Go big on data and digital (2023)

As the digital revolution gains momentum in our industry, one of our strategic priorities is to be a leader in harnessing data science and digital technologies to boost effectiveness and efficiency across our enterprise. Digital technologies are helping improve how our scientists discover and develop innovative new treatments, how we make decisions, how we engage with customers and how we run our operations.

As part of our data and digital strategy, we are pursuing 12 major projects to build large-scale digital solutions across every aspect of our business. We are creating a strong foundation to support our digital transformation through such steps as building massive databases and improving our people’s digital capabilities. To reinforce and accelerate our progress, we are forging partnerships with leading technology companies. And we are taking bold bets to prepare for a more digitally enabled future in healthcare.

12

The number of major projects we are pursuing to build large-scale digital solutions across every aspect of our business

In parallel, we are prioritizing responsible use of patient data. In January 2020, we adopted a new, principles-based privacy policy, and we are training employees on its use. And we are developing a specific approach for applying the principles to patient data.

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5000

The number of sales representatives in 11 countries using the ACTalya platform

How we innovate

Artificial intelligence (AI) and other digital technologies are helping us improve and streamline the research and development (R&D) of new treatments. We are working to shave two years from the R&D process, make clinical trials more accessible to patients, and uncover new ways to fight disease by applying powerful analytical tools to data from more than 2500 clinical trials and 2million patient-years of research results.

For instance, through a project called data42 we aim to fundamentally reshape how we discover and develop treatments, using AI to sift through huge amounts of data from our clinical trials and other sources to find new insights into illnesses and how to treat them. To make this possible, we are bringing together diverse data sources, from images to blood test results. And we are building the analytical tools that could help us identify potential new drugs, for example, or ways that existing drugs might be applied to different diseases.

For more detail on how we are using data and digital in R&D, please see the section “Deliver transformative innovation.

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How we engage with customers

Our ambition is to use data science, new communications channels and other digital technologies to help us better serve our customers and ultimately increase revenues.

We continue to expand the use of a digital personal assistant for sales representatives, called ACTalya. It uses AI to search through information in dozens of databases and provide daily suggestions to our salespeople on how to best support doctors with information about our products. The system aims to promote more meaningful interactions with doctors and is now being used by about 5000 people in our top 11 countries for products such as Entresto, Aimovig and Cosentyx.

Initial indications in countries where it has been in use for at least six months show the system helps improve productivity, enabling salespeople to schedule one or two additional doctor visits per day. The system is being adapted for use by others in the organization, such as salespeople working with oncologists, and our medical science liaisons.

In the Novartis Oncology business unit, we are building a data platform that uses AI to help optimize marketing, among other applications. Called DROID, it integrates data from 110 different sources from inside and outside Novartis, and deploys AI in several ways. One application helps marketers decide how best to reach doctors or patients interested in learning more about treatments for specific cancers. The algorithm suggests the optimum mix of marketing approaches for an individual product – whether through television advertising, social media or other means. DROID has been rolled out in our oncology organization in the US and is now being introduced in other countries.

Building on the tools already in place, we began work at the end of 2019 on a platform of digital solutions to support all commercial efforts in innovative medicines.

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How we operate

We are finding new ways to use data and digital technologies to improve our operations, increase efficiency and support our ambitious cost-reduction efforts.

For instance, in the finance function in our Innovative Medicines Division, we are using AI and predictive analytics to forecast sales and cash flow. In our top markets, we generated sales and cash flow forecasts for the period from 2020 to 2022, shortening the budget process. We also used AI to recommend the most effective way to allocate our marketing and sales resources in top markets.

In our manufacturing operations, we’re building an advanced analytics platform to help improve production processes. To enable this approach, we linked together data captured manually, or in different systems that until recently operated independently, providing an end-to-end view.

In 2019, we built a prototype of the analytics platform to help us make better business decisions based on insights from our data. We focused on the end-to-end production process for one of our key products, Cosentyx. And we worked with our Cosentyx production site in Stein, Switzerland, to improve its process execution.

We expect the approach will help us identify and predict bottlenecks, accelerate production and ensure our medicines reach patients faster. In 2020, we plan to expand this approach to additional locations and products.

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Our digital team is also looking at further potential applications whereby we can use data to improve operations or extract useful new insights, such as ways to help us accelerate cultural change in the organization, or streamline commercial operations.

Technology partnerships

To reinforce our adoption of data analytics and digital technologies, we continued to collaborate with everyone from big, leading companies in the field, to small entrepreneurial startups.

