Unlock Your Data’s Potential with Revelate
Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!
Data is everywhere, and with the right tools and strategies, the total value of data can be realized. Whether it’s to provide insurance companies with risk analysis information so they can determine pricing or give healthcare organizations the data they need to develop new and effective medicines, data science is at the core of countless innovations.
But what is data science, exactly, and why is data science important?
Data science is an interdisciplinary field that uses mathematics, statistics, specialized programming, analytics, and newer technologies like artificial intelligence (AI) and machine learning (ML), combined with subject matter expertise to glean valuable, objective insights so data-driven decisions can be made.
A data science platform or data science software isn’t exactly one entity but a combination of different offerings that make up the whole of an organization’s data science strategy.
The phases or stages of data usefulness are called the data science lifecycle. The specific stages involved in a typical data lifecycle before data outlives its usefulness can vary in complexity, but at a high level, include:
Data generation. The creation or generation of data happens from a myriad of sources—web applications, internet of things (IoT) devices, transactions, communications—basically any action performed in today’s world that involves some kind of recorded process that generates data. From a business point of view, customers, employees, suppliers, partners, and distributors are all producing data.
Collection. With essentially infinite data available, organizations have to narrow down what data can and should be collected and how to do it in the most efficient and effective way. Data acquisition for data science can include forms, surveys, interviews, or use online interaction measurement tools like Google Analytics to capture internal company data.
Organizations can also greatly benefit from augmenting their internal data with external data collected through data marketplaces or data exchange partnerships. Augmenting internal data with external data can teach AI and ML technologies faster, get more objective business insights to make more educated and proactive decisions, and much more.
If an organization is getting data from data marketplaces, it can be difficult to find high-quality and relevant data. Not every marketplace utilizes metadata (descriptions of what the data product is) and tags in the most effective way for that data product, making it tough to determine the usefulness of a data set on the description alone or find it in the first place.
Revelate’s data marketplace is a centralized data platform that is highly customizable in terms of cataloging, segmenting, and marketing data products. It facilitates a seamless experience in terms of purchasing, sharing, or exchanging data products, strengthening the relationship between data providers and customers. Learn more about how Revelate’s data marketplace works via our blog: Marketplaces and data marketplaces: Mobilizing the World’s Data Together.
Processing/preparation. Once data has been collected, it needs to be processed and prepared so that it can be understood. The complexity involved in data preparation at this point depends on who wants to use the data and for what purpose.
Large organizations such as enterprises may employ data scientists to interpret and prepare data, but often it falls to IT teams and is done on an ad-hoc basis if no other solution is in place. This can be extremely time-consuming and resource-intensive for IT teams, especially when multiple clients (internal and external) are requesting data in specific formulations. Not only does the IT team have to find and prepare the data, but they also have to consider organizational security protocols alongside regulatory requirements surrounding privacy to ensure that data doesn’t fall into the wrong hands.
As you can imagine, handling data processing and preparation isn’t sustainable long-term. The solution is to use data automation to automate as much of the data preparation process as possible according to your organization’s needs. Implementing your organization’s security and access privileges within Revelate ensures that customers get the data they want while security is maintained.
Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!
Storage. After data has been collected, it needs to be stored. Depending on the type of data entering a data ecosystem (whether data is structured or unstructured), organizations typically have several areas where data is stored, including a data lake, data warehouse, NoSQL databases, and others. To organize data stored in so many different locations and to make it easily accessible and searchable, organizations will use modern data catalog tools that read the metadata of each piece of data so that it can be categorized and searchable for ease of access.
Management. Data management is involved at every stage of the data lifecycle. Whether its retrieving data from a source and ensuring that it’s stored correctly with the right security measures and access privileges, effective data management consists of activity tracking (so digital paths can be maintained that give information on who accessed what data and when) and changelogs so that any updates and changes to data are recorded.
Analysis. Gleaning meaningful insights from sets of data requires different data science tools and approaches. Organizations employ data scientists, engineers, analysts, and business analysts to conduct data analysis and/or use tools such as statistical modeling platforms, algorithms, AI, ML, and more.
Visualization. In order for data to be understood quickly, data visualization tools are often used to convert data into graphs, charts, tables, lists, and more so that it can be understood by a wider audience.
Interpretation. This is where subject matter experts and other professionals get involved in determining how the data is presented and described. The previous data visualization step could be part of this phase as well. The important part of data interpretation is not only to visualize it and understand what it's telling you but also to determine what implications that data may have, whether it’s an adjustment to a process or the source of inspiration for a new product or service.
The importance of using data science for every industry and organization cannot be overstated. By using data science, organizations can make better, more informed decisions to improve their operations and revenue and also to make the world a better place.
Governments all over the world use data science every single day in a myriad of ways to improve the lives of citizens. For example, the government of Canada implemented the Data Science Network for the Federal Public Service (DSNFPS) with the aim of offering a collaborative and dynamic space for organizations to share information for the benefit of all Canadians (as well as the organizations themselves) by giving these organizations access to a powerful data sharing community. The DSNFPS also provides organizations with the data science tools and resources needed to develop their data-sharing strategies further.
