DaaS Vadanya / DaaS Good / DaaS The Way

You know about Software as a Service, (SaaS), now let’s talk about Data as a Service (DaaS).

DaaS collects the wealth of information out there today and makes it available across all departments, anytime and anywhere. Just like anything else in the “as a service” family, DaaS providers serve up its data-centric insights through the cloud securely and affordably. But DaaS isn’t a set-it-and-forget-it service that you can log into whenever you have a spare moment and instantly get access to a list of data-driven insights. There’s a lot of data driven digital marketing strategy, science, and structuring that goes into democratizing datasets so that they’re understandable and actionable.

Digitally Data Driven

Data is now widely seen as a necessary fuel for innovation and company growth in the short-term. Companies mine for data like they’re drilling for oil. And just like a car can’t move without petroleum, a modern-day company can’t progress forward without data. The market for it is evolving rapidly, and an important catalyst for this are the newest, popular, methods of accessing data in its various ways through booming connectivity methods such as mobile phones, IoT sensors (IoT in data mining), etc. All these technologies generate new forms of information and ways of using it, for instance, AI that relies on a large amount of high-quality data to work. This is all why Daas has become so important.

But what is DaaS exactly? In the simplest terms, it is a data management strategy that uses the cloud to provide storage, integration, processing, and/or analytics services over a network connection. As previously stated, it falls within the “as a service family”  Just as SaaS eliminates the necessity of software installation on devices and gives users access to digital solutions over the network, DaaS transfers most of the storage, integration, and processing operations to the Cloud. Data as a service is nothing new. The idea and corresponding market for it is decades old. Many notable data companies such as Oracle and Nielsen have pioneered this area since their inception. Today, DaaS is beneficial to many companies from different industries due to the advent of affordable cloud storage and bandwidth combined with cloud platforms. The combination of all of this allows you to manage and process data quickly and at scale.

DaaS the right way

DaaS consists of a few key elements. Data collection is where it all begins, identifying the best and cleanest methodology and timing for gathering data and insights. This moves on to data aggregation, where the data points are compiled for a specific purpose so that they can be analyzed and summarized into actionable insights. Data correlation occurs next, statistically analyzing points against one another to find correlation. Once that finishes, statistical significance begins to measure risk tolerance and confidence levels within the dataset. Data visualization is the second to last step, identifying patterns and visually displaying insights which enables companies to gain buy-in from teams and stakeholders. Lastly are the advanced analytics which simplifies big data to deepen insights as well as avoid analysis paralysis. This is all usually done through an AI automated process. As demand for AI-enhanced products grows, DaaS will only grow with it, but its data quality, speed, and margins is what separates winning companies from losing companies in the long run. As demand for DaaS grows, so will the marketplaces, data cleansing products, and services built around it.

What can DaaS do for you?

Going over the basics is interesting, but how can DaaS help marketers? Well, businesses have two fundamental goals: to increase revenues and to reduce costs. DaaS helps with both. For reducing costs, DaaS means that you buy processed third party data as a service. Instead of spending on data management and analysis software and employing data scientists to store their own data, companies pay one price to do it all in one package. They still get access to the data, but it’s already analyzed, cleaned and processed for them. For increasing revenue, using Daas means that there is a huge amount of room for data monetization. Many companies today have a lot of data. But they are experiencing some problems in organizing and using this data which can be easily solved using Daas.

Beyond just the general monetary gains, Daas is able to help improve user experience and increase innovation. By developing personalized customer experiences with predictive analytics to understand consumer behavior and patterns, Daas can help companies better serve customers as well as build loyalty. Making a customer journey effortless means that they are more likely to return. Innovation is easy when Daas uses strategies built on a large amount of quality data. This allows you to introduce more innovations with less risk. Ideas based on this data are more likely to be successfully implemented. And most importantly, the speed of this implementation increases significantly, and all thanks to accessing data, which serve as a source of information for new initiatives and stimulate growth.

The do’s and don’ts of DaaS

But with so many benefits there also comes challenges when adapting to DaaS. One of the biggest issues most companies face when implementing this is data hygiene. It prevents some companies from signing off on initiatives with a DaaS provider until they take the data through their own internal system to make sure that it is handled the way they decide is appropriate. This can become an issue as DaaS providers need deep access into your database to do their jobs effectively. Sometimes, the differences in data sets means the figures could be skewed. For example, if a vendor shows you that you’re getting a 30% lift but your DaaS provider only shows a 5% lift, there’s a problem.

Another challenge companies face with data as a service is the innate complexity of data itself. DaaS has been slow to take off because many employees in large corporations, and even in some smaller vendors, don’t have solid knowledge of navigating various datasets. DaaS takes methodical, strategic thinking. It’s strategic because your data must fulfill the overall strategies of the company. It’s methodical because it’s nuanced and must work toward a certain objective. The right DaaS providers will be able to show you the ROI. Choosing your DaaS partners effectively and with the right amount of research will help make sure that you are efficiently navigating the complexities associated with data science and see it pay off in actual revenues.