2017 Events for CIO, CTO, Chief Digital Officers, and Chief Data Officers

I was doing some research last week on 2017 events for CIO, CTO, Chief Digital Officers, Chief Data Officers, and IT leaders. I found articles on CIO.com and TechTarget with a number of events listed and then went looking at other institutions (Gartner, Forrester, CDM Media, Evanta, Argyle, HMG Strategy, O'Reilly and others) for their lists.

I focused on events in the United States (MVP) and aggregated almost 250 events targeting CIO and other data, digital, and technology leaders.

It took some time to put together what I think is a reasonable, but not comprehensive list. I then used Trifacta to merge and cleanse the data, added a dimension on "topics" and developed a Tableau dashboard to review.

Here is the CIO 2017 Events Dashboard. To use the dashboard, click on the map for your location and use the bar charts to drill down by topic, timing, and sponsor. In the grid, click on the Event Name to get to the event's website.


2017 CIO Events
Click the image to see 250+ 2017 events for CIO, CTO, Chief Digital Officers and Chief Data Officers

Insights into Event Topics


  • Chief Digital Officers should look at events under Digital Transformation, Digital Marketing, Customer Experience and Innovation.
  • Chief Data Officers should look at events under Big Data which also includes events on analytics, data science, and data management.
  • Emerging topics include blockchain, AI, IoT, Wearables, and innovation.
  • DevOps includes cloud conferences. All other IT operations such as data centers and service desk are covered under Operations. Security is a separate topic.
  • Events from leading technology vendors are under the topic Technology.
  • There are separate topics for enterprise architecture, software development, and mobile.



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How to Select a Data Visualization Platform for Citizen Data Scientists

Over the last few years, I've been telling readers, colleagues, stakeholders and clients the importance of establishing a data driven organization as part of a digital transformation. To compete today, organizations need to be smarter and faster to strategically target market segments, develop new products, improve customer experiences, and automate operations. Organizations can't get there by shooting from the hip and have to educate and empower a larger number of managers to leverage data in their decision making.

Is establishing a data science team sufficient? Skilled data scientists should be used for the most important and complex data analytics an organization requires, but this only provides part of the answer. First, there is a data science skill shortage, so it's unlikely most organizations have sufficient data scientists to perform all the analytics. Many organizations simply can't afford data scientists or have the cache to recruit them, and enabling citizen development programs is one way CIO can address the technical skill gap. To be successful, most organizations need to consider training and outfitting "citizen" data scientists that can take develop analytics and mentor colleagues to use them in both strategic and tactical decision making.

Kicking off Citizen Data Science Programs


I've already blogged on how to kickoff a citizen data science program. Read this post to see how to find early adopters for the program, get buy-in to support the program, and start developing standards and practices. I've also shared what services citizen data scientists need to be successful, and how to assign data roles/responsibilities between data and technology teams.  I also suggested best practices on developing dashboards and also laid out an agile process to finding value in dark data.

But I haven't spoken about technologies and platforms for citizen data scientists. Selecting platforms is a very important consideration in order to make the program both short and longer term successful. Keep in mind that all organizations already have some tools for business analysts to process data including Excel and other legacy BI platforms.  Organizations should look beyond these tools if they are serious about citizen programs. From my post on data governance challenges around Microsoft Excel, "The issue is, that Excel always made it too easy for business users to create splintered and derivative data sets." This is in addition to the very long list of Excel horror stories aggregated by the European Spreadsheet Interest Group. The other issues CIO fear is that empowering citizens will lead to a proliferation of single-purpose reports and dashboards, similar to what many organizations implemented in their legacy BI solutions.


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Data Visualization Tools Selection Criteria


So if you're going to outfit citizen data scientists, you have to consider some traditional business requirements and some newer, "big data" driven ones in order to pick tools that are appropriate for the size, scale, skill, and complexity of the organization, the underlying data, and the analytics required.

For starters, since becoming a data driven organization is key to successful digital transformation programs consider reading six critical strategies for selecting breakthrough digital transformation platforms and my other post on six digital criteria to evaluate superior technology. These posts highlight a number of generic considerations when selecting technologies such as (i) align on vision, strategic opportunity, and short term needs, (ii) use experts to define solution sets, (iii) perform detailed reviews of the user experiences, (iv) evaluate documentation and the health of the tool's ecosystem (data, integration, and developers), and (v) consider the organizational impact of the tool.