Through this mutually-beneficial partnership, the benefits to both Canadians and organizations, including:
One of the most important uses for data science is arguably how it can be used to speed up and improve processes. When COVID-19 ramped up and caused serious disruption to the world, Pfizer was able to develop a vaccine in less than a year. Using the power of data science, including complex robotics and instruments, as well as statistical analysis and partnerships with laboratory information companies to develop, interpret, and gain insights from a whirlwind of data from complex clinical trials and vaccine trials, Pfizer streamlined processes and received needed information to move vaccine development forward in record time. For example, a PCR assay normally takes about 6-12 months, but they were able to do it in two weeks with the help of a dedicated team and data science. Another essential vaccine development process, a neutralization assay, typically takes 24 months but was able to be completed in two.
The result, as we know, was a COVID-19 vaccine that was fully tested, safe, effective, and ready to roll out to the world faster than a vaccine had ever been developed before.
The infamous James Webb Telescope (JWST) uses the power of data to create images of the cosmos that are hundreds of millions of miles away. Data science has always been a fundamental part of astronomy—from mapping star systems to tracking seasons here on earth—so we can gain a better understanding of the best times to plant crops or simply understand more about the vastness of the universe.
With the JWST, astronomical amounts of data are captured, and coding languages like Python are used to extract, organize, and visualize the data so it can be better understood by scientists, and of course the general public with high-resolution images of space. Because of the data gathered by the JWST, scientists have been able to test and confirm theories (such as what a black hole looks like) and gain a better understanding of celestial structures inside and outside of our solar system, enhancing overall human understanding of our universe.
These are just a few examples that illustrate the importance of data science. There are countless other ways that data can be used to understand our world better, make better decisions, and positively impact our world.
Organizations, from small businesses to large enterprises, can no longer ignore the benefits of implementing a data science strategy. The goal is to take advantage of the plethora of data they generate, but many organizations don’t know how to get started with data science, or they see the vast amount of information available on it and quickly feel overwhelmed.
Before you start delving into the world of data science, you should ensure that you understand the technologies behind it. This includes the following prerequisites for data science:
Once you understand the fundamentals behind how data science works, you can begin developing strategies for using data science effectively within your organization.
Certain data that your organization collects or has access to may or may not be useful in certain circumstances, but it’s important first to identify where data is coming from and which data sets will be useful to advance organizational goals.
For example, if you’re a financial institution that lends money to businesses and individuals, your lending data would be useful for risk analysis. A supplier of perishable food goods may have order records from grocery stores that could be used to shed light on seasonality fluctuations with certain food orders, which could influence future stock and reduce inventory costs. An insurance company could be sitting on claims records that could be used to determine risk and influence certain insurance rates. By determining what data you have access to and how it could potentially be used, you can give focus to your data science efforts.
What do you want your data to do for you? There are endless possibilities in terms of how you can put your data to work. From setting up an analytics dashboard so that your team can easily glean insights from a specific database or providing information to a machine learning model to improve customer support outcomes from website chatbots, you’ll want to set specific goals for what you want your data to do to help reach organizational objectives.
Depending on your organizational goals regarding how you want to use your data, you’ll want to ensure your team has the right skills before you get started. This may mean picking up a programming language or two, including:
Your organization likely already has data science tools that employees use daily to get insights for decision-making. Auditing these tools across departments throughout the organization is necessary to understand where data is coming from and how these tools handle the information that flows through them.
To do so, it helps to follow a step-by-step process, such as the one below:
Gather information on available tools. Information regarding the performance and functionality of each tool should be collected to paint a picture of its overall effectiveness. This may mean speaking to subject matter experts and regular users of each tool and sending out surveys to relevant stakeholders as an efficient way to get answers to a list of questions.
Assess and analyze. Collected information and information from within the tool (such as error reports and tracking data) should be used to highlight known issues with the tool, such as recurring issues with functionality, unexplained errors, and other limitations.
Determine overall usefulness. Evaluate the effectiveness of the tool by considering the business, technical, and cost factors and how each affects your business. For instance, if not a lot of users feel the tool is valuable, it has a higher-than-average learning curve, and the costs for maintenance and user subscriptions are high, then it may not be a tool worth keeping in your organization’s tech stack—and subsequently not useful for your organization’s data science strategy.
The idea behind data models is that organizational stakeholders, such as developers, data scientists, business analysts, and more create frameworks for what data they’ll capture and how they want it to be used in different contexts. Data models help with data integrity (preventing duplicate data, for instance), data governance (security and access), and legal compliance (privacy), so that the organization can maintain standards surrounding the use and movement of their data.
In other words, data models are like blueprints for a building; they make up the foundation for how you want data to be stored and used, like a building’s layout. As the building is built, the more granular aspects, including plumbing, electrical, and who can access which rooms and floors and under which circumstances, are determined. In terms of data and data models, this refers to how data is moved in and out of the organization, who can access it, when, and much more.
