Lastpass

Lastpass is a browser plugin that stores your online passwords.

It eliminates the need to remember usernames and passwords for your online accounts because it auto-fills the forms whenever you login.  Super convenient.

LastPass generates very complex passwords, which helps avoid having your password guessed by hackers’ programs.  You don’t need to remember them, because LastPass does.

LastPass has a great report that inspects all of your stored passwords.  It quickly shows you duplicate passwords you’ve used in two or more accounts.  And it rates each password for its strength.  Don’t like what it found?  Use LastPass to generate new ones.

LastPass runs on Windows and Mac PC’s as well as all smartphones.

LastPass also has a sister product Xmarks. It’s a bookmark manager and syncs across all of your devices.  This is great when you change devices, such as buying a new smartphone or PC.  One click and all of your bookmarks show up on the new device.

One drawback to LastPass: you must use a master password to access the LastPass repository that contains all the other passwords.  Even though they encrypt everything, you’ll need to choose (and remember) a sufficiently complex password to your LastPass account.  There’s some extra features that can make access to LastPass even more secure, if you’re so inclined.

AnchorFree

AnchorFree establishes a secure, encrypted connection from your laptop to a wifi network.  This is important if you use a public wifi network, and is useful even on a password-protected one.

Like LastPass, AnchorFree gives you a lot of security without a lot of hassle.  Once you install it, it auto-connects over your network connection.  You don’t need to remember to turn it on.

AnchorFree doesn’t noticeably slow down your connection, at least here in the U.S.  They have built out their global network over the years to the point that it should perform well from most countries.

Customers also purchase AnchorFree for two other reasons:

• it provides some anonymity due to IP address obfuscation and encrypted communications;  people living in countries where their internet use is subject to surveillance find this useful
• its IP address obfuscation also enables people living in one country to access online entertainment content in another. For example, streaming Netflix from a country other than the U.S.

One drawback: I had trouble using AnchorFree on my iPhone.  The Mac and PC versions work fine for me.

Rather, IT’s role in managing business applications is ending.  Business users can do for themselves in minutes what used to require an IT programmer hours and days.

What’s radical about SaaS business applications like Salesforce.com is their configurability.  The fact that they run in another data center called “the Cloud” is less significant, imho.

Think about it.  Using a browser, business users with admin permissions can do lots of stuff to tailor how the SaaS application behaves:

• bulk import of data
• add new fields to the database
• create templates for workflows and business processes
• provision new users
• modify the role-based access model
• design dashboards
• …. and more

Contrast this to legacy on-premise applications like SAP.   Any change in application behavior required source code programming in “ABAP”.  Dozens of IT people would care and feed the beast, accumulating a long list of modification requests from the end-user community.  Upgrade cycles would require re-implementation of all of these changes against the new release.  Slow.  Expensive.  Brittle.

Thanks to the power of configuration, business department leaders are gradually and systematically dismantling on-premise ERP suites with a group of SaaS applications.  They are happy to be freed from the grip of a centralized IT organization.  And they’re voting with their feet (or, budgets) by consuming SaaS, IaaS and PaaS at an accelerating rate.

If IT organizations don’t re-invent themselves they’ll face extinction.  More in a future post on what re-invention might look like.

Because when the full engineering team is here soon, we’ll be in heads-down development mode along with our early customers.  No time for other stuff.

The results are pretty staggering:

• Everything we implemented is software-as-a service; it lives in the cloud
• Everything is “industrial strength” in terms of feature/functionality; we’re not going to outgrow these tools and apps anytime soon
• Everything was implemented within minutes or hours.  Enter your credit card number and go.  Tweak the configurations now or later
• Little or no installed software on laptops
• No servers required
• Everything is licensed as a monthly or yearly subscription (often I had a choice of either).  Easy on the cash-flow and easy to budget for growth

All of this was done without an IT employee or consultant.  All of this was done without owning a server.  All this was done without installing (and maintaining!) software.

We get immune to the hype surrounding the Cloud, but this experience reinforced the immense power of this trend.  Think about what this means to small businesses and their ability to “act big” on a budget.  Or, what this means to the IT department of a mid-size or larger corporation.

