Data science, machine learning, and analytics all help organizations understand their data and make effective decisions. Each discipline is effective on its own, but what happens when you combine them? Their synergies enrich data insights while improving productivity so businesses can get ahead of the competition.
Using data to make better business decisions depends on four factors:
- Asking the right question
- Finding data that is acceptable to answer the question
- Putting this data in the correct form for further analysis
- Correctly using the method that provides the answer to the question
These four requirements are the bedrock of all methods used to understand data. They provide the common operating principles that bind together data science, machine learning, and analytics.
There’s a natural overlap between these three disciplines, in that they all use data sources to develop value. But they also have functional overlap. By the end of this article, you’ll see how fusing machine learning and analytics environments allow for the faster production of high-quality models. This union allows data science to be closer to the business, better validates your data, and decreases the chances of your models not working.
Let’s begin by discussing the relationship between data science, analytics, and machine learning. We’ll determine where they run parallel, then explore the potential they have when they intersect.
Data Science, Analytics, and Machine Learning
The importance of data science spans analytics, statistics, data engineering, machine learning, and programming. Data scientists extract insights from data found in these different sectors and present the results in a way that stakeholders can understand and use. But it’s not realistic to have a single data scientist do all this legwork to locate, prepare, analyze, and share the results. With a data scientist’s skillset aside, how can you even ensure that one person has enough time to do all this work?
The way that businesses have adapted and ensured that they’re effectively using their data is by splitting the job into multiple separately managed functions: data science, analytics, and machine learning. Let’s explore what each of these environments does individually before we discuss how they intersect and benefit one another.
Data Science
Data science sometimes entails different responsibilities and concerns for different organizations. For this article, though, let’s say that data science involves preparing data in a usable form. Data scientists combine domain expertise, programming skills, and extensive knowledge of statistics. They utilize these skills simultaneously to extract meaningful insights from data.
Typically, data science involves preparing structured and unstructured data for analysis, which involves extracting, integrating, and cleaning data. From here, data scientists use tools to obtain insights from the data, using statistical methods, predictive analytics, or artificial intelligence, including machine learning. Data scientists may then write an application to automate data preparation and analytic functions.
Analytics
Following this data science work, analytics transforms the statistics identified by data scientists into stories, visualizations, and summary statistics to explain the meaning of the results and let decision-makers know how they can use the results (and their limitations). Analytics covers using business intelligence, visualization, or statistical tools to place this prepared data into a form that answers business questions.
Machine Learning
Machine learning (ML) is a subset of artificial intelligence. ML identifies data patterns to build models. Then it alters those models based on additional data, with minimal human intervention. ML uses historical data to automate the building of models that are used against current data. They use this current data to draw interferences, make predictions about future data, and optimize decision-making.
Machine learning is a perfect augment for analytics, as it can provide many ways to enrich the data for analysis. It complements analytics, which is largely concerned with finding patterns in historical data, by using that historical data to predict the future and automate future decisions.
You can apply ML to various problems — like classification, for example. You can use the historical data patterns highlighted by analytics to “train” (develop) the ML model. Through a series of algorithms, ML develops a model from the data, either from scratch or using an existing model as a starting point. Then you test the trained model.
The goal of a classification model is generally to maximize the number of correct results and minimize the number of false positives. Based on the evaluation results, you may adjust the model or use it unchanged. As more data comes in, or as the model performance deteriorates, ML gives you the option to improve (retrain) the model and test it again.
The Value of Combining Data Science, Analytics, and Machine Learning
ML, data science, and analytics all overlap. They’re all focused on deriving value from data. However, in many organizations, and for a variety of reasons, these three areas work separately. While each area may have carved out its own functional role and territory, consider the power that comes from looking at the process of turning data into good decisions from start to finish.
The data scientist wants to provide the best possible data to make the decision. Doing that requires that they understand the decision that the analyst is supporting. Similarly, the analyst needs to work with the data scientist to understand the possible alternatives for sourcing the data to obtain the best one for their purpose. Without working with the analyst, the data scientist has to guess the answer that suits the decision needed.
Additionally, you may have a near-continuous stream of information and a need for rapid decisions. While the analyst might develop a model based on placement at a given point in time, the machine learning specialist is looking for ways to alter and improve that model’s predictability based on the flow of data. In this case, implementing ML can lead to better faster decision-making on what advertising to place.
If you break down the barriers between these three roles, you can use their respective expertise to provide solutions that support better decision-making. The three areas are mutually advantageous: using each area of expertise collaboratively allows you to decrease the chances of your model not working in the real world by developing and testing your models using contemporaneous business data. Your results can be measured against actual business metrics, which ultimately improves your ability to make decisions about future business activities and predict future data results. By harnessing the synergy of ML, analytics, and data science expertise, you’re going to have better data validation, business insights, and be able to make strategic decisions.
Common Goals, Different Data Needs
Data science, analytics, and ML each provide unique business value. Because they each have specific objectives they’re working to meet, they have varying data needs to accomplish their goals.
Data scientists, for example, need to collect data continuously, profile and understand data, and govern and secure the data. With this data, they also need to put models in production and schedule training and predictions. Then they need to get the results into the hands of users, which is and the need to visualize and publish results — all easily provided by an analytics platform.
