Here at Information Builders, we love celebrating the success of our team, so we are delighted that our own Director of Market Intelligence, Lyndsay Wise, has been included in the DataIQ 100 list of the most influential people in data for the second consecutive year.
The DataIQ 100 includes the professionals who are leading the development of best practices in data and analytics in industry, academia, government, and charities.
For the past two decades, Lyndsay has made it her mission to understand how technology helps organizations gain more value from their data. We took the opportunity to ask Lyndsay what inspired her to start a career in the data industry and find out who she most admires. We inquired about what she sees as the main data challenges that organizations are trying to solve today as well as the common factors that contribute to successful data operationalization and monetization. She also shared her predictions on the broader use of data analytics and business intelligence (BI) as they become embedded within all applications.
Lyndsay explained, "I started off as a business analyst, focusing on business process re-engineering (BPR) and saw value in the way business processes could be augmented to create more efficient work spaces. As time went on, much of my work focused on systems and data.
"The better an organization’s visibility into their data and the easier access they had, the better able they were to make good business decisions. Seeing organizations cut costs, increase profits, or lower customer churn were the proof points I needed to shift my focus from BPR to BI and analytics, as merging both skill sets was a great way to work with organizations and support them through their data journeys.
"During my career, I’ve been inspired by data industry analyst and BBBT founder, Claudia Imhoff. What I admire about Claudia, and anyone who tackles complex issues, is that they do not let challenges defeat them.
"Twenty years on, many of the data challenges remain the same: without effective data management, organizations struggle to scale and hamper their digital transformation. At the same time, organizations have more mature data infrastructures and many of the challenges involve stitching disparate data sources together and creating an overall data value chain. This is actually quite complex, as organizations also need to consider security, privacy, regulatory requirements, and governance.
"One of the key challenges is to address the complexities that exist for data assets across the enterprise. Organizations look to data integration and data quality, but don’t always take an iterative approach to design and maintenance. The reality is that the only way to solve data challenges is to make sure that iterative processes are in place to support the organizational focus on continuous improvement.
"Healthcare, insurance, and government verticals will require more data sharing and open data ecosystems to deal with the aftereffects of the pandemic. That's especially true in North America, where we have yet to see the second and third waves of COVID-19.
"I have seen overall repeatable success in organizations that use their technology as an asset and not simply as a tool. In essence, this means looking at technology and data to drive success. Organizations that actually want to differentiate with their customer experience need to understand their customers. This means analyzing historical information, demographics, products/services, customer satisfaction, and the list goes on. This can be a complex process. But organizations will succeed if they look at the data they need and how stakeholders need to interact with information assets to get that view and understand the customer story. Companies are more likely to plan with outcomes in mind if they understand the concrete value of data about customers, products, revenue, and how leveraging it relates to business.
"There is an increasing focus on open ecosystems and broader access to data across platforms by creating partnerships and API access with competitive and cooperative technologies. As this expands, and organizations continue to look to cloud-native applications and integration for support, embedded analytics will become more commonplace and shift the way we consume data.
"Incorporating data management as a key focus from the outset of any initiative is key. The creation of a single platform is one trend that ebbs and flows, in the sense that the market goes through phases that advocate a single-view-of-the-organization (or single-platform) approach. Because of the complexities of data environments, unless an organization is willing and able to rip and replace its current infrastructure, a single platform may not be possible. At the same time, an organization can simplify its analytics environment by leveraging a single analytics platform.
"From a broader data management perspective, however, the organization needs to evaluate its data ecosystem to ensure it can manage data across its platforms in a way that provides tangible business benefits and a cohesive view. Multiple data providers and vendors may be used, but consumption should be seamless across platforms. In general, more solution providers are developing partnerships and options to leverage data virtualization and more flexible information access points to ensure that organizations have access to an experience that has a similar feel to accessing a single platform. This will help support organizations as they leverage embedded analytics more broadly.
As North America faces the second and third waves of COVID-19, vertical markets will require more data sharing and open data ecosystems. This also applies to the way we look at data privacy and security of data across geographic locations – within international travel, for example. At the same time, the way business is conducted and the competitive landscape may shift. Analysts speak frequently about the need to adjust to a post-pandemic economy.
"Within organizations, the need for analytics to drive business decisions will increase and become more important. Organizations that were not data-driven will need to become data-driven in order to remain competitive."