Massive change is underway and we might be underestimating it.

For the curious, here’s what we deployed so far:

• Salesforce.com
• Webex for conference calls and web meetings
• Accompa for product requirements management
• Rally for Agile product delivery
• Jira for defect tracking
• Github for source code control
• Basecamp for general-purpose project management
• Box for file repository
• QuickbooksOnline for accounting
• ExpenseCloud for expense report management
• Google AdWords for keyword advertising
• Google Analytics
• Algentis for outsourced HR, benefits and payroll administration

As we get closer to market launch , we’ll take the same appraoch for everything else:

• Website content management
• Marekting campaign management
• Various web analytics tools
• e-commerce and/or customer billing
• Various software development tools

Kudos to New Relic for writing about their toolset and inspiring me to write this post.

What’s notably missing?  Any talk on data quality and ways to deal with it.

I’m shocked, given my past and current experiences and the widely circulated anecdote that “80% of an analyst’s / data scientist’s time is spent preparing data to be analyzed”.  In other words, dealing with inbound data quality.

One explanation could be that the Big Data world is still focused on single-source click-stream data.  This is the cleanest data available.

But many of the best insights come from fusing many data sets together to paint a more comprehensive picture of a subject, such as a user or customer.  And this is when it gets messy.

How do you link multiple data sets together to know it’s the same user or customer across the various sources?  How do you deal with CRM and transactional data, which is rife with duplicate records, incorrect categorizations, missing values, etc.?

If we’re to take the next step in generating value from the Big Data ecosystem, the old problems still need solving.  Hopefully Strata 2014 will be a different story.

Why?  Because it makes for a more inclusive culture.  And because bringing many different points of view to bear on important decisions yields….better decisions.

To that end, I started asking friends of mine the following question: “where can we find communities of women in technology?”.  This was for the purpose of including such groups in our recruiting outreach.

The results thus far have been, to put it mildly, underwhelming.  Mostly in the form non-responses.  And a suggestion to search on meetup.com, which is like starting mostly cold.

What’s going on?

I know there aren’t many women in engineering roles, especially in proportion to the percent of men.  But they do exist.  And they exist in even larger numbers in roles like user experience design, another role we’re looking for.

(We interrupt this post to note that as I’m writing this, in the lobby of a hotel, James Brown is singing “It’s a Man’s World” in the background music.  You can’t get more ironic than that!)

But despite the size of the community, why can’t I tap into this network of professional women the way I have done with so many other communities of interest?

Rather than put forward my hypotheses, I’m interested in yours.  And any connections you could make.  The journey of a thousand miles – gender equality in tech – begins with but a single step.

UPDATE

I’m pleased to report that the first two hires we’ve made are women.  Not because we went looking for them in women-specific networks; it just happened.  But it’s a big step toward preventing our early culture from being defined by a homogeneously (young) male team that mirrors the tech workforce as a whole.

In the meantime, I researched and ultimately co-founded a new company in San Francisco called Bluenose Analytics.  And attracted a kick-ass venture investor as partner.  Details on all of this will come with the company and product launch later this year.

A few hints: we’re building an analytics application in the cloud using a Big Data stack.  The application will help companies with subscription business models keep their customers longer and earn more recurring revenue.

This is a huge market opportunity.  I repeat, huge.

In the meantime, we’re hiring.  User experience designers, Java developers and Big Data stack developers.  If you’re interested to know more, contact me.  Or, share this with some friends.

My contact details are on my “about me” page and all over social media platforms.

I’ve tried almost all of the others.  But Pandora’s music matching algorithms have exposed me to lots of new & cool artists from genres I already like; better than the other services.

So why are we getting divorced (maybe)?  Because the streaming of a song in progress is often interrupted by the start of another song.  Or an ad.  Both interruptions are a huge bummer (Ads are ok between songs.  I use the free version, after all).

I tweeted Pandora’s CTO pleading for help in fixing their app.  And he was incredibly responsive.  But I ultimately got put into a process designed to make the user go through all of the hoops.  The latest email I got after previously being directed to uninstall & re-install the app and re-boot my phone:

Sorry for the continued trouble, but thanks for giving those steps a shot. I noticed that you aren’t running the latest version of iOS on your iPhone, which helps address bugs and provides you with new features. 

