My first appreciation of the power of data came in the form of trying to find an almost imperceptible needle in a multiple terabyte haystack. As a physics graduate student at the OPAL experiment at CERN in Geneva, Switzerland, I was charged with the search for the Higgs Boson using Artificial Neural Networks.
I came to the early conclusion that no matter how complex the analysis, sometimes the result needs to be a single number or a yes or no answer.
Since then I have been obsessed with making data relevant to all types of people and have followed that obsession through positions in pre-sales consulting, product management, solution management, and leadership of support, customer success, and global services teams at Business Objects, SAP, Tableau, and MicroStrategy.
At MicroStrategy, I am now part of the team driving Intelligence Everywhere - closing the loop between the analyst and the front-line worker and driving the adoption of data in business decisions by everyone, regardless of their level of data literacy.
I believe that the path to power for data and analytics is the empowerment of non-traditional data users by creating tools and experiences that speak to individuals’ differing levels of comfort with data, while providing a secure and trusted single version of the truth to all users.
As always, human beings are the most important part of any organisation. The proudest achievements in my career have been when people on my team have taken on leadership positions in the industry. I strongly believe that you should coach your talent to the point where you would be excited to work under their leadership, and I am happy to say that many of the people I have mentored over the years are now in senior executive roles in analytics.
While I don’t have an individual role model, I do find that one of the benefits of building highly effective teams is that the team itself becomes a source of inspiration. A team that is working at a high level in product management, product marketing, or sales always generates energy greater than the sum of its members and building such teams has been one of the great joys of my career.
In some ways, the consolidation of analytics vendors in 2019 happened faster than I expected. However, the acquisitions of Tableau by Salesforce and Looker by Google only serve to underline the importance of analytics and business intelligence to cloud platform vendors, even as the consolidation potentially sets up analytics silos since analytics tools are becoming closely aligned to particular data management infrastructures. We also saw much more focus on AI and ML in analytics in 2019, even as the relationship between these technologies and governance is still being worked out.
This year will be marked by two major strategy decisions for enterprises’ analytics leaders. Firstly, they will need to decide how to future-proof their analytics strategy to provide ongoing and growing intelligence for the organisation. Given the speed with which data management technologies are changing, defining a governance and semantics strategy that lies above the data layer is key to the endurance of the intelligence held within the enterprise. Secondly, enterprises will need to implement data consumption experiences that involve everyone in the organisation providing just in time trusted information for key decisions, with little friction or context switching.
Data and technology are only useful insofar as they allow the human mind to go further than was previously possible. The most useful technologies are those that speed repetitive or complex tasks and give an actionable result just when the human mind needs it to make a decision or derive an insight. In the data and analytics industry, the biggest opportunity is to use the emerging artificial intelligence and machine learning technologies in conjunction with strong semantic graphs to provide contextual data within applications people use every day, thus allowing them to make better decisions.
The biggest challenge remains on how to get the data to everyone in the enterprise. While this is a cultural challenge, it is also a technological one given the natural human tendency to act on data only when it is trusted, contextual, and presented at just the right time to make a decision or take an action.
Putting data at the heart of an enterprise’s operations requires an open, modern, and scalable business information platform that provides tools for everyone from the data scientist to the shop floor worker, in context and from a governed single version of the truth.