Skip to main content

Analytics-first Enterprise Applications


This is the story of Tim Zimmer who has been working as a technician for one of the large appliance store chains. His job is to attend service calls for washers and dryers. He has seen a lot in his life; a lot has changed but a few things have stayed the same.

The 80's saw a rise of homegrown IT systems and 90's was the decade of standardized backend automation where a few large vendors as well as quite a few small vendors built and sold solutions to automate a whole bunch of backend processes. Tim experienced this firsthand. He started getting printed invoices that he could hand out to his customers. He also heard his buddies in finance talking about a week-long training class to learn "computers" and some tools to make journal entries. Tim's life didn't change much. He would still get a list of customers handed out to him in the morning. He would go visit them. He would turn-in a part-request form manually for the parts he didn't carry in his truck and life went on. Not knowing what might be a better way to work Tim always knew there must be a better way. Automation did help the companies run their business faster and helped increased their revenue and margins but the lives of their employees such as Tim didn't change much.

Mid to late 90's saw the rise of CRM and Self-Service HCM where vendors started referring to "resources" as "capital" without really changing the fundamental design of their products. Tim heard about some sales guys entering information into such systems after they had talked to their customers. They didn't quite like the system, but their supervisors and their supervisors' supervisors had asked them to do so. Tim thought somehow the company must benefit out of this but he didn't see his buddies' lives get any better. He did receive a rugged laptop to enter information about his tickets and resolutions. The tool still required him to enter a lot of data, screen by screen. He didn't really like the tool and the tool didn't make him any better or smarter, but he had no other choice but to use it.

Tim heard that the management gets weekly reports of all the service calls that he makes. He was told that the parts department uses this information to create a "part bucket" for each region. He thought it doesn't make any sense - by the time the management receives the part information, analyzes it, and gives me parts, I'm already on a few calls where I am running out of parts that I need. He also received an email from "Center of Excellence" (he couldn't tell what it is, but guessed, "must be those IT guys") whether he would like to receive some reports. He inquired. The lead time for what he thought was a simple report, once he submits a request, was 8-10 weeks and that "project" would require three levels of approval. He saw no value in it and decided not to pursue. While watching a football game, over beer, his buddy in IT told him that the "management" has bought very expensive software to run these reports and they are hiring a lot of people who would understand how to use it.

One day, he received a tablet. And he thought this must be yet another devious idea by his management to make him do more work that doesn't really help him or his customers. A fancy toy, he thought. For the first time in his life, the company positively surprised him. The tablet came with an app that did what he thought the tool should have done all along. As soon as he launched the app it showed him a graphical view of his service calls and parts required for those calls based on the historic analysis of those appliances. It showed him which trucks has what parts and which of his team members are better of visiting what set of customers based on their skill-set and their demonstrated ability in having solved those problems in the past. Tim makes a couple of clicks to analyze that data, drills down into line-item detail in realtime, and accepts recommendations with one click. He assigns the service calls to his team-members and drives his truck to a customer that he assigned to himself. As soon as he is done he pulls out his tablet. He clicks a button to acknowledge the completion of a service call. He is presented with new analysis updated in realtime with available parts in his truck as well as in his teammates' trucks. He clicks around, makes some decisions, cranks up the radio in his truck, and he is off to help the next customer. No more filling out any long meaningless screens. His view of his management has changed for good for the very first time.

As the world is moving towards building mobile-first or mobile-only applications I am proposing to build analytics-first enterprise applications that are mobile-only. Finally, we have access to sophisticated big data products, frameworks, and solutions that can help analyze large volume of data in real time. The large scale hardware — commodity, specialized, or virtualized — are accessible to the developers to do some amazing things. We are at an inflection point. There is no need to discriminate between transactional and analytic workload. Navigating from aggregated results to line-item details should just be one click instead of punching out into a separate system. There are many processes, if re-imagined without any pre-conceived bias, would start with an analysis at the very first click and will guide the user to a more fine-grained data-entry or decision-making screens. If mobile-first is the mindset to get the 20% of the scenarios of your application right that are used 80% of the times, the analytics-first is a design that should thrive to move the 20% of the decision-making workflows used 80% of the time that currently throw the end users into the maze of data entries and beautiful but completely isolated, outdated, and useless reports.

Let's rethink enterprise applications. Today's analytics is an end result of years of neglect to better understand human needs to analyze and decide as opposed to decide and analyze. Analytics should not be a category by itself disconnected from the workflows and processes that the applications have automated for years to make businesses better. Analytics should be an integral part of an application, not embedded, not contextual, but a lead-in.

Comments

Popular posts from this blog

Emergent Cloud Computing Business Models

The last year I wrote quite a few posts on the business models around SaaS and cloud computing including SaaS 2.0 , disruptive early stage cloud computing start-ups , and branding on the cloud . This year people have started asking me – well, we have seen PaaS, IaaS, and SaaS but what do you think are some of the emergent cloud computing business models that are likely to go mainstream in coming years. I spent some time thinking about it and here they are: Computing arbitrage: I have seen quite a few impressive business models around broadband bandwidth arbitrage where companies such as broadband.com buys bandwidth at Costco-style wholesale rate and resells it to the companies to meet their specific needs. PeekFon solved the problem of expensive roaming for the consumers in Eurpoe by buying data bandwidth in bulk and slice-it-and-dice-it to sell it to the customers. They could negotiate with the operators to buy data bandwidth in bulk because they made a conscious decision not to st...

Reveiw: Celluon Epic Laser Keyboard

The Celluon Epic is a Bluetooth laser keyboard. The compact device projects a QWERTY keyboard onto most flat surfaces. (Glass tabletops being the exception) You can connect the Epic to vertically any device that supports Bluetooth keyboards including devices running iOS , Android , Windows Phone, and Blackberry 10. On the back of the device there is a charging port and pairing button. Once you have the Epic paired with your device it acts the same as any other keyboard. For any keyboard the most important consideration is the typing experience that it provides. The virtual keyboard brightness is adjustable and is easy to see in most lighting conditions. Unfortunately the brightness does not automatically adjust based on ambient light. With each keystroke a beeping sound is played which can be turned down. The typing experience on the Epic is mediocre at best. Inadvertently activating the wrong key can make typing frustrating and tiring. Even if you are a touch typist you'll still ...

Rise Of Big Data On Cloud

Growing up as an engineer and as a programmer I was reminded every step along the way that resources—computing as well as memory—are scarce. The programs were designed on these constraints. Then the cloud revolution happened and we told people not to worry about scarce computing. We saw rise of MapReduce, Hadoop, and countless other NoSQL technology. Software was the new hardware. We owe it to all the software development, especially computing frameworks, that allowed developers to leverage the cloud—computational elasticity—without having to understand the complexity underneath it. What has changed in the last two to three years is a) the underlying file systems and computational frameworks have matured b) adoption of Big Data is driving the demand for scale out and responsive I/Os in the cloud. Three years back, I wrote a post, The Future Of The BI In Cloud  where I had highlighted two challenges of using cloud as a natural platform for Big Data. The first one was to create a lar...