A few different types of data models can be utilized depending on the context behind how an organization wants to use certain datasets.
|Conceptual Data Models||Logical Data Models||Physical Data Models|
|Shows a high-level view of a system’s content, organization, and business rules in terms that technical and non-technical stakeholders can understand (e.g., flowchart).||Determines how data will be organized in a database or warehouse. Determines the granular aspects of how data is stored, retrieved, and managed within a system.||Specifies the type(s) of data that will be stored along with data type specifications and other technical requirements. This includes factors such as storage needs, access, redundancy, and more.|
Organizations of all sizes deal with large amounts of data, but that data is stored in multiple systems. Manually extracting or relying on dataflow to combine datasets for analysis results in roadblocks and bottlenecks, such as corruption, data loss, and duplication, that affect the overall quality of the dataset. As organizations scale, this problem only grows along with them.
That’s why the movement of data within an organization should be controlled by data science pipelines. Just like any pipeline-based system, a data science pipeline controls how data is moved from a source to a destination, including how it’s transformed, prepared, and optimized along the way. One example is the AWS data pipeline. It works by allowing the creation of data-driven workflow definitions, which allows logic-based data transformations to take place. Highly-complex data processing can take place with little resource management, which is one of the main benefits of a data science pipeline; data science automation eliminates many of of the manual steps and processes with regard to transforming data from one stage to another that would otherwise be handled by data scientists or engineers.
Consolidating data from various sources throughout your organization ensures a single source of truth and consistent data quality, so higher-quality analysis and insights can be gleaned.
A data science pipeline consists of the following components:
Data sources refer to where the data originates and includes any system, program, or application that generates data for your business.
Data collection refers to the process of bringing data into the pipeline for processing. The data collection layer can extract data that’s at rest or streaming (in motion).
Data processing refers to the process of transforming data into usable components. This is where architecture like ETL (extract, transform, load) comes into play.
Data storage refers to how data is stored for the organization. Data warehouses, data lakes, and databases are all typical areas where structured, unstructured, and semi-structured data are stored.
Data consumption refers to how data is fulfilled for orders. A data marketplace is an example of a platform where data can be consumed. Data consumption may also occur at the software or application level, depending on the complexity of the data pipeline.
Data governance applies to every aspect of the data pipeline to ensure that data remains secure, activity is tracked, and access is controlled throughout the pipeline.
Data pipelines are not a new concept for organizations that deal with a lot of data, but the technology behind the data science pipeline has evolved over time.
On the other hand, modern data pipelines are much more versatile in terms of accessibility and management. With a modern data science pipeline, raw data is extracted from various sources and then placed in a data repository, where it can be transformed into usable information for business analytics and other purposes. From there, data is organized into areas such as data warehouses for ease of access by people and programs.
The main advantages of the modern data science pipeline compared to the traditional are:
The shift from traditional data pipelines to modern, cloud-based data pipelines with modern ETL gives organizations the ability to not only improve and simplify their data processing workflows but also allow better data access to those who need it without sacrificing security or control.
As you can imagine, organizational data models and pipelines can get extremely complex with all the moving parts that go into ongoing maintenance and functionality. Data scientists, data engineers, and IT professionals must work together to set up the initial functionality of algorithms and data pipelines and ensure that they are optimized for efficiency and continued functionality over time.
This back and forth is very demanding, and as algorithms evolve and change or new projects are started, new data pipelines may have to be set up to provide the data scientists in an organization with fresh new data to analyze.
Modern data pipelines are a key component for quickly and efficiently providing new data for data scientists quickly and efficiently. But getting new, usable data quickly, straight from the source, is essential to keep data pipelines flowing with information.
Revelate is platform agnostic, which means that it can integrate with any BI, analytics, or data science tools and data scientists can self-discover new data when needed by accessing an internal data marketplace. In other words, rather than relying on IT or data engineering to constantly provide new data for analysis from various closed and open sources, such as Databricks, Snowflake, AWS, and more, Revelate facilitates instant access to data for data scientists so that business insights can be gleaned from fresh data without heavy workloads on other departments.
Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!
While both disciplines are focused on data interpretation, there are some key differences between data science and business intelligence that should be understood, as the two can be mistaken for the same thing, as they both analyze data.
Here are the main differences between data science and business intelligence:
|Data Science||Business Intelligence|
|Attempts to discover hidden patterns within data to make predictions for the future||Uses data analysis to make actionable business decisions that save money, identify opportunities for making more money, or allow for process, customer service, and workflow improvements|
|Focuses on the scientific method||Focuses on the analytical method|
|Deals with structured and unstructured data||Mainly deals with structured data|
|Experts include data scientists, data engineers, and IT professionals||Experts include business analysts, digital marketing professionals, SEO analysts, and other professionals that interpret data from structured sources|
|Has a higher complexity (requires highly-trained technical experts to handle the flow of both raw and structured data through data modeling, data pipelines, and data storage)||Less complex than data science as professionals use structured data sets|
|Uses a variety of tools and programming languages, such as SAS, BigML, MATLAB, Excel, Python, SQL, etc.||Uses a different set of tools focused on data interpretation, such as Google Analytics, Tableau, Sisense, Microsoft Power BI, etc.|
In general, data science is more technical in that it relies on highly-trained professionals in the fields of mathematics, statistics, and computer science, as well as modern technologies like machine learning and artificial intelligence to analyze data to look for patterns and make predictions. Data science is focused on the future, answering the question, “this is what will likely happen based on the data we’ve seen so far.”
