A common denominator for pricing and negotiating Business Intelligence (BI) and Analytics software

Boris Evelson

BI and analytics software packaging and pricing are a Wild West with few common practices among the vendors. Comparing and contrasting vendor prices and negotiating with vendors is challenging because

  • Few vendors publish list prices, so when a vendor tells you you are getting a certain discount you can’t really verify whether the discount numbers are valid or not.
  • Vendors base their prices on multiple variables such as
    • Total number of users
    • Concurrent users
    • User types
    • Connectivity to certain types of data sources
    • Number of CPU cores or sockets
    • CPU clock speed
    • Amount of RAM
    • Server Operating System (OS)
    • Environments such as development, test, QA (quality assurance), UAT (user acceptance testing), production, and DR (disaster recovery)
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What Do Business Intelligence Consultants Mean By “Solutions”?

Boris Evelson

Management consultants and business intelligence, analytics and big data system integrations often use the terms accelerators, blueprints, solutions, frameworks, and products to show off their industry and business domain (sales, marketing, finance, HR, etc) expertise, experience and specialization. Unfortunately, they often use these terms synonymously, while in pragmatic reality meanings vary quite widely. Here’s our pragmatic take on the tangible reality behind the terms (in the increasing order of comprehensiveness):

  • Fameworks. Often little more than a collection of best practices and lessons learned from multiple client engagements. These can sometimes shave off 5%-10% of a project time/effort mainly by enabling buyers to learn from the mistakes others already made and not repeating them.
  • Solution Accelerators. Aka Blueprints, these are usually a collection of deliverables, content and other artifacts from prior client engagements. Such artifacts could be in the form of data connectors, transformation logic, data models, metrics, reports and dashboards, but they are often little more than existing deliverables that can be cut/pasted or otherwise leveraged in a new client engagement. Similar to Frameworks, Solution Accelerators often come with a set of best practices. Solution Accelerators can help you hit the ground running and rather than starting from scratch, find yourself 10%-20% into a project.
  • Solutions. A step above Solution Accelerators, Solutions prepackage artifacts from prior client engagements, by cleansing and stripping them of proprietary content and/or irrelevant info. Count on shaving 20% to 30% off the effort.
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What does Business Intelligence integration with R really mean

Boris Evelson

“A little prediction goes a long way” wrote Eric Siegel in his popular Predictive Analytics book. True, predictive analytics is now part and parcel of most Business Intelligence (BI), analytics and Big Data platforms and applications. Forrester Research anecdotal evidence finds that open source R is by far the most ubiquitous predictive analytics platform. Independent findings and surveys like the ones by KDNuggets and RexerAnalytics confirm our conclusions (and I quote) “The proportion of data miners using R is rapidly growing, and since 2010, R has been the most-used data mining tool.  While R is frequently used along with other tools, an increasing number of data miners also select R as their primary tool.”

To jump on this R feeding frenzy most leading BI vendors claim that they “integrate with R”, but what does that claim really mean? Our take on this – not all BI/R integration is created equal. When evaluating BI platforms for R integration, Forrester recommends considering the following integration capabilities:

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The Forrester Wave™: Enterprise Business Intelligence Platforms, Q4 2013

Boris Evelson

The majority of large organizations have either already shifted away from using BI as just another back-office process and toward competing on BI-enabled information or are in the process of doing so. Businesses can no longer compete just on the cost, margins, or quality of their products and services in an increasingly commoditized global economy. Two kinds of companies will ultimately be more successful, prosperous, and profitable: 1) those with richer, more accurate information about their customers and products than their competitors and 2) those that have the same quality of information as their competitors but get it sooner. Forrester's Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012 (we are currently fielding a 2014 update, stay tuned for the results) survey showed that enterprises that invest more in BI have higher growth.

The software industry recognized this trend decades ago, resulting in a market swarming with startups that appeared and (very often) found success faster than large vendors could acquire them. The market is still jam-packed and includes multiple dynamics such as (see more details here):

  • All ERP and software stack vendors offer leading BI platforms
  •  . . . but there's also plenty of room for independent BI vendors
  •  Departmental desktop BI tools aimed at business users are scaling up
  •  Enterprise BI platform vendors are going after self-service use cases.
  •  Cloud offers options to organizations that would rather not deal with BI stack complexity.
  •  Hadoop is breathing new life into open source BI.
  •  The line between BI software and services is blurring
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How To Have The BI Cake And Eat It Too: A (Or The) BI Prediction For 2014

Boris Evelson
Rather than going with the usual, ubiquitous, and often (yawn) repetitive “top 10 BI predictions” for the next year, we thought we’d try something different. After all, didn’t the cult movie Highlander prove beyond the shadow of a doubt that “in the end there will be only one”? And didn’t the Lord Of The Rings saga convince us that we need one prediction “to rule them all”? The proposed top BI prediction for 2014 rests on the following indisputable facts:
  • Business and IT are not aligned. Business and IT stakeholders still have a huge BI disconnect (after all these years — what a shocker!). This is not surprising. Business users mostly care about their requirements, which are driven by their roles and responsibilities, daily tasks, internal processes, and dealings with customers (who have neither patience nor interest in enterprises’ internal rules, policies, and processes). These requirements often trump IT goals and objectives to manage risk and security and be frugal and budget minded by standardizing, consolidating, and rationalizing platforms. Alas, these goals and objective often take business and IT in different directions.
  • Requirements are often lost in translation. Business and IT speak different languages. Business speaks in terms of customer satisfaction, improved top and bottom lines, whereas IT speaks in metrics (on a good day), star schemas, facts, and dimensions. Another consideration is that it’s human nature to say what we think others want to hear (yes, we all want our yearly bonus) versus what we really mean. My father, a retired psychiatrist, always taught me to pay less attention to what people say and pay more attention to what people actually do — quite handy and wise fatherly advice that often helps navigate corporate politics.
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Evaluating BI Services In Asia Pacific

Michael Barnes

Business decision-makers in Asia Pacific (AP) are increasingly aware of the importance of business intelligence (BI) and broader analytics to business strategy and execution. However, lack of internal expertise remains a significant barrier to BI project success.

