Tag: analytics

Are You Ready for AI?

Are you ready for AI?Artificial Intelligence (AI) is coming.  It is not a question of if, but when.  Are you ready?

While consumer AI is already in use by companies such as Netflix for movie/episode suggestions, very few companies have implemented AI.  A lot of companies claim to have AI and some offer AI solutions, be careful with what AI really means.

Artificial Intelligence refers to process automation, letting computer programs and hardware take over repetitive tasks.  Machine Learning is add-on to AI in respects of letting the machine learn from new data automatically.  AI can be enhanced through further human based programing.

AI Requirements

What does your company need for Artificial Intelligence?

To start, you need to identify one or more business processes which would add value to automate.  Start with the simplest and most repetitive tasks for your account managers, sales reps, and other operational employees.  Start with a task as simple as retrieving information needed to make a decision.  Many of our clients have discussed the concept of the virtual analyst, a robot like tool which dispenses insights instead of logging on to Tableau to retrieve.

Next, you need good data.  As the saying goes, data out is only as good as data in.  While data is never perfect, even in the most highly controlled environments, there needs to be a fair amount of stability.  While most AI can learn to deal with bad data, minimizing errors is key.

You will also need a good data science team.  You will need team players who can code using a variety of tools, such as Python and R.  You will also need resources familiar with the cloud as AWS offers many advantages, particularly for big data projects.  Even with a giant budget capable of purchasing off the shelf tools like IBM Watson and DataRobot, your data science team will bring the knowledge and experience needed to make these tools even better.

AI Suggestions

When getting started with AI, we have some suggestions.  First, start small.  It is far better to under-promise and over-deliver.  Start with a well focused use case which offers data stability and drives value to the organization. Something like the virtual analyst is a popular place to start.

Second, be flexible with your roadmap.  Count on delays, mistakes, and learnings throughout your implementation process.

Third, find a passionate, progressive executive to be your champion.  Having executive sponsorship not only helps to secure funding, but helps give the project visibility and defense at the highest level possible.  Having an executive on board helps tie the project to company wide goals.

Finally, be patient.  AI doesn’t happen overnight.  In fact, many of the most hi-tech and innovative companies with huge budgets are struggling with AI.  AI is possible, but there isn’t a well refined recipe available, yet.  Keep your eye on the prize and keep learning.

The first one to master true AI will have a huge advantage as the world of technology and analytics progresses.   It is a pretty sweet prize.

 

Let’s Discuss Data Rights

Data Rights and youData rights is becoming a topic we can’t avoid discussing any longer. With the rise of AI and algorithms, the data companies collect from us is now starting to be used, if I dare use the word, against us. We are in a digital economy which needs boundaries.

We need to start talking about data rights.

First, let’s understand what data companies collect. The last few EULA’s I read mentioned collecting data, but didn’t spell out exactly what data that was.

Second, we need to understand how they use this data. Are they just storing it? Doubt it. Are they using it to help target products to us? Are they using it to push us away from news? Inquiring minds want to know!

Third, who uses this data. It is one thing for an internal analyst to use an anonymized version to understand buying and usage patterns. However, it is something completely different for a company to sell/give the data to a 3rd party who then uses it in a variety of ways. Example: Alteryx buys a data dump of the Equifax data to resell to marketers. This is fine, as long as they are transparent.

Fourth, lastly, we need an opt out method. Just like unsolicited magazines and emails, consumers must have a choice. A choice beyond cancelling their account.

Let’s start the conversation and make it into a movement. I welcome your comments. Please use hashtag #datarights

Thank you!

Why We LOVE Alteryx

Alteryx Data BlendingAlteryx is a data blending tool.  Much like your Black & Decker blender makes short work of blending your favorite fruits and vegetables into your morning protein shake, Alteryx makes short work of disparate data sets. In fact, Alteryx loves the most horrible data you can throw into it!

There are a few more reasons why we love Alteryx:

  • Automatic Documentation – each tool module is easily readable and the step by step process breaks complex data transformation down to small bits.  If you are anal about documentation, you can also use a Comment box.
  • Colors – Using Comment boxes and Tool Containers, you can color code sections of your workflow to discern different data flows.
  • Data Everywhere – at the click of a button, you can pull in your nastiest of data and tame it.  Then, you can push it out to as many output as you want.  What the heck, in addition to Tableau, let’s put the data result out in Excel on three different network drives, push it to PowerBI and why not just drop it in a shared Google Doc.
  • Fast, Pretty Data – Alteryx can take horrible data and transform it into a thing of beauty in a matter of hours or days, not weeks or months.  If you are tired of waiting for your Business Intelligence team “to get around to it”, get Alteryx and sidestep them.
  • Analyst Style, Preserved – There are a number of different ways to do the same thing.  If you need to summarize your data, join it, pivot it, and do a few jumping jacks, the combination of tools allows for multiple solutions to the same problem.  Very much like Analysts love their VLookup or their Index/Match (both do the same thing), Alteryx lets the Analyst be who they are… and that translates to productive creativity.
  • Server Means Collaboration – Analysts across the company can save their workflows to a centralized enterprise server and access each other’s work.  They can even execute workflows.
  • Alteryx is just a great company. – I worked with Alteryx and am a former employee, so I might be a bit biased.  But, Alteryx is a great company.  The “Alteryx for Good” campaign gives back to the community.  They also make Alteryx free to qualifying non-profits.  Awesomeness!

If you haven’t explored Alteryx yet, do!  Don’t let the possibilities overwhelm you.  Take the time to research it and test it.   We guarantee you, you WILL love Alteryx as much as we do.