential scalability challenges. In most cases, taking the time for diligence leads to solutions that help organizations solve the business pains being faced.

For SMBs specifically, time and budgetary constraints may cause organizations to take shortcuts by evaluating product capabilities without taking the time to gather business requirements and understand the needs of the various stakeholders within the organization. The reality is that IT related projects should be connected directly to solving business challenges. As an extension of this, organizations need to start by focusing on the needs of business units and understanding how technology can address and help solve business challenges and not the other way around.

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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http://www.b-eye-network.com/blogs/wise/archives/2014/11/driving_technology_projects_the_right_way.php http://www.b-eye-network.com/blogs/wise/archives/2014/11/driving_technology_projects_the_right_way.php Wed, 26 Nov 2014 18:17:46 -0700
Moving beyond common SMB BI implementation knowledge challenges Just over a week ago I attended the Enterprise Data & BI Conference Europe in London. The conference focused on many different BI and data management related topics, many of which support mid-market goals to achieve greater visibility and better analytics. The takeaway most interesting to me was the fact that many SMBs are still struggling with their BI implementations. Many are either starting from scratch or trying to figure out how to expand their traditional BI implementations. Neither of which are easy as both require the ability to translate business requirements into technical needs and apply the right set of tools to meet the needs of business.

The reality is that many organizations don’t even know where to start. This includes a general inability to develop the proper cohesion between business units sponsoring projects and IT required to develop the BI infrastructure. Within the European market, this seems like one of the most common situations among SMBs. One in which the market does not seem to be taking advantage as much as it could be. With the technology available today and the pricing coming down to be able to meet the budget restrictions of smaller organizations, midsize organizations require the guidance to understand the technology and key differentiations in the market. More than once, I have been approached to help organizations with their shortlist requirements to be asked about vendor offerings that should not be considered during the same evaluation due to the fact they were developed to address completely different business challenges. The issue remains that many organizations are still uncertain what key differentiations exist among products.

Part of implementing the best fit choice requires this level of knowledge. Basically, organizations need to take a step back and look deeper than what they see via online marketing and develop an understanding of how specific offerings can meet their needs and help them address their business challenges. The ability to combine this level of understanding with what business users require is what can help organizations develop BI applications that are easy to interact with and flexible to take into account future scalability requirements.

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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http://www.b-eye-network.com/blogs/wise/archives/2014/11/moving_beyond_common_smb_bi_implementation_knowled.php http://www.b-eye-network.com/blogs/wise/archives/2014/11/moving_beyond_common_smb_bi_implementation_knowled.php Mon, 17 Nov 2014 16:24:11 -0700
Understanding Business Requirements Are Essential For Analytics Projects Analytics projects and the associated data preparation, storage, and management require continual effort. Sometimes mid-market organizations have a good sense of what they want to achieve and how they need to get there, but overlook the value of gathering in-depth business and technical requirements before diving into software and hardware selection. Many companies I have worked with base their choices on which solution provider is making the most noise in the market, their previous experience, or what their friends are doing within their respective organizations. Although a potential good first step, some of these organizations forego any additional evaluation to identify what would fit best based on the organization’s needs. Not evaluating requirements increases risk. Organizations may end up selecting a platform that can’t meet SLAs, doesn’t scale, or simply doesn’t offer essential product capabilities. This in turn can lead to longer implementation times, greater expenditures, lack of adoption, and the inability to meet changing requirements over time, just to name a few.

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

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

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

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

 

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http://www.b-eye-network.com/blogs/wise/archives/2014/10/understanding_business_requirements_are_essential.php http://www.b-eye-network.com/blogs/wise/archives/2014/10/understanding_business_requirements_are_essential.php Tue, 28 Oct 2014 13:39:12 -0700
Increasing Importance of Governed Data Access More solution providers are starting to integrate the concept of governed data discovery into their product offerings. After years of trying to adopt data governance initiatives as part of a larger data management framework within organizations, software vendors are integrating similar capabilities into their solutions. The reality, however, isn’t as simple. Organizations need to understand what governance within the framework of data discovery or business intelligence means to make informed decisions in relation to software selection and solution design. Sometimes organizations think that governed data access is a blanketed statement that will apply to all of their analytics use. The reality, however, can be much different.

