Saturday, January 21, 2017

Has Agile Lost It's Way?

When I got started with Agile in 2007 it was new. Most of us had been delivering software for awhile and while we loved developing solutions we hated the often tyrannical circumstances we endured doing it. We knew what worked: lots of unit testing, iterative/incremental development, lots of collaboration, transparency etc. But until Agile (for me, XP and Scrum), we didn't know how to pull it all together. What found then, even before Daniel Pink's gave simple ways to say it, when we had the opportunity to 'do it right', was the outline of a path to mastery, autonomy, and purpose in our daily work.

Agilists associate the birth of "Agile" with The Snowbird Conference in 2001 and The Agile Manifesto. If we focus on the unmet needs of business' and governments relying on software then it goes farther back still.  Let's just say Agile's about 16, a teenager. It shows.

Agile has become a big business promising homogenized faster, better and cheaper delivery to the masses. I would guess that 70% or more of teams who call themselves Agile aren't. At least by my definition but, I'm a tough grader who expects the use of XP engineering practices.

In 2012 Thoughtworks' published an Agile Fluency Model to describe levels of Agility from One Star Teams focused on delivering business value to Four Star Teams optimizing entire eco-systems. Their data suggested that 15% teams were doing something, but it wasn't Agile. 45% of teams, while focussed on business value, weren't meaningfully improving software quality. As a result, due to Scrum or Scrum-like process, these teams are only marginally more productive.

As a result of this homogenization, the tyrannical circumstances Agile was to dispell are back for too many. As the market grew, "consultants" with no more than a scrum class and a half-read, frequently derivative book popped up to feed on the Agile sales frenzy, the essence started getting lost.

Turns out that greater transparency can also lead to micro-management and ever more unrealistic expectations on a team. You get worse, not better code. You get less not more shared understanding. Even when you've had some success by focussing solely on visible business value, you may hear, "Wow, we've gotten faster since leaving waterfall. Bet you can go twice as fast! Indeed, you must! Find more process improvements!"

If this is sounding familiar to you I'm sorry. Here's what I suggest you might reference to find the essence:
  1. Read the original sources:
  2. If you are a developer or architect then also check out:
  3. If you are an Architect add these to the above:
    • Read Clean Code
    • Watch the Architecture, Tech Debt, and TDD videos at Clean Coders
    • If you haven't written a line of code in 10 years, and think this is all craziness then spend the next month coding, fulltime in your enterprise applications then read and watch the videos again.
Once your done, happy New Years 2006! Please don't stop now. Next up:
  1. RSA ANIMATE: Drive: The surprising truth about what motivates us
  2. Kanban: Successful Evolutionary Change for Your Technology Business
  3. The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses
  4. Large-Scale Scrum: More with LeSS
  5. The DevOps Handbook
  6. BADASS: Making Users Awesome
Ok, happy New Years 2017! We're not done. So much has happened since Agile was born. Not all of it captured above. Keep in mind that you have to know the rules before you can successfully break them. You should also check into Modern Agile

Friday, January 13, 2017

Finally Finished the Kanban Book Summary!

Yes! Finally finished the seven-part summary of "Kanban: Successful Evolutionary Change for Your Technology Business by David Andersen. Part one starts here.

Of all the summaries so far, this is one of my favorites. The summaries are written as study guides or review materials.

The Kanban book fills in the blanks regarding why Scrum and XP work the way they do and how to improve their processes in a way that minimizes resistance to change and maximizes value delivered.

Kanban is not a methodology for software delivery. It is a change management system for improving the implementation of any delivery framework or process. It is reasonable to say that using the system to improve a waterfall process would lead to Scrum/XP/Lean Start Up like processes. This may explain why some think Kanban is a delivery methodology - applying its principles will suggest process improvements that likely converge towards the processes that are taken to define today's "Agile."

But that too is perhaps misleading. Agile is a mindset growing from of a set of principles and values we apply to improving our ability to deliver value through software delivery. Is is not a process or a framework - despite industry marketing machine claims to the contrary. It uses processes and frameworks and it is our job to improve them. If you agree then you'll enjoy getting involved with Modern Agile.