On the other hand, business intelligence is focused on gleaning insights from data. These insights can be new and different opportunities for business, like an increased understanding of a target audience or market, product optimizations to save money, or improvements to existing processes and workflows. In other words, business intelligence interprets data to make actionable decisions instead of interpreting data to make predictions.
Data science as a discipline is used in every single industry throughout the world. The power of data needs to be harnessed and used, regardless of the organization, to drive innovation and increase productivity, revenue, efficiency, and more.
Here are different applications of data science according to industry:
The finance world has long understood data’s power in profitable decision-making and determining risk. In fact, the finance industry has often been viewed as a pioneer in data science, as the industry as a whole was one of the first to recognize and capitalize on the opportunities that embracing STEM professionals could bring to the space—from Fintech innovations to development of software and applications to enhance data analytics.
Data scientists and related positions within the financial and investment sectors are only expected to grow, as the industry always needs highly-skilled individuals to interpret the vast amount of data that flows through the industry and glean valuable insights to predict future trends to protect economies, ensure reliable income streams, and further innovate in every aspect of financial services, from fraud detection to customer service.
Data science finance includes technologies such as machine learning and artificial intelligence assist in managing and transforming data into usable information, as well as developing algorithms and systems to automate and further develop the usefulness of analytics.
Specific areas where data science is useful in the data science finance industry include:
The finance industry benefits greatly from real-time analytics, as large volumes of data from transactions, market prices, trading, and more are the norm, and the faster that financial institutions can analyze this data and make decisions based off of it, the better.
Real-time finance analytics is useful in various areas, such as customer service and forecasting, but especially for risk management and fraud detection and prevention. By fully taking advantage of instant access to data to train machine learning algorithms and artificial intelligence technologies, for instance, real-time analytics allow financial institutions to make quick, data-driven decisions to keep security initiatives working seamlessly and to make quick pivots with resources where needed.
Previous events such as the global financial crisis of 2009 and the recent pandemic are just a couple of recent examples that have had a major impact on the state of the world’s financials, highlighting how its more important than ever before for financial organizations to be able to calculate the risk involved with any financial business decision accurately.
Risk analytics is a core component of data science in finance and involves designing, engineering, and utilizing complex data infrastructures to make sense of unstructured data and glean insights from it. One example of this is incorporating unstructured data into real-time risk detection systems. These systems use the incorporated data to simulate credit and market risk exposures and help banks and other financial institutions provide effective risk analysis for a variety of situations, from lending to investments.
With the large number of financial transactions occurring at any given time, it’s extremely important that a financial organization is able to identify unusual transactions and take action as needed quickly. Machine learning utilizes transactional data, for instance, to recognize existing fraud patterns and use this base foundational knowledge to detect future fraud patterns and act on them accordingly.
This technology combined with real-time analytics, which provides algorithms and fraud detection models with a continuous stream of data, allows financial institutions to act on fraudulent activities such as speculatory trading, rouge trading, and other regulatory violations with swiftness, even when millions of transactions are being performed at one time.
Providing a great customer experience is paramount to the success of financial institutions. By using data science, institutions can be at the forefront of consumer behavior understanding by utilizing real-time analytics to make personalized service and investment recommendations and better understand banking habits. Machine learning, for example, can be used to understand the drivers of consumer behavior, which leads to initiatives like cross-selling opportunities and better fraud detection.
Managing customer data properly is of utmost importance for the finance industry. Ensuring that customer data that’s coming in from different sources and stored in different places follow company and regulatory standards for access and security is essential. This is another area where data science is applicable and especially important for protecting customer data and maintaining a high level of customer trust.
Data governance tools are especially important for the financial services industry. Ensuring that customer data is protected while at the same time not stifling innovation and growth by allowing no access to data is paramount.
There are valuable insights to be gleaned from customer data across the financial services value chain that can benefit financial services systems worldwide by providing better access to personalized services, cost savings, more efficient processes, and more. The appropriate use of customer data has been seriously considered, including who should be able to use what data, in which situations, and why. When enacted effectively throughout an organization’s data pipelines, data models, and data storage procedures, data governance policies are the most effective tools for ensuring that data is accessible by the right people while maintaining a high level of security.
Delivering personalized service experiences in the financial services market has become essential to maintaining customer loyalty. In the past, when face-to-face transactions were the norm, it was easier for institutions to build relationships and deliver personalized services. But online technology removed that face-to-face experience, causing customers to shift their focus to doing business with whoever offers the lowest rates rather than doing business based on trust and loyalty.
