With the explosive growth of social media in recent years, the entire marketing landscape has changed drastically and become more cut-throat. Today businesses have various avenues through which they can market themselves like “social media influencers,” commonly found on social media platforms like Instagram and Facebook with millions of followers—all of whom you can potentially reach by extension of these individuals.
This is, however, just a small piece of the entire internet marketing industry pie. Most of the companies are still heavily reliant on internal marketing, which requires a dedicated team and plenty of insightful data to create any value for your business. To ingest and process this data, you need ample computing resources and robust data management tools to integrate, store and analyze your datasets.
Data-as-a-Service or DaaS has emerged as a resourceful method through which you can reduce the burden of data management on your IT and marketing teams. DaaS is a data management strategy that relies upon cloud computing resources to provide data storage, processing and analytics over the internet.
With traditional IT infrastructure, this type of data processing was cost-prohibitive. The rise of cloud computing in recent years has negated this problem with low-cost database storage and high availability of network bandwidth for the transfer of large-scale datasets.
When it comes to marketing, DaaS allows you to source Hard-to-Find Data (HTFD) assets more efficiently as compared to traditional data management strategies. With DaaS, these HTFD assets can be structured to supply a constant stream of insightful data, which you can leverage to boost the efficacy of your marketing endeavors. This is achieved through a selection of data types, which include:
Foundational Data – As the name suggests, this data type offers a foundation on which you can rely on to make critical business decisions. Foundational data is composed of internal and external sources, including HTFD assets, and acts as a centralized repository from which you can reference various business software tools. Some categorization fields you may see with foundational data include reference types and metadata.
With foundational data, you can make decisions in real-time, improving enterprise agility. This data also allows compound KPIs to be generated, which increases visibility into the performance of your marketing efforts.
Onboarded Data – Most of the marketing data that businesses store is collected from online sources, but there is also significant value in customer-captured offline data. And data onboarding is a way to do exactly that.
Data onboarding is the process of converting offline data sources into a format that can be used in an online environment. This data undergoes a hashing process to remove personally identifiable information (PII), before being uploaded to your database. From here, you correlate this data with existing category fields to identify users and expand the scope of your data analytics.
To be clear, this data is not “offline” in a real-world sense. Rather, it refers to unique identifiers that aren’t tracked across the internet. You would take these identifiers and convert them into cookies, which are anonymous text files that sit with the client rather than your server. From here, you can track users as they navigate through your website and the wider internet, improving the depth of marketing insights and the performance of marketing campaigns.
Trianz is a leading data management consulting firm with over a decade of experience in helping our clients manage their growing datasets. We can help you implement a DaaS strategy that maximizes the effectiveness of your marketing endeavors. Our dedicated team of data management consultants will assess your existing data management procedures and help you identify ways to extract maximum value while maintaining data compliance.
Get in touch with our data management consultants and start building a foundation for marketing success 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