David Andersen's application of Lean principles to software delivery and their synthesis with the Agile mindset makes Kanban a valuable tool. Here are the summaries:

Thursday, December 8, 2016

Big Data Technology Impacts of NoSQL, Distributed Data Stores...

The last 30 years have brought dramatic change to the world and technology.  Moore's law and the internet's relentless march connect more devices than there are people on the planet. Cisco estimates 50 billion connected devices by 2020.  To many cloud computing, big data and NoSQL tools sit hidden behind Facebook, Google and ubiquitous smart phone apps . Even those in IT may sometimes struggle to synthesize these innovations.

In this post we'll take a brief look at the implications of some of these changes on traditional business intelligence and analytic capabilities. Think of this as a quick survey of the landscape and not a deep dive into its many facets. Let's start with a seemingly esoteric NoSQL capability: graph processing and databases.

Graph Processing and Databases

We benefit from graph technology every day. Let's look at a visualization and some use cases.

Real time recommendations on retail web sites, the sometimes creepy "friend recommendations"  on Facebook.  Both echo chamber and gold mine: social network analytics for marketing and advertising. GPS applications like Waze or google maps. A little less obvious are use cases like network and operations root cause analysis, contextualizing real time streams of event and other data from the internet of things' sensors. Finally, saving money and sometimes lives via fraud detection, cybersecurity, and medical research.

When the relationships of things to each other are as important as the things themselves - graphs are attractive. Beyond the ubiquity of this hidden technology notice that unlike traditional BI and analytics for some key use cases, e.g. recommendations, identity and access management, it can be real time.

Before we move on to look at NoSQL more generally, it's important to recognize a few things about graph processing and graph databases. Graph databases often compete as OLTP tools e.g. Neo4j. Large scale graph processing is more an OLAP capability e.g. Giraph, Pregel, so the graph's data may sit in another NoSQL distributed data store. Having said that, the intuitive nature of the graph database lends itself to OLAP.

Depending on the scale and type of the analysis graph queries using languages like cypher or gremlin are often dramatically simpler to write than similar queries in SQL. Also, queries across many layers of related things perform orders of magnitude faster in a graph database than in a relational database.

Before we go any farther we'll need to have at least a passing understand of how NoSQL enables big data.

NoSQL - A Quick Overview

What's happened to the "relational" world? Data warehouse data marts and multi-dimensional cubes for OLAP, along with SAS, once ruled the BI and data analysis landscape.  Yet open sourced projects like R, Hadoop, Cassandra, Spark are in the forefront of the now "big" data world.  Even SQL database OLTP dominance may be challenged by the modern graph databases like Neo4j's ACID support, ease of use, and lightening speed for some use cases. 

NoSQL - could anyone have picked a worse or more misleading name? Every major cloud provider offers SQL based API's to many of their "noSQL" data sources.  SQL tends to connote relational data stores and tables. Yet,  graph databases are intrinsically relational and the open sourced versions of google's distributed, non-relational data store (now categorized as Column based noSQL) is called BigTable/MapReduce. Oh well, at least Column based noSQL is not really tabular.

NoSQL type characteristics overlap suggesting innovation will continue. My advise is to think "not only SQL" when you think of NoSQL.