You can install the free update by connecting your iPhone to your computer. Now, click ‘Update’ on the main iPhone screen in iTunes. You can also update your phone by installing the update directly by going to Settings -» General -» Software Update (it’s recommended that you plug your phone in during the update). 

If the issue still continues, then network congestion or a signal strength issue is the most likely cause.

If you’re having trouble when using a 3G or EDGE connection (in other words, not Wi-Fi), you can often get better performance with the “higher quality audio” option turned off. (Tap the arrow in the upper left of the Pandora “Now Playing” screen to reach the Station List page, then tap “Settings” -» “Advanced” -» “Higher quality audio” -» Off). This will ensure the minimum bandwidth is used to stream music when using a cellular-data connection (Wi-Fi connections are always automatically streamed in “higher quality audio” whether this option is on or off. For best results, use Wi-Fi whenever possible — e.g. at home, work, a coffee shop or a friend’s house).

If you are still having issues with “higher quality audio” turned off, then this is almost always due to poor cell reception. Note that the “number of bars” is often not an accurate measure of bandwidth. You can test your actual iPhone bandwidth by visiting http://www.testmyiphone.com on your Safari web browser. It will tell you your upload and download speeds. A consistent download speed of over 80kbps is generally required to stream Pandora smoothly. If you have less bandwidth than this, please change location — even a few feet can sometimes make a difference — or wait and try Pandora again later.

The user – me – isn’t the issue.  Half of these steps don’t even address the fact I’m using 3G networks.  And the problem has been occurring for months on a state-of-the-art phone over 3G networks around San Francisco that are amongst the densest in the world.

The issue is something much more technical and out of the user’s control.  It’s probably rooted in cacheing and compression algorithms that deliver data to my phone.

Anyone try Skype 10 years ago?  Remember the crappy sound and video quality?  Skype has since spent tons of time and money to write great algorithms that now deliver a wonderful service that overcomes lots of network problems.

Pandora has yet to, based on my experience.

I’m the kind of person that probably would have installed diagnostic software on my phone to give Pandora a hand.  Instead, I get asked to do a bunch of stuff that skirts the real issue and ignores the actual usage scenarios.

Is Pandora alone in delivering technical support this way?  Certainly not.  I’ve seen it in companies I worked at too.  But it doesn’t make it right.  Vendors delivering support should take an active role in troubleshooting instead of exhausting the users’ efforts (and loyalty) before owning the problem.

CloudCare is a managed services platform that enables local IT resellers to become managed services providers to their small business customers.  This article did the best job of describing the great value proposition for security and IT support, for resellers and customers alike.

A lot went right in getting there.  The product was rooted in a clear and well-thought-out strategy.  We made a directed investment in building it from scratch.  We focused on a customer segment that was under-served by existing solutions.  A lot of research and interviews went into understanding both the customer and resellers’ needs.  And it got tested and refined in partnership with our users along the way.

Congrats in particular to Mirek, Vikas, Alan, Darren and David.  You pulled off something special.

It’s nice to have others recognize the importance of this.  One of the audience members, Paul Costanza,  had this to say:

“A few outstanding presentations at the Social Media Intelligence Summit highlighted and validated this. The first was a session in which Jill Hunley of AVG and her former colleague Don MacLennan demonstrated that by integrating a set of social data inputs from online product ratings into its customer data analysis, AVG significantly redirected the product development map of one of its star products, AVG Mobilation.

Specifically, AVG determined that more than 90 percent of the product’s negative sentiment was due to just six product attributes. The most interesting aspect of these findings is that not one of these attributes was being addressed for correction by the existing product roadmap! This is the type of insight that marketers dream of providing to product development efforts.”

You can read Paul’s full blog entry here.

I spent some time at the Hadoop Summit this week, and spent lots of time in the prior weeks with entrepreneurs, practitioners and VC’s in this space.  My prediction: we are entering what Gartner would call the “trough of disillusionment” right about now.  The hype has left reality behind.

