Joe DosSantos, chief data officer at Qlik, a business intelligence and data visualization software company based in King of Prussia, Pennsylvania, is steeped in the growing focus on data and analytics. But, he argues, not all companies know how to get the most out of their information.
DosSantos spoke with StrategicCIO360 to discuss how data analytics is evolving—and how information professionals can best leverage AI cloud-based technologies to enhance their organizations.
Data is the buzzword across business these days. But what does it take to get the most out of a company’s information?
It’s become trendy recently to talk about AI and data science, but the trick to creating meaningful analytics is the combination of unlocking the right data in real time, enabling analytics and using this to trigger business action. Without all three of these pieces, businesses will struggle to find value from data.
When data is converted into real-time insights, it can solve complex business problems, guide the development of new initiatives, anticipate risk and identify ways to reduce loss and waste. The multinational pharmaceutical Novartis is a great example of what real-time, continuous and relevant data can achieve. After employing data and data analytics across the entire organization, Novartis realized six-figure savings by identifying areas for budget and process improvements.
Advancements in the ease of use in modern analytics is also enabling every employee—not just data scientists—to be data literate enough to understand what the data and analytics are telling them and understand how best to act.
How does the information they’re getting from these insights compare to, say, five years ago?
The old era of data warehousing required months or even years to build something worthwhile. Data needed to be carefully fed into a single repository and modeled into a common structure. In addition, the batch-oriented nature of this technology left people feeling like they were always looking in the rear-view mirror of their business. And the effort required to manage the data was so significant—and the required skill sets so specific—that it made it difficult for all but the biggest and most well-capitalized firms to take advantage of the data they were collecting.
In recent years we’ve seen the rise of modern data warehouses and data lakes that leverage the cost structure, scalability and flexibility of the cloud. When combined with data catalogs, access to more relevant and real-time data is now a reality for more and more organizations.
Data analytics has also evolved in parallel, leveraging a SaaS model to deliver augmented analytics, natural language processing and machine learning that helps transform data into actionable insights that inform decision-making across now more intelligent enterprises. Modern analytics can now deliver more streamlined and continuous access to data that is contextually relevant and hence more useful to more and more employees.
So, in summary, businesses are shifting to real-time data that can drive real-time decisions in cloud-based architectures.
What do you expect will be the new demands from customers, especially as businesses get back after the Covid pandemic subsides, and the world opens to its new normal?
As we return to the office and our new normal, businesses will look for support in two areas. First, they will want to tap into more of their data to navigate the new hybrid work reality, understanding how their employee, customer and supply chain needs are evolving in our new norm.
Second, developing new business models for the new normal will be a top priority. This will include headcount planning, inventory management and supply chain logistics. Nobody really knows completely how business will work in the next decade and businesses will be anxious to understand so as to shift their business models and decision making accordingly.
Tackling each of these will mean an increased use of predictive analytics, where variables are assessed to create forecasts from an organization’s data sources and external data sources, like weather reports, to identify potential risks and opportunities and allow organizations to be proactive. The benefits of predictive analytics include being able to quickly evaluate multiple potential scenarios, greater agility in reacting to changing conditions and overall improved risk management posture.
A great example is what Direct Relief, a nonprofit organization that provides medical resources in emergencies, is doing with contextual, continuous data. The organization used real-time data analytics to continuously track the pandemic’s changing dynamics, including the growth rate of new cases and fluctuations in Covid-19 testing. Direct Relief was able to target new and evolving areas of need in a rapidly changing global situation and effectively deliver 2,400 tons of medical supplies to more than 100 countries during the pandemic where and when it was needed most.
How does all that impact the role of a CDO and other information professionals?
As data continues to take a more central role in businesses’ everyday decision making, the role of the CDO has become more central to the future of the enterprise. CDOs have shifted from back-office management and reporting to achieving measurable results required by the C-suite. Today, CDOs have three things on their agenda: ensuring data availability across the business, driving data literacy from the top down and accelerating speed to deliver high-value analytics insights.
CDOs should examine processes to ensure data is in a constant state of readiness. This includes deploying automated onboarding and cataloging that leverages data governance, which clearly defines, classifies and provisions data assets to people who are authorized to see it at speed and scale.
The CDO needs to help executives convey how they are connecting strategy to data-enhanced executable moments. CDOs must also remove the roadblocks that hinder the creation of insights in real-time. CDOs are taking a serious look at DataOps, the emerging practice that takes a DevOps approach to data by addressing the people, process and technology challenges to creating a data culture.
