Not All In-Memory Analytics Tools Are Created Equal

I get many questions from clients interested in evaluating different in-memory technologies. My first advice is not to mix apples and oranges and clearly understand the differences between in-memory indexes, in-memory OLAP, in-memory ROLAP, in-memory spreadsheets, and other approaches. See more details in my recent blog entry "I forget: what's in-memory?" to understand the differences. Then once you zero in on a particular segment, you can indeed do an apples-to-apples comparison. Let's say we pick the category of in-memory associative indexes, which would include Microsoft PowerPivot, QlikTech, and TIBCO Spotfire. We also sometimes run across Advizor Solutions, but typically in smaller clients (and we do not include them in The Forrester Wave™ process). I recommend a three-step approach to compare these four tools:

  1. First, compare all of the commodity features of the vendors and tools like data integration and portal integration, operational features like administration, security, and others. You can leverage the detailed evaluation behind our slightly outdated 2008 BI Forrester Wave, if you are in a hurry, or you can wait for another month or so and the 2010 update will be published (it's in the last stages of editing at this point). Or if you are a Forrester IT client — not a vendor — client, send me a note and I'll share a draft preview with you.
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Results Of The Forrester Wave™: Open Source Business Intelligence (BI), Q3 2010

Open source software (OSS) and business intelligence (BI) are two related market segments where Forrester sees continually increasing interest and adoption levels. BI specifically continues to be one of the top priorities on everyone's mind. The main reason? Enterprises that do not squeeze the last ounce of information out of their data stores and applications, and do not focus on getting strategic, tactical, and operational insight into their customers, products, and operations, risk falling behind competition. And when it comes to open source, 2009 could best be described as "the year IT professionals realized that open source runs their business." The reason is simple: Over the past few years, we've seen that developers adopt open source products tactically without the explicit approval of their managers. This has shown up in numerous surveys where the actual adoption of open source ranks higher than what IT managers report. Well no longer: Forrester's Enterprise And SMB Software Survey, North America And Europe, Q4 2009 shows that management has caught on to the fact that developers increasingly use open source to run key parts of their IT infrastructure. And management has grown increasingly comfortable with it. In fact, throughout 2009, most client inquiries Forrester received regarding open source were focused on how to move from tactical adoption to strategic exploitation.

Yet, when you put the 2 and 2 together (OSS and BI), you mostly get a mixed market, where one unfortunately has to compare apples to oranges. Why? Before plunging into a tool evaluation and selection process, ask yourself the following questions, and make sure you are doing a like-to-like comparison:

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Decision Management, Possibly The Last Frontier In BI

Just read an excellent article on the subject by Tom Davenport. We at Forrester Research indeed see the same trend, where more advanced enterprises are starting to venture into combining reporting and analytics with decision management.  In my point of view, this breaks down into at least two categories:

  • Automated (machine) vs. non automated (human) decisions, and
  • Decisions that involve structured (rules and workflows) and unstructured (collaboration) processes
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Why I Don't Want To Research BI Market Size

I know, I know, this is what analysts do. But I personally would never want to get involved in doing a BI market size – it’s open game for serious critique. Here are some of the reasons, but the main one is a good old “garbage in garbage out.” I am not aware of any BI market size study that took into account the following questions:

  • What portion of the DBMS market (DW, DBMS OLAP) do you attribute to BI?
  • What portion of the BPM market (BAM, process dashboards, etc.) do you attribute to BI?
  • What portion of the ERP market (with built-in BI apps, such as Lawson, Infor, etc.) do you attribute to BI?
  • What portion of the portal market (SharePoint is the best example) do you attribute to BI?
  • What portion of the search market (Endeca, Google Analytics, etc.) do you attribute to BI?
  • What is the market size of custom developed BI applications?
  • What is the market size of self built BI apps using Excel, Access, etc?
  • On the other side, what is the % of licenses sold that are shelfware and should not be counted?

Plus many more unknowns. But, if someone indeed did do such a rough estimate, my bet is that the actual BI market size is probably 3x to 4x larger than any current estimate.

Use A Four-Step Approach To Select The Right BI Services Provider

BI projects are never short, and, alas, many of them don't end since a fast-paced business environment often introduces new requirements, enhancements, and updates before you're even done with your first implementation. Therefore, we typically recommend doing sufficient due diligence upfront when selecting a BI services provider — as you may be stuck with them for a long time. We recommend the following key steps in your selection process:

  1. Map BI project requirements to potential providers. Firms should use Forrester's "BI Services Provider Short-Listing Tool" to create a shortlist of potential providers. With the tool you can input details about your geographic scope, technology needs, and the type of third-party support you need (i.e., consulting versus implementation versus hosting/outsourcing).  The tool then outputs a list of potential providers that meet the criteria. For each potential fit, the tool also generates a provider profile summary that offers key details around practice size, characteristics, and areas of expertise.
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Join Me For A BI Strategy Workshop In Cambridge, Mass., On October 19, 2010

Business intelligence (BI) continues to be front and center on the agendas of businesses of all sizes and in all industries and geographies. Ever-increasing data volumes, complexity of global operations, and demanding regulatory reporting requirements are just some of the reasons. But also, more and more businesses realize that BI is not just a tool but rather a key corporate asset that they can use to survive, compete, and succeed in an otherwise increasingly commoditized global economy.

However, we consistently find that many BI initiatives fail and even more are less than successful. Well, maybe we can help. Even if just a little bit. Come to our interactive one-day BI Strategy Workshop to learn the fundamentals and best practices for building effective and efficient BI platforms and applications. The Workshop will also include hands-on exercises with tangible deliverables that you can take back to your teams to help you jump-start or adjust the course of your BI initiatives.

