One of the recent business obsessions going around is the idea of “digital transformation.” If marketing has ever taken over a business concept, this is it. Digital transformation has been going on since the advent of the IBM PC in 1981 — some would argue before that. The business world has been transformed by technology for the last 35+ years — from the PC to the Internet to the iPhone — we are all walking around with the world’s knowledge in our pocket these days.
In the last year, I have been inundated with solicitations for seminars, conferences, and consulting around this idea of digital transformation. I have been asked if my firm has a digital transformation strategy. There are, of course, many organizations that want to sell services to help figure all of this out.
Someone recently remarked that in five years artificial intelligence would be referred to as “software.” Well, in five years, digital transformation will be referred to as “management.” The application of technology to drive opportunities and efficiency is nothing new, and what was for a number of years viewed as a competitive advantage is now a given. Not utilizing technology will be the end of your company or even your industry. I’m not sure how in 2018 that needs to be explained to folks by “experts” — we have been collectively marching down this road for quite some time now.
What is becoming more vital to organizations is the ability to utilize internal systems for the mining of data. Data analytics technology — while not new — has progressed substantially in recent years thanks to big data (NoSQL) technologies. To the point where companies can devise business strategies, investment decisions and employee hiring/retention practices based on the detailed understanding of their information. Where this fails is when the data that an organization analyzes is of low quality. Low-quality data can be the result of a few problems such as a lack of data collection processes, poor systems architectures, and lazy employees. If you took an intro to computer stuff class 20 years ago one of the first concepts you would have been exposed to was “Garbage In, Garbage Out.” It typically refers to writing poor application code and thus getting poor results. I don’t hear that term much these days, but it has never been more appropriate.
If the data recorded in your business during day-to-day operations is incorrect or incomplete, you can almost guarantee your direct competitors will benefit. That is the best case scenario. The worst case is a new player emerges and finds a better way to do things and takes your industry altogether.
This data-centric approach to competition is not new. However, its impact like all things touched by technology is accelerating — much like a wave that has rolled along for a while. The crest of the wave is what people are referring to as disruption.
Even if an organization is great at what it does — client service, manufacturing, etc. but isn’t focused on understanding the intricacies of their operation via data analysis — it is very susceptible to someone coming along and taking their customers.
The best way to defend against this from happening is to have a systems architecture that allows data to move easily through systems within your organization. This allows for minimizing human input and maximizing the extraction of that data for easy (dashboard) analysis. The architecture has to be one where all business systems and touch points are accounted for up front. Gone are the days where building a best in breed solution that solves a single business problem is acceptable. Each repository of business data needs to be accessible to the others. The single best way to accomplish this is with a SaaS (Software as a Service) approach to everything. SaaS allows your IT resources to focus on the company’s data vs. infrastructure, application features, upgrades and care and feeding. SaaS also means web services APIs (application programming interfaces) which provides a standard way of moving data around. The proper SaaS systems architecture will even get an organization on the path to putting the resources towards getting rid of the one thing that is sure to sink a business in the future — garbage data.