Friday, December 19, 2014

2014 - The Year the CIO and CMO Partnership Became Mission Critical

I started 2014 with several posts on the CIO / CMO relationship and where IT departments can help with digital marketing initiatives. The main touch point is around data and the technologies that enable CMOs to be more data driven. With so many technology vendors trying to tap into marketing budgets and when most of the key marketing data is is enterprise systems, SaaS products, and other data silos, the CIO has the opportunity to help deliver marketing results in a sustainable way.

Big Data technologies and practices are at the front and center of this collaboration. Every company has CRM(s), financial systems, transaction systems, and now marketing automation platforms that contain information on prospects, customers, and former customers. Big Data platforms can help join this data and be the backbone to cleansing, data mining, and analytics efforts needed for digital marketing efforts.

Click on the image below to learn more. Also, see my related posts on Big Data, Self Service BI, and Agile data practices.

5 Tips On Starting a Killer
CIO - CMO Relationship
CIO CMO Partnership -
The Modernized
Technology
Selection Process
How to Clean
Your Inbox
and Drive Marketers
Crazy
Last Call for Collaborative
CIOs and CMOs -
Winning at
Data Driven Marketing
5 Ways IT Pros Can Help Marketing
Dear CIO,
Here's How To Help the CMO
with Marketing Automation
3 Big Data Platforms Needed for Marketing Automation Success

continue reading "2014 - The Year the CIO and CMO Partnership Became Mission Critical"

Monday, December 15, 2014

2014 Big Data Posts - Self Service BI, Agile Data Management Practices, and Killing Data Landfills

In 2014, many of my posts on BigData were actually about "small" data management practices. Specifically, I hope these posts educated readers that tools of "Data Science 1.0" such as Microsoft Excel, PowerPoint and especially Access can disrupt - even kill - efforts to make organizations become more data driven.

In addition to calling out this problem which is something I have been doing for a couple of years now (see my classic posts on Spreadsheet Jockeys and my plea to Stop Creating More Access Databases), this year I offered solutions. Specifically, I think CIOs need to deploy Self Service BI tools and establish more modernized data management programs. I support data scientists that act like disciplined hikers, adopt agile practices, and avoid creating new data landfills.

Click on the images below to read related posts. Good luck everyone in 2015.

The Database Decision that Will Hurt Your Company's Big Data Opportunities
Succeeding in Big Data Transformation - It is a Journey, Not a Destination!
Friend or Foe? How Microsoft Excel 2013 Creates New Data Governance Challenges
Why is Data Sooo Messy and How to Avoid Data Landfills
Killing Bad Data Practices - Acknowledging The Problem is Half The Battle
5 Agile Leadership Practices Where CIOs Can Help Data Scientists
Five Data Management Practices IT Needs to Better Support Data Driven Organizations
What Technologies Work Best for Decentralized Data Scientists?
The Agile Data Organization - Balancing Responsibilities in Data Science Programs
Agile Data Scientist, A Disciplined Hiker or Reckless Hunter?

continue reading "2014 Big Data Posts - Self Service BI, Agile Data Management Practices, and Killing Data Landfills"

Tuesday, December 09, 2014

The Four Strange Leadership Momentos on My Desk and What They Mean

For the last few years as CIO of McGraw Hill Construction, I've had four relatively strange items on my desk that I've kept around for special reasons. A few people on my staff have some understanding on why I kept them, but few have asked about them. I think everyone working in IT might have some interest in the stories behind these items as they represent different leadership principals. So here is why this CIO has bottles of Heineken, Bardolino, and Napa Valley Mustard along with a toy dump truck on his desk.

  • The Bottle of Heineken - In my first year leading technology at BusinessWeek, we launched a business oriented social network Business Exchange that enabled people to collaborate on business topics and view articles that were rated highest. This was a controversial project because until then, most of BusinessWeek's content was created by journalists and Business Exchange was a significant step to stretch the brand and capture audiences looking for a wider breadth of business coverage. To develop the product, we successfully brought together collaborators from all departments - editorial, marketing, product, technology, and sales - to shape and launch it. We held a big internal celebration the week it launched.

