Tuesday, February 16, 2021

How global data availability accelerates collaboration and delivers business insights

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BriefingsDirect data strategy insights discussion explores the payoffs when enterprises overcome the hurdles of disjointed storage to obtain global data access.

By leveraging the latest in container and storage server technologies, the holy grail of inclusive, comprehensive, and actionable storage can be obtained. And such access extends across all deployment models – from hybrid cloud, to software-as-a-service (SaaS), to distributed data centers, and edge.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

Stay with us here to examine the role that comprehensive data storage plays in delivering the rapid insights businesses need for digital business transformation with our guest, Denis Kennelly, General Manager, IBM Storage. The interview is conducted by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner:  Denis, in our earlier discussions in this three-part series we learned about IBM’s vision for global consistent data, as well as the newest systems forming the foundation for these advances.

But let’s now explore the many value streams gained from obtaining global data access. We hear a lot about the rise of artificial intelligence (AI) adoption needed to support digital businesses. So what role does a modern storage capability -- particularly with a global access function and value -- play in that AI growth?

Kennelly: As enterprises become increasingly digitally transformed, the amount of data they are generating is enormous. IDC predicts that something like 42 billion Internet of things (IoT) devices will be sold by 2025, and so the role of storage is not only centralized to data centers. It needs to be distributed across this entire hybrid cloud environment.

Discover and share AI data


For actionable AI, you want to build models on all of the data that’s been generated across this environment. Being able to discover and understand that data is critical, and that’s why it’s a key part of our storage capabilities. You need to run that storage on all of these highly distributed environments in a seamless fashion. You could be running anywhere -- the data center, the public cloud, and at edge locations. But you want to have the same software and capabilities for all of these locations to allow for that essential seamless access.

That’s critical to enabling an AI journey because AI doesn’t just operate on the data sitting in a public cloud or data center. It needs to operate on all of the data if you want to get the best insights. You must get to the data from all of these locations and bring it together in a seamless manner.

Gardner: When we’re able to attain such global availability of data -- particularly in a consistent context – how does that accelerate AI adoption? Are there particular use cases, perhaps around DevOps? How do people change their behavior when it comes to AI adoption, thanks to what the storage and data consistency can do for them?

Kennelly:  First it’s about knowing where the data is and doing basic discovery. And that’s a non-trivial task because data is being generated across the enterprise. We are increasingly collaborating remotely and that generates a lot of extended data. Being able to access and share that data across environments is a critical requirement. It’s something that’s very important to us.

Then -- as you discover and share the data – you can also bring that data together into use by AI models. You can use it to actually generate better AI models across the various tiers of storage. But you don’t want to just end up saying, “Okay, I discovered all of the data. I’m going to move it to this certain location and then I’m going to run my analytics on it.”

Part 1 in the IBM Storage innovation series
Part 2 in the series
Instead, you want to do the analytics in real time and in a distributed fashion. And that’s what’s critical about the next level of storage.

Coming back to what’s hindering AI adoption, number one is that data discovery because enterprises spent a huge amount of time just discovering the data. And when you get access, you need to have seamless access. And then, of course, as you build your AI models you need to infuse those analytics into the applications and capabilities that you’re developing.

And that leads to your question around DevOps, to be able to integrate the processes of generating and building AI models into the application development process so that we make sure the application developers can leverage those insights for the applications they are building.

Gardner:  For many organizations, moving to hybrid cloud has been about application portability. But when it comes to the additional data mobility we gain from consistent global data access, there’s a potential greater value. Is there a second shoe to fall, if you will, Denis, when we can apply such data mobility in a hybrid cloud environment?

Access data across hybrid cloud

Kennelly:  Yes, and that second shoe is about to fall. The first part of our collective cloud journey was all about moving to the public cloud, moving everything to public clouds, and building applications with cloud-based data.

What we discovered in doing that is that life is not so simple, and we’re really now in a hybrid cloud world for many reasons. Because of that success, we now need the hybrid cloud approach.

The need for more cloud portability has led to technologies like containers to get portability across all of the environments -- from data centers to clouds. As we roll out containers into production, however, the whole question of data becomes even more critical.

That need for more cloud portability has led to technologies like containers to get portability across all of these environments – from data centers to clouds. As we roll out containers and these workloads into production, the whole data question is more critical.

You can now build an application that runs in a certain environment, and containers allow you to move that application to other environments very quickly. But if the data doesn’t follow -- if the data access doesn’t follow that application seamlessly -- then you face some serious challenges and problems.

And that is the next shoe to drop, and it’s dropping right now. As we roll out these sophisticated applications into production, being able to copy data or get access to data across this hybrid cloud environment is the biggest challenge the industry is facing.

Gardner: When we envision such expansive data mobility, we often think about location, but it also impacts the type of data – be it file, block, and object storage, for example. Why must there be global access geographically -- but also in terms of the storage type and across the underlying technology platforms?

Kennelly: To the application developer, we really have to hide from them that layer of complexity of the storage type and platform. At the end of the day, the application developer is looking for a consistent API through which to access the data services, whether that’s file, block, or object. They shouldn’t have to care about that level of detail.

It’s important that there’s a focus on consistent access via APIs to the developer. And then the storage subsystem has to take care of the federated global access of the data. Also, as we generate data, the storage subsystem should scale horizontally.

These are the design principles we have put into the IBM Storage platform. Number one, you get seamless actions and consistent access – be it file, object, or block storage. And we can scale horizontally as you generate data across that hybrid cloud environment.

Gardner: The good news is that global data access enablement can now be done with greater ease. The bad news is the global access enablement can be done anywhere, anytime, and with ease.

And so we have to also worry about access, security, permissions, and regulatory compliance issues. How do you open the floodgates, in a sense, for common access to distributed data, but at the same time put in the guardrails that allow for the management of that access in a responsible way?

