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Q&A: BI Needs of Midsize Firms Sometimes Overlooked

BI vendors sometimes overlook mid-market firms in favor of large enterprises, though smaller organizations wrestle with many of the same issues.

Mid-market firms wrestle with many of the same issues as the largest enterprises. For example, says John Whittaker with Dell Software, "All organizations are struggling with the fact that they have data in a variety of different silos. ... It's one of the big challenges of our time."

Whittaker goes on to say, "For analytics to really provide the best benefit, you need to find a way to integrate that data, and to mask the complexity of the environment." In this interview, Whittaker discusses the challenging issue of data integration, and how mid-market companies can address them and many other issues they face.

Whittaker is the executive director of product marketing for Dell Software's Information Management group, where he oversees marketing activities for a portfolio that includes database management, data integration, business intelligence, and big data analytics.

BI This Week: When we talk about the mid-market and information management tools, what size company are we including in that definition?

John Whittaker: We generally define the mid-market as organizations with between 500 and 5,000 employees. The term mid-market is fairly broad but that's how Dell tends to define it; it's generally organizations that are large enough to have specialized IT departments. An example that comes to mind is healthcare. Tere are a lot of mid-market firms in that sector.

It's a key market for Dell. ... We have a mid-market design point in mind for most of our software. Although certainly large enterprises and small businesses also use our tools, we tend to orient ourselves toward what we see as an underserved market -- and it's one that Dell has a solid relationship with. It's a very important market for us, and aligns well with what we're trying to do in terms of our products.

How is the mid-market under-served when it comes to information management and BI, and why do you think that is?

We certainly feel -- and the research we've done aligns with this -- that the mid-market has been ignored to some degree by the legacy BI and information management platforms. It's not necessarily an intentional thing, it's just the design point. When [vendors] were building [solutions], they tended to target large-scale deployment solutions that focused on large enterprises. Certainly, large enterprises were the first to recognize BI and information management opportunities and were the first to focus on it. They already had large IT staffs, and so it was a natural step.

Some mid-market companies, of course, have some very robust and sophisticated solutions, but we tend to find that the organizations in the mid-market are somewhat fractured, with a mixed infrastructure. They are moving ahead very rapidly and are very agile ... but there's a high bar to implementing many of these legacy solutions in the marketplace.

Those are a few of the many reasons why the space is under-addressed. We seek to remedy that -- we see it as a terrific opportunity.

Are mid-size firms wrestling with the same data complexity issues as large firms?

The data complexity issue defies segmentation. Going back a generation or two, there was no data complexity issue. It was very straightforward and structured -- you picked a platform and that's what you ran with. Modern IT infrastructures are no longer like this. Even if they are still relational platforms, they typically have multiple relational database platforms in their enterprise,. and they utilize platforms based on price points or performance or other decisions. It's led to a fairly complex environment throughout.

Even small businesses now, with the advent of cloud computing options, have a variety of data platforms. You would be hard pressed today to find an organization that didn't have components in the cloud and multiple relational databases in their environment.

It's created some extraordinarily complex environments.

What about analytics? Where is the mid-market there?

It's a mixed bag. We've been surprised in a few instances where even some very large institutions have not had the robust analytics capabilities that we might have anticipated. That's particularly true for advanced analytics -- predictive and prescriptive.

The reason for that probably is that you really need to have a maturity model in place for the organization to be successful with predictive analytics. You need to get your arms around the data; you need some semblance of data quality in your environment before you can reasonably expect to get good analytics results out of that.

Some organizations are on the way, but are struggling in those areas. With that backdrop, it makes analytics difficult. The payoff, of course, for having all of this data -- and everything promised by the big data segment -- is really on the analytics side of the house. If data is the new oil for the economy, then analytics is the combustion engine.

We're definitely at a stage where analytics has come into its own. It's re-emerged, almost. The technology has been around for 30-plus years, but it's really been rediscovered, and it presents all sorts of interesting capabilities and possibilities for the mid-market.

The opportunities are in more than just optimizing, say, or more than just controlling costs and increasing quality. With predictive analytics, you can actually improve top-line revenue growth. That's an eyebrow-raiser for a lot of organizations -- that there's a technology out there that literally can increase potential revenue, help them make smarter decisions as they interact with customers, and so forth.

As you meet with mid-market customers, where should they be focusing their efforts as they wrestle with analytics and big data? What mistakes are you seeing?

For middle-market organizations, it's largely the same as large organizations: there should be a sense of what you want to achieve with the programs and how you're going to scale and support them. That means line of business and IT coming together -- that's really the best way to implement this. Having a center of excellence or some methodologies that allow you to inject good governance and to scale as needed on the knowledge and support side -- those things have to be part of the conversation.

IT can't just go off on its own and acquire a technology that's going to be difficult to implement or to scale. It's really the collaborative aspect that will drive a more successful outcome.

We've seen it again and again with our customers -- the one aspect that we definitely want to drive home is getting those groups together and communicating. We don't want IT to dominate the conversation. If you don't start with a business challenge in mind, then you're just exercising an academic program on the technology side. That's a mistake. It needs to be business-challenge driven, and you really need collaboration between the two groups. Without both perspectives, the final outcome will be sub-optimal.

One challenge large firms have with big data is integrating it, perhaps with existing data already stored in data warehouses. Is that challenge any different for mid-size firms?

All organizations are struggling with the fact that they have data in a variety of different silos, from enterprise data warehouses all the way down to the spreadsheet level -- data exists throughout the organization. For analytics to really provide the best benefit, you need to find a way to integrate that data and to mask the complexity of the environment.

It's one of the big challenges of our time. If you're a CIO or chief data officer or a DBA, or anybody really serious about data and analytics, no problem is as big or important as this data integration challenge that we're facing right now. The data complexity issue is the big issue of our day. It's not the amount or even the rate of data coming in; it's not a velocity or volume problem. Most of that has been resolved through different technologies. The big problem today is one of data variety -- the fact that we have all this data in different locations. How are we going to get the best analysis out of it? How are we going to integrate the data in a way that allows us to analyze it?

We spend a lot of time on lots of tools and platforms and capabilities to solve some of this. We expect it to continue to be something that we work with our customers on solving into the foreseeable future. With the Internet of things, it's going to be even more interesting and challenging as we try to figure out how we're going to run analytics at the edge.

What does Dell bring to the table regarding the mid-market and information management?

Dell offers an end-to-end capability. Rather than piecing together an environment, an organization can come to a large, trusted vendor for everything from the servers to the storage to the services to integrate it, and finally the software that gives you the technology to deal with these problems.

I'm in the information management group within Dell Software. That's where our tools align and are curated together to provide a platform that businesses can use to manage all the data in their environments. No matter what platform it is, with the Toad data base management and data base development tool, you're able to manage the servers and the database and whatever else you need to manage. You can integrate that data using our technology, such as Boomi and SharePlex, and finally to analyze that data with tools such asToad Data Point and our most recent acquisition, the advanced analytics and predictive analytics platform Statistica, which just joined us in March. That brings advanced predictive analytics capabilities to the marketplace today and wraps it all together in a solution that allows an organization to solve for some of their data complexity issues and to start to take advantage of predictive and prescriptive analytics.

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