There's no technological innovation more powerful at the moment than advanced analytics. Machine learning's ability to aggregate, summarize and use Big Data to predict outcomes and prescribe actions to get the desired results introduces extraordinary possibilities to lower operational expenses, improve efficiency and ultimately boost return on investment. So why aren't more businesses using advanced analytics as a way to make their enterprise more intelligent, agile and lucrative?
For starters, it's not due to a lack of trying. Massive investments are being made in the Big Data and analytics services market. By 2019, the market value will be just shy of $190 billion. Businesses of all sizes are determined to make smarter use of their data to glean insight and facilitate better business outcomes. What's missing is the data integration that's necessary for making this happen.
The data silo dilemma
According to CIO's Thor Olavsrud, business leaders are struggling to execute Big Data and analytics strategies because they're having trouble breaking down data silos. Without the ability to integrate with the many isolated functions within an organization, it's incredibly difficult to implement a strong Big Data and analytics strategy.
The good news is that many enterprises are still stuck on this same integration dilemma. Nevertheless, there are some businesses that are clearly pulling ahead, and it's all due to how they wield Big Data and analytics. For example, Uber's meteoric rise was only possible because of how it uses these technologies. Granted, Uber has the benefit of being a relatively young company, which means they probably didn't have as much legacy IT infrastructure or data silos in place as a more mature enterprise.
Another example of a company that has made analytics the cornerstone of its most recent successes is Amazon. The company is now able to complete deliveries in some regions within two hours of an order being placed, which is an as-of-yet unrivaled supply chain accomplishment.
But enough about Uber and Amazon. The important question is how can you dissolve silos in order to start leveraging all of your data to its fullest extent?
Culling unnecessary functions and processes
As pointed out in the Harvard Business Review, the idea of "a data lake" is more of an ideal than anything else. The modern enterprise uses hundreds, if not thousands, of applications with varying file formats, many of which are scattered across "data islands." This makes it difficult to address this problem all at once. The better solution, according to HBR contributor Edd Wilder-James, is to use what he calls "a progressive, pragmatic approach."
"Analyze your business needs, and choose a problem where data could provide a tangible benefit, perhaps in enhancing sales or preemptive incident response," Wilder-James wrote. "Draw in the data from around the organization and invest in these use cases first."
In other words, don't try to reinvent the wheel all at once, but instead go spoke by spoke, starting with the most essential or immediate opportunity. That said, Wilder-James cautioned businesses away from creating entirely new silos in this endeavor, noting that "Each progressive step should build also toward an integrated platform for your enterprise data."
"The goal is to make your enterprise IT leaner."
It's also worth noting that the opposite is also true: You should also be keeping an eye out on redundant functions, or those that are no longer contributing ROI. The ultimate goal is to make your enterprise leaner. Much like trying to build muscle in the human body, this is a bit of a give and take. Yes, you're adding mass to the enterprise, but you're also actively trimming away the fat in order to become leaner, more agile and ultimately more capable.
Working toward data integration can be an incredibly complex endeavor, and one that you might not necessarily be able to achieve alone. To that end, accept when you've hit a brick wall with what you can do internally. At that point, consider consulting a third party that can help you more effectively streamline your data integration efforts, perhaps through the implementation of a custom enterprise service bus.
Funneling what's left into an analytics engine
As you're working to dissolve silos, you also need to be proactively implementing your Big Data and analytics strategy. As much as we'd like to tell you that this is the easier part, ensuring that your analytics engine is well structured across your enterprise IT infrastructure, and that you have the applications you need to summarize data into polished visual insight, and then optimizing your workflow structures so that these insights can actually lead to swift action via data-driven functions, is hardly a walk in the park.
Nevertheless, the first days are the hardest. Once you've successfully navigated the earliest phases of digital transformation, and you have enterprise nervous system that responds to change more fluidly, it only gets easier to find new ways to leverage Big Data and analytics for better business outcomes.
As Wilder-James put it, "If it was easy, it wouldn't be important." Streamlining your IT infrastructure to facilitate a predictive enterprise may be difficult, but in the end, the ROI is well worth the struggle.
So don't wait to get started. Contact Neudesic today to learn more about how digital transformation can help your enterprise overcome its unique set of challenges.