Wikipedia's current taxonomy of relevant types of NoSQL along with example implementations.
  • Column: Cassandra, HBase
    • This type is often part of a distributed data store which has many benefits beyond the scope of this conversation. For now we'll just say it allows reliable, fast querying agains huge amounts of data across many computers, or nodes, at once
    • Map Reduce often handles querying where "map" sorts and filters and "reduce" summarizes.  Other common query tools include Hive and Pig
    • A column of a distributed data store is its lowest level object. It is a tuple (a key-value pair) consisting of three elements: A unique name, a value (or set of values), and a time stamp to determine if the content is valid or stale.
    • Example columns: 
      • street: {name: "street", value: "1234 x street", timestamp: 123456789},
      • city: {name: "city", value: "san francisco", timestamp: 123456789}
    • This data often resides on a Hadoop filesystem and may be performance optimized via Spark
  • Graph: Neo4J, Apache Giraph, MarkLogic
    • Graph databases uses nodes which represent entities like person, edges which represent relationships, and Properties which can be associated with both nodes and edges
    • Working with graph databases is generally intuitive as the storage model directly reflects natural language rather than being burdened with the physical implementation details of most other data stores e.g. relational or column
    • Relational databases use keys to relate entities to one another leading to "joining" one to many tables. Graph databases use pointers to relate one entity to another and may capture properties about the relationship.  The deeper the levels of relationships the more this database differentiates itself from relational stores.
    • Compared with relational databases, graph databases are often faster for associative i.e. highly related, data sets and map more directly to the structure of object-oriented applications. They can scale more naturally to large data sets as they do not typically require expensive join operations. As they depend less on a rigid schema, they are more suitable to manage ad hoc and changing data with evolving schemas. 
    • Conversely, relational databases are typically faster at performing the same operation on large numbers of data elements.
  • Multi-model: MarkLogic, OrientDB
    • Supports multiple data models against a single, integrated backend. Document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database.
    • An article by Martin Fowler suggests this type will become more prevalent over time Polyglot Persistence
  • Document: MarkLogic, MongoDB
    • A document-oriented database, or document store, is designed for storing, retrieving, and managing document-oriented information
    • Graph databases are similar, but add another layer, the relationship, which allows them to link documents for rapid traversal.
  • Key-Value: Redis, Oracle NoSQL
    • Manages data in associative arrays, a data structure more commonly known today as a dictionary or hash. 
    • Dictionaries contain a collection of objects, or records, which in turn have many different fields within them, each containing data. These records are stored and retrieved using a key that uniquely identifies the record, and is used to quickly find the data within the database.
    • Very easy for developers to use e.g. to persist objects.  Computationally powerful for some use cases. Some graph databases underlying implementations are key-value store.

Dealing with the Big in Big Data

A simple example:
Let's take a simple comparison: doing analysis of invoices versus sensor data from the internet of things. Traditionally for invoices you might extract the invoice data from your transactional data store, transform it into dimensions and facts, load it into your data mart to run reports or do sophisticated analysis by loading it into a cube. For gigabytes of data this works great (if requiring a lot of money to set up - more on that in a minute). 

Sensor data sets are often much, much larger, think terabytes. Yet, we can simplify and say the data has many dimensions (e.g. time, sensor type) and sensor readings (facts) to assess.  Using ETL to create a data mart is impractical. Enter "big data" with its distributed data stores and analytic capabilities.

Let's take a more accessible, if over simplified, example than sensor data. Perhaps Macy's wants to do analysis of the last 50 years of sales invoices.  Rather than put all of that data into a data mart they might leverage the distributed computing power of Hadoop and MapReduce. They could create a text file for each invoice and then distribute the invoices across many data stores in the cloud. Next they'd leverage MapReduce to do their summarizations across many computers in parallel.

Turns out this processing can be done very quickly. Programming is involved but a no where near the cost of using a more traditional approach. The aggregations etc. that this processing does can then be fed into a tool like SAS (or R) to do more in-depth, ad-hoc analytics. 

Getting more granular:
The term "text file" above could use some expansion. These could be realized as NoSQL columns, hive "tables" etc.  Hive is a great example of why we might choose "not only SQL" as the expansion of the term NoSQL. It exposes a SQL API to interact with virtual tables.  

As the complexity of the problem solved grows so does its implementation details. For example, designing the structure of an HBASE database is non-trivial.  

In the simple example above we say the outputs of MapReduce can then be input to a tool like SAS for ad-hoc querying.  Apache SPARK enables Hive to present users complex, OLAP style ad-hoc query capabilities agains huge distributed data stores. 

