Managing Data and Optimizing Marketing Using Multiplatform Data Architecture

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Read the latest blog on iTMunch titled. 'Managing Data and Optimizing Marketing Using Multiplatform Data Architecture'

What is Multiplatform Data Architecture?

A “multiplatform data architecture” (MDA) is created when diverse tools, technologies, platforms, and data sets are integrated. Mulitplatfrom data architectures are not a new concept. They emerged with the earliest client or server implementations. The new aspecct is that they have managed to achieve an extreme diversity, sophistication, and complexity recently. This makes all the early MDAs seem pale in comparison.

The assumption is that data is heavily distributed in an MDA. This means that data is spread across many databases, clouds, and other storage platforms physically. But at the same time we also assume there should be some form of large-scale, cross-platform architecture which would help in unifying an MDA and its data on a logical level.

The architecture should ideally be designed by data architects. Data should also be guided by some form of governance. Without direction and control, an MDA could possibly deteriorate into an unmanageable and ungoverned swamp. This would result in minimal business value at high risk.

The Challenges that Come with a Multiplatform Data Architecture

The complexity of a multiplatform data architecture is difficult to design, maintain, govern and integrate with other systems. Many organizations face this problem. But still, the users manage to succeed with MDAs. This is done by relying on best practices in data architecture and data governance. Along with this, to make it high performance, technologies such as data and application integration, central metadata and cataloging, and virtualization techniques that stitch together the grand design are put to use.

TDWI regularly encounters modern data warehouses that are actually multiplatform environments of integrated platform types. The experience that comes with data warehouse professionals from having to deal with complex data environments since the early 1990s has made them well equipped to succeed with the multiplatform warehouses that are needed for the diverse requirements of advanced analytics. All this is done while still satisfying all the requirements that come with traditional practices, for example reporting.

Besides all the complexities, an MDA can come with substantial costs for its range of tools and platforms. The way to combat this challenge is by the relatively low cost of open source and cloud-based data platforms and tools. These are especially common when it comes to all hybrid MDAs.

The Opportunities that Come with Multiplatform Data Architecture

Businesses are continuously deploying more sensors, customer channels, applications, and social media. With this, the diversity of source data drives up. All types of relational and other structured data are combined using a widening range of unstructured as well as multistructured data types. The main benefit that comes with an MDA is that it provides various options to handle rapidly diversifying data and its business use.

There are a lot of businesses that are expanding their use of analytics, reports, and data-driven business monitoring. All of these come with their own unique requirements for data capture, storage, processing, and delivery. These requirements are what drive the requirement for diverse data platforms and all related tools.

More and more users are now succeeding with MDAs. Large, complex, and hybrid MDAs are already common in different areas of marketing and other industries that are data-driven enterprise programs such as data warehousing, analytics, multichannel marketing, the digital supply chain, IoT.

Why Multiplatform Data Architectures Become Necessary

A solution that meets every need would be the ideal scenario. The reality is different. There are multiple applications and software packages which are needed to be able to meet all the complex company-driven standards.

Another option, especially when it comes to fulfilling unique requirements, would be to use these multiple platforms to collect the data with direct or indirect redundancy. There could be many reasons for using this method such as an oddball piece of equipment that will only talk to a particular application. Another reason could be that a manufacturer never got around to migrating a system. These scenarios are actually quite common where where only a specific application can be used due to equipment limitations.

Before looking for a software package, take stock of what is already available or being used by other groups to fulfill similar requirements. It is most likely that expanding the use of an existing solution will be more cost-effective and quicker to implement than adopting a new solution altogether.

Diverse Data Management Tools and Storage Platforms

Many user organizations are diversifying their portfolios of data management tools to use as a strategy to capture and leverage new data assets for new practices. No single data storage platform can be optimized due to the extreme diversity of data structure, latency, and analytics purpose that exists today. There are many organizations that prefer being able to select platforms and tools combinations that are most appropriate for any given type of data.

This drives organizations toward numerous data platforms where data is physically distributed across multiple database servers, file systems, and storage. The extreme complexity stems from the sheer number of systems involved, which regularly includes multiple brands of database management systems and tools for data integration, analytics, and stream processing. These data management systems could be on-premises, in the cloud, or in hybrid.

The result is a combination of old and new data which is managed on traditional and modern platforms by using tools from different providers which are then stitched together by some type of distributed data architecture.

Five Strategies for Effectively Marketing for Multi-Platform Data

Organizations need to be equipped with the right metrics to be able to fully understand how their audiences behave online if they are going to implement successful strategies. Below are five strategies that can be used to reach and engage audiences, acquire and retain customers and grow their business using multiplatform data architecture.

SEE ALSO: How to Drive a Data-Driven Marketing Strategy

Develop Customer-Centric Marketing Strategies

The main objective for marketers is to reach, engage and influence consumers and not platforms. They need to answer how consumers consume content, what content they consume, and when they’re accessing it to be able to obtain an accurate view of their behaviors.

The new measurement capabilities and analytics tools come with the insights that help them understand the complete customer journey. Once this is done, companies can start to adapt their strategies on the necessary platforms.

The main goals and corresponding digital experiences should be created to the consumers’ needs first and then adapted to the respective platforms.

Create Consistent and Integrated Experiences for Your Customers

Businesses should aim to build products, content and marketing experiences that work flawlessly and are complementary across all platforms. This can be in the form of responsively designed digital properties or even native ad formats that render the same across screens. It is possible to deliver a consistently engaging experience to their users.

Maximize Effectiveness by Leveraging Multi-Platform Synergies

By providing key employees with third-party multi-platform data can help a business take a broader look at its own current and potential impact. This type of a holistic approach can be conducive to creating an environment that fosters a ‘surround sound’ marketing approach. This means that the campaign will reach the right people on the right platforms and ultimately generate greater awareness, engagement and conversion.

Understand Demographic Differences Between Platforms to Drive Audience Targeting Efficiencies

Different audience segments behave differently across media platforms. Therefore publishers and advertisers can both improve the efficiency of the way they reach their core audiences by using multi-platform targeting strategies. Publishers can also create their content strategies that will align better with the audiences who are more likely to consume content on various channels.

By improving your understanding of audience demographics, companies can optimize their content and determine the best strategies.

Re-Evaluate Business Metrics and KPIs

The market is constantly changing, which means that business KPIs need to be updated to reflect that. When you do not, it can lead businesses away from optimal business strategies. By re-evaluating your KPIs, you can get a sense of how and when the problem occurs. A unified view of the digital consumer takes into account their behavior across platforms. Using this, businesses can measure their work according to the best metrics, as well as develop product and marketing strategies that align with customer behavior.

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Conclusion

The changes that are being made in data and its management are being used to drive up the scale, scope, and complexity of modern data ecosystems in many organizations. But still, these changes come with new opportunities for data management professionals as well as their business counterparts who are willing and able to accept and put to use new data-driven practices, especially those for big data and advanced analytics.