Here’s how to transition away from your current “transactional” sales approach that’s getting you nowhere.

Data is not an unfamiliar concept in global business and it’s used for many purposes. From hyper-personalized, targeted marketing to real-time financial dashboards, data has become an essential asset for companies of all sizes.

While business leaders often consider how data impacts the bottom line, there is another benefit shaping how businesses engage with their customers, clients and partners: data for good. This notion isn’t new, but it’s becoming increasingly important for organizations that want to make a positive impact on the world.

What Does “Data for the Greater Good” Mean?

At the outset, one could argue that all data is for the greater good. When used efficiently, it improves the health of the organization, creates a highly engaged community of customers, and builds a more efficient work environment. But, organizations that want to use data to advance the greater good need a strategic roadmap to ensure it all comes together. It starts with asking: What, how, and where? When these questions are answered, data becomes a secret superpower, with the ability to create hope in seemingly hopeless situations.

The International Myeloma Foundation (IMF) has made its mission to unify data to build a powerful education program for patients and their families who have received a Myeloma diagnosis or relapse. In partnership with LatentView Analytics, IMF is shortening the time to hope—the period from diagnosis or relapse to a patient’s realization that they can live with myeloma.

Non-profit organizations and foundations with a focus on philanthropy, curious about how their data could create a positive impact, can ask three critical questions to learn how they can use their data to impact the greater good: What, how, and where.

Leveraging Data and Overcoming Challenges—Defining the What.

Organizations such as the IMF must identify what they’re trying to accomplish. For non-profits that may not be as data-savvy, understanding the goal of the effort is crucial for creating a strategic plan with a data foundation.

The main question the IMF is trying to solve is: What do we need to do to reduce the time to hope for our patients? Their ultimate goal is to be able to accurately measure and reduce this time in a repeatable and scalable way.

As IMF made this data journey, they knew their patients and their families needed personalized, educational information that was easily accessible upon diagnosis. With such expediency, patients can immediately utilize educational information to make informed healthcare decisions. Data shows that better-educated patients have more positive outcomes and are more capable of advocating for themselves when using a shared decision-making model.

Non-profit organizations seeking this same goal should remember that their “what” is unique to the community they are building. Unlike influencing a paying customer base, non-profits’ efforts should be focused on how they can be the best advocate for their base. Ranking the community’s most immediate needs can help leaders zero in on where to focus their initiatives.

Starting with a concrete patient-centered goal made it easy to map the road to get there. Organizations that are unclear on their communities’ greatest needs have several low barriers to entry solutions for collecting this information. A brief survey in an email or a poll on social media to a select audience can help project leaders zero in on their focus.

Understanding How

With speed, accuracy and personalization in mind, the next question to ask is how? The International Myeloma Foundation chose to build a knowledge platform that aggregates data to serve as a custom home base for every patient.

Powered by AI and ML, this knowledge platform allows patients to find the information they need to make an informed medical decision—without adding undue stress. Patients can ask questions like what they should be reading and watching, the questions they should be asking their doctors, and several treatment options they can consider.

The platform shows patients a hypothetical six-month journey based on their unique data and acts as a digital touch point to help them navigate their journey more informed. IMF found that patients that follow this journey may have improved outcomes over time.

Similarly, other humanitarian organizations are using aggregated data to improve social outcomes. The United Nations, through its Global Pulse initiative, is using real-time data from different sources to estimate the likelihood of different humanitarian scenarios and help localities better prepare to serve their communities.

As organizations consider creating similar digital interfaces, it is important to consider that every tool—particularly in health care and life sciences—needs privacy compliance and patients’ rights baked in.

Those that don’t know where to start should lean on external partners and consulting for support with data analytics, AI/ML modeling, software/app development, and design.

Last But Not Least…Where?

Finally, once the organization has a clear goal and a plan for execution, the last critical step is to understand where the data insights will be manifested and how many variations of the data should be made available. Forward-facing platforms that are easy for patients to use and interact with are essential for success. For IMF, its comprehensive dashboard is designed to help patients access their information through HIPAA-compliant voice assistance, chatbots, and QR codes.

As the IMF developed its dashboard, it recognized that not every patient has the same physical abilities. Voice assistance allows patients to get the information they need verbally, while chatbots give clear answers to specific patient questions and provide recommendations based on the history of questions or similar questions. QR codes are used to concisely communicate information with families when necessary. Where an organization hosts its data will be unique to the needs of the community it serves.

