PODCAST #20. How Product Management is Mirrored in the Pharma Business and Tech Divisions

Welcome to our podcast! Today, we chat with Tamara Snow, who went from working on cancer research clinical trials to being a Director of Product Management at Flatiron Health. We’ll talk about how she moved into product management and how she helps big pharma companies.

Tamara’s wide range of experiences gives her a special viewpoint on these areas, showing us the nitty-gritty of the pharmtech and business worlds.

This article concisely captures key points from our lively dialogue.

A Career Path from Clinical Trials to Leading Pharma Product Management

Tamara has spent over ten years in the healthcare industry, making transitions as she encountered new problems and challenges. Initially, she aspired to be a doctor, gaining exposure to patient care and clinical work as an EMT and clinical research coordinator. She discovered her passion for solving operational and strategic challenges in healthcare environments. One area that surprised her was the manual and expensive nature of clinical trials despite being crucial for innovation in drug development.

After graduating, Tamara pursued a career in life sciences consulting to better understand the economics and strategy behind drug development. During this time, she learned about Flatiron Health, a company working on data sets to streamline decision-making in cancer research. Intrigued by the mission and vision of making clinical trials more efficient, she joined the company in 2017 in a partnership role. While she gained valuable sales and negotiation skills, she felt it took her away from solving the operational and strategic challenges she was passionate about.

“I definitely think the product hat is the one I love the most, and I will definitely continue wanting to play that role in the future.”

Tamara Snow – Director of Product Management at Flatiron Health

Recognizing that the product management role aligned with the problems she wanted to solve, Tamara pivoted to become a product manager at Flatiron Health. She initially focused on scaling one of their real-world data products linked to external genomic data for precision medicine. Seeing the impact of these data products on customers’ clinical and drug development decision-making was rewarding. However, a recurring issue was customers lacked the internal resources and skills to analyze the data products effectively.

In response to this need, Tamara seized the opportunity to expand her scope and build a team to develop dashboards and analytic tools on top of the data products, providing customers with valuable insights. Building and managing this new team has brought fresh challenges, focusing on the user interface and delivering insights alongside the underlying data.

After spending several years in the healthcare industry, Tamara firmly believes that the product management role is the one she loves the most and intends to continue playing in the future. She finds joy in solving operational and strategic challenges and appreciates the ever-changing nature of product management.

The Key Tips for a Successful Transition in Pharma Industry

According to Tamara, there are various ways for individuals to transition into a product role. While it may not be the standard path, she personally achieved it through an internal transfer, a route she has observed others taking as well. Successful internal transfers have come from diverse backgrounds, including business (such as sales and finance) and technical roles (like engineering and data science).

Some major tips for a successful transition into the pharma industry include

  • Identify a product role or team that aligns with one’s existing skill set.
  • Seek advice from experienced PMs.
  • Volunteer for challenging tasks, network, and ask the right questions.

Identifying a product role or team that aligns with one’s existing skill set.

“I also just took the time to teach myself things like Python and just took the liberty to dig into Flatiron and the products on my own and was able to demonstrate my ability to learn a new skill set and willingness to do it.”

Tamara Snow – Director of Product Management at Flatiron Health

Tamara believes the key to a successful transition is identifying a product role or team that aligns with one’s existing skill set. This reduces the risk for the company when transferring an employee to a new function. In her case, coming from a sales and partnerships role, joining an external-facing product team made the most sense. It required a strong understanding of the company’s customers, products, and business model. 

However, she also recognized her lack of technical skills, so she took the initiative to teach herself Python and familiarize herself with the company’s products. By demonstrating her willingness to learn and bridging the gap in her skill set, she differentiated herself from others and showcased her abilities.

Seek advice from experienced PMs

Tamara acknowledges that there is no perfect science to this transition process. She advises aspiring product managers to seek advice from experienced PMs in roles they are interested in or individuals who have gone through a similar career evolution. Learning from their experiences and strategies can be valuable.