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

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

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

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

     

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    http://www.b-eye-network.com/blogs/wise/archives/2014/10/increasing_importance_of_governed_data_access.php http://www.b-eye-network.com/blogs/wise/archives/2014/10/increasing_importance_of_governed_data_access.php Tue, 14 Oct 2014 20:02:34 -0700
    Governed data discovery – looking at the importance of managing data validity The role of governed data discovery is becoming increasingly important as organizations manage more complex and diverse data that they want to gain insights from. Self-service BI access and broader data discovery capabilities means that BI is deployed to more users who leverage data in the way that best suits them and not according to pre-defined analytics. Being able to trust this data is essential as it is one of the main ways to guarantee information validity and correct results. Unfortunately, I have worked with several organizations using BI that continue to develop their own analytics, yet admit to knowing about inaccuracies in their data. In these cases, establishing the value of analytics becomes difficult because without trust, it becomes impossible to validate analytical outcomes.

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

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

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

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    http://www.b-eye-network.com/blogs/wise/archives/2014/10/governed_data_discovery_looking_at_the_importance.php http://www.b-eye-network.com/blogs/wise/archives/2014/10/governed_data_discovery_looking_at_the_importance.php Wed, 01 Oct 2014 16:10:00 -0700
    The importance of BI specific skill sets BI implementations are becoming more commonplace as organizations realize they cannot overlook the management of and access to their data in order to facilitate better decision-making. What this means for many is the re-evaluation of resources, skill sets, and project planning to ensure that the proper resources exist to support BI and analytics development. Unfortunately, many businesses overlook the fact that IT development expertise does not equal BI savvy. There are SMBs without the IT resources to facilitate a project but there are also companies with an IT department and developers on-hand, but without BI development experience or skills. The reason why this is an important consideration is because in order for solutions to be effective, they need to be designed right – and right includes understanding data, analytics, and design in a way that promotes BI best practices. In general, two types of organizations exist, and identifying and leveraging the right skill sets are equally important for both.

    Organizations that don’t care

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

    Organizations willing to invest

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

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

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

     

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    http://www.b-eye-network.com/blogs/wise/archives/2014/09/the_importance_of_bi_specific_skill_sets.php http://www.b-eye-network.com/blogs/wise/archives/2014/09/the_importance_of_bi_specific_skill_sets.php Wed, 24 Sep 2014 15:16:12 -0700
    Why are organizations still struggling with their data? This is a question I’ve been asking myself for a while. The data infrastructure exists to support Big Data, operational data streams, data quality practices, and the list goes on. Best practices exist for organizations to follow to achieve a strong information management framework and tie data to business processes enabling decision makers the ability to take actions on the insights they’ve gleaned. A variety of solutions exist in the market place providing BI access to any type of user and that are geared towards a strong IT infrastructure or small business with little to no internal IT support. Additionally, organizations understand the value their data brings to the table. Yet, many companies still struggle with silos of data, lack of visibility, the inability to consolidate information assets and develop the essential correlations between the data they need to drive strategic business value.

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

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

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

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

     

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    http://www.b-eye-network.com/blogs/wise/archives/2014/09/why_are_organizations_still_struggling_with_their.php http://www.b-eye-network.com/blogs/wise/archives/2014/09/why_are_organizations_still_struggling_with_their.php Fri, 19 Sep 2014 13:32:56 -0700
    Leveraging information assets and the need for collaborative decision making The struggle between business units and IT seems to be a challenge that will never fully be solved within some organizations. In a recent study by IBM Global Analytics, 79% of respondents stated that cohesion does exist between business and IT within the organization, while 50% said that most decisions were left to IT or individual departments. These responses highlight a disconnect between a collaborative approach to working and actual decision making. Additionally, 29% (click here to access the InfoGraphic referenced) admit to a disconnect between business acumen and analytics know-how, meaning that gaps definitely exist with the current approach towards analytics. All of this makes sense because, until more recently, analytics and BI implementations were driven and managed primarily by IT resources. As business units start to take more responsibility for their data assets, departmental struggles are more likely to occur. Additionally, employees who were solely focused on the business side of things have to become more involved in data. Understanding analytics and being able to interact with data to get answers to business questions is becoming more essential – not only for organizations on a whole to become successful, but for individuals as well.