In 2019, we began a multiyear alliance with Microsoft to create the Novartis AI Innovation Lab to bolster AI capabilities across our organization, from research through commercialization. Data scientists from both organizations will work together to apply the power of AI to fundamental challenges, such as improving the design of drug molecules to make treatments more effective, finding smarter dosing patterns and improving the production process for cell therapies. The alliance also aims to empower people without data science backgrounds to use AI to help make better, faster decisions, whether they work in laboratories, factories or our commercial operations.

We also began a collaboration with Amazon Web Services to use its cloud services for an enterprise-wide data and analytics platform to help transform our business operations. The first application will be in manufacturing, where the platform is designed to give employees access to real-time information that can help increase efficiency in production processes and our supply chain.

At the other end of the scale, we continue our work with health technology startups that are finding creative new ways to harness the power of digital technologies in strategically important areas. Through a program called the Novartis Biome, we’ve created a bridge between startups and our own teams to help accelerate digital programs across our business.

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During 2019, the Novartis Biome added an outpost in Paris, France, to complement the existing one in San Francisco in the US, with additional locations planned for 2020. Through collaboration with groups such as technology accelerator Plug and Play, as well as pitch events for entrepreneurs, we are engaging with startups to find creative solutions to some of our difficult challenges. One of them is Aidar Health, whose respiratory platform we are evaluating for inclusion in our drug development program. Another is Hemex Health, which is developing a portable diagnostic device for malaria and sickle cell disease.

We’re also pursuing bolder bets with partners. For instance, we’re working with technology giant Tencent to create an AI-powered digital nurse to support heart failure patients in China, where increasing life expectancy and an aging population are driving increased demand for healthcare.

Using Tencent’s WeChat application, the collaboration aims to provide heart failure patients with personalized education materials and help them manage their disease, schedule follow-up appointments with their doctor, and order prescription medication refills. During 2019, we collaborated to build a prototype of the application. In 2020, we plan to pilot it with selected patients, and then roll it out more widely.

FAQs

What is an example of big data answer? ›

Big data comes from myriad sources -- some examples are transaction processing systems, customer databases, documents, emails, medical records, internet clickstream logs, mobile apps and social networks.

What is big data very short answer? ›

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.

What does big data mean in digital sales? ›

In marketing, big data comprises gathering, analyzing, and using massive amounts of digital information to improve business operations, such as: Getting a 360-degree view of their audiences. The concept of “know your customer” (KYC) was initially conceived many years ago to prevent bank fraud.

How can I succeed with big data? ›

Organizations everywhere have big data of all shapes and sizes.
...
Their techniques for success can be summarized in seven tips.
  1. Think long term by thinking short term. ...
  2. See through the false choice. ...
  3. Bring big data down to eye level.

What are the 3 types of big data? ›

Table of Contents
  • Structured data.
  • Unstructured data.
  • Semi-structured data.

What is an best example of big data? ›

Big Data powers the GPS smartphone applications most of us depend on to get from place to place in the least amount of time. GPS data sources include satellite images and government agencies. Airplanes generate enormous volumes of data, on the order of 1,000 gigabytes for transatlantic flights.

What are big data skills? ›

In Big Data Market, a professional should be able to conduct and code Quantitative and Statistical Analysis. One should also have a sound knowledge of mathematics and logical thinking. Big Data Professional should have familiarity with sorting of data types, algorithms and many more.

Why big data is so important? ›

Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. Businesses that use big data with advanced analytics gain value in many ways, such as: Reducing cost.

What are the five big data? ›

Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.

What are the 4 features of big data? ›

There are four major components of big data.
  • Volume. Volume refers to how much data is actually collected. ...
  • Veracity. Veracity relates to how reliable data is. ...
  • Velocity. Velocity in big data refers to how fast data can be generated, gathered and analyzed. ...
  • Variety.

Why is big data important in digital marketing? ›

With the help of three types of big data, namely financial, operational, and customer data, marketers and companies understand their target audience better, improve their business processes, and, more importantly, measure their performance while ensuring efficient operations.

Is big data a digital technology? ›

Big data is a marketing concept that refers to the technologies and processes used to collect, store, organise, generate insights from, and take action on the large amount of customer information available thanks to the digital transformation of an industry.

What does it mean to work with big data? ›

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.

What is the most goal of big data? ›

The goal of big data is to increase the speed at which products get to market, to reduce the amount of time and resources required to gain market adoption, target audiences, and to ensure customers remain satisfied.

