Embedded analytics leads to improved productivity

While augmented intelligence capabilities may one day dominate analytics, business users are already using embedded analytics more than traditional business intelligence capabilities.

Many organizations also are reporting improved performance resulting from data embedded within a custom application and more end-user productivity as a result of embedded analytics.

Those were among the key findings of a new report from Enterprise Strategy Group (ESG), a division of TechTarget.

Similarly, a January survey by IDC on behalf of analytics vendor Sisense found that embedded analytics was extremely important to users.

Recently, Mike Leone, senior analyst at ESG and author of the report, discussed how embedded analytics helps a broad array of users and what technologies have the potential to make analytics accessible to an even wider audience in the relatively near future.

In addition, he spoke about organizations that haven't yet broadly adopted analytics and what might be holding them back.

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

Mike Leone

Most business analytics users are now using BI broadly across their business -- what does that level of analytics adoption signify to you?

Mike Leone: It shows the significance of empowering more individuals to take the reins and be able to control their own data destiny. Maybe they have had access in the past, and maybe they haven't due to cost restraints or they haven't had the right training, but because organizations are now putting such a level of importance on leveraging data, we're seeing that number rise and really expand throughout organizations. Maybe it was just sales or marketing teams that were really diving in deep in the past, but as other business units are starting to see success from leveraging BI, within those business units it's making a lot more sense to start extending that usage and empower more generalists to leverage data.

As analytics becomes more mainstream, what are the common barriers holding back organizations not yet broadly using BI?

Leone: We term them the data laggards, but that shouldn't be interpreted as them not prioritizing data, not prioritizing analytics. Maybe there's only one person at the core of a particular business that needs access to data. There are always going to be cost restrictions, especially in a pay-per-use model. Part of it is understanding the value of analytics to the company. Maybe they could open it to a lot more people, but they have to take into account the bang for the buck. Is exposing more people to the data going to be valuable today? Maybe not. Maybe they need training first, or people would need a lot of guidance. What you can infer is that those companies will be investing in embedded analytics where they can expose generalists across business units to BI features, to BI capabilities and interactive dashboards where they have the right guardrails in place.

Today, we're at the very early stages of that happening. I don't think it's that the laggards don't want more access to data. I think it's only a matter of time.

So, is it safe to say it's not about denial but rather a matter of logistics?

Leone: I think in 99% of the cases we've seen, that's the case.

Are there certain types of organizations that are slower to adopt analytics than others, whether they are small or medium-sized businesses (SMBs) or perhaps in a particular industry?

Leone: What's going to happen ... for those midmarket companies that don't have the resources to invest in data-centric personnel, in data-centric employees, is they're going to lean more heavily on self-service analytics and embedded analytics and things of that nature. What you're starting to see is vendors come out and focus purely on SMBs, you're going to see some of the big vendors that have traditionally focused on the enterprise start trickling down to SMBs. So many vendors have prioritized enterprises because they make just a few deals and make a ton of money, but SMBs are a big deal, and I'm starting to see a lot more vendors look at SMBs as an area of opportunity, asking how they can cater their products and services to SMBs that need help and guidance and are struggling to compete with massive enterprises.

What is it about embedded BI that makes it powerful and attractive to enterprises?

Leone: The thing that really sticks out to me is productivity. It's easy to get lost in looking at the productivity of an end user, or a developer, but what the research found is that productivity is being improved and gained across the board. It's all the personas. It's the generalists, it's the experts, it's the data teams, the data engineers, the data scientists and the developers who are increasingly working with both the data teams to make sure that the right data is available and also the end users to make sure that the capabilities are available them as they request them.

That level of productivity is absolutely massive.

Today, embedded analytics is in a place to improve productivity across the board more than a lot of other technologies.

Are there other technologies, other trends that have the potential to help various kinds of business end users in the same way as embedded analytics?

Leone: I would say that today, embedded analytics is in a place to improve productivity across the board more than a lot of other technologies. Augmented analytics, and I throw natural language processing in there, is close, and we're expecting to see massive growth in the adoption of augmented analytics. I'd say a very early stage capability that we've been hearing about over the last year -- natural language query where you're interacting with data the way you interact with Siri -- is going to benefit a lot of folks, but that's really catering toward the generalists. Augmented analytics has the potential to help everyone, but we're still early there.

How important is broad use of analytics to the culture of an organization?

Leone: The culture is critical. This is one of those areas where it starts at the top, with the executives, with the CEO. And frankly, it's up to the business to enable even the laggards within the business who are pushing against something new. One of the biggest challenges preventing more employees from using analytics and BI platforms is a skills gap. It's lack of training. Organizations need to make sure that the people being forced to leverage data are being set up for success. That's why self-service, why embedded analytics, why guardrails are really being emphasized and put in place so people can have their analytics sandbox to play around with and explore.

The other component to culture is collaboration. I always think about the data rock stars. There are people within organizations who are just unbelievable and super dynamic with being able to gain insight. By being able to share what they're doing, and how they're doing it, you can arm the generalists or the people who are pushing back and are timid about embracing data.

Did any of the survey results surprise you?

Leone: One of the components that I thought was really interesting was the importance of accessibility, making sure that end users have access to BI platforms and access to data. But that's not all. It's much more than accessibility. It's the frequency with which the platform is being accessed. That's where the success lies. It's not like the data leaders have more people accessing BI platforms. It's the frequency. The data leaders are [much] more likely than the data laggards to have employees using and accessing their BI and analytics platform on a weekly basis.

Organizations should push for access on a more frequent basis -- get in there and use it more than once a week, more than once a day. That frequency of access is really tied to greater success.

What are you seeing in terms of enterprises starting to really use augmented intelligence capabilities effectively?

Leone: That is going to be incredibly hot over the next couple of years and the area that we expect to see the greatest growth when it comes to capabilities that a company plans to invest in. Augmented analytics has been around for a few years now, but it's been early stage, so when I saw that data I was impressed.

That said, it's easy to get lost in all these new capabilities. When it comes to the expected adoption of capabilities, one of the top five is still static dashboards and reporting. That speaks to the generalists that are starting to get involved.

Is there anything else you'd like to add?

Leone: One of the funny things about embedded analytics is that I'm starting see vendors start to shy away from using the term itself. It's often lost on end users. If you ask, 'Are you leveraging embedded analytics today?' you're going to see far fewer hands go up than if you ask, 'Are you leveraging analytics from a custom application that incorporates dashboards and reports?'

I actually think a lot more organizations are leveraging embedded analytics than we know -- I think it's a lot more pervasive. And I think it's going to expand even more.