    Unfortunately, I missed this celebration. We had kinks to knock out in the product's infrastructure and we were experiencing some kind of issue during the celebration. I stayed back to work with the IT team that found and fixed the issue but by then the celebration was over. Thankfully, one of my lieutenants brought us some beverages from the celebration.

    I kept my Heineken as a reminder not to miss these celebrations. Truth be told, this wasn't the first time in my career that I missed a celebration for an IT accomplishment because of a systems issue. At the time, I was angry and promised to make sure that my next products had sufficient "alpha" periods to insure that launches were seamless and successful.
  • A Bottle of Bardolino - This was a gift from a consultant who brought it back from his trip to Europe and has been sitting in my office since then as a reminder to travel. Travel personally and for business - just get out there, meet some people, be inspired, and explore. Visit your offshore teams, go on a sales call, or speak at a conference. 
  • A Bottle of Napa Valley Mustard - This came in a gift basket from a CIO colleague as a thank you for helping him interview candidates for a key lieutenant on his team. I kept it around as a reminder to thank people for their extra efforts and accomplishments.
  • The Dump Truck - When I came over to work at McGraw Hill Construction (now Dodge Data and Analytics) a colleague gave me this toy as a gift. Yes, I would be working in the construction industry, hence the dump truck but his hidden message was, "Prepare to clean up a mess." The CIO's job is one of constant cleaning, improving, and building toward the future and sometimes you do need an over-sized dump truck to blaze a new path. At Construction, we launched five new products, re-platformed the flagship product, and established several new technology platform capabilities.Glad I had a staff up to the challenge, the right agile tools, the ability to select platforms, and of course, this dump truck!
Now the dump truck and the mustard sat in the back of my office capturing little attention but most of my staff knew what it meant if I had either the Heineken, the Bardolino, or both on my desk. Watch out!



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Wednesday, December 03, 2014

The Most Simple, Agile, Portfolio Management Tool

Today, I am going to share the most simple, agile, program management tool. I've implemented a versions of this tool in every organization I've been a part of for the last ten years. It doesn't require an expensive enterprise portfolio management tool and can be easily implemented in almost any SaaS database or workflow tool that can support lists and forms.

This Tool Solves One Very Simple Problem


Here is the question I ask every Monday morning, "What did everyone accomplish last week?" 

Usually I am asking this question in the context of working agile teams, running programs, and active projects that have multiple people working on them and have durations from several weeks and greater. This tool is not for individuals working on tasks or managing requests or incidents - there are plenty of other tools for these purposes. 

Here it is - The Most Simple Agile PMO Tool


Ask everyone in your organization that has some management responsibility to do the following:
  • Once a week (I like Fridays), log into a tool that you've configured
  • Create an entry for something that they worked on that week. Give it a name. I call these Initiatives so that people don't get hung up on what is a program or a project. Reuse this name (ideally implemented by a drop down) when providing Updates in subsequent weeks.
  • Write 2-3 sentences on what was accomplished. 
  • Mark the status as Red, (I am in trouble), Yellow (some issues, but things are under control), Green (things are progressing as expected), or Done! 
  • Click Submit.

That's it! Extra points for those of you that have more sophisticated tools and can implement Initiatives and their Updates in a parent/child table structure. As for process, I nag people Monday mornings if they miss entering an Update and I review the full report at my staff meeting.

Why This Works


It's simple to implement, simple to contribute to, easy to consume, and straightforward to use as a tactical management tool. Team leaders can use it to raise their hands and say they need help (yellows) or if their programs are really going off course (reds). It lends to communication and collaboration. It provides some basic measures (% green, # initiatives done) without becoming overbearing. 

Need more detail? That's the best part... Starting simple and you can add more detail where it is needed and valuable.  


continue reading "The Most Simple, Agile, Portfolio Management Tool"

Wednesday, November 19, 2014

How Artificial Intelligence Will Solve IoT's Big Data Challenges

If IoT is going to deliver on its transformational promise, it will have to provide greater value and importance than a single internet enabled sensor such as a wearable device. The technology to create a central hub around a small collection of sensors, for example in home automation, has been around for decades. What is revolutionary today is that home automation is cheaper to implement and gives home owners better software to monitor and control their homes remotely.

