The Power Of Data Science: How It Drives Innovation And Decision-Making

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Data Science has emerged as a game-changer in today's digital age, transforming the way businesses operate and paving the path for unprecedented innovation. Whether you realize it or not, data science is silently revolutionizing various aspects of our lives, from personalized online experiences to cutting-edge medical breakthroughs. In this blog post, we delve into the power of data science – its ability to drive innovation and decision-making – uncovering how this seemingly magical field can unlock hidden insights and help shape a brighter future for us all. So fasten your seatbelts and get ready to embark on an exhilarating journey where numbers meet creativity, possibilities know no bounds, and the power of data changes everything.

Data science is a rapidly growing field that uses data to provide insights and recommendations for solving business problems. Data scientists use algorithms and models to analyze large sets of data in order to find patterns or insights. This information can then be used to improve business processes or make decisions.

Data science can be used in a number of different industries, including healthcare, retail, finance, and marketing. Its uses are only limited by the amount of data available. Data science has the potential to drive innovation and change across all sectors of society.Areas of study in data science include machine learning, data preparation, statistical analysis, and data visualization.

How is Data Used in Business?

Data is one of the most important tools in business. Companies can use data to improve their operations, personalize products for customers, and make better decisions.

Operations. Data allows companies to optimize their operations by understanding customer behavior and trends. For example, retailer Walmart uses data to ensure that shelves are stocked with the right items and that employees are working efficiently. In addition, data helps companies optimize their marketing efforts by understanding consumer preferences.

Product customization. Customers want goods and services that fit their individual needs. By using data, companies can customize products for each customer segment based on their previous purchases and preferences. For example, clothing retailer Abercrombie & Fitch uses customer data to determine which styles are popular among teenage boys and girls.

Decisions made with data usually lead to improved efficiency or better customer service.

The Role of Data in Decision Making

"For years, business decision-makers have turned to data for insights that can help them make better choices. But what is actually driving the innovation and transformation in our economy? How can businesses harness the power of data science to improve their operations? The answer lies in understanding how data science works and how it impacts decision-making."

The Role of Data in Decision Making

Data plays a fundamental role in modern business operations. It provides insights that can help managers make better decisions about where to allocate resources, what products to offer customers, and how best to compete against rivals.

Data analytics play a key role in extracting these insights. By mining large volumes of data sets, analysts can build sophisticated models that can identify patterns and trends. This information can then be used to make informed decisions about the next action to take in business decisions.

In addition to providing managerial insights, data also drives innovation. Scientists and engineers use data to develop new products and services. They also use data to test hypotheses about how things work – whether that's studying the behavior of big chunks of matter like planets or tiny particles like atoms. By testing theories using data, scientists are able to better understand the world around them and figure out ways to solve problems.

Importantly, this process isn't limited to scientists & engineers – many businesses rely on data analytics as well. For example, retailers use customer data collected through loyalty programs to survey customer preferences before making product choices. This knowledge helps keep stores stocked with the right items and attracts new customers, who are more likely to return in the future.

businesses that can harness data science can improve their operations and compete on a global scale.

Conclusion

Data science has come to dominate many parts of our lives, from business to governance. So why is it so valuable? And what does that mean for us as citizens and consumers? In this essay, I will discuss the power of data science in relation to innovation and decision-making, and explore some of the implications for society at large. We live in an age where big data is omnipresent and often unpredictable, making it difficult for humans to understand or navigate. By relying on algorithms, data scientists help us make sense of this complexity. As we continue to rely on technology to make decisions for us—from what products we should purchase to how best manage our resources—we need information that is reliable, accurate, and trustworthy. Data science provides just that.

How The Science Of Choice Can Boost Innovation

Ever since the late 1990s, social psychologists have known a surprising truth: If you want to maximize someone's satisfaction with a choice, don't give them unlimited options. Instead, you should give them some choice but with clear constraints. This added structure is crucial for picking a desired option with confidence. This research also translates to innovation, which often results from 1) identifying a big problem to solve; 2) breaking it down into sub-problems; 3) identifying how those subproblems had been previously solved; and 4) combining the subproblem solutions in a unique and novel way. This process, which the author calls "choice mapping," adds helpful constraints to your process.