How can a beginner start big data? ›

  1. Step 1- Learn Unix/Linux Operating System and Shell Scripting. ...
  2. Step 2- Learn Programming Language (Python/Java) ...
  3. Step 3- Learn SQL. ...
  4. Step 4- Learn Big Data Tools. ...
  5. Step 5- Start Practicing with Real-World Projects. ...
  6. Intro to Hadoop and MapReduce– Udacity. ...
  7. Spark– Udacity. ...
  8. Introduction to Big Data– Coursera.

Who Uses big data? ›

Big data is the set of technologies created to store, analyse and manage this bulk data, a macro-tool created to identify patterns in the chaos of this explosion in information in order to design smart solutions. Today it is used in areas as diverse as medicine, agriculture, gambling and environmental protection.

What is the most important characteristic of big data? ›

Value. Among the characteristics of Big Data, value is perhaps the most important. No matter how fast the data is produced or its amount, it has to be reliable and useful.

What are the 3 characteristics of big data? ›

What are the Characteristics of Big Data? Three characteristics define Big Data: volume, variety, and velocity. Together, these characteristics define “Big Data”.

What is use of big data in real life? ›

With the evolution of the internet and technologies like big data, this field of marketing also went digital, known as Digital Marketing. Today, with big data, you can collect huge amounts of data and get to know the choices of millions of customers in a few seconds.

How does big data affect our daily lives? ›

Big data helps us save money on what we eat too with loyalty card schemes, cashback sites and money-off coupons all designed to reduce the weekly food bill. The same goes for transport, recreation & leisure and holidays. A staggering 90% of the world's data has been created in the past two years.

What are data give 5 examples? ›

In our day to day life, we can collect the following data.
  • Number of females per 1000 males in various states of our country.
  • Production of wheat in the last 10 years in our country.
  • Number of plants in our locality.
  • Rainfall in our city in the last 10 years.
  • Marks obtained by students.

How do I add big data skills to my resume? ›

Big Data Engineer Skills:

Knowledge of Hadoop ecosystem and different frameworks inside it – HDFS, YARN, MapReduce, Apache Pig, Hive, Flume, Sqoop, ZooKeeper, Oozie, Impala and Kafka. Real-time processing Framework (Apache Spark) Database architectures. SQL-based technologies (e.g. MySQL/ Oracle DB)

What 3 skills are involved in data analyst? ›

Here are the eight most important data analyst skills:
  • Data cleaning and preparation.
  • Data analysis and exploration.
  • Statistical knowledge.
  • Creating data visualizations.
  • Creating dashboards and reports.
  • Writing and communication.
  • Domain knowledge.
  • Problem solving.
27 Jun 2022

Why big data is the future? ›

In the future, big data analytics will increasingly focus on data freshness with the ultimate goal of real-time analysis, enabling better-informed decisions and increased competitiveness.

What is the most important thing in data? ›

The correct answer is b) Question . Questions asked in the process of data science are the most important part because they command the answers we...

What is the benefit of using digital data? ›

What is the benefit of using digital data? (1) It can help you make informed decisions and improve online performance (2) Digital data is always 100% accurate. (3) Digital data allows you to save money on offline analytics (4) Using digital data allows you to reach more customers automatically.

What are the four common terms for big data? ›

Big data is now generally defined by four characteristics: volume, velocity, variety, and veracity. At the same time, these terms help us to understand what kind of data big data actually consists of (ABN Amro, 2018).

What are the challenges of big data? ›

But, there are some challenges of Big Data encountered by companies. These include data quality, storage, lack of data science professionals, validating data, and accumulating data from different sources.

What size of data is big data? ›

“Big data” is a term relative to the available computing and storage power on the market — so in 1999, one gigabyte (1 GB) was considered big data. Today, it may consist of petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of information, including billions or even trillions of records from millions of people.

What are 6 characteristics of big data? ›

Big data is best described with the six Vs: volume, variety, velocity, value, veracity and variability.

What are the five characteristics of good data? ›

There are five traits that you'll find within data quality: accuracy, completeness, reliability, relevance, and timeliness – read on to learn more. Is the information correct in every detail?

What is the role of big data in social media? ›

With big data analytics, marketers can identify social media trends that they can use to effectively make decisions. For example, they can determine which ad campaign or email newsletter to launch next or when to launch their discount schemes and many others.

What is an example of digital data? ›

Digital data, in information theory and information systems, is information represented as a string of discrete symbols each of which can take on one of only a finite number of values from some alphabet, such as letters or digits. An example is a text document, which consists of a string of alphanumeric characters .

What are the types of big data or digital data? ›

Big data is classified in three ways: Structured Data. Unstructured Data. Semi-Structured Data.

How is big data used in digital transformation? ›

The value of big data in digital transformation comes from an organization's ability to combine both in their efforts to enable the digitization and automation of business operations. It enables organizations to be more efficient and innovative and create new business models through digitization and automation.