When selecting data  visualization tools for data scientists, a number of more specific criteria emerge based on the people, data, analytics, and other constraints.

1. People, Skills, and Organizational Impact


These criteria require you to understand the needs of three types of users (i) citizen data scientists that will be the primary developers of dashboards and analytics, (ii) data scientists, quants and statisticians who may also use this tool but may have additional integration requirements, and (iii) end users of the completed dashboards and analytics


CriteriaImpact
Number of citizen data scientistsMore training and governance will be required for larger teams.
Skill levels of the citizen data scientistsIf low skill, less sophisticated tools with easy user experiences will yield faster results. Tools that heavily rely on programming models may be difficult for novice groups.
Organization also has skilled data scientists, quants, or statisticiansDecide on whether they are in scope and if yes, consider their integration needs. Advanced data scientists may prefer data visualization tools with programming models that offer more flexibility and integration capabilities.
Number of departments that will leverage completed dashboardsMore departments imply disparate use cases. Consider tools that have programming models or have mechanisms that enable reusing visuals. 
Number of users that will access completed dashboardsIf large audiences, then user experience of the dashboards and visuals should be a top criteria.

Bottom line: These criteria should help you decide whether you need a simple and easy tool for a small, less sophisticated group or a more comprehensive tool aimed at  higher skilled developers and greater organizational needs.

2. Data Management Considerations


You can't complete data discovery work, perform analytics or create dashboards without some consideration of the underlying data sources and their complexities.

CriteriaImpact
Number of data sourcesQuantity is important, but more important is whether citizen data scientists will be incorporating new data sources on a regular basis
Big data considerations?Are you handling larger volumes, higher velocity, or greater variety of data sources and types?
Real time data?Does your organization require processing data in real time?
Data quality, transformation, or master data consideration?Are you connecting to relatively clean data sources or do you expect significant data processing and preparation will be needed? If yes, you may need a data preparation tools such as those offered by Informatica, Talend, Alteryx, and Trifacta.
Enterprise data sources?Most organizations will look to secure and automate data connections from enterprise data sources.
SaaS data sources?Many SaaS providers have APIs for pulling data. Review whether the data visualization tool offers a direct connection to your SaaS platforms or if one is available through platforms such as IFTTT or Zapier.
IoT data sources?Sensors often produce a large volume and velocity of data. You'll likely need data storage and stream processing technologies to handle IoT sources before connecting a data visualization tool.
Confidential, privacy considerationsWill you need to consider mechanisms to secure data and manage entitlements? If yes, then you need to review the security capabilities of the data visualization tool and also consider adding tools that mask and encrypt data elements.

Bottom line: These criteria all speak to whether you require additional data integration, preparation, processing or management tools in addition to any data visualization tools. Many of the data visualization tools come with some data preparation capabilities and some market themselves as an end to end data management tools. A few will try to sell you on the concept that all you need is them, no other databases, ETL, or data integration tools because they come with all the required capabilities.

So these criteria should help you flush out whether this is a realistic proposition. The more data sources, the bigger the data, the more enterprise sources, and the complexity of the data preparation work are all indicators that you will likely need additional data management tools. On the other hand, if you're working with relatively few, less complex data sources then make sure to evaluate the data preparation capabilities of the data visualization tools and see if they are "good" and "easy" enough.

3. Constraints


Before getting to the heart of the analysis, you'll want to consider other selection constraints.

CriteriaImpact
Legacy toolsDoes your scope include phasing out any legacy BI or reporting tools? If yes, you'll want to consider what dashboards, reports, or analysis are in scope for conversion and where there is flexibility to modify output formats.
Business modelIn addition to overall cost, you'll want to consider how the vendor prices with usage and whether that will create higher than expected costs as usage increases. This is a very important criteria for customer facing analytics especially if customer will receive access to the data visualization tool.
Costs and budgetPricing models may box out smaller organizations from selecting the more sophisticated tools. Can you afford it?
RegulationRegulations may pose requirements on how and where data is stored and accessed. It may also require auditing, analytics lifecycle, documentation, and other data governance capabilities.
Hosting optionsSaaS? Cloud? Data center? What options are available and your organization's requirements?

