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Principles of Last Mile Data Management Solutions | GridBuddy

Posted by Marc Aubin on Oct 2, 2016 5:25:16 PM
Marc Aubin
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In our previous post, we described what the last mile problem was and what last mile solutions are. 

We see that in the electricity industry, solutions at what can be defined as the last mile hold the most promise for saving our electric grid and achieving a goal of a sustainable energy future.

In this and the next post, we take some lessons learned from the electricity industry and rolling out customer life cycle management solutions at scale to outline 8 principles for last mile solutions.

Ready? Let’s get started!

1. Renewable and Reusable

Renewable electricity is that which is generated from a source that is not depleted when used, such as wind and solar power. These hold the most promise for a sustainable energy future, and by their very nature involve many producers or very decentralized solutions, for example, rooftop solar power generation which is at the consumer location. In other words, these solutions are true last mile solutions.

Reusable solutions in data management systems means they have the potential to be reused for many use cases. These solutions include code that is generically written so it can be reused for new use cases (unfortunately, a lot of Apex and Visualforce out there isn’t written this way), or applications that can be easily configured to meet new use cases.

Here we have an example of a screen in GridBuddy which enables admins to configure efficient data management experiences for an any multi-object or multi-record use case. This means GridBuddy can be configured for tailor-made user experiences at the last mile, whether it’s for different geos that have to manage opportunities in their own ways, or account management and renewal teams who work with account related data in different ways to conduct different tasks at different stages in the life cycle. 

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2. Smart, Efficient Consumption

Despite more electronic gadgets connected to the grid and an huge increase in population, electricity consumption is “predicted to remain flat until 2040,” according to Gretchen Bakke. This is in large part because our electronics are becoming more and more efficient. For example, advances in lighting, heating/cooling systems, and appliances have them all running much more efficiently than they did even 5 years ago. In addition to more efficient, they are running and will run smarter. For example, with the help of smart meters and computer software, lights in businesses will automatically dim in response to peak consumption on the grid as a whole. Home lighting systems that turn on and off at a certain time or based on motion detection are already making it to the mainstream.

In data management systems, things usually get bogged down because of performance. Smart and efficient consumption means being more focused in how we consume data per use case. It is not necessary to bring back all 50,000 rows for an end user in most cases. Rather, apps should be designed to retrieve only what users need at any given time. This helps users cut down the noise and efficiently focus on what is most important for them to do next.

With smaller data sets, apps can free up their their query cycles to combine data in the ways end users need to see (for example by cross-referencing many objects together to give data more meaning and context), instead of having to worry about performance considerations that traditionally limit users to data sets that are too simplified (e.g., single-object data sets).

3. Integrative and Modular

This principle says that anything we add on must have capacity to interface effectively with everything that was there before.

In electricity production, the centralized grid is a fact of life, and any new innovative solution needs to work with it. In fact, home solar does just this -- all the power it generates goes into the grid. However, home solar is not enough by itself (and as we mentioned in earlier posts in this series, it’s actually causing a lot of the problems in the grid). Home solar with local battery storage, however, could smooth out peaks and valleys in production in ways that don’t necessitate the firing up of inefficient fossil fuel plants when the sun goes down.

A good example of modular solutions in enterprise data management systems are apps on the Salesforce AppExchange. These apps are made to work with your existing Salesforce instance and its data. They can be installed in a few button clicks, and individual setup nuances aside, take a lot of the legwork out of ensuring they work with your existing setup. These apps provide a great way of not “reinventing the wheel” when implementing solutions to manage the customer life cycle by using additive plug-and-play solutions to solve gaps in the core Salesforce system.

Another modular concept has to do with how you grow your data model over time. Some of the smartest enterprises out there including NetApp introduce new, purpose built objects that hang off of core “enterprise objects”, like the Account and Opportunity, in order to support incremental process needs. The reasoning is clear: so many systems and user groups depend on the core enterprise objects that changes to these can introduce ripple effects. It’s also not very good data modeling to flatten all of your fields on these objects (e.g., you know you probably have a problem with this if you have over 300 fields on your Opportunity object).

For example, if you have an SDR team that is making calls, you might have a “Qualification” object that is specifically geared towards capturing information related to the qualification questions they ask. This object can be associated to a Lead, a core enterprise object that many groups including marketing, SDRs and AEs depend on, and therefore should be touched lightly. This object can only be visible to those who need to interact with it. Likewise, when a Lead becomes an Opportunity, you might have a “BANT” object that AEs can use to capture the budget, authority, need and timeline questions that are specific to their calling activities.

4. Relatively Simple To Understand

Another way of saying this is that things should just work.

Power customers don’t care about the inner workings of how our power works, they just want their cold drinks and the TV on. Having simple solutions means that electricity customers shouldn’t have to think about what to do to reduce consumption. It should be natural or just happen automatically. This is what smart appliances achieve. Likewise, they shouldn’t have to know the inner workings of how to fire up different power sources in the home provided one source fails. Failover from one power source to another should be seamless and automatic.

In business process automation, simple to understand means when I look at a process, I know what it does. It speaks to me. It walks me through what I need to do. Most users don’t care where data is stored; they just want to be able to do their work productively, and work on what is relevant to them.

This means don’t expose raw components to users that they don’t understand and tell them that’s their business process. This image shows an example of a “single-object user experience” which violates this principle. Notice that the business process is articulated in terms of raw system components. Your users may or may not know what these mean.

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This diagram below shows an example of how we need to abstract those raw system components into the language of the business. Notice how the business process is articulated in a language that users understand because it corresponds to what they have to do.

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This is a hugely important point that all too often does not get taken into consideration in enterprise software design. Not to rag on IT and operations, but there is a widespread assumption that if you give them the raw components to do their job, it’s a good enough user experience for them to hunt and peck and memorize where they have to enter their data, no matter how much time it takes. Our users are teaching us that we need to rethink this assumption.

But wait, you’re not out of the woods yet. There’s just 4 more principles you need to follow to make sure you’re rolling out business processes for managing the customer life cycle that won’t come back to haunt you.

Read the next post, 4 (More) Principles of Last Mile Data Management Solutions.
 
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