As we transition to 2020, I wanted to take a moment to reflect on an incredible year. From our perspective, analytics and data topics dominated the business world.

“Companies are in the midst of many profound changes,” wrote research and analytics firm BARC in their Top Business Intelligence Trends 2020 Report. “The amount of data available and the speed of producing new data has been increasing rapidly for years, and business models as well as process improvements increasingly rely on data and analytics.”

It’s no surprise, then, that organizations of all kinds (healthcare, retail, manufacturing, insurance, finance—you name it) are doing everything in their power to stay ahead of the massive digital transformation currently underway. In fact, 94% of enterprises say data and analytics are vital to their business growth and transformation.

We know that data and analytics are the keys to driving innovation, and that the most innovative companies are often the most successful. While there’s reason to celebrate the abundance of data, what’s less clear is how to execute the most effective strategy using both traditional analytics (data extraction, cleansing, blending; modeling; dashboards and reports) and advanced analytics (machine learning, AI, augmented analytics, etc.).

In conversations with analysts, partners, prospects, and customers over the last year, we heard two key challenges:

  1. How can organizations scale traditional analytics capabilities within their organizations?
  2. How can organizations implement and operationalize advanced analytics capabilities within everyday business scenarios?

For me, analytics culture is a key component for each.

Challenge 1: How can we scale traditional analytics?

Today, many companies have hired a chief data officer, established a data strategy, and maybe even selected a BI platform to help them make sense of their data assets. Time and time again, though, we saw organizations struggle to implement analytics across the last mile of the implementation. That’s the point where data has been piped in, modeled, and transformed into visualizations or dashboards.

For many, this last juncture marks the moment where people can finally begin to see and understand their data. And this is generally the point where decisions can be made. And this is really important, because it takes an incredible amount of hard work to get to this point. However, most acknowledge the work is far from done. For the true value of analytics to become manifest, this whole process must be iterative, and it must involve participants and users from across the business. In short, it must be scaled.

From my perspective, scaling analytics is hard. And the problem is usually cultural. Consider statistics from NewVantage Partners’ annual Big Data and AI Executive Survey 2019. While 48% of respondents said their organization competes on data and analytics, 31% had a “data-driven organization,” only 28% possessed a true “data culture.”

In many organizations, collaboration suffers because data is siloed. In January, I wrote about how to combat these “data fiefdoms,” scenarios in which data is trapped at the individual or departmental level. In these organizations, users often have access to multiple—sometimes competing—analytics tools, further compounding the problem. It’s hard to scale when there’s internal friction.

Ultimately, successful organizations in 2020 and beyond will be the ones that are able to establish solid data governance protocols, promote data stewardship (as opposed to data ownership), collaborate across departments using shared tools, focus on analytics training and literacy, and establish transparent KPIs and targets.

And while success does largely rest on an organization’s ability to execute on this enterprise-wide data strategy, the analytics tools in place must focus on the user experience and get people excited to use them. This will in turn encourage collaboration, which increases the likelihood an analytics culture will scale.

Enthusiasm and collaboration. Yes, these are squishy concepts, but they are often overlooked. Until BI platforms focus on the user experience (for all users), organizations will continue to struggle to scale analytics within their ranks.

Challenge 2: How can we operationalize AI/ML/augmented analytics?

The other key challenge we heard a lot about in 2019 was how to best integrate advanced analytics into everyday business practices. We know that companies that compete—and win—on analytics are the ones that innovate more quickly than their competitors. Today, innovation requires the incorporation of advanced data analytics technologies—such as machine learning, AI, and augmented analytics that predict future events and behaviors—in regular business operations.

However, these techniques are often difficult to implement and scale in traditional production environments. It’s an obvious point to anyone: success requires much more than simply investing a lot of money in ML and AI initiatives and expecting to reap the rewards.

According to Spiceworks in their annual IT survey, “Businesses’ adoption of AI technology is expected to nearly triple.” Despite this enthusiasm, many organizations struggle to gain traction. According to a recent VentureBeat article, 77% of respondents said that business adoption of AI initiatives continues to represent a challenge for their organizations. The same article cited the awful success rate on advanced analytics projects, pegging the failure rate at an alarming 87%. Ouch.