But delivering personalized experiences to customers at scale can be challenging without effective data management across different lines of business (LOBs). Customer interaction management (CIM) initiatives are strengthened by data science in that privacy and security, regulations, and management of vast amounts of data can all be handled effectively across different LOBs without resulting in silos.
Data scientists can employ complex mathematical formulas and computations to help financial institutions build more effective strategies for trading, even with constantly growing amounts of data. Like other applications in finance, algorithmic trading models are backed by machine learning and are able to quickly process large datasets and determine the best trades using historical and real-time data by identifying patterns and trends. All of this is done while at the same time adhering to regulatory requirements and laws to prevent unauthorized trading. The main benefit of better trading affects both the institution and their customers, as the institution can make better, quicker, and more efficient trading decisions that make themselves and their customers more revenue.
The healthcare industry is constantly innovating in terms of processes and procedures and using the latest technology and information to develop new and effective treatments, medications, preventative measures, and more to keep people healthy and safe. Ensuring that healthcare organizations and medical and scientific research initiatives have access to the latest available data is paramount to continue to drive innovation and revolutionary new treatments.
Patient data is especially important for healthcare professionals to extract insights that can inform future care. Data science in healthcare allows information to be extracted from a variety of sources, including Electronic Health Records (EHR), prescriptions, clinical reports, medical insurance, laboratory reports, and more, while at the same time following strict data governance to ensure patient privacy.
Specific use cases for how data science is used in healthcare are explained in more detail below:
Research and development of medications have been growing exponentially thanks to data. Data from new and emerging technologies such as molecular profiling, imaging, AI, and ML are responsible for streamlining drug discovery and allowing development to happen faster than ever before, mainly by using patient-driven data and biology to create better hypotheses rather than relying on a more traditional trial-and-error approach.
One effective way that drug discovery has been optimized over time is through the development of Hit identification. The Hit process identifies molecules that act upon a target allowing researchers to make faster progress, and often acts as a starting point in drug discovery research. One of the ways that researchers are able to find Hit molecules is through large chemical libraries, which, when available to a wide variety of researchers, can help them identify which compounds are viable for their project. What this demonstrates is that when data-sharing initiatives are combined with data science, the effects on important societal advancements like healthcare can be compounded. In other words, data science and data sharing can be attributed to being directly responsible for advancements in medical science.
Data sharing is one of the keys to effective data science. Learn more about how Revelate can help your organization facilitate quick and effective data sharing without loss of control.
Using aggregated patient data to inform new treatments or improve existing ones is one of the foundations of how data science relates to patient treatment optimization. Genetic data, as well as information about patient lifestyle, previous treatments and outcomes, medications, and more granular information, can be considered when developing a treatment, allowing for more targeted and personalized approaches rather than broad ones.
An example of one of the ways that patient treatment can be improved is through the optimization of patient scheduling. Continuity of care is extremely important for positive patient outcomes, so ensuring patients move through clinical settings in the most efficient and effective ways possible is paramount. Interestingly, researchers have taken inspiration from another industry, consumer supply chains, to help develop the mathematics and computational formulas needed to optimize patient scheduling for diverse clinical settings. In this way, factors such as patient no-shows and patient flow from doctor to nurse or other healthcare professional (such as getting blood work after an appointment, for example) can be optimized to ensure that a) clinics run at max efficiency, allowing more patients to be seen in a shorter time frame, and b) patients receive a consistent level of continuous care.
Determining the best treatments and medications to use is a process called Geonomics. One example of this technology being applied is GAP, which is an initiative dating back to the early 2000s with the goal of making Whole Genome Sequencing (WGS) more accessible to patients by reducing the overall costs associated with it. By using advancements in data collection and data science, WGS for patients, even at a large scale, can be a reality, resulting in rare diseases being found and diagnosed faster, treatments being actioned sooner, and the overall health and wellbeing of patients increasing.
Technologies like AI, ML, and predictive analytics have allowed healthcare professionals to make better decisions regarding patient care, including which tests to administer for diagnostic investigation and which treatments and medications to administer. Predictive analytics, specifically, analyzes past and real-time data to help healthcare providers make better predictions about future outcomes in the areas of patient care, enable more effective clinical decisions, give the ability to predict and develop responses to trends, and much more.
For example, using predictive analytics, the University of Michigan Rogel Cancer Center is developing a blood test that will be able to predict if patients with metastatic HPV-positive throat cancer will respond to treatment months earlier than the typical throat scans. In other words, if the blood test proves to be effective, patients with this particular throat cancer will likely see more positive outcomes from the disease. This is because doctors will be able to quickly switch gears to another treatment sooner if one is not working effectively.
From being able to track the progression and outbreaks of a highly-transmissible disease and enact effective containment measures to learning more about diseases like Cancer and Heart Disease to aid in prevention, data science enables healthcare professionals to not only develop better responses and treatments but also develop better screening and preventative measures to reduce the risk of certain diseases significantly.