Bottom line: Technology selections need to consider financial, legal, logistical and other constraints. It's best practice to identify these up front to help limit the scope of the review.

4. Data Visualization and Analytic Capabilities


You'll spend most of your time evaluating data visualization tools based on their visualization capabilities, ease of use, and sophistication of analysis.


CriteriaImpact
Chart types availableEvery visualization tool comes with a toolkit of chart types. All will have bar charts, pie charts, data tables, etc. but some will include geo mapping, heat maps, node graphs and other more sophisticated visuals. What's required versus nice to have?
One time or ongoing analysisIf you're conducting more one time discovery work, then you'll want to consider how easy it to use "out of the box" analytics and review the tool's story telling capabilities. (Some good examples of story telling are here and here.)
Internal or customer facingIf you intend to develop customer facing analytics then this has implications on the type of delivery expected (direct access versus pdf outputs for example), whether there are style or branding considerations of the final product, security considerations (how to enable data entitlements), and performance considerations (speed becomes more critical). 
Analytics needsAggregations? Trends? Modeling? Machine learning? You'' want to consider not only whether the tool has the capability, but how easy it is to use and whether you'll need to integrate with programming environments such as R or Python to implement these algorithms
Visual configuration needs?It's one thing to have the chart types desired, but then you should consider how easy they are to configure and the overall configuration capabilities. If you're doing customer facing visuals, then reviewing the visual configuration capabilities is important to ensure that the output meets minimal customer expectations.
Reusability? StandardsIf you plan to develop a large number of dashboards or analysis, you'll want to consider how to reuse and standardize elements such as dashboard layouts, chart configurations, calculations, expressions and other elements that are programmed.

Bottom line: These criteria all address the core capabilities of the tools and separate out less sophisticated needs versus more flexibility and analytical capability. You'll want to invest considerable effort investigating these capabilities, but be prepared to make compromises. Most tools can't be all things to all people but many will try to sell you that they can handle your requirements. The best way to evaluate these tools is to run proof of concepts.

Data Visualization Tool Selection Process


The figure below provides some guidelines on a selection process




In summary:

  • Define a tool selection committee and have them propose a charter - Keep this team small, but empower them to make decisions to avoid stakeholder conflicts.

  • Use primary selection criteria to short list the tool set - There are a large number of data visualization tools in market today, so use the criteria from people/organization, data management, and constraints to help narrow down the list.

  • Commission proofs of concepts to evaluate the visualizations and analytics - It's better than doing a paper evaluation, Have a small group of your proposed data scientist use the short listed tools against some of the short term needs and evaluate the output, effort, performance, and end user satisfaction. 

The Gotchas in Selecting Data Visualization Tools


I promise you that selecting a data visualization tool isn't as easy as I just laid out and there are a number of significant "gotchas" that can steer you in the wrong direction. I'll first share these in my newsletter, so please consider signing up!

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Hot Twitter Hashtags for CIO [DataViz]

We're tweeting about key technologies like blockchain, artificial intelligence, IoT, big data and analytics.Transformation topics on digital transformation, agile, social business, and smart cities are also top of our tweets. Click here to use the data visualization on CIO Influencer Hashtags.

CIO Influencers Hashtag Data Viz
Click on the image to use the dashboard

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What do CIO Influencers Tweet About?

I love a good data science challenge!

An image from Leadtail floated around identifying "Who Influences CIOs?"

Who Influences CIO



This was followed by a question posed by David Bray, @fcc_cio

Request for Tweet Cloud


I love a good big data question, so I decided to roll up my data science sleeves and see if I could develop this tag cloud. I wanted to do this without coding and use as many tools the average data scientist could leverage to perform this analysis.

I learned a lot, and was able to develop a lot more analytics than just a tag cloud. If you're interested in seeing my dashboards or learn about the methodology, please sign up for my newsletter!

So, here is an aggregate of what all of us are tweeting about. This is looking at everyone's last 1000 tweets, a total of 20K, over varying time frames ending last week. The more an influencer tweets, the shorter the time window.


What CIO Influencers Tweet About


You can see that I grouped hashtags into several categories so that you can get some insights. No surprise that the top hashtag is #CIO followed by the #CIOChat that @MyleSuer hosts on Thursdays and Saturdays. After that, you can see that we tweet a lot about transformation, (#DigitalTransformation, #Collaboration, #Innovation), different technologies (#blockchain, #ai, #bigdata, #cloud), industries, and events.