When reaching out to PMs for guidance, Tamara recommends avoiding cold outreach and instead making warm introductions. Personalizing the outreach and offering something in return, such as industry insights or skills, can increase the likelihood of PMs wanting to assist and provide advice.

Volunteer for challenging tasks, network, and ask the right questions.

Tamara appreciates the emphasis on volunteering for challenging tasks, networking, and asking the right questions. These qualities are often associated with successful product managers. She also highlights the importance of building strong relationships and trust with customers, as it facilitates sharing information and understanding their needs. Asking open-ended questions during user research helps uncover the root of the problem and avoid biases. Active listening and focusing on important insights gathered from responses are vital in solving the core problem.

Strategies for Streamlining the Process in Challenging Circumstances

According to Tamara, when it comes to their customers, particularly in the context of a complex linked clinical plus genomic data set, clear training and documentation are crucial for understanding and interrogating the data. It is important to give customers a well-defined understanding of the data product. Additionally, having a robust process for addressing customer questions and resolving issues promptly is essential. 

Tamara suggests recognizing when a customer’s question requires more in-depth support, such as scheduling a call and screen sharing to collaboratively work through the problem. The goal is to unblock customers and enable them to conduct effective research using the data product.

“I think having really strong customer support and customer guidance is definitely core.”

Tamara Snow – Director of Product Management at Flatiron Health

Tamara emphasizes the significance of strong customer support and guidance. Providing examples of how others have utilized the data product to answer similar questions, whether through publications or key studies, can be immensely helpful. Sharing these use cases with customers helps them see the practical applications and possibilities of the data product.

The Journey to Becoming a Successful Product Manager

“In those first few months, really build strong trust with your core stakeholders and take the time to have them explain to you how they operate and what their pain points are.”

Tamara Snow – Director of Product Management at Flatiron Health

According to Tamara, being a product manager (PM) involves constant learning and encountering new challenges. Embracing this aspect is part of the fun of being a PM. One important advice Tamara offers to new PMs is to build strong trust with core stakeholders in the first few months. 

Understanding their operations and pain points allows the PM to identify tasks they can take off their plate or collaborate on to achieve quick wins. Tamara shares an example of how she learned basic R programming to handle easy client requests, which relieved her data science stakeholders and allowed her to better understand customers and data products.

Tamara also recommends finding a committee of advisors early on, including mentors and other PMs within the organization. These advisors can help tackle issues, provide guidance, and offer insight into the new role and function. Celebrating both wins and failures is another crucial aspect highlighted by Tamara. Acknowledging accomplishments, no matter how small, is important, but it’s equally important to learn from failures and treat them as opportunities for growth.

In the discussion, Tamara mentions that PMs can positively redefine the concept of failure, transforming it into a learning experience and an opportunity for product improvement. This mindset shift can be particularly impactful in the health tech industry. Additionally, the importance of empathy in interactions with stakeholders is highlighted, emphasizing the need to understand their perspectives and needs.

What Businesses Truly Want from Product Managers

“Regardless, I think there are a few key roles or tasks that I think the business will probably want regardless.”

Tamara Snow – Director of Product Management at Flatiron Health

According to Tamara, the role of a product manager (PM) can vary depending on various factors within a company. These factors include whether the product is internal or external-facing, the stage of the product (early concept or mature business line), and its significance to the company’s overall economics. However, a few key roles and tasks are generally expected from PMs.

Firstly, PMs are responsible for owning the vision and strategy of their product. They need to develop and articulate a compelling vision that justifies the company’s investment in the product. Additionally, PMs should create a strong roadmap aligned with the overall company strategy and vision.

Secondly, PMs act as the voice of the customers, both internally and externally. They must deeply understand the customers and advocate for their needs and insights during product development. PMs are crucial in making challenging product and resource tradeoff decisions, using customer insights to guide their choices.

