Key Takeaways
- Demystifying Data as a Service starts with a consistent process for collecting, analyzing, and sharing data across your organization.
- Data is abundant. Data as a Service helps your organizations simplify raw data into clear, actionable insights.
- Data looping, data visualization, and data mapping are crucial parts of the Data as a Service process; they need to be executed properly in order to successfully take advantage of your data’s potential.
How Important is Data to Your Business?
Before we explain the concept of Data as a Service (also known as DaaS), let’s explore data’s significance to business success.
Today, you can gather data from unlimited sources, including B2C and B2B interactions. In fact, roughly 2 million megabytes of new information is created every second for every person on the planet. Unless you can make any sense of it, the existence of the data alone is meaningless though.
To create value from the massive amount of data created daily, you need a system for collecting, analyzing, and sharing it across your organization. And while turning data into insights is easier said than done, successful data transformation projects result in a competitive edge for the company. For a typical Fortune 1000 company, just a 10% increase in data accessibility can lead to over $65 million in profits.
So, it’s safe to say—data, or more specifically the ability to successfully collect, analyze, and share data—can accelerate growth across the business. Making data-driven decisions can be the difference between crushing the competition or becoming irrelevant in your industry.
Making use of data is overwhelming. Understanding what tools to use, how to connect them, how to analyze the data correctly, and how to act on what it’s telling you is a complicated but critical process for any business. Unless you invest in Data as a Service, of course.
For a typical Fortune 1000 company, just a 10% increase in data accessibility can lead to over $65 million in profits.
What is Data as a Service?
The simplest technical definition of Data as a Service is a data management strategy used to store data and analytics. However, practically speaking, you can think of a DaaS company as one that helps you accomplish these objectives:
- Make sense the overload of information and data that exists
- Make data available and easily accessible across all departments
- Provide actionable insights from your data
- Do all of these things through the cloud securely and affordably
At its core, the purpose of DaaS is to make data useful for your business.
While DaaS providers offer cloud-based services like the rest of the “as a service” family (e.g., SaaS, IaaS, etc.), DaaS isn’t a set-it-and-forget-it service. Every company has different data to analyze, different tools in its current stack, and is in a different stage, both in its data journey and its business altogether.
Your DaaS solution will involve strategy, science, and structuring before the raw data can be successfully turned into insights.
Understanding The Data as a Service Process
The process of translating information and signals into insights for your company can be broken down into a few key steps: data looping, data visualization, and data mapping.
Data Looping
Data looping is a systematic process for collecting information, gathering insights from the information, and acting on the insights. The first step is to create a system for collecting data, both qualitative and quantitative. Both simple and complex methods exist. The simple method includes creating a word cloud generator from survey responses and customer conversations.
Data looping is more than data collection; the data must be shared across the necessary departments. If this doesn’t happen, teams and departments operate in silos and can end up wasting valuable time and money on initiatives because they lack information.
For example, if data that is collected by the marketing team is not shared with the sales team, the sales team could spend too much time on unqualified leads or hurt deals due to misaligned messaging. Thus, ensuring your data is shared across departments is referred to as closing the data loop.
Data Visualization
Data means nothing if it’s too difficult to understand or interpret, which is why data visualization is crucial to a successful DaaS program. Once data is gathered and analyzed and the loop is closed, the next step is to translate the raw data into simple visuals— to tell a clear story. This makes insights easier to understand across all departments, keeping everyone on the same page.
Dashboards are a reliable method for showcasing insights because your options for visualizing data with charts and graphics are endless. Dashboards even allow you to integrate several visuals in one digital pane of glass, making it easy to paint a cohesive story and provide a clear understanding of what the data revealed. Dashboards also help a marketing team break down the data by geography, channel, etc.
Data Mapping
The final step in the process is to turn your insights into actions through an exercise called data mapping. This is where you identify what opportunities are available based on the data and the steps your company should execute.
For example, if the data shows YouTube and Instagram ads generate positive experiences for your audience, while Google and Facebook ads create negative experiences, your next steps might be to double down on YouTube and Instagram ads while cutting back or eliminating Google and Facebook ads. Your next project could be to analyze which types of ads perform the best (and why).
Rally Your Organization Around Data
The steps outlined above demystify the inner workings of a Data as a Service program. Gathering, analyzing, and sharing data across your organization are necessary processes that, when executed successfully, can accelerate business growth and affect the bottom line. Therefore, working with a DaaS provider, like NetWise, helps you reach your goals sooner, including innovation.
Want to get started using NetWise for your Data-Driven Marketing?
Start by signing up for a demo here.
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