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6 Takeaways From Forrester’s Data Strategy & Insights Forum

Posted by Peter Eliason on Jan 2, 2019 11:50:00 AM
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Forrester’s Data Strategy & Insights Forum took place December 4th and 5th in Orlando, FL. The maiden voyage of this event was jam-packed with incredible speakers, interesting sessions, and most of all—important ‘aha moments’. Leaving the event I put together a list of key takeaways that will have the most impact in data science in the coming year.

The positioning of this event towards the end of the year was timely. I got the sense that many people attending the conference were looking to make a change with their data strategy in the New Year. Attending this conference was a great place to start. Speakers at the event several Forrester analysts including Brandon Purcell and also various industry thought leaders driving the field forward.

For a full recap of my time at the Forrester Data Strategy & insights Forum, take a listen to Episode 3 of RadioRev—I break down the conference and share all of my favorite moments and top takeaways. 

 

My key ‘aha moments’ boil down to 6 main themes. Here are the highlights.

1. There’s a difference between being data driven versus insights driven.

There’s an additional step that’s required and that’s moving data into action. We talked a lot about data driven organizations versus insights driven organizations. Ultimately, that means that the things we are learning aren’t valuable unless we actually do something about it and take action based on what the data is telling us.

 

2. Understand the maturity curve.

Forrester will interview and analyze different organizations, then plot them all on a spectrum in terms of how well they use data, how well they value data, and how they make decisions based on data. This is the maturity curve. It’s a great way for organizations to understand within their industries where competitors stand and where they, as an organization sit and what they can do to improve to remain in the game.

 

3. A test and learn mentality isn't optional.

It’s difficult to make decisions without having fundamental reasons why you're making a choice. A test and learn culture is absolutely the best way to make sure we’re making informed decisions and moving in the right direction. This is something we talk about a lot within Revel and it's important to keep it a central focus as we continue to grow and improve.

 

4. Data prep is still really hard.

This has been a challenge for nearly 15 years and we still haven’t figured out how to solve this problem. It hasn’t helped that the abundance of data has exploded over the past 10 years (even the past year alone). We’re continuing to find better ways to co-mingle data and make it easier to digest, but there's still a lot of work that needs to be done. Andrew Lee with IMB Analytics noted that 60% of his data scientists' time is spent preparing and wrangling data. 60%! Their time should be spent doing data science, building models, and working with the data. This is something we need to get on top of and solve.

 

5. Data quality is the #1 challenge in AI.

Even the best machine learning platform will fall on its face if fed bias or incorrect data. The same is true for humans. If we’re given incorrect information, how do we make good decisions? It’s also similar to wild animals raised in captivity. Everything they experience is a departure from the real world. If they are faced with real data and real experiences they can’t survive. The same is true with machine learning. The machine needs to be pointed to real data in order to be successful.

 

6. The impact of micro journeys on macro journeys is important.

Brandon Purcell offered an interesting example. He was working with the Bank of Montreal when they found that a negative experience resetting your password led people to be 4x less likely to buy a home. Little things like that can deteriorate an experience with a brand and lead to detrimental effects. This shows how important brand experience is and how important every single touchpoint on a consumer's journey informs decisions . What they’ll actually do and how they’ll interact with that brand is based on each experience along the way.

Overall, the first Forrester Data Strategy & Insights Forum was a great event. I look forward to seeing it grow and evolve in the coming years.

If you didn't catch the interview on our podcast, RadioRev, here's a peek at my interview:

This was Forrester’s first Data Strategy & Insights Forum. What was the primary reason you felt it was important to attend?

They had some great presenters with very important, diverse messages to convey—that was a big draw for me. And, they did a great job with it—from the caliber of thought leadership to the quality of sessions, it was well worth it to attend.

 

A big part of Forrester’s message at the Forum were relevant to the characteristics of a truly insights driven enterprise. What was your take on this?

It takes a lot of diverse players to enable insights. Brandon Purcell, one of the Principle Analysts at Forrester, compared a data team to the Beatles and what it takes to build a truly insights driven organization. The combination of John, Paul, George, and Ringo formed the band - they each played their specific roles, but together they were able to make great music.

That's how data organizations should be structured. There’s not a single individual that has every skill. We have individuals that can be storytellers with data, individuals who are data scientists building predictive models, engineers that are required to move data around, data visualization experts. When this group comes together they create a strong, data informed practice.

 

Were there any speakers that struck you as particularly forward thinking?

So many great ideas came from the conference, but there were two that stood out.

The first was Kjell Carlson, a Principle Analyst with Forrester. He talked about AI and how to use it to drive business insights. He talked about what AI really means in a business context. Organizations can realize value from machine learning and predictive modeling, but the automation of those components is really where we begin to industrialize the value of those things. I haven’t yet heard anyone define AI as succinctly as Kjell did, so that was nice to takeaway.

The second was Fatemeh Khatibloo, another Forrester analyst. She spoke about a digital twin—a digital representation of a widget or object. Many industrial companies will create a digital twin for a particular machine that they’ve built to capture the data of the machine as it’s being used. The sensors on the machine will continuously send data back to the data environment, and the digital twin will replicate what’s happening in the real world. This allows the organization to predict or recommend the next best action based on what we’re seeing from the usage patterns of this machine.

In terms of a human context, organizations can use the data they have about their consumers to create a personalized digital twin to recommend features on their behalf. But, it’s more so to recommend things that we would want, including things that would align to goals or needs that individual humans have beyond just marketing.

One of her examples was about New Year's resolutions. People generally want to lose weight, so through the use of a digital twin, Google Maps serves routes that avoid fast food chains on your way home, or suppresses ads for unhealthy foods or bubble up ads for healthier options. When an organization can drill down into its personalized digital twin and make recommendations or create experiences that act on an individual’s goals and beliefs—that’s pretty cool and absolutely something that we could integrate into healthcare.

 

Can you tell us about the attendees?

I was surprised—there were many from retail and the financial sector, but I didn’t meet many folks from healthcare. There’s an opportunity for healthcare to use data, and adopt a test and learn culture within their organization. I hope next year we’ll see more people from healthcare as the trend grows.

On the flip side, it gives Revel the opportunity to demonstrate ourselves as a leader and help coach healthcare organizations in terms of what being "insights driven" really looks like.

  

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Topics: Data Driven Healthcare

 

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