Big data is driving businesses today in an unprecedented way. However, big data is growing at an exponential rate and exists mostly in silos, making it hard and complicated for companies to unleash its actual potential .
Let’s dig deeper into Data-as-a-Service and how it can enable companies to tap into the transformative potential of big data.
What is Data as a Service (DaaS)?
Data as a Service is a cloud-based data arrangement and distribution model that aims to make business-critical data accessible for all departments, from anywhere and at any time.
Benefits of DaaS
As per Forbes, for a common Fortune 1000 organization, only a 10% increase in data accessibility will bring about more than $65 million extra net income. Following are some of the advantages of Data-as-a-Service:
Improved Customer Experience
Improved customer experience is one of the key benefits of DaaS. While bringing the data to an enterprise is only one aspect, setting up robust systems to process the garnered data and knowing how to act upon it, is what will enable companies to provide positive and better customer experiences. This is where data analytics becomes an important factor.
Data analytics has proven to be instrumental in providing better customer experiences in client-facing businesses as well as B2B industries. According to a research conducted by Econsultancy and Adobe, 65% of respondents state that data analytics played a major role in improving customer experiences for client-facing marketers, and 41% of B2B professionals posited the same.
Intelligent initiatives to mitigate costs
The world underwent a new revolution when Apple launched its first iPhone in 2007. A decade ago, smart devices that were used by people, are now influencing the behaviour of the masses.
Google has coined a term called micro-moments – which occur when people turn to devices, increasingly smartphones, on an urge to do various activities such as watch something, buy something, learn something, etc. Today, micro-moments are fuelling predictive analytics while creating tremendous opportunities for brands.
Combining the power of enabling technologies like AI, machine learning, and deep learning with micro-moment insights can enable companies to run intelligent initiatives at reduced costs and with ease.
For example, a high-end gym – Orangetheory Fitness experienced profound benefits by deploying an AI platform in 2017. AI was a game-changer for Orangetheory as it enabled them to reduce the cost per lead from $20 to $8. With the help of their AI platform, they were able to increase the size of their audience and run a new media campaign called “More Orangetheory more life”.
Unbiased Insights
According to a recent BI-survey, 58% of respondents state that their organizations make decisions based on experience or gut feelings. Though disagreeable, humans have innate biases that make them take decisions based on their gut and thoughts, which is highly problematic and ineffective in today’s information age.
As biased decisions put many things at stake such as brand image, financials, relationships, etc, companies can no longer use speculations in determining market demands and consumer needs or making investment decisions.
With DaaS, companies can carry out a more strategic and methodical approach to collect, mine, and analyze data and extract unbiased insights into customers, markets, competitors, etc. Walmart is a perfect example for understanding the relevance of data-driven business decisions. In 2004, when Hurricane Frances was nearing Florida, Walmart, after analyzing a terabyte of customer history data, made data-driven decisions in choosing products and shelf-items to stock up before the arrival of the storm. Besides generating profit, Walmart was able to help the people of Florida during the dire situation.
Challenges to Data as a Service
Though critical to businesses, Data as a Service comes with a set of challenges. Following are some of them:
Data hygiene
Maintaining data hygiene is an uphill struggle. This is because most companies face challenges while integrating the data provided by the vendors according to their cleansing standards and scientific rules. Also, they are confused if the data is comparable or combinable.
Furthermore, changes in data sets could mean that the figures are skewed. For instance, a vendor showing a 20% lift while the DaaS provider shows 5%, indicates a problem. The other concern that almost all companies have with their vendor is – Is the vendor handling the data as per the company’s standards?
Breaking down the complexity of data
Data is complex, which is one of the greatest challenges to DaaS. Also, Data-as-a-Service hasn’t gained momentum yet because employees cutting across enterprises lack the knowledge to navigate through different datasets.
Data as a Service requires strategic and methodical thinking. Strategic because the data must fulfil the company’s overall strategies; methodical because it is nuanced and must help in realizing business objectives.
Often, at a certain point after using DaaS, most companies seek an instant answer to one constant question – Is the analysis working? However, the right Data-as-a-Service provider will be able to show the ROI and ensure that the client can effectively navigate the complexities related to data science.
Data security & privacy concerns
With data transmission comes data security concerns. In most cases, the data that is garnered and processed to extract meaningful information and insights is transactional data. This means that the data includes private information that neither businesses nor customers can afford to lose to any cyberattack. Data breach not only affects an organization’s reputation but also creates effortless opportunities for competitors to innovate.
As threats to data security are becoming more sophisticated, cybersecurity is getting extremely crucial for companies to handle data cautiously and with due diligence. Ensure that your DaaS providers pay immense attention to data security and comply with data governance regulations like GDPR. Though this requires extra efforts and research from the client-end, the research will pay off in the long run.
Furthermore, in recent times, customers are concerned about personal data privacy. According to Accenture, more and more customers are opting out of personal data taps, making it even more challenging for organizations to collect data to improve customer experiences and operations.
Wrapping Up
It is next to impossible to find an executive who doesn’t acknowledge the importance of data-centric insights in today’s world. However, selecting the right DaaS provider is as hard as gathering and cleansing big data. Therefore, make every effort to choose the right DaaS provider, as only the right data can fuel business transformation, drive buy-in, and improve customer experience.