Spark creates a flexible abstraction called the "resilient distributed dataset" that aggregates data across many computers in a cluster. This aggregation overcomes MapReduce's limitation of requiring a linear data flow where you map a function across data and then reduce the results onto disk. It overcomes this limit by creating shared memory across the computers in the cluster enabling iterative passes over the data. Put more simply, it brings OLAP (and machine learning capabilities) to cloud tools including Hadoop's HDFS, Cassandra, Amazon S3, Open Stack's Swift...

However, Spark also necessitates additional complexity such as the use of cluster managers. It has its own native manager as well as provides support for Apache Mesos and Hadoop YARN.  Once again we see google innovation at work. Mesos conceptually descends from google's omega scheduler. It has used Omega to manage its services at scale. We'll stop here as cluster managers are a topic onto themselves.

Finishing up

This post summarizes a huge space in a few pages. I hope you found it useful. I've tried to describe a very complicated space in a simple manner.  If I've misrepresented rather than simplified, please comment so I might update this post.

So we started big lets end big.  Facebook's network of friends has caused it to be a pioneer in the graph processing. They have scaled graph processing up to handle a trillion edges ie. relations! Read about that here.  Here's a visual of their conceptual architecture.

Monday, December 5, 2016

Big Data Business Intelligence and Visualization 2016

Early in my career at Accenture I spent a lot of time consulting in Enterprise Information Management or more specifically: Content Management and Publishing (IA), Data Strategy and Governance, and bit in BI.  After spending some years in mostly Agile, OLTP, and SOA, job opportunities present themselves that have me looking into the space again. This post is the first of a few on developments here. This one looks at Gartner's 2016 vendor POV and some self-service Data Visualization vendors.

With the advent of big data technologies like Hadoop, Map Reduce, Spark, Graph DB's, Neo4JPregel... visualization, multi-dimensional networks, IOT and cloud this space is ripe with opportunity. Gartner's 2016 Magic Quadrant for Business Intelligence and Analytics Platforms illustrates some of this. They have fundamentally retooled how they look at the space.

Gartner's view is generally: 

  1. The market is moving from centralized IT BI capability delivery to decentralized self-service business capabilities often baked into business processes. Business self-service data preparation and analysis is a good example. 
  2. From a technology perspective: "By 2018, smart, governed, Hadoop-based, search-based and visual-based data discovery will converge in a single form of next-generation data discovery that will include self-service data preparation and natural-language generation."
  3. The vendor market is fragmented. Trying to rely on one big vendor will likely not meet all of your needs. In the near term businesses should assess their needs and find the products that match them.

Gartner's five use cases reflect this view:

  1. Agile Centralized BI Provisioning
  2. Decentralized Analytics
  3. Governed Data Discovery
  4. Embedded BI
  5. Extranet Deployment
Before we look at the Magic Quadrant let's look at overall vendor group's performance against Gartner's 2016 "Critical Capabilities for Business Intelligence and Analytics Platforms" publication. It is interesting that the rankings, using a 1-5 scale, on average are not that good. I am not sure if this is a common occurrence in their assessment methodology. The rankings may simply reflect market conditions.

The group's only "excellent" capability average comes in "BI Platform Administration." Looks like cloud may be helping to level the playing field a bit for new entrants.  

The overall vendor results are similarly mixed:
Gartner provides customer reviews of many of these vendors here.

Gartner Magic Quadrant

Note that the Leaders rank only "fair" on average across Gartner's 14 critical capabilities.  Of those with an average rank of "good" most are in the Visionary quadrant and 45% in Niche.  Our deep pocket product vendors are mostly average "fair" as well. Interesting market right now.

Something to consider: What do you really want? 

Gartner assessment criteria are somewhat conservative focusing as much on vendor stability, size etc. as on the quality of the underlying product implementation.  To some extent that unmeasured quality  characteristic dictates the vendor's  ability to innovate and deliver capability differentiation. This suggests Gartner may be a poor guide if you are after differentiation. For example,  graph processing and databases as well as graph based analytics delivered via Cloud shows promise. Look at what Facebook, Ebay, AWS and Google have been up to. More on this in a future post. Another example is in the Data Visualization space. Let's take a quick look there now.

