(I Want) Real Hybrid Data Management
The Hybrid Data Management (HDM) mission is unassuming: Make Data Simple & Accessible… Yet remember we’re in information technology, NOT data technology — meaning that the mission implies an inherent value path for turning Information Architecture (IA) into insight, analytics, and Augmented Intelligence (AI).
In reality, the complexity behind “simple” can be overlooked. We take it for granted that data is everywhere: public, private, social. Leading industry research suggests that by 2020, 60% of mission-critical data will reside outside of business’ walls, yet still leaving 40% behind firewalls. Data is stored in all formats: structured, unstructured, semi-structured; and in both operational and warehouse structures. Some data is event driven, other data sits in cold storage wasting away. That all spells chaos for those in the business of harvesting data.
Making data simple just got bleak, didn’t it?
So, we’ve characterized a challenge, let’s characterize a solution. The ubiquitous nature of data lends itself to the theory of Data Gravity: data attracts other data (coined by software engineer Dave McCrory). Applications and business logic are drawn to data relative to its mass, almost as if it’s new physics. As a result, we recommend a hybrid solution that places analytics and AI wherever data lives.
We already know that HDM must handle many differentl data types and workloads, and support hybrid environments (private and public cloud, appliance, embedded IoT, and edge computing). If I’m looking for an HDM solution, I’m going to require a complete set of capabilities that collects and manages all data, organically. “Organic” by my definition is a simple solution that just works without attention.
What I WANT is for an HDM solution to grow alongside my business, organically. In fact, let’s be self-serving. We’re footing the bill. We’re the customer, right? Here’s what I DEMAND:
- A HDM platform with a common stack across ALL form factors
- Write once, run anywhere paradigm with compute and storage elasticity
- Hybrid Transactional Analytics Processing (HTAP)
- Push-button data movement in any direction
- A single control panel across all deployments that is persona-adaptive and cognitive
- Unified governance and industry-leading security simply built-in
- AI or Machine learning-induced, supporting a self-service analytics model
Notice I didn’t mention autonomous DB, worry-free availability, and unequalled performance. These are table stakes, not even deserving mention. In fact, the trusted partner I choose must be an innovator, developing new technologies well before they reach my business requirements. That is, my investment should be safeguarded, both now and in the future. I want a trusted adviser driving inventions like:
- Natural Language Query. Forget traditional SQL, Imagine a database that can be queried by the ordinary end-user through natural language: Querying democratized.
- Blockchain security optimized for data management. Blockchain by itself is slow with no querying capabilities. Blockchain with an enterprise database? Problem solved.
- Distributed Analytics for Internet of Things (IoT). Query across millions of devices with machine learning and near real-time decision making at the edge. That’s IoT at your service.
But wait, there’s more. Developers need data science at their fingertips (download and go) with open source integration. We’re talking a unified client experience from discovery through support. Give me flexible offering plans, seamlessly allowing deployments anywhere. Oh, and did I mention personalized support when I get the itch for expert advice?
No imitators but real hybrid data management that places me, the client, first.
That’s all I want… Easy.
___
Follow me on twitter, @amartin_v or listen to the podcast series (Making Data Simple) I host on Analytics Insights available on iTunes or via the IBM Machine Learning Hub.
