Joanna Hughes :
Data. It’s one of the most powerful four-letter words out there, but it’s useless without the ability to derive meaning from it. Enter data science. This new field harnesses the power of statistics and deep learning for near-endless applications, including facilitating better corporate decision-making and development.
Here’s a closer look at how companies are using data science, along with one way aspiring business leaders can position themselves to maximize results through data science.
What are Data Science Platforms?
Google’s Chief Economist Hal Varian once said, “The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate-it’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the [complementary] scarce factor is the ability to understand that data and extract value from it.”
Indeed, data is everywhere, and understanding it is the key to using it. But as the world becomes increasingly data-driven, so do the challenges of wrangling it into results. Bridging this gap are data science platforms.
Dataconomy explains, “Data science platforms are meant to encompass the whole of a data scientist’s work. That means they typically provide tools that help users integrate and explore data from varied sources, build and deploy models, and make the outputs of those models operational. Essentially, this suite of tools is meant to keep data science work transparent, reproducible, and scalable – and make it easy for a data scientist to push dynamic results (like the predicted outcomes of ad campaigns) to the people who make decisions based on those results, replacing or supplementing static (and quickly outdated) reports.”
In other words, gaining access to data is only part of the equation. Companies must also have mechanisms through which to integrate that data into their operations and outcomes. Those that are adapting data science platforms as a strategic enterprise-taking the emphasis off of the data itself and focusing on data-driven insights instead-are gaining the competitive advantage. Because of this, adoption of data science platforms is expected to skyrocket from 29 percent to 69 percent over the next year, according to a recent study conducted by Forrester Consulting on behalf of DataScience, Inc.
Described as “insights leaders” by Forrester, companies already embracing data science platforms are leading the pack for profit and growth expectations.
Quality over Quantity
Another imperative when it comes to making the most of your data? Putting high-quality data first. As American Express chief risk officer Ash Gupta told McKinsey, “The first change we had to make was just to make our data of higher quality. We have a lot of data, and sometimes we just weren’t using that data and we weren’t paying as much attention to its quality as we now need to. That was, one, to make sure that the data has the right lineage, that the data has the right permissible purpose to serve the customers. This, in my mind, is a journey. We made good progress and we expect to continue to make this progress across our system.”
While there is a time and a place for quantitative data-particularly for developing and testing – the importance of qualitative data should not be overlooked within the context of corporate development. TechRepublic explains, “Qualitative research is about exploration and in terms of data science that, at least in part, translates to exploratory data analysis. Qualitative research data scientists are looking for themes in a vast sea of data. This data could be structured, unstructured, or unavailable (data that hasn’t been captured yet). If they’re successful, they’ll uncover concepts and patterns in your data that you never knew existed.” The Fine Line
With so much attention focused on how to make the most of data, another critical topic is sometimes overlooked: using data without compromising customer trust and loyalty. This means that forward-thinking organizations must also acknowledge ethical implications of using all of the data at hand. After all, just because you have access to data doesn’t mean it’s yours to use-or that your customers will respond positively if they perceive that you have crossed a line and/or been careless with sensitive information.
Tech Bytes & Insights concludes, “Therefore, organizations should take extra care in ensuring that ethics and customer sensitivity are part of data science planning discussions, along with adherence to standard data governance policies, which include safeguarding data from compromise.”
Becoming a Data Science Leader
With the world of data science so full of opportunities and obstacles, how can you position yourself to maximize the former while overcoming the latter? Bologna Business School’s Master in Data Science aims to produce the next generation of business leaders with the knowledge, skills and training to use data to guide the decision-making processes of their organizations.
Staffed by distinguished team of professors, guest speakers and managers from top global businesses, the Master in Data Science’s international and interdisciplinary approach is not just for students who want to learn how to manage data, but also about how to generate value and measurable results with abilities which will position them at the forefront of this key sector.
Consisting of two terms of classroom-based lessons in English as well as 500 hours of work experience totaling 12 months of study, the program comprises three core competency areas: business economics, IT, and statistical mathematics-adding up to exhaustive theoretical and practical understanding.
LinkedIn’s annual roundup of “top skills that can get you hired in 2017” declared that “data and cloud reign supreme” for one reason above all else: Companies require these cutting-edge skills in order to stay competitive. Data scientists, meanwhile, just claimed the top spot on Glassdoor’s roundup of the “50 Best Jobs in America.” (Not to mention that whole “Sexiest Job of the 21st Century” thing.) The overall takeaway? Not only will the latest data science know-how-backed by a Master in Data Science degree from Bologna Business School-help you land a great job, it will also help you make a major organizational impact.
(Joanna worked in higher education administration for many years at a leading research institution before becoming a full-time freelance writer. She lives in the beautiful White Mountains region of New Hampshire with her family).