For example, Parkinson’s, a neurodegenerative disease that affects more than a million Americans, currently has no cure. However, wearable sensors have been developed to give researchers valuable information on involuntary movements, sleep patterns, and more. The data collected can be used to improve disease diagnosis, improve ongoing monitoring, and determine if medications are working.
Think about the logistics that go into creating a product. From ready-mix cement to food products, modern manufacturing both produces and uses a huge amount of data, all of which is used to increase manufacturing efficiency and effectiveness to get products made and delivered to customers faster. Just-in-Time (JIT) manufacturing also reduces manufacturing costs, makes products more affordable, and increases availability.
The specific use cases for data science in manufacturing include:
Keeping machinery working so that products can be produced is arguably one of the most important aspects of manufacturing. Data scientists can utilize data from various sensors located on machinery to determine when and where maintenance needs to take place and determine if a machine is about to fail.
Collected sensor data, alongside other data sources such as technician data logs, is used in predictive analytics, which helps manufacturers make the most accurate and cost-effective decisions regarding when a machine needs to be maintained and how.
An example from machinemetrics effectively illustrates how this works. In the example, a machine would experience tool failure when amperage was increased. Tracking the amperage was difficult, but they found that they could track another related metric—spindle load data. They found an 80% correlation between spindle load and transducer amperage by tracking spindle load data. Using this information, they were able to predict how many parts could be made before the equipment failed, which they determined was 1 to 68. Further adjustments of the load could potentially expand this range.
The key takeaway from this example is that using correlating data to determine patterns helped the manufacturer optimize their production to the maintenance schedule for a piece of equipment and build an algorithm to automatically detect the failure and take corrective action to prevent it.
It’s not unusual for the price of products to shift over time. Whether it’s the availability of raw materials, consumer demand, or changes in delivery schedules, many factors can affect market pricing, directly affecting how a manufacturer prices their product.
Manufacturers can use data science, more specifically ML, to predict price fluctuations and determine demand so that they can accurately price their products and prevent excessive inventory costs due to over-manufacturing while at the same time ensuring that they are manufacturing enough products to meet market demand.
While warranty claims on products aren’t exactly what manufacturers would like to see, it’s an inevitable part of manufacturing that can help these organizations better understand their products, suppliers, clients, and every other logistical step between creating and selling their products.
One example of how data science helps with warranty claim analysis is the automotive industry. After a vehicle is sold, dealerships provide post-sale service to customers, and both the dealership and the manufacturer sustain the costs involved with providing warranty service.
To optimize how much is spent on vehicle warranty services and determine when warranty service is likely, data science is used in the following ways:
By using ML to recognize potential patterns in warranty claims, manufacturers may be able to provide faster and more effective warranty service, including preventative maintenance, and potentially allow them to increase the duration of vehicle warranties (for certain components).
Lean Six Sigma has long since been a methodology used in a variety of industries, including manufacturing, to identify where processes could be improved to reduce costs and allow for more efficient manufacturing. Using data science for a lean six sigma mindset makes sense, as it allows more information to be gleaned from various sources throughout the manufacturing process to help manufacturers become more efficient and competitive.
For instance, value stream mapping is a lean manufacturing technique that relies on analytical information regarding work and information flows, detecting which are adding value to a specific manufacturing process and which are not, and mapping them accordingly. Visualizing a process to see where roadblocks and bottlenecks lie helps to improve communication and collaboration between teams, leading to higher productivity and more efficient processes.
Ensuring a quality product that performs properly is an obvious goal for any manufacturer. Targeting potential issues in either category and taking action as quickly as possible is invaluable for ensuring that production rates and timelines continue to run smoothly, even when adjustments need to be made.
By utilizing real-time data analytics, manufacturers can zero in on where issues are occurring and take action to fix them before products reach the end of the line. For instance, let’s say that a part is consistently coming up defective during end-of-line checks. Checking the historical data from each individual machine doesn’t prove effective in determining the problem, as each machine seems to be running as it should. But data science can go beyond simply analyzing the data from one location and perspective. One provider, Sciemetrics, utilizes data science to give their customers datasets based on different perspectives, in this case, the perspective of the part, helping them identify where hidden issues may lie that affect the quality of the end product. With the issue identified, action can be taken to address the issue and prevent huge disruptions to the manufacturing process.
From being able to accurately predict consumer behavior to offering personalized buying recommendations based on past purchases, and general interests, data is arguably one of the most valuable commodities that the retail industry has to gain insights.
Both brick-and-mortar stores and online retailers benefit from data science, but the applications may be different. For example, retailers with physical stores must consider location when opening up new stores, while both physical and online stores need to consider strategies around inventory management. In either case, data science in retail industry can be used to make sense of demographic information, foot traffic for specific commercial areas, and seasonality trends to help retailers make informed decisions.
