BI In The Cloud: Separating Facts From Fiction

Boris Evelson

“… and they lived happily ever after.” This is the typical ending of most Hollywood movies, which is why I am not a big fan. I much prefer European or independent movies that leave it up to the viewer to draw their own conclusions. It’s just so much more realistic. Keep this in mind, please, as you read this blog, because its only purpose is to present my point of view on what’s happening in the cloud BI market, not to predict where it’s going. I’ll leave that up to your comments — just like your own thoughts and feelings after a good, thoughtful European or indie movie.

Market definition

First of all, let’s define the market. Unfortunately, the terms SaaS and cloud are often used synonymously and therefore, alas, incorrectly.

  • SaaS is just a licensing structure. Many vendors (open source, for example) offer SaaS software subscription models, which has nothing to do with cloud-based hosting.
  • Cloud, in my humble opinion, is all about multitenant software hosted on public or private clouds. It’s not about cloud hosting of traditional software innately architected for single tenancy.
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Oracle Leapfrogs BI Competitors By Acquiring Endeca

Boris Evelson

This is a very smart move by Oracle. Until the Siebel and Hyperion acquisitions, Oracle was not a leader in the BI and analytics space. Those acquisitions put them squarely in the top three together with IBM and SAP. However, until this morning, Oracle played mostly in the traditional BI space: reporting, querying, and analytics based on relational databases. But these mainstream relational databases are an awkward fit for BI. You can use them, but it requires lots of tuning and customization and constant optimization — which is difficult, time-consuming, and costly. Unfortunately, row-based RDBMSes like IBM DB2, Microsoft SQL Server, Oracle, and Sybase ASE were originally designed and architected for transaction processing, not reporting and analysis. In order to tune such a RDBMS for BI usage, specifically data warehousing, architects usually:

  • Denormalize data models to optimize reporting and analysis.
  • Build indexes to optimize queries.
  • Build aggregate tables to optimize summary queries.
  • Build OLAP cubes to further optimize analytic queries.
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How Do You Sell BI To The Business Executives?

Boris Evelson

Whoa! Hold your horses. If this is indeed a key challenge that you’ve tried to address in the past without much success, consider switching jobs. This is not a joke. Business intelligence (BI) is an employee market right now; a key challenge for most BI employers is finding, recruiting, and retaining top — or actually any, for that matter — BI talent. Consider that IBM BAO alone added more than 4,000 (!) BI positions in just over a year! Every other major, midsize, and boutique BI consultancy I talk to is struggling to find BI resources. So if you’ve been fighting this uphill Sisyphean battle for a while, consider new channels for your noble efforts.

Now, some more practical advice — albeit not as exciting. Start from the top down. In a few minutes I am getting ready to talk to yet another large client whose CEO does not “get” BI. Can you rightfully blame him/her? Yes and no. Yes, because how can you manage any business without measurement and insight into your internal and external processes? So if your CEO didn’t learn that in his/her MBA 101, suggest that he/she look for another job. And if you’re still standing after that and have suffered only a mild concussion, consider that many BI projects have been less than successful, and ROI on BI — one of the most expensive enterprise apps — is extremely difficult to show. So can you really blame your CEO?

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Question On BI Total Cost Of Ownership

Boris Evelson

I need your help. I am conducting research into business intelligence (BI) software prices: averages, differences between license and subscription deals, differences between small and large vendor offerings, etc. In order to help our clients look beyond just the software pricese and consider the fully loaded total cost of ownership, I also want to throw in service and hardware costs (I already have data on annual maintenance and initial training costs). I’ve been in this market long enough to understand that the only correct answer is “It depends” — on the levels of data complexity, data cleanliness, use cases, and many other factors. But, if I could pin you down to a ballpark formula for budgeting and estimation purposes, what would that be? Here are my initial thoughts — based on experience, other relevant research, etc.

  • Initial hardware as a percentage of software cost = 33% to 50%
  • Ongoing hardware maintenance = 20% of the initial hardware cost
  • Initial design, build, implementation of services. Our rule of thumb has always been 300% to 700%, but that obviously varies by deal sizes. So here’s what I came up with:
    • Less than $100,000 in software = 100% in services
    • $100,000 to $500,000 in software = 300% in services
    • $500,000 to $2 million in software = 200% in services
    • $2 million to $10 million in software = 50% in services
    • More than $10 million in software = 25% in services
  • Then 20% of the initial software cost for ongoing maintenance, enhancements, and support

Thoughts? Again, I am  not looking for “it depends” answers, but rather for some numbers and ranges based on your experience.

Our Take: What The Verint-Vovici Merger And QuestBack-Globalpark Merger Mean For The Market Insights Professional

Roxana Strohmenger

The past three weeks have been quite busy within the enterprise feedback management (EFM) vendor landscape, with two major acquisitions. The first occurred on July 19th between Verint and Vovici; the second was announced today between QuestBack and Globalpark. These mergers make sense and are in line with how I see the EFM vendor landscape evolving over the next five years.

One part of the EFM vendor evolution will be the creation of what my colleague Andrew McInnes calls “comprehensive customer experience solution sets.” The Verint and Vovici merger demonstrates this. Here you have two distinct vendors, each with their own sweet spot within the EFM world. Verint is primarily known as an actionable intelligence solutions vendor that focuses on creating enterprise workforce optimization software and services to evaluate customer communications, especially in the contact center. Vovici is primarily known as an online survey management and enterprise feedback solutions vendor that focuses on helping companies obtain customer feedback from different channels and bring it all together to create a more holistic view of the customer. Essentially, Vovici had what Verint lacked — and Verint had what Vovici lacked. The result is now a more well-rounded and robust EFM offering.