    As information becomes more important and businesses collect more diverse data, the way decisions are made will also shift. Silos are no longer acceptable. IT acting on its own can only act from its perspective. The same exists for individual departments. Even decisions that seem to be limited to one department require broader analysis. For instance, increasing sales requires knowing more than previous sales. To be successful, the organization needs insights into customers, competitors, products, supply chain, weather patterns, etc. Much of this information may be made available to one department, but people with a variety of experiences are required to make decisions that will effect the broader sales channel. 

    What all of this means for mid-sized businesses is that companies seem to be making headway towards the right direction:

    1. Cohesion between IT and business does exist within many SMBs.
    2. Half of identified businesses seem to have organization wide decision making strategy.
    3. 70% of organizations have resources that can link business and data related insights to leverage analytics successfully.

    As data becomes more widely available to more employees within more departments, organizations will begin to share information with each other and increase collaborative efforts. After all, working in silos or relying on IT will only limit the benefits that can be realized by analytics. Big data is just an extension of the fact that organizations need information from a variety of sources in order to gain the visibility required to remain successful. The reality, however, is that data only represents half of what is required to maintain success. People and departments collaborating more broadly and sharing data across the organization are also required for data to remain valuable.

    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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    http://www.b-eye-network.com/blogs/wise/archives/2014/08/leveraging_information_assets_and_the_need_for_col.php http://www.b-eye-network.com/blogs/wise/archives/2014/08/leveraging_information_assets_and_the_need_for_col.php Mon, 25 Aug 2014 17:12:58 -0700
    With all of the data available – why do companies still fall short? It is slowly becoming easier to tie in business analytics with real world value to enhance customer experiences. Technologies support diverse data collection, storage, management, analytics, and delivery. Organizations can identify trends, customer preferences, and customer satisfaction levels by looking at a variety of factors and information sources. Algorithms can be defined to take into account a variety of scenarios and identify outliers. Advanced analytics, big data storage, and the like seem to be the answer to many company challenges that exist in a competitive marketplace where customers can choose similar products and services from multiple providers. If this really is the case, then why does it seem as if many businesses are missing the boat?

    Case in point: Although I normally don’t use personal examples, this one jumped out at me. I am in the middle of a move and after an unhappy journey with my telecommunications provider I have decided to use a different provider. About a week after giving notice, I received a lovely letter in the mail inviting me to call their office to see if anything could be done to win me back. Although a very nice gesture, the reality of the situation is that had they evaluated me as a customer over the past several months, they would have realized that I was lost to them before formally cancelling. Therefore, the after the fact letter was just a waste of paper and stamp. If this service provider really cared about keeping me as a customer, they would work towards identifying dissatisfied customers, the reasons they are not happy, and which ones have a potential high customer lifetime value to know which ones to invest in trying to retain. If I fall into that category, then sending a letter after I’ve already cancelled is too little too late. 

    This situation reminded me of a case study presentation at a conference by a leading North American Bank that was worried about competing with other large financial institutions and their struggle with customer satisfaction ratings. A trend was occurring whereby long term customers with good standing would want to deposit a check and withdraw money automatically without having the bank hold the check. What ended up happening is that there was a higher than average turnover rate with these customers who were looking to other banks to provide them with immediate transactions. In order to maintain high customer ratings, the bank began to analyze what was happening. The results of the analysis were that the customers with large investments in the bank were the ones who decided to leave because they weren’t being serviced the way they wanted, as opposed to a number of customers who complained a lot but didn’t leave. It was found that the customers who stayed didn’t have as much invested with the bank. Consequently, the bank decided to take the risk and provide the long standing, high investing customers with immediate access to their funds because they were low risk. The result was higher satisfaction and less turnover. The lower investing customers would be addressed based on their individual situations, but the main effort was placed on the customers the bank felt would do more business with the bank over time.

