Businesses of all shapes and sizes have come to rely on one thing to understand consumers: data. But for all the computing power, data mining, and customer insights available, most companies stumble trying to leverage the full potential of their findings.
Part of this comes down to resources: No matter how little data is collected, you still need the manpower to analyze and segment it into actionable data points. And if you do have the resources to make sense of it all, there’s a good chance the departmental silos within most companies will prevent organization-wide access to potentially beneficial information.
But just as businesses moved procuring and managing software to software as a service (SaaS), they’re now moving the same data processes to data as a service (DaaS).
At its core, DaaS allows subscribers to pull data streams whenever the need arises. You’re no longer required to devote time or energy to inputting and cleaning up records; they’re already collated and compiled into relevant data points, allowing for greater agility and productivity in your business efforts.
And with an ever-increasing number of companies moving toward cloud services, the market for DaaS will continue to evolve, just as it did for SaaS; your team can access whatever information it needs whenever it’s needed.
Besides, we’re now seeing the evolution of data into the open-source format, and this trend will continue to fan out. We’ll likely see DaaS move away from its closed and proprietary data to free and open-source data. This then brings us to the question:
How does a company prepare for this next stage of data evolution?
1. Flush your data. Having bad data is worse than having no data at all. When moving forward with DaaS, accept that you’ll abandon much of your existing data. Even if it has taken years to gather and compile, bad data is unusable. Understand that it can’t be salvaged, so save yourself the headache of trying.
2. Provide the right support. While you could hire data scientists to complement the rest of your team or even train your existing team in analytics interpretation, I’d suggest supplementing your team’s skill set with a third-party vendor.
Sure, you’ll need to vet companies to ensure that you find one that meets your business needs. But it’s a shorter process than your other two options, and you’ll gain almost immediate access to the technical and statistical skills necessary for your next data-driven project.
3. Focus on simplicity. When it comes to analyzing your data, simplicity is key at the start. Decide what you need to know to achieve your business objectives, and then set up everything according to your most straightforward use case. This allows you to collect and measure the right data.
That’s not to say, of course, that you can’t add greater complexities with each additional use case. But you must start somewhere; otherwise, you’ll find yourself collecting everything “just in case,” which costs more time and money.
4. Connect all systems. When integrating any new system into your organization, you want to guarantee it can “talk” to all other systems. With DaaS, the easiest way is to attach a unique identifier (e.g., a primary URL) to your data that’s closely aligned to all branches of your business. Using URLs not only allows all systems to speak to one another, but it also guards data against duplicates.
Smart companies understand the importance of data and how essential it is to their business trajectories. By not implementing a DaaS strategy, you waste valuable time, energy, and money, and all the data informing your strategic decisions isn’t as fresh as it could be. That’s not the best way to run a business — especially one you want to build for sustained success.
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