Advice on how to operationalize advanced analytics is abundant. Name your reputable source—McKinsey, Gartner, Forrester—and you’re sure to find a top five list of recommendations for success. One post from Chris Brahm, who leads Bain’s global Advanced Analytics practice, was particularly practical: “Many will rush to invest in the latest analytics software and infrastructure vendors and hire data scientists, but the ultimate winners will align these investments with their strategic and organizational needs in ways that lead to action and results.”

His point about “the last mile” was particularly incisive. He wrote, “Design the analytics with the last mile of adoption in mind.” The best analytics solutions emerge when data scientists and business stakeholders work together, set success requirements early and keep end users central to decisions.”

It’s clear that AI and ML initiatives require more than just tools, people, and processes—they require strategic direction and a roadmap that builds consistency and accountability. A sound strategy begins with the end in mind.

However, there’s more to it than that: I believe the key factor for success is cultural. And the key comes down to collaboration between two key personas. Earlier this year, I wrote an article about how data scientists and citizen data scientists can work in tandem to achieve success. I described the explosion of a new breed of analytics-minded professionals: the citizen data scientist. According to research from Gartner, 2019 was the year citizen data scientists were supposed to outproduce traditional data scientists.

Whether or not that prediction bore out, I see the collaboration between  pedigreed data scientists and citizen data scientists as a net positive: as the overall pool of people doing data science goes up, network scale comes into effect, heralding the advancement of an analytics “mindset.”

However, for this collaborative mindset to take root and flourish, it paradoxically comes back to the right technology investments. There was a time when only trained professionals knew how to use analytic and BI applications. Today, there are fewer barriers obstructing access to these advanced toolsets.

As such, I strongly believe the most favorable path for adoption of advanced analytics is one where the greatest number of people are confident using their platforms. At Pyramid that’s our goal: to create a truly codeless experience where any user—regardless of skill—can perform advanced analytic techniques and make better decisions with data.

Pyramid in 2019

To recap, we saw organizations struggle to implement and scale both traditional and advanced analytic technologies in 2019. This featured across a backdrop of profound market consolidation in the business intelligence space, which made things even more complicated for buyers. With the acquisition of Tableau by Salesforce, and Looker by Google, major changes in the analytics market were clearly afoot.

We’re not shocked by the acquisitions, or the general trend toward consolidation in the analytics space. BI is simply big business these days. However, these acquisitions inserted uncertainty into organizations’ strategic analytics and data initiatives.

Things were far from uncertain with Pyramid. In fact, despite the tumult in the BI market, we focused wholly on developing customer-focused analytics software. In October, we launched Pyramid v2020, our latest version. Simply put, it was a massive release. It introduced over 150 new enterprise-grade features and capabilities to improve the way organizations make data-driven decisions. In particular, we sought to create an adaptative analytics application with a focus on customer experience. The result? No other competing BI solution packs as much analytic punch—without overwhelming users—as Pyramid.

In 2019, we also worked hard to connect and build relationships with partners, vendors, customers, and prospects across the globe. We attended dozens of events to meet analytics professionals and understand their business challenges. And in late September, we brought in over 100 Pyramid customers and partners together outside of London for our first-ever user conference.

We came away from ANALYTICON more energized—and more committed—than ever to make Pyramid our customers’ and partners’ go-to analytics resource. The enthusiasm we saw experienced was inspiring, and it’s that kind of energy that motivates us to get better every day.

What’s next in 2020?

Today, we’re more committed to our customers than ever. In 2020, you’ll see and hear a lot about Pyramid Analytics. You’ll find us exhibiting at events across the globe, producing dozens of educational and topical webinars, and hosting user conferences and training workshops across multiple continents.

And we’ll continue to execute on a product roadmap that puts our customer at the center of everything we do.

We realize organizations have lots of choices in this crowded BI market, and that they do not choose analytics platforms on functionality alone. We understand customers are seeking the best user experience possible, and we intend to make that true for you, our customers and partners, in 2020 and beyond.

That’s our singular focus, and we can’t wait to show you all the amazing things we have planned for 2020.