When discussing where the data insights should live, organizations should think about meeting their communities where they are. Are they regularly visiting your website? Do they meet regularly with employees in person? Wherever the insights will live, make it intuitive.

Ultimately, utilizing data for the greater good is a journey that requires a strategic roadmap founded on three questions: what, how, and where. The International Myeloma Foundation serves as a blueprint for using data to advance the greater good.

Organizations eager to embark on their own “data for good” journeys should acknowledge that IMF’s Time to Hope project was a multi-year endeavor that required resources and collaboration from across the foundation, including patients, healthcare providers, community, and data partners. Together, they created a unified data platform and ultimate patient resource that is educating patients and their families, fostering hope, and improving outcomes.

Whether machine learning, robotics, artificial intelligence, or any other buzzing technology, almost every road in digitopia leads to data. Every entrepreneur constantly asks the question; how do I improve my business using technology? If data was an enabler before, today, data & analytics is increasingly becoming the cornerstone of business decision-making worldwide. Not convinced yet? If you love football, you will probably remember the German national football team’s unbeaten run in the 2014 FIFA World Cup. Following a 7-1 victory against Brazil in the semi-finals, Germany brought Argentina to their knees in a nail-biting final that went on to win 1-0 in extra time. But the real hero, the 12th man, during their dream run, was big data analytics. Using SAP HANA, Germany recognized their average ball possession time and reduced it from 3.4 seconds to 1.1 seconds. If big data analytics can help a national football team win the world cup in 2014, imagine the limitless possibilities of data in 2023.

As data-driven solutions continue to transform businesses worldwide, Rajan Sethuraman, CEO, LatentView Analytics, walks us through the happenings in the data analytics industry. With more than three decades of professional experience, Rajan clutches an illustrious history in the management consulting industry. He holds an MBA in Business Administration, Management, and Operations from Indian Institute of Management, Calcutta.

In conversation with Rajan Sethuraman, CEO, LatentView Analytics

Even though the concept of data and analytics has been around for a while now, it has really started to kick on over the past few years. How do you perceive this evolution and the latest trends?
As a company, we have been around for 16 years. Even during 2006, organizations were starting to realize the potential of data generated by the ever-increasing IT systems and applications, including ERPs, transaction processing systems, operating systems, HRMS, and whatnot. Today, organizations have moved significantly in terms of deploying technology and applications across their processes, generating tons of data. But despite most organizations swimming in the sea of data, they are starved for actionable information and insights that can improve the quality of their decision-making. That is the strong trend that sets us off right in this direction. Data analytics can be crucial for organizations to make high-quality business decisions and optimize their processes. That’s been our founding philosophy. In fact, that trend itself is still playing out today. However, most organizations are still scratching the surface regarding the quantum of data they analyze and leverage. For instance, today, data generation has sky-rocketed with the increasing level of online interactions between organizational stakeholders via digital mediums. This leaves much room for analyzing this data and producing actionable insights on both sides of the business.

One of the biggest challenges with data analytics is ensuring people’s privacy and their data’s confidentiality. Whether I am walking into a company as an employee, a retail (or e-tail) outlet as a customer, or a hospital as a patient, I am always in pursuit of personalized experiences.

On the other hand, processing data has gotten simpler over the years. Barring a few high-quality, sophisticated algorithms being developed, many of the current algorithms have been around for decades. However, the ease with which data can be stored and manipulated has changed. The cost of data storage, computation, memory, and processing has decreased significantly. This has enabled more organizations to leverage the power of data. In some sense, we are talking about a bunch of secular trends. However, most organizations are yet to leverage the potential of data comprehensively, and it creates enormous demand as well.

How does LatentView cater to this trend? What are the areas that you focus on?
A lot of our work, for example, is around understanding human behavior in various shapes or forms, whether consumer, employee, patient, or citizen. However, human behavior is a fairly complex topic. Although we generate a lot of data around human interactions, we still have a long way to go in terms of understanding the nuances of the data and uncovering the mysteries of human behavior. Hence, we are still in this space’s nascent stages of evolution. The good thing is that there is a lot of focus and attention from the industry. Several organizations are doing extensive research in the area. We all know about generative AI and GPT. All of these initiatives are coming together to enable high-quality decision-making.