Thirdly, stakeholder management and collaboration are essential for PMs. They must effectively work with various stakeholders, such as engineering and design teams, to build the product efficiently. In health tech, where a mix of experts like oncologists and clinicians collaborate with engineers, managing stakeholders and consolidating different perspectives into a cohesive vision is particularly important.

Challenging the Problem Space and Unveiling Opportunities to Drive Product Success

According to Tamara, when looking for new opportunities as a PM, it is crucial to engage with stakeholders. The first and most powerful stakeholders to approach are the customers. By actively listening to customers and understanding their needs and preferences, PMs can identify pain points and opportunities for improvement.

In addition to existing customers, expanding to new customers or segments requires consideration. Monitoring competitors’ product offerings and partnerships can reveal potential gaps to address. Staying up to date with industry trends can generate new ideas. Conducting lightweight market research and seeking time with target customers enables direct conversations and a deeper understanding of their requirements.

Tamara suggests clearly defining the problem and opportunity when rallying the team for the job. It is essential to motivate the team by presenting a compelling vision highlighting their work’s impact. Early and regular engagement with stakeholders, including engineers, is crucial to gaining their buy-in and involving them in shaping the project. 

Leveraging Data Awareness to Address Pushback in Problem Solving

According to Tamara, data awareness refers to having a solid set of objective data that supports your argument and clearly defines the opportunity and problem space. While data is important, Tamara believes it only takes you so far. 

It is crucial to drive the vision and demonstrate why your team is well-positioned to execute the solution. This involves explaining why the problem needs to be addressed now and highlighting the qualitative aspects of the opportunity, not just the numbers.

“Yeah, in my opinion, if there is a pushback, there is a level of interest.”

Tamara Snow – Director of Product Management at Flatiron Health

As challenges and opposition are expected, anticipating pushback and objections and preparing responses in advance is also important. Tamara emphasizes the need to go beyond a rosy picture and be transparent about the risks, assumptions, and potential challenges associated with the opportunity. You must acknowledge the unknowns and openly discuss the potential bumps along the road. 

From her personal experience, Tamara has learned the value of transparency and managing expectations. She further opines that if the opportunity requires collaboration with other parties or forming partnerships, it’s essential to consider company fit and strategic alignment. The terms of the agreement should be carefully evaluated to ensure that collaboration makes sense for the envisioned opportunity.

Defining Product Management and Keeping Abreast of Current Trends in the Health Tech Industry

According to Tamara, staying on top of industry trends involves reading newsletters, participating in industry-specific conversations on social media platforms like LinkedIn and Twitter, attending conferences and speaking events, and networking with professionals in the industry. These activities help her stay informed and identify new opportunities.

“The role and the skill sets you need as a PM and how you would define that role, I think, definitely differ depending on where you really sit within the organization and what your product looks like.”

Tamara Snow – Director of Product Management at Flatiron Health

When it comes to defining product management, Tamara believes it is not a one-size-fits-all role. The responsibilities of a product manager depend on the specific needs of the product line. For external-facing products, the PM must be able to pitch, sell, understand customers, filter feedback, and guide and influence the team accordingly. For internal-facing products, the focus is identifying and prioritizing platforms that benefit the broader organization and gathering feedback from various teams.

Tamara emphasizes that a product manager’s role and required skill sets vary based on the position within the organization and the nature of the product. Adapting and shaping oneself based on the product’s needs is important. She mentions Bruce Lee’s quote about being like water, which can take any shape depending on the container. While martial arts and product management may not directly correlate, the idea of being adaptable and flexible resonates with the role of a product manager.

Tamara also notes that the role of a product manager evolves over time. As the product and business line mature, different skills and activities become relevant. The role of a product manager constantly changes, presenting new opportunities and challenges, which Tamara finds exciting.