Other specific use cases for data science in the retail industry include:
Thanks to major players in the online retail space, such as Amazon, today’s customers are increasingly expecting a personalized shopping experience. Using proprietary data, retailers are able to meet this expectation, and when done well, it’s difficult for competitors to imitate. Personalization has more benefits than just giving retailers a good competitive advantage—research from McKinsey shows that when personalization is fully implemented at scale, businesses can be 10 to 20% more efficient with marketing initiatives, including greater cost savings, and see a 10 to 30% increase in revenue and retention. In the long term, customer satisfaction has also been shown to increase.
Data science is at the heart of personalized marketing. One of our partners, Snowflake, has a Retail Data Cloud where customers can leverage internal data as well as external data to power marketing strategies regarding personalization and other initiatives. Snowflake’s customers can gather data from the Snowflake marketplace, such as weather and geolocation data, to aid in determining whether customers are more likely to purchase products to work directly with suppliers and distributors to reduce food waste.
Revelate’s marketplace can provide a similar repertoire of information but to a potentially wider audience. Revelate customers can create their own customized data marketplace that has the ability to extract data from any platform and provide it to anyone who needs it. If a data fulfillment order is within the Snowflake ecosystem, Revelate can process datasets to be used within that platform.
Brick and mortar retail stores have long struggled with the problem of fraud. POS (Point of Sale) manipulation, credit card fraud, employee and customer theft, fraudulent transactions and returns, vendor fraud, and much more has been a reality in retail since retail has been an industry. According to the LexisNexis Risk Solutions True Cost of Fraud Study, there has been a 19.8% increase in retail fraud in the United States since 2019.
Detecting and preventing fraud should be a top consideration for retailers. By using data analytics, fraud can be predicted and prevented. For example, predictive analytics can be synchronized with retail POS systems, and ML algorithms linked with fraud detection systems can learn to identify specific transactional characteristics, patterns, and trends that often result in fraud. To take it one step further, AI can restrict access user access, cancel a transaction, or inform the appropriate parties that fraud is likely to occur so that action can be taken.
Retailers have often struggled with price optimization. Striking a clear balance between generating maximum revenue without discouraging purchases is as much of a skill as it is an art. Traditional price-setting strategies have relied on price-setting logic rules, which require extensive manual monitoring and optimization to ensure that they are creating logical pricing based on market rates, seasonality, and other data.
By using ML, retail organizations can feed real-time data into the system, automatically allowing the algorithm to continuously learn and adjust pricing based on the most up-to-date data regarding market conditions, inventory levels, current marketing campaigns, seasonality, and more. This eliminates the need for manual maintenance of a pricing system and allows for more accurate pricing that can easily provide the best balance between being too expensive and inexpensive.
Now that we have insight into a variety of industry-specific use cases for data science, let’s take a closer look at the tools that data scientists use on a regular basis.
The job of a data scientist is to use, analyze, and glean actionable insights from that data that can be applied in a myriad of ways to benefit an organization, whether it’s a private business, healthcare facility, or government. The tools that a data scientist uses on a regular basis to gain access to and analyze data are essential considerations to ensure that the data scientist is working with high-quality data sets.
Data scientists rely on the availability of high-quality data to glean insights. Without high-quality data and the surrounding data architecture to allow ease of access and aid in the development and productization of data models, data scientists are not able to perform their jobs effectively.
Another expert, the data architect, is responsible for managing the hardware and software involved with data storage and building the data architecture framework for the data scientist. A data architect could effectively be the answer to the question, “who oversees the data science process?” Using the analogy of a building, the data architect builds the foundation, walls, and rooms of a building. The data scientist designs the internal components of the building according to business needs, like how desks and waiting areas should be set up, where people should enter and exit the building, and more.
|Data Scientist||Data Architect|
In order to build effective data models, data scientists need the right tools for the job. Perhaps one of the most illustrative examples of data modeling tools is a story from Built in regarding a developer from DoorDash. When asked to show his data model to an executive, he didn’t present a fancy visualization or diagram—he held up a piece of paper to the camera.
Data modeling, at its core, describes and controls the relationship between data-consuming entities within an organization, from storage systems like data lakes and warehouses to how applications and programs use available data. The purpose of data modeling is to visualize the abstract, in this case, the movement of data, so that people can understand and control how it flows through an organization.
Beginning the process with pen and paper is actually not a bad idea—after all, if you’re mapping out your data model on paper, the urge to make it more complex than it needs to be is less tempting than starting with a modeling program, and it’s easier to visualize the start and end points. Of course, the next step is choosing the right data modeling tool to execute the vision and fill in the more granular details of the final data model.
Choosing the right data modeling tool depends on your business needs, the situation surrounding its expected usage, your organization’s existing tech stack, and of course, data security.
Data visualization tools make sense of cleaned and prepared data so that it can be understood by its intended audience. Data visualization tools also allow organizations to process massive amounts of data, making that data interpretable and actionable across every area of the organization.
For the data scientist, communicating their findings with stakeholders across an organization—including those unfamiliar with data science—is important to ensure that the right people understand the data scientist’s insights. In that sense, data visualization tools that are flexible and have robust feature sets are preferred, as the data scientist is able to apply their technical skills and expertise to present information in any way they wish to increase understanding.