As the industry whisks efforts to drive data-powered transformation, how should leaders develop working structures that attract and retain talent to score organizational and people goals?
“In God we trust. All others must bring data.” These words, made by W. Edwards Deming, an American engineer, today have more relevance than ever. The most vital aspect of data transformation is to believe in data and act upon the idea that data & analytics can make a huge difference in the organization’s decision-making. We can call it the data analytics maturity of an organization. The organizations on the higher side of this maturity index equip themselves with high-quality data & analytics and act up the actionable information across the functional departments, organizational policies, and customer engagements, among others. Unfortunately, the organizations that reside low on this meter still resort to traditional ways and end up making crucial business decisions based on gut feeling and experience. They don’t trust the data. That’s a challenge organizations must overcome to succeed in the digital era.

On the other hand, organizations must also ensure access to high-quality data analytics platforms because data is often scattered across the enterprise—multiple silos and systems, different functional departments, and geographical boundaries, among others. It takes world-class analytics systems to combine data from different platforms and churn out high-quality, actionable insights to help you make well-informed decisions. Additionally, you have to plant, cultivate, and firmly root the philosophy of bringing data to the center of decision-making within the organization. There will be instances wherein you cannot be 100 percent objective, which is all right. The important aspect is having a general philosophy around objectivity and understanding.

How do we create a balance between leveraging digital advances and fulfilling ethical responsibilities to lead an organization effectively in the digital age?
One of the biggest challenges with data analytics is ensuring people’s privacy and their data’s confidentiality. Whether I am walking into a company as an employee, a retail (or e-tail) outlet as a customer, or a hospital as a patient, I am always in pursuit of personalized experiences. I expect them to tailor-make services and products. But they can’t provide a personalized experience unless they know about you and get access to your data. At the same time, everybody rightly wishes to control revealing information about them and give access and revoke it when necessary. The challenge for organizations is to crack the ideal balance between offering personalized experiences and the privacy of people & confidentiality of their data.

The latest artificial intelligence and machine learning models are also beginning to drag in many challenges, especially the Black box AI systems. It is like a child growing up amidst a violent home environment. The child is definitely going to inherit some of those behaviors. We can go back and rewire the Black box AI systems, either. It is possible that we might be passing on some of our bad qualities as humans to these models. This uncertainty around it also poses a challenge.

What does the future look like for the industry, and what areas does LatentView focus on?
The data analytics industry is in a nascent stage and still a fragmented space. Large enterprises are ramping up their investments and efforts in the space. Investors are increasingly interested in the space because it is a fast-growing area, and many smaller companies are making their mark. It’s a sure bet that the data analytics industry is poised to sky-rocket soon.

We at LatentView have identified five important pillars of growth for us. From a geography standpoint, we have always been heavily focused on the US. Over the past six months, we have been expanding our footprint across Europe and doubling in the European market. We are exploring opportunities and having conversations with clients and prospects. We are also running a small experiment in India. However, the US will always remain an essential geography for us.

On the other hand, from an industry standpoint, we have been doing most of our work in technology and the digital native space. We are now adjoining more focus areas, especially BFSI and Retail segments. We see these two areas as crucial future prospects.

From a capability standpoint, there are two areas that we are pushing hard on—supply chain analytics and data engineering. We are also stepping up our efforts in the marketing analytics space—around full-funnel marketing or growth marketing. We are investing a lot of resources into advanced analytics and data science, including graph theory and applying graph theory around natural language generation & processing and image analytics.

What would be your advice to budding industry leaders hacking new opportunities?
While there may be 200 things that might seem interesting, it is crucial to pick and focus on a few things that you believe can change the game in terms of quality decision-making within the organization. Any organization or leader embarking on analytics initiatives should first analyze what the data that they already have access to is, what kind of use cases and problem statements are my internal clients coming to me with, whether they can evaluate with the available data and help clients address the pain points or opportunities, and what will be the likely business impact while using the available information. People want to see tangible benefits without any delays. Hence, you need to show proof that data analytics-driven decision-making can be a lot more impactful, generate more revenue, unlock more value, and save costs.

by Prashant Vishnupad – Global Head of Industrials, LatentView Analytics

Picture this: The number of connected cars is set to increase to 352 million by the end of 2023, compared with 119.4 million in 2018. By 2030, more than 95 percent of passenger miles will be served by autonomous cars. And by 2025, the globally connected car market is projected to reach $166 billion—up from an estimated $54 billion in 2020.