Analytics has a large domain of decisions to support. While some analytics are prescriptive, many are advisory: they tell business leaders what’s happening rather than what they should do about it. Analytics answers questions such as, “What is the sales trend?” or “What percent of orders are unfilled?” Their purpose determines the data they require.
ML focuses on specialized problems that are prescriptive. ML applications make decisions that someone may or may not act on. For example, the models may advise a physician or respond to a question in a chatbot. ML brings value in the form of specific recommendations.
All three environments are data-driven, needing to rely on massive amounts of data. The more information/data that each can offer, the more effective and efficient they will be, individually and collectively, at producing results. They all identify trends and patterns on a detailed, granular level. Pinpointing trends in particular areas allow you to examine the data of these trends, gain valuable insight, and establish actionable outcomes. These three areas are also invaluable for making predictions for your organization’s future. Using the data from these three areas helps you to make informed decisions about the future based on current, existing data.
While data science, analytics, and ML have different data needs, they’re all working towards one common goal: using good, clean data to perform effectively and add value to the business in better decision-making. The key to all three disciplines working together is a unified data and analytics platform that can support all their needs. If data science could span the needs of both ML and analytics, a greater gain could be made by leveraging a single data stream. This prompts us to ask how these three can work closer together for better results.
How Does a Unified Data and Analytics Platform Help?
If you’re managing your data, analytics, and machine learning in different platforms, you’re likely stuck in a time-consuming cycle of needing to wrangle your data every time you want to analyze your data. You might also be working with data that’s not in its original form, preventing you from establishing a single source of truth, creating challenges when you’re making operations and strategy-based business decisions. The way to use data science, analytics, and ML most effectively and efficiently is to use a unified data and analytics platform (UDAP).
A UDAP simplifies data ingestion into a centralized repository while preserving the original data schema. It maintains a metadata map that allows users to understand the repository. The map translates the transactional data to business-friendly terms. A UDAP’s in-memory architecture makes it possible to do something that normally couldn’t be done: You can manipulate data on the fly at a sub-second speed without preprocessing.
Performing queries and joining data elements usually require a data warehouse to pre-aggregate the data, but a UDAP eliminates this requirement. It only allows data transformation after extraction, so it preserves the repository’s data integrity. This feature provides both analysts and ML personnel with the data they need. And it does this without one group’s needs preventing the other’s from being met.
A unified platform creates data sets that any available analytics tool can consume and use, whether it’s data visualization, business intelligence, or ML. This support for business tools enables data scientists, analytics professionals, and ML experts to work more efficiently, increasing business data value.
This means that data scientists no longer have the job of organizing and aggregating the raw data, freeing them for more complex tasks. They can also work closely with ML and analytics professionals to provide business value.
A Practical Example
To further illustrate how all three disciplines can work together effectively, let’s look at an example in the insurance industry. The highly data-driven insurance industry has several major components, each with its own role and data needs, such as risk assessment, underwriting, policy creation, and agent assistance.
The insurance business needs to respond to events happening in real-time and adjust its business procedures accordingly. But they can’t react quickly when they have to spend time collecting and integrating data from different data warehouses. The key to supporting all insurance areas is collecting the data in a single unified platform.
Insurance companies use risk assessment to determine your premium, usually assigning policies to risk categories according to a variety of factors. For example, homeowner insurance risks include area, type of home, and roof composition. ML can classify each home to a risk level based on these variables.
However, emergent variables in the environment — such as mudslides and wildfires — can affect the risk assessment. Insurance companies must ingest this new data into the repository. Then, data scientists can extract and prepare this data for further study. Analytics can use the new data to revise risk probabilities, and ML professionals can use the data to retrain their existing models or create new models to improve their predictability. These risk assessment results then feed into underwriting, which determines the policy price.
Underwriting must also consider the type of loss. For example, burglary is selective. Fire isn’t. The loss from a fire is likely to be higher than from a theft. Underwriting needs to consider the climate risk assessment elements and combine them with each area’s estimated price of possessions to determine the potential cost of a loss. Again, the ability to ingest new data into the repository and integrate it enables organizations to augment their existing models.
Creating policies uses data for a different purpose than risk assessment or underwriting. Policy creation must also incorporate a layer of marketing data into its development. For example, you need to be aware of what the competition charges, and if your organization can sell enough policies to distribute the risk. Workers creating the policies must respond quickly to changes in the competition’s pricing policy. The UDAP supports this price pivot with additional information that is quickly accessible.
Further, a unified system leads to better agent assistance. Agent assistance involves summarizing the complexity of the data models developed above into rules that sales agents use to determine the client’s policy, coverage, and price. Many agents are independent, selling policies from a variety of companies. The company that’s most responsive in terms of tools and speed of providing the latest information to agents gains an advantage in the marketplace.
By constantly gathering new information and making this information accessible to all departments, a unified data and analytics platform helps insurance companies make the most of the available data and respond quickly to change — with all departments working in sync.
Summary
While synergies exist for data scientists, machine learning, and analytics professionals to work together, companies often treat them as separate entities for various reasons. One reason is that they need different types of data to do their jobs.
A UDAP provides a structure to bridge these gaps as all three groups quickly access exactly the data they need. These UDAP efficiencies create opportunities for the three groups to work together, enhancing data’s value and driving better business decisions.
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