Thursday, September 8, 2016

Agility Has Its Limits

Automation and orchestration have given us free two-day shipping via Amazon Prime; next day shipping via the major postal companies; delivery via Uber or drone; and so on. These are all concepts that we are either intimately familiar with as consumers of these services or at least are cognizant of them because of news reports, TV commercials or YouTube videos.

Convenience and Digital Transformation

The value of convenience cannot be overstated.  However, there will always be times when these methods of delivery will not be enough.  For example, a freak snow blizzard in my area wasn't a big deal for me since I have snow removal equipment.  But as I drove past kids sledding in the field at the end of the street I wanted to buy a sled for my children also.  Next day shipping wouldn't have satisfied my need to have this now. Of course, Uber or drones could have delivered such an item theoretically.  But unless you live in NYC or a similarly sized metropolitan area and the goods purchased were something that either service would deliver then you're still forced to get in your car and drive to the nearest Walmart.

Measuring your way to revenue
It is during times like these that we must keep in the proper perspective the benefit that agility from a technology perspective gives a company.  For online shopping, the ability to deliver new functionality on an as-needed basis is critical to staying ahead of the competition.  For example, Amazon has a production deployment once every 25 seconds on average. For the brick and mortar companies, however, the need for agility is somewhat different since the quality of the end user experience isn't dictated by how good the Point of Sale software looks.  The focus in this situation is in the quality of the in store inventory, the cleanliness of the store, the helpfulness of the staff, etc.

Regardless of what defines "a quality experience" for a business, it can be measured.  And if it can be measured, then the measurements can be gathered and analyzed.  And if the measurements can be gathered and analyzed, then there is a need for the gathering and analysis.  This need exists whether you are a brick and mortar business, an online business, or a hybrid of the two.  The results can be stunning.
  • Take Netflix, for example.  Netflix does real time analysis of their customers' viewing habits to suggest new movies they may want to watch.  This starts with an ETL process that gathers the data and normalizes it into a format that is more easily digestible by the balance of the algorithmic analysis engine that results in recommendations.

  • Or consider LinkedIn.  LinkedIn does real time analysis of their users' network to suggest people that they may know based on degrees of separation.  This starts with several ETL processes that run on Hadoop, Teradata, and other data sources to enable this functionality to work.

  • Finally, look at AMC Theatres.  AMC does real time analysis of ticket and concession sales to automate their social media feeds.  This starts with an ETL process on Hadoop to gather, normalize, and process information to post updates to various social media sites with no user intervention.
Digital Growth

Anything that defines "a quality experience" for a business is quantifiable and should be measured. Why not? After all, this need exists whether you are a brick and mortar business, an online business, or a hybrid of the two like AMC. The process of measuring, gathering, analyzing and reporting unearths what is and is not working so success can be capitalized on in real time.

As you can see from the examples above, the results can be stunning. But the quantity of data that these companies generate means none of this would be possible without data automation, which is geared specifically at real-time execution of jobs to gather and then process large amounts of data in real-time.

Orchestration Backbone

There are a number of platforms to process large amounts of data (Hadoop, Hortonworks, and Cloudera, to name a few). However, the orchestration to coordinate the activities around gathering and reporting, is critical. Without it, you would be stuck with the task of building a custom set of solutions to do these two very important activities. It is this orchestration that distinguishes a large data processing platform like Hadoop from a data automation platform that enables you to effect a change in your company based on what the data contains.

The other thing all the above examples have in common? They all chose Automic to provide the orchestration capabilities to allow them to transform their businesses into something that responds in real time to the needs of their customers and users.

As Vijay Aruswamy, Big Data Operations at LinkedIn, put it, “We're using Automic to automate data transformation on Hadoop, Teradata, User Input and External sources of information. It supports our data driven products like ‘People You May Know’ or ‘Jobs You May Be Interested In’.”

Automation, when properly harnessed, means the difference between being able to continuously innovate and rapidly becoming irrelevant. And though large scale adoption of automation is not an endeavor to be undertaken on a whim, examples of data automation like those listed above show the outstanding benefits that come as a result of this investment that you make for your company’s future.