What exactly is big data and why should you care? ›

“Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” Volume refers on the actual storage space this data occupies.

Why do you want to work in big data? ›

Huge range of opportunities

Once you decide to work for a Big Data company, your opportunities are likely to grow and grow from day one. In the world of Big Data, there are three main types of data analytics areas you could start with: Predictive analytics, prescriptive analytics, and descriptive analytics.

What is your definition of big data? ›

Similarly, in the United States, the National Science Foundation (NSF) refers to Big Data as: large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future.

Can I learn big data in one month? ›

It will depend on the level of your intellect and learning skills. Still, you can expect it will take at least 4-6 months to master Hadoop certification and start your big data training.

Is big data hard to learn? ›

While it's not the simplest skill set in the world, it is certainly not hard to learn how big data works and what a data scientist does.

What are the four steps in preparing big data? ›

Making Big Data Manageable: Four Steps to Implementation
  1. Collect. The first step seems simple, but there's a caveat: Look beyond your immediate data sources and immediate needs when collecting and compiling data. ...
  2. Validate. Raw data should be complete and consistent. ...
  3. Analyze. ...
  4. Find Your Golden Thread.

What is an example of big data Brainly answer? ›

Examples of big data applications are :

Advertising and Marketing. Banking and Financial Services. Government. Media and Entertainment.

What is an example of big data tracking the work hours of 100 employees with a real-time dashboard? ›

tracking the work hours of 100 employees with a real-time dashboard. entering and tracking a company's daily transaction records in a spreadsheet. sending user survey responses from various store branches to a single central database. providing real-time data teeds on millions of people with wearable devices.

What are 5 data examples? ›

In our day to day life, we can collect the following data.
  • Number of females per 1000 males in various states of our country.
  • Production of wheat in the last 10 years in our country.
  • Number of plants in our locality.
  • Rainfall in our city in the last 10 years.
  • Marks obtained by students.

What are the 10 examples of data? ›

10 data types
  • Integer. Integer data types often represent whole numbers in programming. ...
  • Character. In coding, alphabet letters denote characters. ...
  • Date. This data type stores a calendar date with other programming information. ...
  • Floating point (real) ...
  • Long. ...
  • Short. ...
  • String. ...
  • Boolean.
21 Jul 2021

What is big data in your own words? ›

What exactly is big data? The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources.

What are the 5 Which of big data? ›

Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.

What is data * Your answer? ›

In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject.

What are the three steps that involved while working with big data? ›

Let's explore these phases in more detail.
  • Phase 1: Understanding the Health of the Business. In this first phase, organizations use big data and diagnostic analytics to dig deeper into how various aspects of the business are executing. ...
  • Phase 2: Experiencing Operational Excellence. ...
  • Phase 3: Shaping Competitive Strategies.
23 Feb 2018

How do you track employees daily activities? ›

Simple Ways to Track and Measure Employee Productivity
  1. Measure tasks. ...
  2. Create short term and long term goals. ...
  3. Feedback cycle to and from employees. ...
  4. Keep tabs on sales productivity. ...
  5. Measure time management. ...
  6. Communicate expectations. ...
  7. Manage quality of work. ...
  8. Update each other daily.
28 Apr 2022

Why is data so important? ›

Good data allows organizations to establish baselines, benchmarks, and goals to keep moving forward. Because data allows you to measure, you will be able to establish baselines, find benchmarks and set performance goals. A baseline is what a certain area looks like before a particular solution is implemented.

What are the two types of data? ›

There are two general types of data – quantitative and qualitative and both are equally important. You use both types to demonstrate effectiveness, importance or value.

What data means to you? ›

In statistics data is defined as facts or figures from which conclusion can be drawn. IT professionals will describe data in terms of entities and attributes. In layman's terms, data describes a person, place, object, event or concept in the user context or environment with its meaning dependent on its organization.

What is the most important type of data? ›

Understanding the most important types of Data
  • Big data. Big Data is a term heard often in the last couple of years. ...
  • Smart data. In contrast to Big Data, Smart data is actionable and does make sense and has a clear purpose. ...
  • Dark Data. ...
  • Machine data. ...
  • Transaction data. ...
  • Master data. ...
  • Reference Data. ...
  • Reporting data.

What are three uses of data? ›

They treat all efforts as similar by the mere fact that they use “data” and seek to do something “good”.
...
The three things are:
  • Observe: Take a snapshot of the world. ...
  • Reason: Draw conclusions about how the world works. ...
  • Act: Physically change the world.
14 Jul 2021

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