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Howard Dresner talks current trends in analytics

In an interview on the state of business intelligence, longtime analyst Howard Dresner said cloud migration and AI features like natural language processing remain major trends.

If there's been one overarching trend in analytics in 2020, it's been vendors' efforts to help organizations navigate the COVID-19 pandemic.

Beginning in March when the novel coronavirus started its worldwide spread, analytics software vendors began developing general resources to inform both organizations involved in the fight against the spread of the virus and the general public. As the pandemic continued to grow, vendors began working directly with healthcare organizations and governments to build specific tools to help them make data-driven decisions related to COVID-19.

But other analytics trends have either developed in 2020 or continued from previous years as well. Cloud migration continues to be significant, as does the development of augmented intelligence and machine learning features.

Howard Dresner, founder and chief research officer of Dresner Advisory Services, has been monitoring analytics trends for more than three decades and seen just about every new development in business intelligence in that time.

Dresner recently took time to talk about current trends in analytics, including the importance of analytics during the COVID-19 pandemic when data is perhaps more valuable to organizations of all types -- including healthcare providers and governments involved directly in fighting the spread of the virus and businesses hoping to survive the significant economic downturn -- than ever before.

What has been the biggest analytics trend through the first half of 2020?

Howard DresnerHoward Dresner

Howard Dresner: There's no one big trend, but what we've been telling people is that it's time to double down on data.

A lot of organizations, reacting to the pandemic, have done so without the benefit of data. In many cases they've overreacted, and in other cases they haven't reacted in a way that was commensurate to the severity of the pandemic. As a result you see companies make across-the-board reductions in force instead of knowing where they ought to be reducing head count and where they ought to perhaps be increasing head count. Knowing all of the details of your business, understanding the dynamics of the respective market you play in, is really critical so you can react appropriately. That's why we've seen some organizations continue to lay off people while others are actually hiring people. Why is that? The latter knows something that the former doesn't. Data is really critical, understanding all of your internal data as well external sources to build a more complete picture of what's going on in your respective markets.

What we've been telling people is that it's time to double down on data.
Howard DresnerFounder and chief research officer, Dresner Advisory Services

Beyond COVID-19, what are some other analytics trends you've seen this year?

Dresner: Well, of course everything is moving to the cloud, and all the vendors support cloud to one degree or another. Some have been behind [and] are investing heavily so they can be truly cloud-native, but there have also been a number of vendors out there who have been hyping cloud support, but they weren't really cloud-native. They weren't multi-tenant; they weren't really designed for the cloud. Also, a lot of vendors are moving toward multi-cloud and microservices and embracing Kubernetes and containerization because they have to in order to be cloud-native. If you have an old architecture you can't do things like multi-cloud -- it's not possible.

Other things like natural language processing have shown up as more prominent. Natural language has been around for decades -- I think the first time I saw a demo for a natural language query had to be back in the early 1990s, but it was extremely high maintenance. It's still fairly high maintenance, but the technology has matured a lot and there's a lot more AI and machine learning built into it. They're much more mainstream than before.

I know you hesitate to single out vendors, but is there a group of vendors who are doing interesting things with NLP?

Dresner: I think smaller vendors always do better than larger vendors because it's just easier for them to innovate. They have less process to go through. But I've seen some pretty interesting demos. We've had a lot of conversations with some vendors in that particular realm. It's encouraging, but there's still lots of work that needs to be done to automate and simplify and generalize the technology. It's promising.

Beginning in March, COVID-19 of course became the dominant event that all organizations needed to react to, but if by fall there is some better control over the virus, do you see any new trends emerging over the final few months of 2020?

Dresner: I tend to be an optimist, but at the same time I don't think we're going to be back to any semblance of normal anytime soon. We're actually actively tracking the impact of COVID-19 and we have a website -- covidbusinessimpact.com -- and people can come in and take a 30-second survey and then get instant results to see how they compare to the rest of the sample. We've been tracking COVID-19 since March and doing this specific survey since April, and we'll keep tracking it until this pandemic is behind us, whenever that is.

Whenever post-pandemic is, those organizations that survive -- and that's a real thing; not everyone is going to survive -- are going to have to take stock as it relates to data so that they are more well prepared when another cataclysm or major disruptive event emerges, because there will be another one and another one after that. We just don't know the nature of it and we don't know the timing, so you have to have full perspective so you can act and execute with precision, and there hasn't been a whole lot of precision going on. Organizations should probably be doing that preparation now -- it's a good time to think, 'What do we need to do to be as fully prepared for the next one?' because business is maybe a little slower. And I think data is at the center of everything. Businesses are just far too complex to look out the window and know what's going on. You have to have all the relevant data you can possibly get your hands on and able to combine it and make sense of it as quickly as possible.

If you're going to be a digital enterprise, you also have to be hyperdecisive. You have to be able to act as quickly as possible with the most complete information all the time.

Are there any new features that have come out this year you've found particularly innovative?

Dresner: It's interesting. If you look at the statistics, most organizations are just looking to do a better job with what they have. There are some new products that are cloud-native and are more useable, easier to deploy, more scalable, more cost-effective, but the top things are still the top things. People still want reports, dashboards and visualization tools. They still want data warehouses. Those things have been at the top forever, and that's the dirty little secret. Everyone wants to talk about the shiny object, but the truth of the matter is most organizations are still trying to do what they were trying to last year but do a better job of it.

That said, some of the advanced techniques in data science play into that but support those existing environments. You can have data scientists that are developing interesting features from the data, but those features still end up in a data warehouse and get used by more traditional tools. And that's fine because not everyone is going to be a data scientist, nor should they. Back in the 1990s when data mining first emerged, you had vendors saying there would be data mining on every desktop -- well, that's just dumb. It doesn't help the organization. Having dedicated data scientists that are out there trying to find the needle in the haystack and then pushing that into environments using more traditional tools, to me, makes a great deal of sense.

Things shift around, certainly, but the top things are still front and center. And we still haven't done a good enough job even with those.

What excites you about analytics right now in this moment?

Dresner: I'm always excited about data, because data always challenges us to know what's really going on. If you have data, it speaks truth, as opposed to the last five people you spoke to or your own biases. Data always challenges those, and we're always collecting data, and that's a great way to learn. COVID-19 is horrible, and we really wish it was gone, but it's generating so much interesting data. If you look at what Johns Hopkins and Harvard and others are doing, it's fascinating data -- that's why testing is so important, because we need the data. That's the only way you can really understand how this disease is traveling. Even in our own little patch looking at the data month after month, it gives us perspective.

And whatever the topic is, data is always interesting because it gives us an opportunity to learn from a broader segment of the marketplace than we can access any other way.

Last, is there something I should have asked but didn't?

Dresner: Yes, how are the vendors doing out there? And it turns out, pretty well, especially performance management vendors. It's not as mainstream as BI and analytics and more focused on finance, but they're doing really well because it turns out planning is a big part of the puzzle. If you know everything, you need to translate that into plans and then you need to execute against them, and you need to constantly refresh them and revise them. That's how you run a well-run organization. But the vendors, in general, that have to do with data and analytics, data preparation, data quality, data catalogs -- those guys are actually doing really well. The business is shifting around a bit so some of their traditional customers may be struggling a bit, but new ones are showing up, coming out of the woodwork.

So they're going to do just fine.

Editor's note: This Q&A has been edited for clarity and conciseness.

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