Five Business Challenges Data Can Solve
Examples of when data strategy makes a difference | June 15th, 2017
Editor’s note: Welcome to Throwback Thursdays! Every third Thursday of the month, we feature a classic post from the earlier days of our company, gently updated as appropriate. We still find them helpful, and we think you will, too! The original version of this post can be found here.
It’s 2017, there’s an AI assistant in your pocket. You don’t need convincing that there’s power in data to change your business; you’ve probably got one or more big data projects running. Kicking the tires has been relatively straightforward, and you’re ready to take things to the next level.
The next level, however, is a much bigger deal than those islands of encouraging success. Moving the business forward meaningfully—new product offerings, for example—usually means collaboration across functions and silos, and the IT and business sides of the house working closely together. That’s not an everyday experience, and the journey to using data effectively means adopting many of the traits of successful software and web companies: figuring out how to deploy technology in strategic support of the business.
At SVDS, we’ve helped numerous clients through these data transformations. Before we get going writing code or unleashing our data scientists, the first step we take is to create a modern data strategy. For us, that means a living roadmap of what to tackle, and in which order, to get results.
In today’s business climate, executives understandably want to see both early results and a long-term direction. A data strategy helps meet business needs, while ordering work in a way that respects constraints and creates future opportunities. If that sounds a little like motherhood and apple pie, get some business and IT leaders in the same room and see if they see priorities and resource requirements the same way! Even if it provides nothing else novel, creating a data strategy enables agreement across these stakeholders.
When do you need a data strategy?
While it’s not hard to see the need for data strategy, it can be difficult to map that to your current position. In this section, we present some of the more common situations that lead to charting a data strategy; use these to start planning your own path. The key similarity in all these situations is that the end goal is to create new data capabilities that enable progress, and that the stakeholders are drawn from across the business.
Infrastructure is inhibiting growth
The more customers you get, the more data you get. The problem is that traditional data systems rarely scale linearly: when you reach a certain point, scaling issues become pathological, and it’s time to move to a new platform. Doing that at the same time as maintaining existing systems is a challenge that requires careful planning.
Infrastructure is inhibiting development
It might not be data size that’s the challenge, but keeping your offerings competitive. In an era where great user experiences, personalization, and “always connected” are the expectations for both consumer and business customers, keeping up is essential. Typically, these capabilities are not simple additions, but part of a move towards a new platform. And new data streams often means the involvement of many more stakeholders than before, across the organization, from R&D through to marketing and finance.
Undergoing an analog to digital transformation
The march of digitization is uneven: though we interact via smartphones every day, many processes and systems still operate in the analog world. Through moving paper processes into software, or bringing hardware online into the Internet of Things, great efficiencies and insight can be gained. The challenge is not just to catch up to the last decade, but to harness the capabilities of today’s technologies, such as machine intelligence, to leapfrog into a competitive position.
Changing business models
There are now new models for businesses, created by the directly connected internet consumer, the scalability of big data technologies, and the application of machine intelligence. Ask anyone if they thought five years ago that a Silicon Valley firm would reinvent the taxi industry. Stores have progressed to make the things they used to just resell, and publications can now sell the things they used to just advertise. Businesses can find new models thanks to the new capabilities available, but they require a technology path that supports and validates these ambitions.
Unifying fragmented offerings
No business grows in perfectly planned evolution. They grow lumpily, adding on developments that met a market need at the time, but don’t quite connect up to new product offerings. Or by M&A, where every acquired system and product generates both redundant cost and operational incompatibility at the same time. Operational costs are raised as a result, but crucially, it’s the customer that suffers as well. For example, consider the operational and patient impact caused by the lack of integration between medical devices. New data capabilities mean we can create a unified infrastructure, giving new life to existing products and creating future opportunity.
Digital is in the driving seat
The rapid sweep of digitization across industries means that data infrastructure is a key component for creating efficiencies, growth and future potential. It can’t be managed in isolation as an IT concern, but as part of the overall company strategy. That’s where data strategy comes in: bridging and coordinating between business ambitions and the necessary investments in systems, code and data science.
If any of the situations in this article resonate with your challenges, and you’d like help or advice, please reach out to us to talk. To discover more about how our approach to data strategy can help, you can also download our position paper.