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While history would like you to believe breakthroughs are the result of genius inspiration, or divine intervention, the truth is far more prosaic. Whether it's the invention of basketball or an organization-wide system for learning and innovation, our greatest minds have arrived at their Eureka! moments by way of clever choosing. That is, they identified their big problem, broke it down into several subproblems, searched to find options for how each subproblem had been previously solved, and combined those options in unique ways to arrive at a novel solution.

I call this method the Think Bigger method. I first began teaching it to my students at Columbia Business School, but quickly realized how much value it had beyond the classroom. For instance, organizations that have dedicated innovation committees have been shown to outperform those who lack such a committee. But these results are not guaranteed, and I believe even these firms are leaving revenue on the table, based on society's collective poor understanding of how innovation really happens. Research suggests we can do even better, if we infuse the science of choice into the processes that produce world-changing solutions.

The Power of Constraints

Ever since the late 1990s, social psychologists have known a surprising truth: If you want to maximize someone's satisfaction with a choice, don't give them unlimited options. Instead, as my colleagues and I have shown, you should give them some choice but with clear constraints. This added structure is crucial for picking a desired option with confidence.

Almost 30 years later, I've been applying my research on choice to understand how innovation happens. To create a solution, you must start by defining a problem. The caveat here is that each big problem is secretly made up of several subproblems in disguise, so you must search for options that solve for those various subproblems. It turns out, choice is the active ingredient for innovation. Every great innovator in history has solved their problem by way of these subproblems, whether consciously or unconsciously.

In 1899, for example, when Henry Ford founded his own car company, he didn't just toil away on his big problem — how to make automobiles affordable for everyone. He broke the problem down into subproblems:

How do I reduce the cost of labor?

How do I reduce production time?

How do I reduce the cost of materials?

He found options to solve those subproblems in things that already existed. Oldsmobile had developed an assembly line that reduced the number of workers needed to assemble a single car. Ford was also inspired by a "disassembly line" at a Chicago slaughterhouse in which animal carcasses were broken down by specialty trained workers. He realized he could reduce production time by moving the product, not the workers, along the line. He was able to reduce the cost of materials by using a black lacquer paint that dried quickly on metal and could produce a brilliant sheen in smaller amounts than typical automotive paint. From the combination of these solutions to his subproblems, the price of Ford's car went from more than $1,000 in 1900 to $265 in 1924.

Structuring your problem-solving in this way adds helpful constraints to your process. It allows you to choose between novel solutions, particularly in ways that traditional brainstorming doesn't allow.

Brainstorming is useful when you or your team members have all the necessary information to make a decision. But it's not so helpful when information is missing. As a result, brainstorm sessions usually produce fewer, lower-quality ideas that don't solve big problems.

To illustrate the power of constraints, let's look at an example: the invention of basketball. In 1891, James Naismith was a physical education teacher in Springfield, Massachusetts. He wanted to offer his students a game they could play indoors during the harsh New England winters. That was his big problem. Like Henry Ford, Naismith didn't just rack his brain dreaming up new activities from scratch. He broke his problem down into four subproblems:

It had to fit in an indoor room, not on a vast field outdoors.

It needed the speed, effort, skill, and complexity of a field sport in order to keep the students in shape physically and mentally.

It couldn't be rough, because the players would fall down on a hard floor, not soft earth.

It had to be a team activity that involved lots of students at once in the confined space.

These constraints limited Naismith's menu of options in ways that helped him solve his problem. He couldn't reinvent football because it was too rough when played on the gym's hardwood floor. He couldn't reinvent baseball because the gym was too small.

Ultimately, Naismith realized he could combine elements from lacrosse, football and rugby, soccer, and an element from a game Naismith played as a child, called "duck on a rock"— passing a ball between teammates (lacrosse and soccer), penalty shots for excessive roughness (soccer), speed and complexity (football and rugby) and throwing at a target ("duck on a rock") — to solve his subproblems. And solving them all is what allowed him to invent a game that would last for more than 130 years and become an industry worth billions.

Creating a "Choice Map"

Choice mapping is an exercise based on the science of choice that helps you produce as many possible solutions as your imagination can come up with. You can use the below template to create a choice map. Here's how it works.

Start by identifying your big problem and breaking it down into a list of subproblems. Let's say you work for a hospital and your problem is: What is the best method for transporting donated organs safely? Your subproblems might be:

How can we ensure the organs are transported in sanitary conditions?

How can we keep the organs preserved in the right climate?

What are the greatest risks associated with improper transport?