Global data access opens doors

Kennelly: That’s a great question. As we introduce simplicity and ease of data access, we can’t just open it up to everybody. We have to make sure we have good authentication as part of the design, using things like two-factor authentication on the data-access APIs.

But that’s only half of the problem. In the security world, the unfortunate acceptance is that you probably are going to get breached. It’s in how you respond that really differentiates you and determines how quickly you can get the business back on its feet.

And so, when something bad happens, the third critical role for the storage subsystem to play is in the access control to the persistence storage. At the end of the day, that is what people are after. Being able to understand the typical behavior of those storage systems, and how data is usually being stored, forms a baseline against which you can understand when something out of the ordinary is happening.

Part 1 in the IBM Storage innovation series
Part 2 in the series
Clearly, if you’re under a malware or CryptoLocker attack, you see a very different input/output (IO) pattern than you would normally see. We can detect that in real time, understand when it happens, and make sure you have protected copies of the data so you can quickly access that and get back to business and back online quickly.

Why is all of that important? Because we live in a world where it’s not a case of if it will happen, it’s really when it will happen. How we can respond is critical.

Gardner: Denis, throughout our three-part series we’ve been discussing what we can do, but we haven’t necessarily delved into specific use cases. I know you can’t always name businesses and reference customers, but how can we better understand the benefits of a global data access capability in the context of use cases?

In practice, when the rubber hits the road, how does global data storage access enable business transformation? Is there a key metric you look for to show how well your storage systems support business outcomes? 

Global data storage success

Kennelly: We’re at a point right now when customers are looking to drive new business models and to move much more quickly in their hybrid cloud environments.

There are enabling technologies right now facilitating that. There’s a lot of talk about edge with the advent of 5G networks, which enable a lot of this to happen. When you talk about seamless access and the capability to distribute data across these environments, you need the underlying network infrastructure to make that happen.

Customers are looking to drive new business models and to move much more quickly in their hybrid cloud deployments. There's a lot of talk about edge with the advent of 5G networks. When you talk about seamless access and the capability to distribute data across these environments, you need the underlying network infrastructure to make that happen.

As we do that, we’re looking at a number of key business measures and metrics. We have done some independent surveys and analysis looking at the business value that we drive for our clients with a hybrid cloud platform and things like portability, agility, and seamless data access.

In terms of business value, we have four or five measures. For example, we can drive roughly 2.5 times more business value for our clients -- everything from top-line growth to operational savings. And that’s something that we have tested with many clients independently.

One example that’s very relevant in the world we live in today is we have a cloud provider that needed to have more federated access to their global data. But they also wanted to distribute that through edge nodes in a consistent manner. And that’s just an example of why this is happening in action.

Gardner: You know, some of the major consumers of analytics in businesses these days are data scientists, and they don’t always want to know what’s going on underneath the covers. On the other hand, what goes on underneath the covers can greatly impact how well they can do their jobs, which are often essential to digital business transformation.

For you to address a data scientist specifically about why global access for data and storage modernization is key, what would you tell them? How do you describe the value that you’re providing to someone like a data scientist who plays such a key role in analytics?

Kennelly: Well, data scientists talk a lot about data sets. They want access to data sets so they can test their hypothesis very quickly. In a nutshell, we surface data sets quicker and faster than anybody else at a price performance that leads the industry -- and that’s what we do every day to enable data scientists.


Gardner: Throughout our series of three storage strategy discussions, we’ve talked about how we got here and what we’re doing. But we haven’t yet talked about what comes next.

These enabling technologies not only satisfy business imperatives and requirements now but set up organizations to be even more intelligent over time. Let’s look to the future for the expanding values when you do data access globally and across hybrid clouds well. 

Insight-filled future drives growth

Kennelly: Yes, you get to critically look at current and new business models. At the end of the day, this is about driving business growth. As you start to look at these environments -- and we’ve talked a lot about analytics and data – it becomes about getting competitive advantage through real-time insights about what’s going on in your environments.

You become able to better understand your supply chain, what’s happening in certain products, and in certain manufacturing lines. You’re able to respond accordingly. There’s a big operational benefit in terms of savings. You don’t have to have excess capacity in the environment.

Part 1 in the IBM Storage innovation series
Part 2 in the series
Also, in seeking new business opportunities, you will detect the patterns needed to have insights you hadn’t had before by doing analytics and machine learning into what’s critical in your systems and markets. If you move your IT environment and centralize everything in one cloud, for example, then that really hinders that progress.

By being able to do that with all of the data as it’s generated in real time, you get very unique insights that provide competitive advantage.

Gardner: And lastly, why IBM? What sets you apart from the competition in the storage market for obtaining these larger goals of distributed analytics, intelligence, and competitiveness?

Kennelly: We have shown over the years that we have been at the forefront of many transformations of businesses and industries. Going back to the electronic typewriter, if we want to go back far enough, or now to our business-to-business (B2B) or business-to-employee (B2E) models in the hybrid cloud -- IBM has helped businesses make these transformations. That includes everything from storage to data and AI through to hybrid cloud platforms, with Red Hat Enterprise Linux, and right out to our business service consulting.

IBM has the end-to-end capabilities to make that all happen. It positions us as an ideal partner who can do so much.

I love to talk about storage and the value of storage, and I spend a lot of time talking with people in our business consulting group to understand the business transformations that clients are trying to drive and the role that storage has in that. Likewise, with our data science and data analytics teams that are enabling those technologies.

The combination of all of those capabilities as one idea is a unique differentiator for us in the industry. And it’s why we are developing the leading edge capabilities, products, and technology to enable the next digital transformations.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: IBM Storage.

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