As I've said in a previous post, the real magic happens when a hub of managed sensors can easily communicate with other neighboring systems. Each hub has to be programmed to intelligently broadcast signals to its neighbors and also make intelligent decisions on how to process signals from its neighbors. 

Examples of IoT Magic


Here are some examples. Maybe there are heavy rains and my basement is beginning to take on water. Can my home automation system alert neighboring systems that flooding is eminent and that the home owner should consider precautionary measures? What if my fire alarms also made similar communications if there is an emergency? These are all examples of neighboring systems sharing information.

Information sharing scales regionally as well. What if the town's facility's team was alerted when multiple homes on a block were flooding. They could then come on sight, review the environment for issues, and maybe fix sewer drains that were flooding. What if fire departments received information on the severity of a fire and its location before a 911 call?

The Challenges of Information Sharing


Information sharing has two fundamental starting problems. First, the security and privacy of what information is shared needs definition. What information and under what circumstances am I willing to share with neighbors and the extended region? It's hard enough for most users to set their Facebook privacy settings, so home automation and wearable device manufacturers will need to consider how to simplify selecting these preferences.

Then there is the question of how various systems will process a larger scope of data coming from neighboring and regional systems. Now for small scale systems with well understood patterns, a rule based approach implementing "if this then that" may be sufficient. But for ecosystems with millions of sensors, thousands of neighboring systems and hundreds of regional systems, rule based systems are likely to be too complex to define and program - assuming patterns are well understood.

Where AI Meets IoT


AI bases algorithms have a better chance to succeed in places where rule based systems are too complex to program or need to process too much data. Neural networks identifying patterns, fuzzy logic based controllers that can respond to local, neighboring and regional inputs, reinforcing learning algorithms instituted at regional systems to identify macro conditions are all AI possibilities to help transform dumb internet connected sensors to intelligent ecosystems.

AI could be used to determine "dangerous" conditions when local systems may be permissioned to "share" additional information with neighbors and regions. For example, upon sensing a flooding danger, a neighbor's home automation may proactively turn on the basement sump pump in response. A health monitoring wearable device could be programmed to seek out a nearby and volunteering doctor, medic or nurse on an emergency condition.

These are all interesting and promising AI applications in IoT. The trick will be in getting enough participants and early adopters to establish data sets, test user interfaces, and validate AI's logic and response.


 
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Thursday, November 13, 2014

The Death of Microsoft Office - When Will Collaborative Tools Disrupt?

Last week, Microsoft announced that mobile and tablet versions of its Office applications would be free. The question is, should we care?

In the short term, the answer is yes.  Most companies and enterprises spend a good deal of money yearly on user computing devices and Microsoft licenses. Now let's say a number of those users are mobile, say the sales team. The new pricing enables IT to experiment and test Office with a small group to validate the experience on a tablet. If the mobile workforce can do enough of their document, presentation, and spreadsheet work on tablet devices then there is a strong likelihood that IT can outfit this group with tablets rather than laptops.

But the reality is that for the last several years, there have been less expensive alternatives to Office including offerings from Google, Zoho, Apple and now Amazon. For light weight users that don't need sophisticated features consumers and enterprises have had options if willing to change to a new user experience.

What about Power Users - What are their Needs Beyond Today's Office?


For power users, the question may be when, not if, there are suitable replacements to Microsoft Office or Office 365. Despite years of effort by Microsoft to enable collaboration and BI features in Office, most business users ignore these capabilities and end up working individually, passing documents back and forth via email, copying data into spreadsheets to do run off analytics and pasting charts into single use Powerpoint presentations. The aggregation of this this wasted digital effort will be the target for productivity improvements over the next several years.

The next generation of "Office" is already here, but the technologies go under different names and none have achieved critical mass as compared to MS Office. Until there are break away leaders that offer enough functionality to substitute MS Office and have a higher level of interoperability with other tools, the switching costs will look high to CIOs who can not afford a user backlash. Until then, these tools are largely additive to the Office experience. 