In Summary

Below are the major takeaways from our chat with Tamara:

  • Transitioning into Product Management: Tamara’s career journey from clinical trials to product management highlights the importance of identifying a product role that aligns with one’s existing skill set and passion for solving operational and strategic challenges.
  • Strategies for Success: Building strong relationships with stakeholders, actively listening to customers, and asking the right questions are crucial for successful product managers. Seeking advice from experienced PMs and making warm introductions can enhance networking opportunities.
  • Key Roles and Skills of Product Managers: The role of a product manager can vary depending on factors such as the product’s nature, stage, and significance to the company. However, PMs generally own the product vision and strategy, act as the voice of the customers, and collaborate with various stakeholders. 








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What is Big Data Analytics? Definition, Types, Software, and Use Cases

You can have all the data in the world, but if you don’t know how to use it for your business benefit, there’s no point in sitting on that raw information and expect good things to happen. The solution – Big Data Analytics – helps to gain valuable insights to give you the opportunity to make business decisions more effectively. 

In a way, data analytics is the crossroads of the business operations. It is the vantage point where you can watch the streams and note the patterns.

But first – let’s explain the basics.

What is Data Analytics?

The term “Data Analytics” describes a series of techniques aimed at extracting relevant and valuable information from extensive and diverse sets of unstructured data collection from different sources and varying in sizes.

Here’s what you need to understand about data – everything on the internet can be its source. For example:

  • content preferences
  • different types of interactions with certain kinds of content or ads
  • use of certain features in the applications
  • search requests
  • browsing activity
  • online purchases

This data is analyzed and integrated into a bigger context to amplify business operation and make it as effective as possible.

Raw data is like a diamond in the rough. Data Mining takes the rough part, and then Data Analytics provides the polish. That’s the general description of what Big Data Analytics is doing. In fact, data mining gets “unrefined” information “from the bowels”, and then it needs to be finalized depending on the tasks. From that moment on, the process of analyzing raw data goes on.

Data Analysis vs Data Analytics vs Data Science

Data Analysis vs. Data Analytics vs. Data Science

Many terms sound the same, but they are different in reality. In case you are confused about what is the difference between data science, analytics, and data analysis, it’s easy to distinguish: 

  • The data analysis primarily focuses on processes and functions

  • The data analytics deal with information, dashboards, and reporting

  • The data science includes data analysis but also has elements of data cleaning and preparation for further investigation (from data mining)

Role of Data Analyst in the Business

Data Analysts are the specialists who control the data flows and make sense of the data using specific software.

Basic data analytics operations don’t require specialized personnel to handle the process (usually it can take care of by stand-alone software), but in the case of Big Data analytics, you do need qualified Data Business Analysts.

The purpose of a Data Analyst is to

  1. Study the information
  2. Clean it from noise
  3. Assess the quality of data and its sources
  4. Develop the scenarios for automation and machine learning
  5. Oversee the proceedings

Use of Big Data in Data Analytics

You don’t need Big Data for Data Analytics since the latter is about analyzing whatever information you have. However, why is big data important? Because it can be a deeper and more fulfilling source of insights, which is especially useful in the case of prediction and prescription analytics.

Data Analytics is all about making sense of information for your business operation and making use of it in the context of your chosen course of action.

It is essential to understand what kind of statistical analysis needs to be applied to make the most of available information and turn a pile of historical data into a legitimate strategic advantage.

There are four big categories of Data Analytics operation. Let’s look at them one by one.

Different Types of Data Analytics

Descriptive Analytics - What Happened?

Descriptive Analytics – What Happened?

The purpose of descriptive analytics is to show the layers of available information and present it in a digestible and coherent form. It is the most basic type of data analytics, and it forms the backbone for the other models.

Descriptive analytics is used to understand the big picture of the company’s process from multiple standpoints. In short – it is

  1. What is going on?
  2. How is it going on?
  3. Is it any good for business within a selected period?

Because descriptive analytics are so basic, this type is used throughout industries from marketing and ecommerce to banking and healthcare (and all the other.) One of the most prominent descriptive analytics tools is Google Analytics.

From the technical standpoint, the descriptive operation can be explained as an elaborate “summarizing.” The algorithms process the datasets and arrange them according to the found patterns and defined settings and then present it in a comprehensive form.

