Database

Database

Bringing Measurement to Agile

Executive teams and your end clients always want to know, “how good are our development teams?”  Agile development teams usually promise that they can deliver faster and cheaper with better quality.  But how do you truly know this is the case?  The only way to really know is to apply quantitative measurement to agile.  With the SLIM solution you can look at a completed agile project and determine the productivity that was demonstrated.  This productivity metric encompasses all environmental factors, such as how good is the skill level and experience of your development team?  How good are the tools and methodology in place?  What is the technical complexity of the software you are building? 

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Agile Database Productivity

New Article: Leveraging the Power of Historical Data Through the Use of Trend Lines

Size vs. Staffing

Developing software within the DoD presents a unique set of challenges, including but not limited to budget cuts, Congressionally mandated changes, changing software requirements, and so on. It should come as no surprise, therefore, that cost estimators have faced significant challenges when estimating systems in the Defense arena. A recent initiative put forth by the DoD was to improve its estimation process by leveraging historical data collected from forensic analyses of recently completed software development efforts. This article by Taylor Putnam-Majarian and John Staiger, discusses (1) some of the challenges faced throughout this initiative, (2) the data collection process, and (3) how one can leverage data to improve cost estimates. This article was originally published in Crosstalk Magazine.

Read the article!

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Articles Data Database Estimation Government

Vendor Management Is a Two Way Street

Vendor Management

Managing vendors has become increasingly important in recent years.  In my account management role at QSM, I see both sides of the vendor management relationship.  The client wants a proven vendor that will partner with them in achieving their IT goals; and the vendor wants to win that business, employ their workers, and hopefully earn more work.  Unfortunately, that state of client/vendor Zen is not often achieved, usually due to legitimate (and sometimes not) misunderstandings on both sides.

On the client side, they are concerned with selecting a vendor with whom they are confident their tasks and deliverables will be achieved on time, within budget, and of the best possible quality.  After a round of RFI’s, then RFQ’s, then a final down select process, the vendor is chosen and work begins.  Often, at least in my experience, the overriding decision criteria comes down to cost, which makes sense, to a degree.  But in many cases, cheapest, I mean, least expensive bids often rule the day.  This kind of decision-making comes with its own set of risks; the most obvious is you get what you pay for and it’s often an ill-prepared vendor.

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Database Vendor Management

Historical Data Isn’t Playing “Hard to Get”

Historical Data Collection

“No, we don’t have any historical project data collected” is the statement I hear with some frequency when speaking to organizations about their IT project estimating processes.  Ideally we use client history to calibrate and tune the project estimates we provide.  In my quest to spread the word about parametric estimating I often encounter this notion that organizations don’t believe they have historical data in a retrievable form.  In almost every case that I have been involved, it turned out that the historical data was present, just not in the form of a 1,000 rowed spreadsheet.  Often times the data is more available than the client is aware.

Our approach works at a macro level so we are seeking overall project metrics of cost, schedule, size, staffing and defects.  If the actual formal documentation of history is not available for these five core metrics, then it usually is available by leveraging various sources within the organization.  We have found it’s common to resurrect a project’s outcome by seeking feedback from the team that worked the project, however if that’s not possible due to attrition, re-org or other disrupting factors, we can usually find the project metrics through other means.  Those other means may be time and defect tracking tools, requirements analysis tools and accounting systems.  The data is almost always documented somewhere.   

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Database Data Metrics

Ask Carol: No Free Lunch in Software Estimation and Benchmarking

No Free Lunch in Software Estimation BenchmarkingDear Carol: 

Given all your international experience, I’m hoping you can tell me where I can find a large, freely available industry database that project managers could use for software estimation and/or benchmarking.  After 5 decades of software development wouldn’t you think that we could put together a software estimation or benchmarking database that the world could use for free? 

- Hopeful in Hartford

Dear Hopeful:  

Great question – and the dream of many IT project managers.  It might seem like an easy concept (just collect actual effort and project size and use it for future estimates); in practice it’s not that simple.

What I know is that in software estimation and benchmarking, there is no free lunch -- you get what you pay for.  And I’ll explain why…

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Database Ask Carol

Data-Less Decision Making

I rather enjoyed the Google Analytics April Fools prank earlier this month, Welcome to Data-Less Decision Making on Analytics Academy.  Though satirical, this video brings to light an important reason why individuals have such trouble making decisions in a business environment: they don’t have data.

I’ll agree that without data it’s really appealing to turn to the coin flip method and be done with it.  After all, 50/50 odds really aren’t terrible, right?  But project management software such as SLIM-Estimate make empirically-based business decisions possible, even when company data isn’t immediately available.

Leveraging our database that contains over 10,000 projects, QSM has developed and regularly updates 17 distinct industry trends.  When creating an estimate or benchmarking a past performance, simply select the QSM industry trend that most closely reflects the type of system being built.  This will serve as a reference point.

If historical data is available but you’re unsure of which metrics to collect, SLIM-SmartSheets is a new downloadable feature in SLIM version 8.2 that mimics the look and feel of SLIM-DataManager and allows users to collect project data, even when they’re not on a network computer.  Each project can then be pulled into one SLIM-DataManager file using the API.  

