Acceleration Powered by Data: A Fireside Chat with AWS

Aug. 31, 2020 | A Q&A with AWS

How does data fuel and support changing business models?

Thanawala: At the heart of every business model, it’s about winning and retaining customers and employees, while improving efficiencies in your business operations.

One of the key ways of finding new customers is to delight your existing customers and use them to create a community of proponents that will bring in additional customers. Peloton, for example, has a passionate community of riders who are helping each other meet their own fitness goals. At the center of Peloton’s product is their leaderboard where users can see the other riders and their stats: power output, cadence, resistance and heart rate. This requires processing of large amounts of data at microsecond latency. Airbnb’s ability to provide personalized search and Lyft’s ability to provide real-time location updates to riders and drivers are both examples of better customer experiences because of the application developers’ ability to take advantage of the data available to them.

Similarly, hundreds of paper and tissue parent rolls are produced every day at Georgia-Pacific manufacturing facilities across North America. Georgia Pacific uses analytics to predict precisely how fast lines should run to avoid tearing. By reducing paper tears, they have improved business efficiencies and increased profits by millions of dollars.

So, organizations really must challenge themselves to envision their ideal end state and work backward from that to see how data and technology can help them reach it.

How do decision models enable organizations to maximize the value of their data?

Thanawala: Data by itself is not very useful. An organization needs to have a decision model or a framework that provides a guided path to actionable insights based on the information gleaned from the data—what we term as “Turning Data to Insights.”

A decision model that simply provides insights but does not highlight the necessary actions to undertake to achieve the desired outcome has limited value, while a good decision model makes it easy for an executive to understand what actions are needed.

For example, Kinect Energy is a key Nordic energy provider and is dependent on the natural power resources enabled by the region’s windy climate. With good decision models and leveraging analytics, the company can predict the upcoming weather trends and therefore the prices of future months’ electricity, enabling unprecedented long-range energy trading that represents an industry-leading forward-thinking approach.

Autodesk, a leading provider of 3D design and engineering software, is improving its software products and offering better service to their customers by monitoring and fixing software problems as quickly as possible through review of log data and good decision models.

It is important to note that models are not static. Decision models should also incorporate feedback and continually incorporate learnings from prior results for even better results and remove any biases found in the earlier model.

How can organizations leverage their data in times of uncertainty?

Thanawala: Now more than ever, it is important to leverage data to be able to respond to a changing environment and changing customer usage patterns and behaviors.

Organizations will have to balance customer needs and business production with conserving cash flow, rethinking their supply chain, and considering employee safety, for example. Data-based decision-making is going to be a key enabler in better outcomes. If you are in the retail business, what channels and programs do you use for customer incentives, how do you manage employee shifts and training programs for the new normal? Having good decision models that can be tweaked with newer information and an understanding of the failure points will be even more important.

Pfizer, for example, processes over 2 trillion healthcare events per year, such as prescription fills and refills, outputs from wearable devices and apps, patient claims, electronic health records, and targeted clinical trials. Pfizer reduced their costs to store and processed a healthcare event by 58%, increased the amount of events processed by 416% by moving to a cloud-based data lake, and reduced the time needed to gather and prepare data for regulatory submissions by a factor of five and now avoid repeated experimentation, which otherwise would have taken an extra three weeks of scientists’ time.

What are some of the biggest obstacles that companies face when creating a data-driven culture?

Thanawala: A lot of the biggest obstacles companies face in creating a data-driven culture aren’t technical—they’re about people. The biggest differences between organizations that talk about a data-driven culture and those that do it and are having the most success often comes down to a few key things.

First, the senior leadership team needs to be aligned and truly committed that they want to create this culture. And they need to be setting clear directions and expectations with the rest of the organization to get everyone on the same page and working toward the same thing. It’s easy for others to do nothing or block things if the leadership team isn’t making the move a priority and building a culture for change. Then, the most successful organizations creating this change start with an aggressive top-down goal that forces the organization to move faster than it would have organically.

Third, it’s important that organizations are trained on the methodologies and comfortable with the concepts as part of the whole process. And last, sometimes we find that organizations can become paralyzed if they can't figure out how to impact every workload and application. There is no need to boil the ocean. So, we often work with organizations to perform a portfolio analysis to assess each application and build a plan for what to do in the short term, medium term and last. This helps organizations get the benefits of the data-driven approach for many of their applications much more quickly, and it really helps everyone see the benefits of the approach.

How do data and analytics empower organizations’ digital transformation efforts?

Thanawala: Digital transformations typically start with simple changes—companies seeking operational efficiencies, reducing paperwork, putting dashboards together for executive insights. However, as more technologies are leveraged, such as cloud-based services, the speed and access to a breadth of different services empowers and spurs innovation. The art of leveraging data and analytics to cut costs and obtain operational efficiencies then moves to launching new products and services and obtaining top-line growth.

Digital transformation is not complete unless it touches all parts of the organization and touches every employee. It’s not just about providing dashboards for executives, but rather about empowering people to work in new ways with technology and providing them with training and tools to help drive insights for their jobs. This empowerment will also drive decision-making decentralization that slows down organizations and will enable people to leverage newer technologies like AI / machine learning (ML) to gain insights and understand trends that are not obvious.

Organizations that successfully generate business value from their data will outperform their peers. An Aberdeen survey saw organizations who implemented analytics outperforming similar companies by 9% in organic revenue growth. This helped them to identify and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices and making informed decisions.

Atlassian, for example, reduced customer churn by combining customer relationship management, social media and incident tickets to correlate customer satisfaction with sales. Siemens Cyber Defense Center used cloud-based AI and ML to process huge amounts of data and make immediate decisions about how best to counter any detected threats.

Thinking about how organizations mobilize their workforce for reentry plans, what role can data play or how can it inform an organization’s reentry plan?

Thanawala: As the workforce is mobilized, organizations will need employees that can adapt and adjust to the new norm. A key part of that is developing and keeping talent and skills across the organizations, leveraging employees’ talents remotely and working with cross-functional teams. Ensuring leaders can bridge the gaps between traditional and digital parts of the business is also going to be key. Leaders that are business savvy will need to understand and leverage technology for completing the business transformation. Data and analytics will play a key role in the reentry plan as enablers for training, 360-degree feedback and continuous improvement.

Companies that are resilient and have a clear vision and a plan to leverage data to delight customers will emerge stronger and survive the changes.

Milan Thanawala is a principal partner development manager for Amazon Web Services’ AWS Partner Strategic Initiatives team. Milan has 20+ years of experience in the technology sector. He is a business and technology leader with deep skills in enterprise software and systems, data and analytics, and cloud technologies.