For example, you have the results of the marketing campaign for a certain period. In this case, descriptive analytics shows the following stats of interacting with content:

  • Who (user ID);
  • Circumstances (source – direct, referral, organic);
  • When (date);
  • How long (session time).

The insights help to adjust the campaign and focus it on more relevant and active segments of the target audience.

Descriptive analytics is also used for the optimization of real-time bidding operation in Ad Tech. In this case, the analytics show the effectiveness of spent budgets and shows the correlation between spending and the campaign’s performance. Depending on the model, the efficiency is calculated using goal actions like conversions, clicks, or views. 

Diagnostic Analytics - How It Happened?

Diagnostic Analytics – How It Happened?

The purpose of diagnostic analytics is to understand:

  • why certain things happened
  • what caused these turns of events.

Diagnostic analytics is an investigation aimed at studying the effects and developing the right kind of reaction to the situation.

The operation includes the following steps:

  • Anomaly Detection. The anomaly is anything that raises the question of its appearance in the analytics, whatever doesn’t fit the norm. It can be a spike of activity when it wasn’t expected or a sudden drop in the subscription rate of your social media page.
  • Anomaly Investigation. To do something, you need to understand how it happened. This process includes the identification of sources and finding patterns in the data sources.
  • Causal Relationship Determination. After the events that caused anomalies are identified – it is time to connect the dots. This may involve the following practices:
    • Probability analysis
    • Regression analysis
    • Filtering
    • Time-series data analytics

Diagnostic Analytics is often used in Human Resources management to determine the qualities and potential of employees or candidates for positions.

It can also apply comparative analysis to determine the best fitting candidate by selected characteristics or to show the trends and patterns in a specific talent pool over multiple categories (such as competence, certification, tenure, etc.)

Predictive Analytics - What Could Happen?

Predictive Analytics – What Could Happen?

As you might’ve guessed from the title – predictive analytics is designed to foresee:

  • what the future holds (to a certain degree)
  • show a variety of possible outcomes

In business, it’s often much better to be proactive rather than reactive. Therefore, Predictive Analytics helps you to understand how to make successful business decisions that bring value to companies. 

How do the Predictive Analytics algorithms work?

  • Go through the available data from all relevant sources (for example, it can be one source or a combination of ERP, CRM, HR systems);
  • Combine it into one big thing;
  • Identify patterns, trends, and anomalies;
  • Calculate possible outcomes.

While predictive analytics estimates the possibilities of certain outcomes, it doesn’t mean these predictions are a sure thing. However, armed with these insights, you can make wiser decisions.

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Application areas of Predictive Analytics:

  • Marketing – to determine trends and potential of particular courses of action. For example, to define the content strategy and types of content more likely to hit the right chord with the audiences;   
  • Ecommerce / Retail – to identify trends in customer’s purchase activities and operate product inventory accordingly.  
  • Stock exchanges – to predict the trends of the market and the possibilities of changes in various scenarios.
  • Healthcare – to understand possible outcomes of disease outbreak and its treatment methodology. It is used for scenario simulation studies and training.
  • Sports – for predicting game results and keeping track of betting;
  • Construction – to assess structures and material use;
  • Accounting – for calculating probabilities of certain scenarios, assessing current tendencies, and providing several options for decision making.
Prescriptive Analytics - What Should We Do?

Prescriptive Analytics – What Should We Do?

Not to confuse prescriptive and predictive analytics:

  • Predictive analytics says what might happen in the future
  • Prescriptive analytics is all about what to do in the future

This digging into customer data presents a set of possibilities and opportunities as well as options to consider in various scenarios.

Tech-wise, prescriptive analytics consists of a combination of:

All this is used to calculate as many options as possible and assess their probabilities.

Then you can turn to predictive analytics and look for further outcomes (if necessary). It is commonly used for the following activities:

  • Optimization procedures;
  • Campaign management;
  • Budget management;
  • Content scheduling;
  • Content optimization;
  • Product inventory management.