    These examples are simple and yet highlight the value of information insights. Organizations may struggle with their BI investments, but the reality is that BI can no longer be a separate tool set within the wider organization. Businesses need to understand the fact that they risk losing business and missing opportunities without the insights they require to identify trends and opportunities. Broader access to information is key, and now the market is flooded with solutions that can address the variety of needs that exist. All of which can help align organization goals with higher customer satisfaction.

    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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    http://www.b-eye-network.com/blogs/wise/archives/2014/08/with_all_of_the_data_available_why_do_companies_st.php http://www.b-eye-network.com/blogs/wise/archives/2014/08/with_all_of_the_data_available_why_do_companies_st.php Tue, 12 Aug 2014 16:47:42 -0700
    Understanding User Expectations Self-service access to analytics is becoming a key component when trying to expand BI and analytics access throughout the organization. For SMBs, this is especially important. Organizations need access to information that is relevant to business pains being experienced and to plan for the future. Analytics can also be used to identify trends and whether the right steps are being taken to move to the next level. The reality however, is that data and the use of analytics are only as good as what’s done with them. Information is required on a daily basis within most, if not all, job functions. But simply having BI doesn’t lead to business value. Organizations need to implement solutions and integrate them within business processes so that they can be acted upon. From the perspective of users, this means meeting end user expectations and delivering solutions that are easy to access, interact with, and reliable. A good starting point is to look at the following:

    Data access

    Most business users do not have the expertise to join tables, identify the fields they need, apply algorithms, and defined business rules accurately without guidance. In addition, many users struggle with the fact that they are interacting with information they don’t fully trust. Therefore this needs to be done for them by developing a front end whereby the data layer is taken out of the equation. At the same time, the information being acted with needs to be governed in some fashion to ensure its accuracy and validity over time. Anything less means that information over time cannot fully be trusted. In cases where two levels of users exist and there are people within the organization who understand the data layer, there needs to be more flexibility in the way in which data is interacted with.

    Data discovery and interactivity

    Once data is prepared, business users need to be able to explore the data the way they see fit. Interactivity needs to be valid, in the sense that users need to be able to ensure that their data is joined properly (in the way that makes sense for their business questions, etc.). The challenge with this is granting users enough access allowing them to explore data without having to determine a predefined set of pathways, while making sure that they are unable to develop analytics based on wrong assumptions. Organizations, therefore, need to balance these two aspects to make sure that solutions are designed with a high level of flexibility to sort through data, but not too much that allows people to make the wrong joins or develop conclusions based on inaccurate assumptions.

    Self-service and ease of use

    Self-service takes this one step further by making sure that the tools used to support decision making and analytics are easy to use and match the level of expertise of the user. Within organizations this might mean having more than one type of access to ensure that decision makers can access data in the way that best meets their needs. One of the challenges of “ease of use” is that solutions are generally developed by IT developers. What this means is that not all user friendly, self-service solutions are actually self-service for everyone. To really achieve self-service it becomes important to make sure that implemented solutions are intuitive to those interacting with them.

    Although BI users have many expectations, these three areas provide the basic requirements when developing interactive BI and analytics access points that support broader decision making across the organization.  

    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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    http://www.b-eye-network.com/blogs/wise/archives/2014/07/understanding_user_expectations.php http://www.b-eye-network.com/blogs/wise/archives/2014/07/understanding_user_expectations.php Tue, 29 Jul 2014 17:57:37 -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. 

    Analytics

    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.

    Mobile

    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.

    Cloud

    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

    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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    http://www.b-eye-network.com/blogs/wise/archives/2014/07/takeaways_from_microstrategy_world.php http://www.b-eye-network.com/blogs/wise/archives/2014/07/takeaways_from_microstrategy_world.php Fri, 18 Jul 2014 20:02:09 -0700
    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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    http://www.b-eye-network.com/blogs/wise/archives/2014/07/understanding_audiences_for_self-service_consumpti.php http://www.b-eye-network.com/blogs/wise/archives/2014/07/understanding_audiences_for_self-service_consumpti.php Mon, 14 Jul 2014 15:41:49 -0700