What is the next generation of "Office"?


It's collaboration tools that will aim to eliminate internal email dialogue and enable a more open, conversational, and searchable environment. Perhaps it will be a tool like Jive, an unseen mashup of Yammer, SharePoint, and Lync or a context enabled environment like Salesforce Chatter. 

Perhaps Excel 2013 will be more friend than foe in enabling spreadsheet jockeys to follow best practices in data governance and avoid a new generation of data landfills. My bet is on self service tools like Tableau and Qlik that realize that Excel isn't the enemy, it's PowerPoint, and both are investing in storytelling functions to eventually disrupt cutting and pasting into PowerPoint or other presentation tools.

Finally, perhaps either Google, Amazon, or Apple will shift from a Microsoft legacy orientation of documents, presentations, data and email to a more collaborative, mobile, and cloud enabled paradigm. Today, their apps look a lot like lesser versions of the Microsoft tools in order to chip away at Microsoft's dominance and easily switch over users. But if one of them re-imagined the user experience and reoriented their tools, it has a chance to get business users to think and act differently.

Place your bets.











continue reading "The Death of Microsoft Office - When Will Collaborative Tools Disrupt?"

Wednesday, November 05, 2014

Agile Data Scientist, A Disciplined Hiker or Reckless Hunter?

In my last post, I provided some guidance on why and how agile data practices can lead to better Business and IT collaboration. Agile aligns priorities, focuses multidisciplinary teams to hit goals, and enables teams to self organize and figure out individual responsibilities. As the team succeeds, management can then map our roles, responsibilities, and other governance considerations.

Data Insights are a Journey


As I ended my last post, I suggested that there are some fundamental differences between agile teams delivering a software driven product versus the analytics and insights that data science teams strive to deliver. Product teams are optimizing against scope, cost, and time to complete their delivery and agile teams often fix time and cost while making tactical decisions on scope. Unlike product and software deliveries, data teams are less likely to have structured deliverables like product launches or software releases. As I explained to CIOs contemplating their data strategy, data science and analytics is closer to a journey and not a destination or milestone. This key difference leads to a different way of thinking about agile data teams.

Hiking or Hunting Your Way to Insights


Picture a hiker in the wilderness who is trying to find the most interesting locations to photograph and is using her skills and tools to find vistas, waterfalls, and wildlife. When the hiker has a clean line of site to an interesting destination, she will move with vigor to capture it. Other times, she will navigate the dangers of the wilderness in a search, often stopping to check her gear, set up camp, or completing other necessities needed for a long term journey.

Data scientists are in the search for insights, and much like a nature photographer, know they've found something insightful when they see it. Until then they are on a search or hunt using a combination of their skills and data tools to support their discovery efforts. 

Now there are some very disciplined hikers who are well equipped and methodical in their approach. When faced with adversity, they have the tools and skills to address challenges without compounding to the risks they are facing. There are also more adventurous hikers that act more like reckless hunters; they are so fixed on the kill that will take on additional risk in order to meet their objectives. (Note: I should point out that there certainly are reckless hikers and many disciplined hunters out there. My point in the analogy is to illustrate differences in both behavior and persona.)  

Agile Data Scientists


The same is true for data scientists. Complexity lies in the form of slow data processing tools, technical difficulties in getting data integrated, structural issues with how the data is stored, data quality issues and other impediments that complicate data discovery efforts. Some data scientists will collaborate to improve the underlying tools, data structures, data processes, or other infrastructure barriers in order to achieve their current and future goals. Other more scrappy scientists focused on just getting the job done will engage in bad data practices, create silo databases or perform adhoc analytics.

So there in lies two major differences between product and data agile practices:
  • Product organizations march to milestones like launches and software releases, data science is more of a journey.
  • Agile product teams evolve products around a stable set of platforms and infrastructure. Data scientists have to choose if and when to be disciplined because there are many tools that can easily bypass defined data structures and practices.
These two differences are key to managing data science practices and big data technologies. More to come!
continue reading "Agile Data Scientist, A Disciplined Hiker or Reckless Hunter?"

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