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Agile Business Intelligence Solution Centers Are More Than Just Competency Centers

Boris Evelson

By Boris Evelson and Rob Karel

Our latest BI solution center (BISC, which in our definition is more than a BICC/BI COE) report is now live on the Forrester website. Here’s a brief summary.

Forrester firmly believes that tried and true best practices for enterprise software development and support just don’t work for business intelligence (BI). Earlier-generation BI support centers — organized along the same lines as support centers for all other enterprise software — fall short when it comes to taking BI’s peculiarities into account. These unique BI requirements include less reliance on the traditional software development life cycle (SDLC) and project planning and more emphasis on reacting to the constant change of business requirements. Forrester recommends structuring your BISC along somewhat different lines than traditional technical support organizations.

Earlier-generation BI support organizations are less than effective because they often

  • Put IT in charge
  • Remain IT-centric
  • Continue to be mostly project-based
  • Focus too much on functional reporting capabilities but ignore the data
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RFQ For BI Software Pricing Research

Boris Evelson

On my Q3 research agenda is a document reviewing typical BI software pricing configurations. Unfortunately, I find that just asking vendors whether they have this or that pricing policy (by number of named users, number of concurrent users, server type, etc.) usually just gets me “Yes, we have it all” or “It depends” answers. Not really useful. So this time I plan to nail down the vendors to three specific quotes given three very specific configurations. Here’s my first cut at the RFQ. I plan to send it out to:

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Enterprise Data Management Is Not The Holy Grail

Brian  Hopkins

From my first days as a baby architect, I was spoon-fed the idea that enterprise data management (EDM) was the solution to our data woes. Some call it enterprise information management or other names that mean a holistic approach to managing data that is business led and centered on stewardship and governance. The DMBOK provides a picture that describes this concept very well — check it out.

Here’s the problem: Most firms are not able to internalize this notion and act accordingly. There are myriad reasons why this is so, and we can all list off a bunch of them if we put our minds to it. Top of my list is that the lure of optimizing for next quarter often outweighs next year’s potential benefits.

Here’s another problem: Most EAs cannot do much about this. We are long-term, strategic people who can clearly see the benefits of EDM, which may lead us to spend a lot of time promoting the virtues of this approach. As a result, we get bloody bruises on our heads and waste time that could be spent doing more-productive things.

I do think that taking a long-term, holistic approach is the best thing to do; in my recently published report "Big Opportunities In Big Data," I encourage readers to maintain this attitude when considering data at extreme scale. We need to pursue short-term fixes as well. Let me go a step further and say that making short-term progress on nagging data management issues with solutions that take months not years is more important to our firms than being the EDM town crier. Hopefully my rationale is clear: We can be more effective this way as long as our recommendations keep the strategic in mind.

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It's The Dawning Of The Age Of BI DBMS

Boris Evelson

Over the years we’ve learned how to address the key business intelligence (BI) challenges of the past 20 years, such as stability, robustness, and rich functionality. Agility and flexibility challenges now represent BI’s next big opportunity. BI pros now realize that earlier-generation BI technologies and architecture, while still useful for more stable BI applications, fall short in the ever-faster race of changing business requirements. Forrester recommends embracing Agile BI methodology, best practices, and technologies (which we’ve covered in previous research)  to tackle agility and flexibility opportunities. Alternative database management system (DBMS) engines architected specifically for Agile BI will emerge as one of the compelling Agile BI technologies BI pros should closely evaluate and consider for specific use cases.

Why? Because fitting BI into a row-oriented RDBMS is often like putting a square peg into a round hole. In order to tune such a RDBMS for BI usage, specifically data warehousing, BI pros usually:

  • Denormalize data models to optimize reporting and analysis.
  • Build indexes to optimize queries.
  • Build aggregate tables to optimize summary queries.
  • Build OLAP cubes to further optimize analytic queries.
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Big Data Survey

Boris Evelson

Forrester is in the middle of a major research effort on various Big Data-related topics. As part of this research, we’ll be kicking off a client survey shortly. I’d like to solicit everyone’s input on the survey questions and answer options. Here’s the first draft. What am I missing?

  1. Scope. What is the scope of your Big Data initiative?
    1. Enterprise
    2. LOB
    3. Departmental
    4. Regional
    5. Project-based
  2. Status. What is the status of your Big Data initiative?
    1. In production
    2. Piloting
    3. Testing
    4. Evaluating
  3. Industry. Are the questions you are trying to address with your Big Data initiative general or industry-specific?
    1. General
    2. Industry-specific
    3. Both
  4. Domains. What enterprise areas does your Big Data initiative address?
    1. Sales
    2. Marketing
    3. Customer service
    4. Finance
    5. HR
    6. Product development
    7. Operations
    8. Logistics
    9. Brand management
    10. IT analytics
    11. Risk management
  5. Why BigData? What are the main business requirements or inadequacies of earlier-generation BI/DW/ET technologies, applications, and architecture that are causing you to consider or implement Big Data?
    1. Data volume
      1. <10Tb
      2. 10-100Tb
      3. 100Tb-1Pb
      4. >1Pb
    2. Velocity of change and scope/requirements unpredictability
    3. Data diversity
    4. Analysis-driven requirements (Big Data) vs. requirements-driven analysis (traditional BI/DW)
    5. Cost. Big Data solutions are less expensive than traditional ETL/DW/BI solutions
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