Once you list these subproblems on your Choice Map, you can start searching for what I call "precedents," or past solutions that either come from the same world as your problem (in-domain) or from other worlds (out-of-domain).

For the subproblems of organ transportation we just listed, under "in-domain" you might research what other hospitals have done in the past and list those in your Choice Map. Under "out-of-domain," you might go looking for answers to questions like the below:

How do food companies keep food fresh in transit?

What is the fastest way to ship glass sculptures?

What is the best way to travel with a newborn?

How do bakers transport wedding cakes to the reception?

Seeing all these options in front of you should spark your thinking so that you can start mixing and matching solutions to your subproblems. For instance, you may borrow a solution from food service that keeps food at safe temperatures, or the cradling system used by a car seat manufacturer to keep it secure in transit.

By combining options for each subproblem in the right way, you may end up with a solution like "Heart in a Box," an FDA-approved device developed by TransMedics that allows for organ donation after the donor has already died. It does this through a technique called perfusion, which replicates the body's warm climate to extend the organ's shelf life, rather than simply freezing it. Through Heart in a Box and similar technologies, doctors can now access a wider donor pool and save more patients in need of transplants.

To be sure, the Choice Map can't help you determine how your solution satisfies your various stakeholders, such as investors and customers. Nor does it give you a sense of whether other people view the problem in the same ways you do. The main utility of the Choice Map is to help you create multiple possible solutions to choose from. In fact, a five-by-five Choice Map can generate up to 3,125 potential solutions.

As we've seen, it does this by allowing you to constrain your thinking in ways that decades of research have proven will put you in the Goldilocks zone of decision making. You aren't brainstorming for just any wild idea. You've collected verified solutions from different worlds to give yourself a range of choices, which you can then combine and recombine to find a solution that is greater than the sum of its parts.

In the process, you are more empowered to innovate because you freed yourself from the burden of coming up with something wholly original. Which, as Mark Twain wisely observed more than a century ago, is impossible anyway.

"There is no such thing as a new idea," he said. "We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely, but they are the same old pieces of colored glass that have been in use through all the ages."

Science Funding Falters A Year After Landmark CHIPS And Science Act

When the CHIPS and Science Act was signed into law last August, colleges and universities saw an opportunity for the government to make a transformative investment in science and innovation. The ambitious legislation promised to bring tens of billions of dollars in new research money to American colleges and universities through federal science agencies, which fund more than half of their research and development.

"We really felt like it was an acknowledgment of what I would describe as the reinvigoration of the government-university-industry partnership, which has really driven our nation's economy and our competitive position in the world on innovation for decades since World War II," said Barbara Snyder, president of the Association of American Universities, which supported the legislation.

But one year later, Congress is already falling short of the ambitious funding targets called for in CHIPS. Snyder and others worry that lawmakers have focused on the investments in the semiconductor industry and forgotten about the "and science" part of the legislation, which authorized but did not fund tens of billions in new money for federal science agencies. It appears unlikely that the next federal budget will fully fund CHIPS, given Congress's self-imposed fiscal constraints.

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CHIPS authorized $200 billion in spending over 10 years for scientific research and development and commercialization, including $81 billion for the National Science Foundation, which would double the agency's budget.

The act also outlined new investments in colleges and universities to develop the country's workforce for science, technology, engineering and mathematics jobs. Some of the planned investments would also benefit historically Black colleges and universities, community colleges, and other types of institutions beyond the big research universities.

The first federal budget since the CHIPS and Science Act passed fell nearly $3 billion short of what the act called for. As Congress grapples with caps on funding and partisan divides over spending, meeting the CHIPS funding goals will be difficult.

Federation of American Scientists, May 2023

Touted as a key to maintaining a technological edge over China and other international competitors, CHIPS also put more than $50 billion into semiconductor research, development and manufacturing, some of which would flow to colleges and universities. That money was authorized and appropriated in the legislation, but the bigger potential boon for higher education institutions was expected to come from the science provisions of the bill.

Groups that backed the bipartisan legislation say that CHIPS won't reach its full potential without full funding and are calling on Congress to stick with the legislation's goals.

"Without funding, without support for things like graduate fellowships, that just means fewer opportunities for students, for young researchers, for postdocs," said Matt Hourihan, an associate director at the Federation of American Scientists. "There's a lot of tangible effects here in terms of lost opportunity. Once we start leaving these opportunities on the table, it's hard. You can't just go back and make up for them in the future. We seem to be digging ourselves a progressively greater hole."