Data Visualization Product Landscape

Data Visualization is key to modern Analytics. Roughly half of the top products made the Gartner list. Again - makes sense given Gartner's assessment criteria - but it sure seems there is opportunity out there to leverage!

Additional References

Here is link to the google sheet used to do the analysis above. It has a bit more data and links to more as well.

Friday, November 25, 2016

Kanban: Successful Evolutionary Change for Your Technology Business

Kanban: Successful Evolutionary Change for Your Technology Business by David Andersen.

If you are an Agilist then this is a must read book. This is another case of my experiencing key concepts from a book prior to reading it. The importance of slack, focussing on cycle time, empowering teams...

I wish I would have read this the day it came out. I'm working on a summary now. It starts here.

Friday, November 18, 2016

DevOps Business Cases - Chatting with Robert...

I had a chance to catch up with my friend Robert Boyd. We had a nice chat about DevOps business cases. Prior to talking I threw together a Google Slides deck to have something for us to react to. It was a very nice chat and it turns out we were seeing things very much the same way. We made a couple of tweaks to the ideas as we talked, namely we added Robert's favorite three top down metrics at the end.

The deck would not imbed in a way that looked nice so this post has the content from the deck in simple outline form:

Top Down DevOps Benefits

  1. Reduce time to market - realize value faster, less coordination costs
  2. Hypothesis test value and growth assumptions using actual customer behavior, improve feature ROI
  3. Reduce/eliminate production incidents from changes, faster mean time to recover (MTTR):  avoid costs, build customer goodwill
  4. Reduce/repurpose operating costs- automate tasks across development, infrastructure and operations

 DevOps Levers

  1. Establish a Lean, self-improving culture
  2. Loosely coupled, intrinsically testable architectures, applications and services that are easy to change and scale
  3. Automated, self-service build pipelines of production like environments reduce environmental errors/defects
  4. Automated test suites with high test coverage to reduce defects
  5. Automated deployments to production reducing opportunities for error
  6. Enhanced telemetry tied to change events and self-service reporting reveal cause and effect thus improve MTTR
  7. Release automation and controls e.g. feature toggles, canary, blue green enabling flexible releases
  8. Split testing capabilities enabling customer hypothesis testing

 DevOps Enablers 

Organizational models

  • *Market based (value stream aligned) integrated teams (infra, ops, dev, test, product)
  • Functional (capability aligned, e.g. dev, ops, test, platform) deeply integrated lean process e.g. test first, ops requirements defined up front, phased production transition to run, platform team owns shared self-service capabilities
  • Matrix - mix of above two


  • Scrum/Kanban
  • Lean analysis e.g. A3, Value Stream Analysis Five Why’s
  • Statistical analysis of operations
  • ...


  • Cloud
  • Infrastructure as code
  • Continuous Integration
  • Deployment and release automation
  • Cluster management (e.g. improve utilization via oversubscription) 
  • ...

Three Top Down Metrics

Focus whole organization on a shared goal, improve:
  • Deploys/day
  • Prod issues/month
  • MTTR
These metrics create a punch list of inefficiencies in the system to attack via solid monitoring, test coverage, sustainable architectures etc.

Map the value stream, identify and subordinate constraints...

If you'd like to see the deck it's here:  DevOps Business Case Chat (looks nicer - I like Google Slides new "explore" formatting feature)...

The DevOps Handbook

While we didn't call it DevOps back in 2012, when I joined Gap Inc.,we working towards achieving some of its goals then. Over the ensuing years we got reasonably good at it. I read Gene Kim's book, The Phoenix Project, a couple years back and so was excited to learn he was working on a new book entitled The DevOps Handbook.

I pre-ordered the book on Amazon and wrote a four part summary as I read it. This is as must read book if your in IT today. The book is long so if you are in a hurry and want to read my take on the most important concepts the summary is for you. It also serves as a good review if you've read it and just want to keep it in your head.