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Liya Hult, Senior Strategic Partner , Oracle, explains how data analytics can help improve your customer engagement strategy in the Digital Age. SaaS companies need to address issues in areas like data privacy, AI, data engineering, and governance with current and new software and technology approaches to better serve their clients and customers. In today’s market, there is a need for a high level of business intelligence and effectiveness and a learning ability where data science can support processes to help grow the business. When asked the solution of how data analysis can improve the customer experience and engagement model, Liya Hult, Senior Strategic Partner , Oracle, explains how data analytics can help improve your customer engagement strategy in the Digital Age. The Digital Dilemma: Data was the new currency of 2019.

Are SaaS customers facing the same challenges today as they did in 2018? Data was a recent commodity, a commodity that was a scarce resource until recently.

Today, it is much harder to get data in today’s market. The challenge that data security is different due to different industries and different data techniques is where the need for data analytics comes in. What customers are asking for from their data is data privacy. It is increasingly accepted that they don’t want their data being shared with third parties and they want the control, visibility, and control over data. With all the buzz around data privacy, and securing your data, how can you ensure that your customers have access to accurate and up-to-date data? Does your data show the number of products your customers are buying, the number of transactions they made? Are you able to see the demographic breakdown of the users of your software product? Does the data show whether an individual could be a good candidate for your campaign, or the product you are using? By adding detailed and real-time data from the customer and from your customers using your product or service you have the opportunity to understand the needs of your customers and provide that insight to drive our customers data model together with data product and customer service.

Big Data doesn’t mean big number – and a big number doesn’t necessarily mean a data problem. A data problem is a group of fragmented data elements with little value. Big data can solve a data problem but it’s not usually the primary problem because you are trying to provide the best experience for your customer. It’s not always about a lot of data , it’s about the right kind of data being available with the right context. GDPR and GDPR 2. 0: GDPR 2. 0. Changes in data practices for large and small companies alike and the impact of GDPR to your business

I refer you to a blog post by our co-lead, Dr Karl Brandt, our VP of Customer Success for Redshift and Automation. Karl writes:

From a customer lens, GDPR 2. 0 has given companies a new set of rules and regulations that govern the handling of their customer data and which they need to comply with in order to operate.

Many companies are looking to update or upgrade their data management processes, considering the best fit for the GDPR 2. 0 rules. It is important for organizations to understand that GDPR is not about big data and analytics but about the sensitivity of processing and ability to control data through careful collection and access controls that minimize the risk of personal data misuse or other data security issues. And while you must comply with both platforms, changes should be developed specifically for the GDPR. When you look at the complexity of GDPR 2. 0 and requirements across organizations, the key elements are an emphasis on consumer choice, an emphasis on data usage tracking across multiple stakeholders, and an emphasis on data protection.

So for software providers, that means letting customers know their data can be managed across the organisation and encouraging them to create an effective data management plan. I have been seeing a lot of engagement data with the impact of personal data protection and the ability to ensure there is adequate oversight of data use and how it is used by the company. In our SaaS sales pipeline, we are seeing more organizations building an all-encompassing approach to data excellence. All-encompassing of whom you allow to access it, all the applications you have in place, and so on. It’s very important for enterprise companies to be great at that. You will be amazed at the most impactful interactions and thought leadership companies are doing around data control.

This piece illustrates from Data Science at the border of IOT data to the mapping and visualization software industry.

How to build this across organizations? This is a foundational role that data science can play as a business software vendor. I am very excited to see SaaS solutions take on data management here.

However, why is GDPR important for SaaS companies? GDPR requires everyone in a company to be in control of their data.

This includes all parties who have access to the data and can securely access it (the company). These are called ‘end users’. Now, think of IT as a pair of hands who are interacting with and ultimately collecting information and never stops managing the interface. And there are many of those in SaaS right now who are in the middle of that having access to the data, usually GDPR rules don’t require they be in control of the end user. The messaging here is that your enterprise will have to hold some form of data minimisation policy at your enterprise level. That’s something that needs to be OK’d out of the gate.

So, how do I conduct a data management policy? The truth is that every part of your business requires working with information, people and data. Each of these have different dimensions, and they’re rarely seen as the same thing. A role that is not doing this is not doing data management, your data strategy and your data governance. So how do I talk about how data management is a business with data? The ideal way to define business with data at its core is to follow data governance patterns, making sure these correlations are straightforward and gaining control over the data processes for the organisation to be successful.

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