Prescriptive analytics is used in a variety of industries. Usually, it is used to provide an additional perspective into the data and give more options to consider upon taking action, for example:

  • Marketing – for campaign planning and adjustment;
  • Healthcare – for treatment planning and management;
  • E-commerce / Retail – in inventory management and customer relations;
  • Stock Exchanges – in developing operating procedures;
  • Construction – to simulate scenarios and better resource management.
Use Cases for Data Analytics

Big Data Analytics Use Cases

Now let’s look at the fields where data analytics makes a critical contribution.

Sales and Operations Planning Tools

Sales and operations planning tools are something like a unified dashboard from which you can perform all actions. In other words, it is a tight-knit system that uses data analytics on a full scale.

As such, S&OP tools are using a combination of all four types of data analytics and related tools to show and interact with the available information from multiple perspectives.

These tools are aimed specifically at developing overarching plans with every single element of operation past, present, or future is taken into consideration to create a strategy as precise and flexible as possible.

The most prominent examples are the Manhattan S&OP and Kinxaxis Rapid Response S&OP. However, it should be noted that there are also custom solutions tailor-made for the specific business operation.

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Recommendation Engines

Internal and external recommender engines and content aggregators are some of the purest representations of data analytics on a consumer level.

The mechanics behind it is simple:

  1. The user has some preferences and requirements, noted by the system.
  2. Web crawling or internal search tools for relevant matches based on user preferences. 
  3. If there is a match, it’s included in the options.

There are two types of user preferences that affect the selection:

  • Direct feedback via ratings;
  • Indirect via interacting with the specific content from the various sites.

As a result of this, the user gets the content s/he will most likely interact with offered.

One of the most prominent examples of this approach is used by Amazon and Netflix search engines. Both of them are using extensive user history and behavior (preferences, search queries, watch time) to calculate the relevancy of the suggestions of the particular products.

Also, Google Search Engine personalization features enable more relevant results based on expressed user preferences.

Customer Modelling / Audience Segmentation

The customer is always on the front stage. One of the most common usages of data analytics is aimed at:

  • Defining and describing customers;
  • Recognizing distinct audience segments;
  • Calculating their possible courses of actions in certain scenarios.

Since the clearly defined target audience is the key for a successful business operation – user modelling is widely used in a variety of industries, most prominently in digital advertising and e-commerce.

How does it work? Every piece of information that the user produces keeps some insight that helps to understand what kind of product or content he might be interested in.

This information helps to construct a big picture of:

  • Who is your target audience;
  • Which segments are the most active;
  • What kind of content or product can be targeted towards which of the audience segments;

Amazon is good at defining audience segments and relevant products to the particular customer (which helps it to earn a lot of money, too.)

We have our case study regarding user modeling and segmentation with the Eco project. In that case, we did a cross-platform analytics solution that studied the patterns of product use in order to determine audience segments and improve user experience across the board.

Market Research / Content Research

Knowledge is half of the battle won and nothing can do it better than a well-tuned data analytics system.

Just as you can use data analytics algorithms to determine and thoroughly describe your customer, you can also use similar tools to describe the environment around you and get to know better what the current market situation is and what kind of action should be taken to make the most out of it.  

Fraud Prevention

Powers of hindsight and foresight can help to expose fraudulent activities and provide a comprehensive picture.

The majority of fraudulent online activities are made with the assistance of automated mechanisms. The thing with automated mechanisms is that they work in patterns and patterns are something that can be extracted out of the data.

This information can be integrated into a fraud detecting system. Such approaches are used to filter out spam and detect unlawful activities with doubtful accounts or treacherous intentions.

Price Optimization

One of the critical factors in maintaining competitiveness on the market in e-commerce and retail is having more attractive prices than the competition.

In this case, the role of data analytics is simple – to watch the competition and adjust the prices of the product inventory accordingly.