In response, Fred Giron and I have just published The Forrester Wave™: Business Intelligence Service Providers In Asia Pacific, Q4 2013. In it, we identified eight companies that offer strong capabilities and services for AP-based organizations seeking BI service support.

To succeed in the region, BI service providers must provide guidance on how to translate data access into actual insight and information into business value. This requires a strong understanding of local cultures, business practices, regulatory frameworks, and market dynamics. When evaluating providers, understand how their capabilities are likely to evolve across five categories:

  • People. To minimize project risks, understand who will be the on-site business and technical leads on BI projects and how many successful implementations this staff has led in a similar industry and similar technical environment within the region.
  • Technical expertise. Service providers need to demonstrate region-specific knowledge of the technical characteristics of various BI tools, platforms, architectures, and applications. Most companies will not have all of the necessary skills on site, so closely evaluate ease of access to remote staff from the service provider as well.
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SAP Takes Another Step Towards Agile BI With KXEN Acquisition

Boris Evelson

Business intelligence (BI) is an evergreen that simply refuses to give up and get commoditized. Even though very few vendors try to differentiate these days on commodity features like point and click, drag and drop, report grouping, ranking, and sorting filtering (for those that still do: Get with the program!), there are still plenty of innovative and differentiated features to master. We categorize these capabilities under the aegis of Forrester agile BI; they include:

  • Making BI more automated: suggestive BI, automatic information discovery, contextual BI, integrated and full BI life cycle, BI on BI.
  • Making BI more pervasive: embedding BI within applications and processes, within the information workplace, and collaborative, self-service, mobile, and cloud-based BI.
  • Making BI more unified: unifying structured data and unstructured content, batch and streaming BI, historical and predictive, and handling complex nonrelational data structures.
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Will Privacy Concerns Stop Or Stunt The Power Of Predictive Analytics

Mike Gualtieri

The power of predictive analytics in the age of Big Data is super-cool, but will privacy concerns stop or stunt it's adoption? Watch this episode of Forrester TechnoPolitics with Eric Siegel, author of Predictive Analytics: The Power to Predict Who Will Click, Lie, Buy, or Die to find out. 

About Forrester TechnoPolitics

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Maximize Your Chances Of Business Intelligence Success

Martha Bennett

Too little data, too much data, inaccessible data, reports and dashboard that take too long to produce and often aren’t fit for purpose, analytics tools that can only be used by a handful of trained specialists – the list of complaints about business intelligence (BI) delivery is long, and IT is often seen as part of the problem. At the same time, BI has been a top implementation priority for organizations for a number of years now, as firms clearly recognize the value of data and analytics when it comes to improving decisions and outcomes.

So what can you do to make sure that your BI initiative doesn't end up on the scrap heap of failed projects? Seeking answers to this question isn't unique to BI projects — but there is an added sense of urgency in the BI context, given that BI-related endeavors are typically difficult to get off the ground, and there are horror stories aplenty of big-ticket BI investments that haven’t yielded the desired benefit.

In a recent research project, we set out to discover what sets apart successful BI projects from those that struggle. The best practices we identified may seem obvious, but they are what differentiates those whose BI projects fail to meet business needs (or fail altogether) from those whose projects are successful. Overall, it’s about finding the right balance between business and IT when it comes to responsibilities and tasks – neither party can go it alone. The six key best practices are:

·         Put the business into business intelligence.

·         Be agile, and aim to deliver self-service.

·         Establish a solid foundation for your data as well your BI initiative.

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Get ready for BI change

Boris Evelson

Market conditions are changing quickly; firms need to make the best possible business decisions at the right time and base them on timely, accurate, and relevant information from business intelligence (BI) solutions. The repercussions of not handling BI change well are especially painful and may include lost revenue, lower staff morale and productivity, continued proliferation of shadow IT BI applications, and unwanted employee departures. Ineffective change management often lies in the process of preparing the people affected by change rather than in planning the technology implementation. Firms that fail to prepare employees for enterprise BI change early enough or well enough will be left behind. They need to implement a multifaceted series of activities ranging from management communication about why change is needed to in-depth, role-appropriate employee training. 

 
Why change management is so critical? Most strategic business events, like mergers, are high-risk initiatives involving major changes over two or more years; others, such as restructuring, must be implemented in six months. In the case of BI, some changes might need to happen within a few weeks or even days. All changes will lead to either achieving or failing to achieve a business result. There are seven major categories of business and organizational change:
  • People acquisitions
  • Technology acquisitions 
  • Business process changes 
  • New technology implementations 
  • Organizational transformations
  • Leadership changes
  • Changes to business process outsourcing or IT sourcing 
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