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 an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
A Winning Base for Successful Digital Transformations When it comes to developing a successful digital strategy, it is not just corporations planning to maximize the benefits of data assets and technology-focused initiatives. The Government of Western Australia recently unveiled four key priorities for digital reform in its new Digital Strategy for 2021-2025.Explore
Engage Your Workforce with a Modern Employee Intranet Solution The employee intranet has changed significantly since it was first introduced in the early 1990s. What started as HTML-based static portals have now evolved into intuitive communication tools complete with search engines, user profiles, blogs, event planners, and more. Today, many organizations are taking a second look at employee intranets to bridge gaps between teams, build company culture, centralize information, increase productivity, and improve workflow.Explore
Adopting emerging cloud technologies, consolidating resources, and improving processes is the key. “IT no longer just supports corporate operations as it traditionally has but is fully participating in business value delivery. Not only does this shift IT from a back-office role to the front of business, but it also changes the source of funding from an overhead expense that is maintained, monitored, and sometimes cut, to the thing that drives revenue,” said John-David Lovelock, research vice president at Gartner.Explore
Deliver Powerful Insights Instantaneously with Federated Queries - No Matter Where Your Data Resides The concept of federated queries isn’t new. Facebook PrestoDB popularized the idea of distributed structured query language (SQL) query engines in 2013. Over the years, AWS, Google, Microsoft, and many others in the industry have accelerated the adoption of a distributed query engine model within their products. For example, AWS developed Amazon Athena on top of the Presto code base, while Google’s BigQuery is based on Cloud SQL.Explore
What is Unstructured Data? Almost 80% of the data that enterprises and organizations collect is unstructured - data without a set record format or structure. Unstructured data includes data such as emails, web pages, PDFs, documents, customer feedback, in-app reviews, social media, video files, audio files, and images.Explore