Why attend? Because hundreds of organizations have already benefited from reading Forrester research and working with Forrester analysts on the topics covered in this Workshop. I plan to present Forrester’s most recent research on:

  • Why are BI initiatives at the top of everyone's agenda, while many of them still fail?
  • What are some of the best practices necessary to achieve successful BI implementations?
  • What are some of the next-generation BI technologies and trends that you can't overlook, such as Agile BI and self-service BI?
  • How do you assess your BI maturity so that you can get a solid starting point on the way to your BI vision and target BI state?
  • How do you assess whether your organization has a solid BI strategy?
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Where Do You Draw The Lines Between Business And IT Ownership Of Data And Information?

I get many questions on this subject and it often turns into almost a religious debate. Let's throw some structure into it. Here's a decision-to-raw-data stack.

  1. Decisions
  2. Strategy
  3. Policies
  4. Objectives (e.g., clear understanding of what is driving revenue performance)
  5. Goals (e.g., achieve x% income growth)
  6. Calculated metrics (any combination, variation of the standard metrics or KPIs)
  7. KPIs (e.g., profitability, liquidity, shareholders value)
  8. KPMs (e.g., enterprise value, trailing/forward price/earnings)
  9. Metrics (e.g., fee income growth %, non-fee income growth %)
  10. Dimensions (part of MDM, e.g., customers, customer segments, products, time, region)
  11. Pre-calculated attributes (standard, cross-enterprise metrics, KPIs, and KPMs)
  12. Pre-built aggregates (used to speed up reports and queries)
  13. Analytical data (DW, DM)
  14. Operational data (ERP, CRM, financials, HR)

Obviously, it's never a clear-cut, binary decision, but in my humble opinion:

  • 1-6 should emphasize business ownership
  • 10-14 should emphasize IT ownership
  • 7-9 is where it gets murky, and ownership depends on whether metric/KPI/KPM is: 1) standard and fixed, 2) fluid and changes frequently, 3) different by product, line of business, or region.

What did I miss? Thoughts?

Best practices for using spreadsheets as BI tools

By Boris Evelson

We all know that the war of fighting the proliferation of spreadsheets (as BI or as any other applications) in enterprises has been fought and lost. Gone are the days when BI and performance management vendors web sites had “let us come in and help you get rid of your spreadsheets” message in big bold letters on front pages. In my personal experience – implementing hundreds of BI platforms and solutions – the more BI apps you deliver, the more spreadsheets you end up with. Rolling out a BI application often just means an easier way for someone to access and export data to a spreadsheet. Even though some of the in memory analytics tools are beginning to chip away at the main reasons why spreadsheets in BI are so ubiquitous  (self service BI with no modeling or analysis constraints, and little to no reliance on IT), the spreadsheets for BI are here to stay for a long, long, long time.

With that in mind, let me offer a few best practices for controlling and managing (not getting rid of !) spreadsheets as a BI tool:

  1. Create a spreadsheet governance policy. Make it flexible – if it’s not, people will fight it. Here are a few examples of such policies:
    • - Spreadsheets can be used for reporting and analysis that support processes that do not go beyond individuals or small work groups vs. cross functional, cross enterprise processes  
    • - Spreadsheets can be used for reporting and analysis that are not part of mission critical processes
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Oracle OBIEE 11g Launch: "We are back!"

Whoever said BI market is commoditizing, consolidating and getting very mature? Nothing can be farther from the truth. On the buy side, Forrester still sees tons of less-than-successful BI environments, applications and implementations as demonstrated by Forrester's recent BI Maturity survey. On the vendor/sell side, Forrester also sees a flurry of activity from the startups, small vendors and large, leading BI vendors constantly leapfrogging each other with every major and minor release.

In terms of the amount of BI activity that Forrester sees from our clients (from inquiries, advisories and consulting) there’s no question that SAP BusinessObjects and IBM Cognos continue to dominate client interest. Over the past couple of years Microsoft has typically taken the third place, SAS  fourth place and Oracle the distant fifth. But ever since Siebel and Hyperion acquisitions, the landscape has been changing, and we now often see Oracle jumping into third place, sometimes leapfrogging even Microsoft in the levels of monthly interest from Forrester clients.

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What SQL Cannot Do

I recently asked my Twitter followers if they had good examples of queries, business questions that SQL can't do. It turns out a better question is "what SQL can't do easily", so I thought I'd share with everyone what I heard and found. Seth Grimes was the first one to provide an excellent answer with some informative examples - thank you, Seth! I also found very useful articles on typical SQL challenges such as avoiding multiple duplicate sets in your SQL results, and why NULLs create tons of headaches for SQL coders.

There's also a typical SQL challenge with ragged, sparse, unbalanced hierarchies and dimensions. For example, a retail store, a wholesaler or a distributor with thousands of products, and a manufacturer with thousands of parts often struggle with dissimilar data. A pencil in an office supply store does not have the same descriptive attributes (lead type, for example) as a calculator (scientific, financial, etc.) or an office chair (number of wheels, etc.). Or a tire in a car manufacturing supply chain does not have any common descriptive elements (rubber grade, width-to-height ratio) with gear boxes (automatic vs. manual, 4 or 5 speed, gear-to-gear ratios, etc). When looking for correlation between two entities (for example, what is a potential product quality issue that is making my sales go down?) in cases with disparate, dissimilar products (as in retail products or manufacturing parts), the same SQL query cannot work for all products or parts. One would be forced to write multiple SQL queries for each product or part type to find such a sales/quality relationship.