But it’s likely that data scientists and experienced developers and analysts will be the only ones wanting to create data visualizations within an organization. For less tech-focused individuals, tools that focus more on factors like ease of use, drag-and-drop functionality, and storytelling—that is, to convey a message using a combination of data and images—paint a full picture of what the data is telling you.
Throughout the data science lifecycle, tasks can be done automatically using data science automation tools. Using these tools not only reduces errors and ensures 100% consistency of processes but also increases the efficiency of complex and routine jobs. AutoML (automated machine learning) is an example of a tool that data scientists can use to offload routine tasks related to data cleansing, visualization, or data science model building.
Hiring an in-house data science team may not be a realistic option for every organization, whether due to cost or lack of resources to support an internal team of experts. But at the same time, the realization that data is a driving force for innovation and growth for an organization highlights the importance of harnessing the power of your data.
Organizations may want to reach out to a data science consulting services company to help them make sense of their data and discover the best ways to leverage technologies like ML, AI, algorithms, and software development to run experiments on their data to glean business insights.
Even smaller organizations are likely to find team members from various departments (e.g., marketing) requesting data, putting pressure on IT to deliver data sets, which is taxing for them to find and isolate relevant data. One or two requests here and there might be fine to handle in the beginning, but as an organization scales, it becomes more and more difficult to fulfill orders and maintain standards relating to data governance, data quality, and more. This often results in IT professionals delivering a giant file containing a huge amount of data, as it’s easier and less time-consuming for the IT professional, leaving the data received with the monumental task of diving into and finding the relevant data they need.
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A data science consultancy company can help organizations build a team of experts, as well as assist with the direction and development of an organizational data science strategy. Top data science consultancy companies such as McKinsey, Deloitte, IBM, and others are not considered data science platform providers, but they have spent considerable time, money, and resources developing their individual approaches to information gathering and interpretation to suit the needs of diverse sets of clients.
Some important considerations to keep in mind when engaging with data science services include:
Once you determine exactly what you need from a data science services company, choosing one that will meet your organizational needs is easier. Considering how data flows through your organization, who needs access to it, and how streamlined and efficient processes relating to data will allow organizational goals to be achieved, it becomes easier to choose a consultancy that can meet your needs.
In a lot of ways, data science and cloud computing go hand-in-hand. Cloud data science software facilitates the analysis of large amounts of data, and cloud computing allows storing and retrieving large amounts of data.
Plus, cloud computing is flexible in that storage can automatically scale up or dial back to match organizational needs, with many cloud-based storage solutions offering subscription models to meet user needs from enterprise to start-up. This means that technologies such as data lakes, data warehouses, databases, and more can exist in the cloud with near-infinite scalability. Using cloud computing, data can be accessed anywhere and at any time, allowing highly-distributed teams the flexibility to function without interruption.
Every organization should consider how their data flows internally and externally, and this can be achieved through data science. Since data is so important to today’s businesses in terms of making better decisions, gaining a better understanding of their customers and markets, and improving processes, workflows, and productivity, it makes sense that every business, whether large or small, puts serious time and consideration into data science initiatives.
Building data models and pipelines are necessary to determine how data should be used to meet organizational goals, how that data should flow through the organization, including within programs and applications, and how individuals can access certain datasets. When it comes to data fulfillment, however, there still needs to be a solution for getting data orders to individuals quickly and efficiently. Revelate, as a platform, is meant to help organizations fulfill data orders easier and faster, without being limited to a specific platform or system.
Revelate extracts data from a source, whether that be a data lake, warehouse, or database, prepares it according to specific parameters, and creates data products that can then be used to fulfill data orders. This means that Revelate touches on every aspect of the data supply chain—from manufacturing, packaging, selling or sharing, and distribution. It does this by automating the process from start to finish, and it all starts with the Revelate data marketplace.
Data is extracted from a source, refined, and prepared into data products.
Access rights, discoverability, use cases, and purchase options are determined.
A customer requests a dataset from the provider’s web store, where any appropriate security and access checks are performed.
The data order is extracted from any system, including typically “closed” platforms, and distributed to the customer’s ecosystem according to their needs.
Revelate doesn’t store provider data but instead facilitates extraction and downloading from the provider’s ecosystem once the customer requests a data product from the data marketplace.
The importance of organizational data science initiatives cannot be understated. It’s no secret that data is the foundation of our world today, so it’s essential that organizations of all sizes get on board with using data to its advantage. While data science may have once been seen as inaccessible unless an organization was at the enterprise level, current technologies have proven that any organization can utilize data science to gain extremely valuable, actionable insights from their data.
To take full advantage of the data that flows through your organization, it’s important to consider how that data can be accessed by everyone who needs it. With Revelate, data access is democratized without giving up control, meaning that you can provide essential data to anyone inside and outside your organization without worrying about security and access privilege breaches.