IDG Contributor Network: Vertically challenged: the pace of digital transformation across industries
As I’ve outlined in my previous posts, we are in the early stages of a digital enterprise transformation tsunami and data is the new currency. By 2020, IDC predicts 1.7 megabytes of new information will be created for every human being on the planet, every second. That’s more data than five times the print collection of the Library of Congress. Every. Year. Never has there been such a motherlode of data for businesses to use to better serve their users and be more efficient. It is a massive challenge for IT organizations to seize this opportunity and fundamentally transform how they manage data and deliver it as a service to their users. It’s a challenge companies in every industry face, but there are some notable differences in how quickly certain industries have embraced the transformation.
Some industries fall into the category of fast “digital transformers”: take the fashion industry, for example. In an article on technology trends shaping the fashion industry, the World Economic Forum notes it is “one major sector being fundamentally transformed from the inside out by technology,” citing evidence like the fashion capitals of the world New York, Paris, and Milan being usurped in importance by digital platforms like Snapchat, Instagram, Pinterest and Periscope. The very nature of stores and the shoppers who used to occupy stores is changing: now that the same clothing items you used to purchase at a store are available online, what incentive does a consumer have to shop at physical stores? Digital transformation is an essential aspect to the success and future of the business, and it’s something the fashion industry, generally, has been very good at recognizing.
What happens to those who don’t take the leap to transform? Let’s take J.Crew and its struggle to adapt to a tech-first fashion industry as a recent example. Amidst company turmoil, Mickey Drexler, chairman and chief executive of J.Crew Group went as far as to say “If I could go back 10 years, I might have done some things earlier” when referring to embracing technology. Sales at the company have fallen for the past 10 quarters, and the retail veteran who turned J.Crew into a household name actually recently stepped down as chief executive after a failure to stop the brand’s decline.
Competitors with high-tech, data-driven supply chains can copy styles faster, move them into stores quicker, and outmaneuver them. By not pursuing a digital-first approach to its business, J.Crew fell a step behind all the other fashion companies who chose to take the leap sooner. A failure to innovate and digitally transform means companies like this can’t maximize datanomics to their advantage for business intelligence and acceleration; most data simply goes unutilized, gathering dust rather than adding value.
While J.Crew provides us with an example of a digital transformation that may have come too late, the fashion industry does have plenty of examples of faster digital transformations as well. Industries like finance and health care, however, often prove slower to complete the digital transformation necessary to harness modern datanomics. According to a Harvard Business Review article on the biggest health care challenges, health care leaders see outdated or ineffective IT infrastructure as their major roadblock. Oftentimes for these industries battling slow digital transformation, getting sign off from executives to try something new is the biggest hurdle. Knowing the benefits for transforming digitally, regardless of the industry, can often be the first step in convincing your company it’s the right step to take. For health care, critical data underpins the complex interchange of patient records, research, and medical advances, and the controlling data must be reliable, immediately accessible and secure. With this in mind, health care agencies have begun to turn into their own disruptors by implementing data virtualization solutions to collapse costs and time factors, transforming the way they operate and deliver their services in the process.
As an example, a customer of ours, Access Community Health Network, supports 40 community health centers throughout metropolitan Chicago. It is responsible for organizing all patient and financial information. By undertaking digital transformation and implementing a data-as-a-service solution, they were able to streamline, automate, and create 24/7 high-performance access to critical data with no downtime—critical when lives depend on the services they provide.
The finance industry typically mirrors the pace of health care transformation, with the challenge for large banks and financial institutions coming in many forms: for example, a lack of responsiveness and agility in the marketplace worries IT leadership, or slow restores of large databases threaten devops teams. An Actifio customer, a top 20 global consumer and investment bank with data centers around the world, faced the challenge of staying agile in the marketplace and developing new capabilities powered by new software. By taking the leap to transform digitally and improve datanomics, this bank tested and deployed a new database-as-a-service cloud across its entire infrastructure, improving compliance, recovery times, and most importantly the agility of its devops efforts. In the process, the global firm saved well over $25 million in infrastructure, software licensing and operational costs—in the very first year.
No matter the industry and the reputation of industries to transform fast or slow, there is exponentially more data to be managed in digital enterprises today and making sure your organization is equipped to manage that data as a service is crucial. Data is increasingly the most strategic asset of any enterprise, or even an individual. Without proper management of data, it is impossible for organizations to make the most of it to grow a business and leverage the benefits of data-as-a-service. For businesses, managing the datanomics to drive digital transformation is very black and white: either they thrive, or they die.
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Source: InfoWorld Big Data