The system is organized around a couple of mechanisms:

  1. Crawler tool that checks the prices on the competitor’s marketplaces;
  2. Price comparison tool which includes additional fees such as shipping and taxes;
  3. Price adjustment tool that automatically changes the cost of a particular product.

To manage discounts or special offer campaigns, one can also use these tools.

Big Data Analytics Software

Big Data Tools: Data Analytics Software

In addition to custom solutions, there are several useful ready-made data analytics tools that you can fit into your business operation.


  • Excel Spreadsheet – the most basic tool for data analytics. It can be done manually and show enough information to understand what is going on in general terms.
  • Google Analytics is a standard descriptive analytics tool that provides information about website traffic, source and basic stats on user behavior. Can be used for further visualization via Data Studio.
  • Zoho Reports is a part-descriptive-analytics part-task-management tool. Useful for project reporting and tracking progress. Works well to assess campaign performance results.


  • Tableau Desktop – with this tool you can make any scope of data comprehensible in the form of graphs and tables. Good for putting things into perspective. Easy to use, light on the pocket.
  • The demo analytics tool is good for medium-sized operations to gather and analyze data from their own large networks. In addition to visualization, it is capable of assessing probabilities of different scenarios and proposing better fitting courses of action.
  • Style Scope is useful for teamwork and planning. It maps data from a different source on one map and in the process unlocks its hidden possibilities.

Heavy Artillery

  • Microsoft Power BI can consolidate incoming data from multiple sources, extract insights, assess probabilities of various turns of events, and put them into a broader context with a detailed breakdown of possible options. In other words, it turns Jackson Pollock’s painting into Piet Mondrian’s grid.
  • Looker is a multi-headed beast of a tool. With its help, you can combine data from multiple sources – track overall progress, break it down to the element, extract insights, and calculate possibilities – all in one convenient dashboard.
  • SAP ERP is primarily used for sales management and resource planning. With its help, you can map out the goals, combine information, assess performance and possibilities, and decide what to do next.

In Conclusion

Here we figured out – what is data analytics? These days, data analytics is one of the key technologies in the business operation. Data mining provides the information, and Data Analytics helps to gain useful insights from that information to integrate them into the business process and enjoy the benefits.

So take advantage of data analytics as a compass to navigate in the sea of information.

Read also: Big Data in Customer Analytics

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How Predictive Analytics is Changing Healthcare Industry

The Healthcare industry is experiencing a significant leap forward due to the growing adoption of big data and machine learning algorithms. The tools are becoming more powerful, and the results are becoming more informative. One of the most useful machine learning tools is predictive analytics algorithms.

The steady supply of information feeds the healthcare system. The patient shares his or her well being with the doctor; the doctor gets more data from the machines and equipment; the researchers receive the compiled data from various hospitals, and in turn, can work on creating a treatment that would help the initial patient (and all the others). As such, this is a perfect playground for analytics technology like predictive analytics. 

In this article, we will talk about how predictive analytics can bring healthcare to a new level.

What is Predictive Analytics?

The term “Predictive analytics” describes a methodology of getting an insight into the possible future favorable and adverse events based on the available data and statistical analysis, answering the question “What might happen?”

The purpose of predictive algorithms in healthcare is:

  • To find the correlations in the patient’s data
  • To find associations of the symptoms
  • To find familiar antecedents of the symptoms
  • To explore the impact of different factors (genome structure, clinical variable, et al.) on the course of treatment
  • To examine the possible influence of medical care to past and current diseases


How Predictive Analytics helps in Healthcare

The researchers, as well as doctors, can benefit from predictive analytics to see what can happen. Here is a simplified process: 

Descriptive analytics algorithms are the first to the scene. They take the incoming data from electronic health records and present it in an understandable format. The information includes clinical documentation, claims data, patient surveys, lab tests, and so on – everything that already happened.

The processed information is sorted into various datasets by various criteria (for example, drug reaction dataset and genomics dataset.)