The House and Senate's proposed science budgets would spend $7.7 billion less than what the legislation called for in fiscal year 2024. That's on top of a roughly $3 billion shortfall in fiscal year 2023. The deal reached earlier this summer to avert a default on the federal government's debt capped discretionary spending for the next two budgets, which will hamper efforts to meet the targets outlined in CHIPS.

Proposed 2024 budgets in the Senate and House keep funding relatively flat for the National Science Foundation, the Energy Department's Office of Science and the National Institute of Standards and Technology (NIST), the primary science agencies that were allocated new money in CHIPS. Funding from the NSF and the Energy Department made up about 15 percent of total federal support for university research and development in fiscal year 2021.

Under CHIPS, Congress was supposed to allocate $26.84 billion toward those agencies this year, but they would receive just $19.1 billion under the Senate's plan and $19 billion under the House's budget. The House Appropriations Committee hasn't yet signed off on the science budget, though the Senate plan passed the committee in July with bipartisan support.

Fiscal 2023 funding for the government runs out Sept. 30. The fight over spending for fiscal year 2024 is expected to be contentious, with some House Republicans saying they'll force a government shutdown to avert further spending increases.

Debbie Altenburg, associate vice president for research policy and government affairs at the Association of Public and Land-grant Universities, said the proposed budgets do show that science funding is a priority, given that neither cuts the NSF's previous budget significantly. Given the uncertainty around the budget process for this year, she said APLU and others will have to moderate expectations.

We're in a critical moment here. The time really is now to get this done.

—Matt Hourihan, associate director, Federation of American Scientists

"We are likely in a situation where we are not going to see the growth path for NSF that we had hoped for with the passage of CHIPS and Science," she said. "I think we are in a situation where we will have to work very hard to make sure that we protect the level of funding for NSF and NIST and some of these other programs in FY24."

Most of the $52 billion in the CHIPS portion of the legislation is aimed at revitalizing domestic semiconductor manufacturing. The U.S. Makes about 10 percent of the world's semiconductors, which are critical to modern electronic devices, while selling nearing half of them. The Biden administration began to roll out the CHIPS funding over the past year and carry out a number of projects outlined in the bill, such as establishing the National Semiconductor Technology Center.

CHIPS included $13.2 billion in research and development as well as workforce development, and colleges and universities are working to tap into that pot of money.

Dozens of community colleges have announced new or expanded programming related to the semiconductor industry, according to a White House news release. A coalition of university presidents and engineering school deans penned an open letter earlier this year outlining their plans to help build and diversify the semiconductor workforce. Commerce Secretary Gina Raimondo has said that colleges and universities need to triple the number of graduates in semiconductor-related fields, including engineering.

Altenburg said the advancements over the past year are exciting and worth reminding people about, but that the picture for fiscal year 2024 and science funding is concerning. Snyder agrees, noting that the investments in research and development are key to ensuring that the United States remains competitive globally. China and other countries have increased their investments over the past decade and could overtake the United States in funding research and development.

"We think it's critical that Congress gets the message to fund American science to keep our economy robust, keep our national security strong and keep our nation healthy," Snyder said. "We just think that the investment that was called for CHIPS and Science is essential even in times when budgets are tight. In some ways, that's the time when it's the most important, because the results that they produce multiply many times and really can make an enormous difference."

Snyder wants Congress to adhere to the original targets in the CHIPS Act, though she acknowledged that some investment is better than none.

"It was the right thing then because, again, it's a long-term play, and it's still the right thing if we're going to be competitive," she said. "I think the American people want and expect us to be the world leader in innovation. I think they want us to lead the world in science, and I think we want to keep doing that. But that does not come without investment."

Hourihan said the CHIPS Act was intended to address a variety of priorities, from STEM education to research infrastructure to support for emerging technology such as artificial intelligence. A lack of funding will mean thousands of fewer research awards and delays in projects to update facilities, among other consequences.

However, the act itself is bigger than one or two budget cycles, and Hourihan and others aren't calling time of death for the science investments yet.

"Even if Congress drops the ball on investing in CHIPS and Science now, I can only expect there will continue to be a need down the road to invest," he said. "But we're in a critical moment here. The time really is now to get this done."

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