The 4 key pillars of data management
Today, our technology is creating more data than ever. With the rise of “digital transformations” and “data-driven strategies, that number is only going to rise. Sean Bowen explains how you can keep your data in line with four simple guidelines for data management.
By 2025, it is predicted we will produce 180 Zettabytes of data annually worldwide, much of it from IoT. So, it’s not surprising to find that many people believe data to be the basis for competitive advantage. The business landscape has changed and data is fueling the network-connected applications that power corporations. “Digital transformations” and “data driven strategies” are hot topics for good reasons.
Today, data is a mix of historical, event-driven, contextual, and often time-critical information, which is produced and consumed everywhere. Data volumes are growing exponentially which requires scalable, future-proof data delivery/distribution solutions. In our data-driven world, there are a host of new challenges.
The findings in a recent Experian report validate my view. The research paints a picture of a complex data landscape that is disrupting how organizations execute data management strategies. Rapidly expanding volumes of data and security concerns remain front-of-mind. According to the report, 80% of the respondent companies recognize the opportunities the wealth of data can provide; but, almost three quarters (73%) claim it is difficult to predict and manage the data challenges. Other interesting findings are that 55% believe that data has greatly disrupted their organization in the last twelve months and 68% believe that the ever-increasing volume of data makes it difficult to meet regulatory requirements. With these fears in mind, how does a development team formulate a robust data strategy?
From my perspective, there are four key areas of data management on which developers must focus.
1. Application Complexity
When building applications in today’s data-rich environments, you are no longer merely connecting to a single data source. There are many internal and external systems and data sources that will need to be connected, integrated and managed. This makes fast, efficient application development difficult because the development team must understand many different systems, APIs and SDKs when building their applications.
2. Data Security
As mentioned under application complexity, the data that you will need to bring into your applications is sitting in multiple different systems. These systems will need to have their own security policies and procedures setup and therefore you will need to have consistent policies, adding to the security headache. Also, each system will no doubt run on a different port, opening-up security vulnerabilities that you will need to be aware of and have plans in place to secure.
3. Data Delivery
Once you have all the integrations setup, you will then need to focus on the delivery of the data. Of course, the delivery is running over a network you don’t own, it’s a shared resource so changes in capacity can result in disconnects. Therefore, you will need to manage the challenges as well as potentially move large volumes of data through this network.
Deploying and scaling across multiple environments is not an easy thing to achieve especially when you have to take into account the other three challenges listed above.
In summary, data management is complex. There are many types of data sources. Data must be sent to and received from systems, devices, applications, IoT sensors, etc. Each data requires a load balancer to manager communications and each individual data source requires security and authentication and can need a proxy server. The overarching objective for modern application development teams is to establish, monitor, and manage data pipelines that extend Enterprise back-end systems through to the constantly proliferating array of end-user devices, connected over a variety of unreliable networks. To satisfy business mandates, the applications must deliver consistent, reliable, high performance and operate in a cost-effective manner.
One solution to these data management headaches is the Diffusion Intelligent Data Platform. Diffusion provides a centralized, single point-of-access and that changes everything. It simplifies development by giving developers just one platform to develop against. It simplifies security by storing data in one place meaning you only set up policies in one place. It simplifies delivery by taking care of everything for you – it’s all out-of-the-box. It simplifies infrastructure via a single delivery platform. Diffusion Is built for Enterprise performance and scale and can run on minimal infrastructure while removing load from and managing the data locked away in back-end systems.
An intelligent data platform is one answer. If you use technology which is both message-size efficient and data aware, the technology can intelligently, automatically, and optimally manage data transmission and remove out-of-date and/or redundant data. Further, an intelligent data platform can deliver up to 90% data optimization across the Internet, which in turn translates to a substantial reduction in bandwidth and infrastructure requirements, while assuring minimal latency for data transport. When a platform “understands” data, actively manages the data, and only distributes what is required at the application level, this solution is more powerful than any amount of hardware thrown at the problem.