This is not a special insight of mine.  All emerging technologies go through stages of hype just as the Big Data movement is now.  Rather, I’d like to focus on why people will become disillusioned, and how to get past it.

Your data sucks

Big Data can deal with less-than-perfect data in many cases.  On collection, Hadoop doesn’t require parsing data into a schema, so you can leave it unparsed and de-normalized at first.

On analysis, lots of Big Data use cases are based on non-financial data.  So there’s tolerance for approximations (or, “confidence intervals” if you’re into stats).  For example, can I deal with a predictive model that says a user is 95% likely to churn?  You bet.

But the old adage “garbage in, garbage out” still applies.  For example, I see lots of cases where joining data sets remains a challenge because of a lack of serialized keys.  For example, is a visitor to your web site the same one who went to your community forums for help?  By the way, you’d better avoid using IP address as your key because it’s Personally Identifiable Information in many jurisdictions.  So it remains tough to develop a single view of the customer/user, especially when web-based touchpoints are everywhere including in Enterprise business models.

Organizations also get hung up with the approximation game.  Use of the qualifier “Likely” sends a chill through some people when faced with making important business decisions based on analytic insights.  Yet this is inherent when dealing with imperfect data.  So people wait for perfect data to arrive into their analytics systems.  And wait.  And wait.

Your analytics platform requires programmers to operate

This seems innocuous enough.  Aren’t programmers available to hire?

Let’s draw a comparison.  Legacy analytics platforms, namely those built on SQL databases and BI tools, don’t need much programming compared to when they emerged given the maturity of the platforms.  An Oracle DBA has lots of tools to configure and manage that database, and doesn’t need to do command-line programming thanks to the toolsets.

Compare that to Hadoop platforms, where programming is often required to even extract data from the data store.  Until the platform matures and these tasks are abstracted away by good tools, you’re faced with the prospect of hiring programmers.  The people that know how to do this are commanding huge salaries and multiple job offers.  Paying these market rates is not an easy conversation with your boss.

Your data requires statisticians to make sense of it

Statisticians, like the programmers above, are in scarce supply.  And are commanding  their own big salaries and lots of offers.

But to be fair, part of what makes Big Data exciting is the use of statistical analysis methods on business data as a mainstream discipline.  As hard as the work is today, lots of business insights are coming from new ways of looking at data.  So let’s not “throw the baby out with the bathwater”.

The bad news in sum: it’s going to take longer – and more money – to capture the promise of Big Data

What to do?

First, and most important, paint a vision for Big Data that is compelling, and creates unwavering executive support.  This is a marathon, not a sprint.  You will need executive support for a long time.

Second, make the support conditional on interim results.  Chunk up the journey into phases, where you can declare victory against interim  milestones.  No executive likes to take risks that will take years to prove.  So make sure the phased plan delivers good news along the way, and early detection of things when they go awry.

Speaking of continuous wins, don’t forget to visualize the results.  Pictures are vital in getting the results across and sustaining the excitement over long periods of time.  If you haven’t thought about hiring a visualization specialist for your team, do it.

Get the job done without experienced programmers and statisticians.  This gets to finding talent absent experience; the kind of talent that can learn these tools and methods in a self-directed way.  Someone with a good computer sceince background can learn Hadoop provided they have the curiosity and the will.  Just be a little patient while they get up to speed, and link your phased deployment to their ramp-up so nothing crashes and burns along the way.

The same could be said for stats skills.  I recently hired a masters graduate in marketing, who had a basic command of math.  But she learned the statistical tools on the fly to get the job done.  You can test for math aptitude by giving assignments during the interview stage, or even to existing employees.  This stuff can be learned.  Like the programmer role above, stage your initiative according to the learning journey of your analysts, so that the tasks don’t outstrip their developing capabilities.

All is not lost

Getting the maximum value out of Big Data is hard, and it’s a long journey.  Data quality is never a quick fix.  Nor is it quick or easy to hire the specialist skills presently required.

It will get easier.  Eventually, the vendor community will deliver point and click capability that abstracts away much of the coding required today by Hadoop admins and statisticians.

In parallel, sell the vision.  Deliver interim results.  Pay attention to visualization.  And look for latent talent.  Do these and you’ll be a Big Data hero.