SLIM-SmartSheets

Ask Carol: How Many Projects Create a "History?"

Dear Carol:

As a project manager who is new to formal project estimating, I’ve been hearing about the importance of having project histories available for accurate estimating.  We just purchased SLIM-Estimate but we don’t have any project history.  Can we still use SLIM, and how many projects do we need before we can get accurate estimates?

– PM in Atlanta

Dear PM:

You may have heard that “history repeats itself” and the adage is true in software development.  Completed projects where the actual software size, effort hours, duration and cost are often the best predictors of future performance on projects – and your own project history gives accurate indicators of how your corporation performs.  However, the majority of QSM clients who purchase SLIM-Estimate start out with little or none of their own project history.  The good news is that the SLIM tool comes preloaded with productivity, duration, staffing, and effort hours trend lines based on thousands of completed real life projects, and delineated by industry and type of project.  When you do an estimate using SLIM, the Monte Carlo simulation models are run, and the results are compared against trend line graphs so that you can see how your estimate of effort, duration, staffing and cost compare to the chosen industry.  This gives you the confidence to know where your estimate falls against comparable completed projects (of a given size.) If your estimates fall outside the bounds of a single standard deviation above or below the industry trend lines, you know that you may want to reassess the assumptions of your estimate.

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SLIM-Estimate Database Ask Carol

Fundamentals of Software Metrics in Two Minutes or Less

A couple of years ago at a lean software and systems conference, I delivered a “lightning talk” about software metrics. In the two-minute time span, I illustrated the folly of gathering data without a measurement plan and the audience grasped the concept immediately.  “Why don’t more companies get this?” remarked several attendees, “it just doesn’t make sense to collect all the data we do without a plan.”

It doesn’t take a rocket scientist to succeed with software measurement; professionals with a straightforward plan can quickly and easily reap its benefits. Two concepts are fundamental to embrace for metrics success:  1. Goal-Question-Metric (GQM), and 2. Simplicity.  

Goal-Question-Metric (GQM) Approach to Metrics

First introduced by Victor Basili as an approach to measurement, and later the subject of a book by the same name by Rini vanSoligen and Egon Berghout, GQM is a straight-forward, stepwise approach to measurement.  While it has applicability to measurement in any industry, Basili created GQM specifically to address the chaos in the software world.  GQM involves three steps:

  1. Establish the Goals for measurement.
  2. Ask the Questions that will answer whether the goals are being met.
  3. Design and collect the Metrics to answer the questions.

The Software Engineering Institute (SEI) at Carnegie Mellon University in Pittsburgh, PA expanded Victor Basili’s GQM approach to GQIM, the “I” being indicator, but that is the topic of a future post.

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Metrics Database

How to Use Big Data to Improve Your Software Projects

In the recent Washington Post article How the Obama Campaign Won the Race for Voter Data, Joel Kowsky writes about how the 2012 Obama campaign used analytics to improve their campaign strategy, and to ultimately secure the presidential victory.  

Regardless of where you stand on the political spectrum, it’s hard to argue that Barack Obama’s campaign strategy was anything short of impressive.  As soon as Obama took office in 2009, his team began preparing for his 2012 campaign.  From the start there was a strong emphasis on measuring the campaign’s progress.  Jim Messina, Obama’s 2012 campaign manager, stated 

“There’s always been two campaigns since the Internet was invented, the campaign online and the campaign on the doors.  What I wanted was, I didn’t care where you organized, what time you organized, how you organized, as long as I could track it, I can measure it, and I can encourage you to do more of it.”

The team began by conducting a postmortem study on their 2008 campaign where they analyzed the number of homes visited, phone calls placed, and voters registered by each field organizer and volunteer.  The result was a 500 page report which highlighted areas of improvement for the 2012 campaign.  

The suggestions led the Obama campaign to invest in building customized software that would integrate all the data the campaign had collected on voters, donors, and volunteers and link to individual voter profile.  This software analyzed previously collected data to calculate the likelihood of candidate support, the likelihood of election day turnout, and the degree of persuasion for each voter.  

Database Validation Best Practices

Database validation is an important step in ensuring that you have quality data in your historical database.  I've talked before about the importance of collecting project data and what you can do with your own data, but it all hinges on having thoroughly vetted project history.

Although it's nice to have every tab in SLIM-DataManager filled out, we really only need three key pieces of information to calculate PI:

  • Size (Function Unit): if the function unit is not SLOC, a gearing factor should be provided (97.3% of projects in the database report total size)
  • Phase 3 duration or start and end dates (99.9% of projects in the database report phase 3 duration)
  • Phase 3 effort (99.9% of projects in the database report phase 3 effort)

These fields can be thought of as the desired minimum information needed, but even if one is missing, you may not want to delete the project from the database. A project that is missing effort data, for instance, will not have a PI but could be used to query a subset of projects for average duration by size. Likewise, a project with no size will not have a PI, but does contain effort and duration information that could be useful for calculating the average time to market for a division. However, if possible, it is a good idea to fill out at least these three fields.

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SLIM-Metrics Data SLIM-DataManager Database