The automotive industry is grappling with greater disruption than ever before. From mobility services to supply chain challenges and the growing pressure to become more sustainable, the industry is at an inflection point. Clearly, the success of the connected vehicle sector depends on how it can drive changes in areas like digital transformation and move towards electrified and autonomous driving.

The CASE for Connected Vehicles 

CASE, or Connected, Autonomous, Shared and Electrified, are the four

megatrends driving the industry overhaul in the automotive sector. Each of these trends involves the use of connectivity to enable new applications and services. The connected vehicle market can be segmented based on the type of service provided, such as Remote Vehicle Diagnostics, Over-the-Air Software, Collision Warning Systems, Automated Parking, Remote Vehicle Operation, Remote Asset Monitoring, Vehicle Design and Development, Vehicle Management, and more.

Software-defined vehicles are pushing the industry to adopt massive changes, not only in hardware but also in the way automotive manufacturers connect with their customers. Primarily, these changes impact the onboard operating systems and the way telematic control units (TCUs) upload data from the car to the cloud.

A Data Intensive Platform

A point to note is that a massive 4,000 GB of data will be used by a single autonomous car … every day! Do OEMs and other stakeholders have the capability to analyze and benefit from this data? Creating an efficient ecosystem to support this voluminous movement of data will require integrated microservices, middleware, and APIs—all necessary for the widespread adoption of autonomous vehicles.

As software architecture shapes the future of the sector, automotive CIOs, CTOs, and CDOs need to take advantage of the current opportunity by prioritizing the capabilities and differentiating features of their vehicles and connecting with service providers to deliver next-gen customized experiences. As car manufacturers begin to think beyond traditional car features like feature design and engine power, they should invest in cutting-edge digital capabilities like personalized subscription services and a range of on-demand features from a centralized digital cloud.

Connected vehicles will play a huge role in driving the future of the industry. And this will happen in conjunction with rapid data proliferation, which can be monetized to help reduce costs or optimize R&D. However, when it comes to assembling substantive data to stay ahead, carmakers need a sizable user base compared with their competitors. At present, the usage levels are insufficient, and carmakers need to clearly establish their place in the connectivity ecosystem.

Re-engineering Mobility

Manufacturers today are also burdened by legacy systems; to enable connected vehicle solutions, cloud based data platforms are needed so that data science based connected applications can be built over them. However, many OEMs are at different levels of maturity when it comes to having the right digital architecture to enable these solutions. Those OEMs that build the right digital architecture for connected mobility, will be able to differentiate their product and service offerings and gain an edge in the future of the automotive industry’s transformation.

Many vehicles are compatible with connected services like Apple CarPlay and Android Auto, which effectively replaces a carmaker’s infotainment system with something more familiar to smartphone users. However, if people are already using third-party applications from major tech players, making these features even more accessible through their vehicle’s interface is key to bringing customers—and their data—back into the fold. But the pace has been slow, and it will need major investments in the connected space to address this challenge.

Looking at the Road Ahead 

While the automotive industry is rapidly adapting to the new business models required to enable CASE—and building the technology stack required to enable the transformation—we have to realize that OEMs cannot do this alone. But the good news is that the transition from hardware to software-supported vehicles has begun.

Traditional tier 1 suppliers are merging with new partners in unexpected areas. For example, Qualcomm, AI providers, and telecommunications companies like Verizon are now part of the connected vehicle ecosystem. Of late, we are seeing a convergence across multiple industries thereby enabling the connected vehicle transformation in the automotive industry. Hi-tech companies like Google and Amazon are partnering with automotive OEMs to build cloud solutions and architectures that are critical to enable connectivity.

Similarly, retail companies are providing solutions like EV charging maps; financial services companies are partnering with ride-sharing companies to enable automotive mobile payment systems; and utility companies are talking to automotive OEMs to ensure the power needed for EVs is available when and where required.

Additionally, pure-play data science and engineering firms are fast emerging, armed with the much-needed expertise in implementing advanced solutions in technology, fintech, retail, and automotive industries. OEMs should leverage the deep knowledge of such service providers to accelerate their own transformation.

The connected vehicle is the future. Technology has made this possible, and drivers can now look forward to when self-driving and electrified cars will enable new connected vehicle applications that will simplify and enhance their driving experience. For connected cars, the ecosystem is not just within the vehicle; automotive leaders should realize a car is just one entity in the consumer’s digital environment. It is not enough to merge connected services into a seamless in-car experience. That experience must also fit seamlessly into their entire digital lives.