Data. It’s one of the most powerful four-letter words out there, but it’s useless without the ability to derive meaning from it. Enter data science. This new field harnesses the power of statistics and deep learning for near-endless applications, including facilitating better corporate decision-making and development.
Here’s a closer look at how companies are using data science, along with one way aspiring business leaders can position themselves to maximize results through data science.
What are Data Science Platforms?
Google’s Chief Economist Hal Varian once said, “The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate-it’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the [complementary] scarce factor is the ability to understand that data and extract value from it.”
Indeed, data is everywhere, and understanding it is the key to using it. But as the world becomes increasingly data-driven, so do the challenges of wrangling it into results. Bridging this gap are data science platforms.
Dataconomy explains, “Data science platforms are meant to encompass the whole of a data scientist’s work. That means they typically provide tools that help users integrate and explore data from varied sources, build and deploy models, and make the outputs of those models operational. Essentially, this suite of tools is meant to keep data science work transparent, reproducible, and scalable – and make it easy for a data scientist to push dynamic results (like the predicted outcomes of ad campaigns) to the people who make decisions based on those results, replacing or supplementing static (and quickly outdated) reports.”
In other words, gaining access to data is only part of the equation. Companies must also have mechanisms through which to integrate that data into their operations and outcomes. Those that are adapting data science platforms as a strategic enterprise-taking the emphasis off of the data itself and focusing on data-driven insights instead-are gaining the competitive advantage. Because of this, adoption of data science platforms is expected to skyrocket from 29 percent to 69 percent over the next year, according to a recent study conducted by Forrester Consulting on behalf of DataScience, Inc.
Described as “insights leaders” by Forrester, companies already embracing data science platforms are leading the pack for profit and growth expectations.
Quality over Quantity
Another imperative when it comes to making the most of your data? Putting high-quality data first. As American Express chief risk officer Ash Gupta told McKinsey, “The first change we had to make was just to make our data of higher quality. We have a lot of data, and sometimes we just weren’t using that data and we weren’t paying as much attention to its quality as we now need to. That was, one, to make sure that the data has the right lineage, that the data has the right permissible purpose to serve the customers. This, in my mind, is a journey. We made good progress and we expect to continue to make this progress across our system.”
While there is a time and a place for quantitative data-particularly for developing and testing – the importance of qualitative data should not be overlooked within the context of corporate development. TechRepublic explains, “Qualitative research is about exploration and in terms of data science that, at least in part, translates to exploratory data analysis. Qualitative research data scientists are looking for themes in a vast sea of data. This data could be structured, unstructured, or unavailable (data that hasn’t been captured yet). If they’re successful, they’ll uncover concepts and patterns in your data that you never knew existed.” The Fine Line
With so much attention focused on how to make the most of data, another critical topic is sometimes overlooked: using data without compromising customer trust and loyalty. This means that forward-thinking organizations must also acknowledge ethical implications of using all of the data at hand. After all, just because you have access to data doesn’t mean it’s yours to use-or that your customers will respond positively if they perceive that you have crossed a line and/or been careless with sensitive information.
Tech Bytes & Insights concludes, “Therefore, organizations should take extra care in ensuring that ethics and customer sensitivity are part of data science planning discussions, along with adherence to standard data governance policies, which include safeguarding data from compromise.”
Becoming a Data Science Leader
With the world of data science so full of opportunities and obstacles, how can you position yourself to maximize the former while overcoming the latter? Bologna Business School’s Master in Data Science aims to produce the next generation of business leaders with the knowledge, skills and training to use data to guide the decision-making processes of their organizations.
Staffed by distinguished team of professors, guest speakers and managers from top global businesses, the Master in Data Science’s international and interdisciplinary approach is not just for students who want to learn how to manage data, but also about how to generate value and measurable results with abilities which will position them at the forefront of this key sector.
Consisting of two terms of classroom-based lessons in English as well as 500 hours of work experience totaling 12 months of study, the program comprises three core competency areas: business economics, IT, and statistical mathematics-adding up to exhaustive theoretical and practical understanding.
LinkedIn’s annual roundup of “top skills that can get you hired in 2017” declared that “data and cloud reign supreme” for one reason above all else: Companies require these cutting-edge skills in order to stay competitive. Data scientists, meanwhile, just claimed the top spot on Glassdoor’s roundup of the “50 Best Jobs in America.” (Not to mention that whole “Sexiest Job of the 21st Century” thing.) The overall takeaway? Not only will the latest data science know-how-backed by a Master in Data Science degree from Bologna Business School-help you land a great job, it will also help you make a major organizational impact.
(Joanna worked in higher education administration for many years at a leading research institution before becoming a full-time freelance writer. She lives in the beautiful White Mountains region of New Hampshire with her family).
