With data generation increasing exponentially, companies who wish to flourish in the digital age have no choice but to be proactive and implement an effective enterprise data management strategy. The recent crackdowns on data protection regulations, in the form of CCPA and GDPR, make this even more vital for maintaining your reputation and avoiding regulatory fines.
There are many ways to build a solid foundation for data storage, protect the integrity of customer information, and extract maximum value from datasets to gain insight. Let’s explore further.
You will need to balance both offensive and defensive utilization of your datasets to maximize value and minimize risk.
Offense - This refers to the more aggressive analysis and extraction of insights from your data. Your business will be looking to increase revenues, profit margins, and customer satisfaction—naturally, you will rely heavily on data to drive these metrics.
This growth will be accomplished through a heavy focus on segmenting your customer base and building a predictive model around their shopping habits, for example. Such an approach would be a staple part of an offensive data strategy. You are trying to predict what your customers want, rather than being reactive and waiting for them to come to you. When you find similarities in customer habits, you can use that insight to create targeted marketing campaigns, offering personalized incentivization to increase sales.
You will typically find retailers using an offensive data strategy, as they process less sensitive customer information when compared to health providers or financial bodies.
Defense – With a defensive strategy, you understand the risks associated with storing said data, and focus on protection and compliance. Your reputation relies on solid data management and security, making aggressive analysis and insight less of a priority.
Rather than using personal information for analysis, you place emphasis on anonymized statistics to understand your customer base. You measure against your whole customer base, rather than pinpointing individuals, drawing broader conclusions to measure performance against your corporate objectives. This can be done through website traffic and performance reporting, measuring average webpage retention times, and click-through rates to determine the success of your current strategy.
Healthcare providers, financial institutions, and other businesses that handle confidential information will use a defensive strategy. The risks associated with security breaches are too high, and maintenance of a reputable brand image contributes more to growth than the acute understanding of customer habits.
Both offense and defense will lie on a spectrum, with businesses being responsible for balancing the benefits of each approach. It is still possible to incorporate some aspects of an offensive strategy into your data management as a financial institution, but you must perform stringent risk assessments to safeguard customer information and quantify the value generated by an offensive approach. In the same manner, a retailer would be missing out on valuable predictive insight through a predominantly defensive approach.
Many businesses encounter the issue of multiple data sources, with numerous versions. This can complicate data validation, particularly when the two sources consist of different attributes.
The consensus is that a single source of data improves data integrity and validation. Rather than individualized repositories for each department, all the data is stored in a single destination, with access controls in place to facilitate proper data protection. This dramatically simplifies database management through more secure and scalable centralized data access.
From this single source of truth, departments can create their own insight and understanding in congruence with their work demands. This is known as multiple versions of the truth, stemming from a central source of the truth.
Trianz is a leading enterprise data strategy consulting firm with a deep understanding of the complex world of big data. Throughout time, we’ve worked with thousands of clients, building a deep understanding of the requirements and potential of enterprise data strategy.
Our expert team of data strategy consultants can step in at any point in your upgrade process, performing in-depth analysis and contributing to development so that you can maximize the value of your growing data pool.
Get in touch with our data strategy team, and take steps towards maximizing your customer insight with Trianz today!
Contact Us Today
What is ITOM? IT operations management (ITOM) can be defined as the process of managing and maintaining an organization’s network infrastructure. An IT team is typically tasked with this work, covering aspects of computing such as compliance, security, and troubleshooting. This team works with internal and external network users, offering advice and remediation to overcome technical obstacles and maintain effective service delivery.Explore
Putting Data to Work Recently, one of the world’s largest global shipping companies was seeking to identify new revenue opportunities; specifically, they were interested in monetizing their data by building other, related business intelligence products for different industries. Like many other businesses, they had found themselves sitting on a mountain of actionable data without any processes in place to explore or leverage said data. Their intentions were now pointed in the right direction, but what they were missing was a data monetization strategy.Explore
The Data Tide Businesses in the digital age are inundated with data as it floods in from multiple channels. This data is both a challenge to wade through and an absolute goldmine. Its tremendous potential can be harnessed to communicate meaningfully with audiences and advance an organization’s brand awareness in the public eye. The problem is, however, that raw data itself can’t tell a compelling story to most people. It needs to be woven together artfully to create a narrative that connects with a specific audience. This is where data-driven storytelling comes in.Explore