David - here's what your cloud looks like

Tweets from fcc_cio

And here is mine (@nyike)

Tweets from NYIke


What else can you see in Twitter data? 


There are other insights I was able to pull from the same data

  • Trends on hash tags and influencers
  • What hashtags and influencers had the highest activity (favorites and retweets)
  • What are some "emerging" topics that influencers are tweeting about

Happy to share more insights through the newsletter.

Updated: The full dashboard is now online here.


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10 ways to Enable QA in Agile and DevOps Teams


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Last year I published a post, DevQOps - Giving QA a Seat at the DevOps and Digital Transformation Table acknowledging that when organizations investing more in application development have to consider the increase in skill and investment required for testing activities.

In my talk last week at AllDayDevOps, I reminded DevOps practitioners that QA is still a critical discipline within DevOps teams. Let me lay out my beliefs -

  1. QA mindset is different than development mindset.  QA is evaluating business benefit and risks to optimize testing while development is balancing their development effort between customer needs and technical debt.

  2. QA skills are distinct from development skills. QA is typically using tools to automate function and load tests then using other tools to validate security and device compatibility. Developers are using tools to develop code and unit tests.

  3. Both developers and QA should be committed to delivering shippable code at the end of every sprint, so every sprint team needs developers and QA working collaboratively toward that goal.

  4. When sizing stories and committing to the sprint, QA should be working with the team and voicing their opinions on the complexity and effort around testing. Story sizes should reflect both the development and testing complexities.

  5. Developers should use coding and documenting principles enabling QA members to understand their code and enable them to diagnose defects.

  6. Teams should collaboratively plan their sprints so that the effort required to test and automate doesn't become time-crunched the last few days of the sprint.  

  7. QA should not only identify the defect, but should take a first pass at reviewing the code and identifying the underling issue.

  8. QA should be providing specific guidelines on whether a release "passes QA". They should become loud and speak their objections when others want to release without this endorsement.

  9. QA should be the first group to triage incidents and defects identified from Ops. At minimum they should take steps to recreate the issue in their testing environments. Ops teams should be providing sufficient computing environments, access to log files, and reports from monitoring systems to enable QA.

  10. QA should develop metrics showing actual test coverage against a defined targeted standards. If this percentage is low and the number of defects discovered in production is high, they should review causes and provide recommendations on improving their practices and identifying where additional investment is need.

You can watch my talk, Driving an Agile and DevOps Culture that Delivers Business Transformation to learn about transformation, team responsibilities, and developing a culture. 
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Driving an Agile and DevOps Culture that Delivers Business Transformation

Thanks to the team at All Day DevOps for giving me the opportunity to speak about this critical topic on the intersection of agile, devops, culture and business transformation. If you missed the talk, I'm summarizing below and including the stream to watch it along with several other great presentations. If you would like access to the slides, I have a newsletter signup at the bottom of this post.


Summary on Driving an Agile and DevOps Culture that Delivers Business Transformation


  • Most businesses needed to rethink their future digital businesses. Many will increase technology investments to deliver improved customer experiences, new products, and new digital means to attract customers.
  • IT budgets are flat and CIO have to balance investments in Dev, Ops and Testing.
  • An operating model needs to balance innovation, operating needs and delivering quality results.

Getting the culture right then requires three steps - 




All Day DevOps 2016 Continuous Integration/Delivery Track East Coast


Here's the lineup for this session: 
  • Speaking for the Dead: Lessons from Waterfall and Monolithic Architectures – Michael DeHaan 
  • Meta Infrastructure as Code: How Capital One Automates Our Automation Tools with an Immutable Jenkins – George Parris 
  • Driving an Agile and DevOps Culture that Delivers Business Transformation – Isaac Sacolick 
  • I, For One, Welcome Our New Robot Overlords – Mykel Alvis Building 
  • Quality and Security into the Software Supply Chain at Liberty Mutual – Edward Webb



You can review all the other sessions Dashboard for Access to All Day DevOps Sessions.

If you would like access to the slides, please sign up for my newsletter where I will provide initial access to them.


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