Predictive analytics algorithms start their work. Depending on the goal of the analysis, a predictive algorithm can produce assumptions based either on available data directly from a given patient or general medical data from the public health datasets.

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The assumptions are usually grouped by their probability – from the most likely to the least likely to happen. It’s important to remember that predictions are, in fact, nothing more than assumptions and probabilities. The more data you have, the more accurate and detailed result you will get (like a trend line or high risk score.)

All these insights give a foundation for prescriptive analytics in healthcare, which also calculates probabilities. The difference is that predictive advanced analytics answers the question “What can happen?” and prescriptive analytics answers “What can we do about it?” 


Healthcare Predictive Analytics Examples

Precise Treatment & Personalized Healthcare – Make Better Decisions

Predictive analytics’ most significant contribution to healthcare is personalized and accurate treatment options. 

Getting the treatment strategy right requires going through a lot of data and taking a lot of factors into consideration. In addition to that, the process is time-consuming, which can be detrimental to the treatment as the patient’s condition may worsen in-between the tests and results.

The predictive analytics algorithms can:

  • calculate what can happen (predictive modeling)
  • say what to expect in certain turns of events
  • tell how to map out the treatment of the disease outbreaks.

Even without implementing the prescriptive algorithms, the doctors can use the results from predictive analytics to treat the patient right (especially in cases of rare chronic diseases that the docs did not have enough experience with before.)

Analytics streamline the process – all you technically need is input data and a clear understanding of what are you looking for. 


Efficient Treatment Testing – Reduce Risks

Predictive analytics aren’t directly involved in the treatment testing process, but it is used to cut out the apparent dead ends and streamline the other tasks that will contribute to the treatment. Considering the amount of information to sift through, any functions that can be done automatically simplify the trial runs and reduce potential risks.

Structured patient data is a treasure trove of information. Based on this information, the predictive algorithm can assess how various types of treatments might affect the organism.

The results of the analysis are processed with the assistance of public health datasets and then interpreted as risk factors for the specific scenarios. The criteria are usually symptom-based, time-based, and treatment type-based.

Risk Factor intelligence is a set of filters, which is utilized during treatment testing and scenario simulation. 

When the time comes to select the proper treatment, the elements that don’t fit the Risk Factor filters are eliminated.  

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Disease Control and Management – Avoid Sepsis

Sepsis is when the body starts to attack its own organs and tissues in attempt to fight off the bacteria or other causes. It is one of the most dangerous threats during any course of treatment. According to the recent Sepsis Alliance study, harmful bacterias and toxins in the tissues kill one person every two minutes.

In the case of a septic shock, healthcare workers need to act quickly and understand the patient’s needs and reactions. Predictive algorithms can help to avoid fatal outcomes.

  • Real-time analytics provide medical professionals with a big picture of what is going on with the patient.
  • The incoming information is analyzed to detect any kind of anomalies.
  • In case of any suspicious symptoms, an early warning health systems informs the doctors and they can prevent the condition from harming the patient.

Such applications as DNA Nanopore sequencers can detect pathogens and toxins in the DNA samples and calculate possible courses of action that avoid the mere possibility of sepsis.

Our developers from the APP Solutions have worked alongside medical research and the Google team on a proof of concept for genomics researchers, data scientists, and bioinformatics developers to fight such sepsis danger, using the breadth and depth of Google Cloud. Read the case study on Google Blog

Workflow Optimization – Predict Patient Utilization Patterns

Besides treating patients, predictive analytics can also help to manage the hospital and other medical institutions’ workflows. 

Managing healthcare institutions, especially on the day-to-day operation level, is a significant undertaking. The predictive algorithm can streamline some of its elements and boost the health services’ efficiency by avoiding operational downtime and stalling.

Predictive algorithms can be used to analyze Patient Utilization Patterns. For instance, it can detect the peak highs and lows as well as the weak points of the workflow. Predictive algorithms can also provide a big picture of the working process and its effectiveness. 

On the other hand, predictions can be used to optimize the workflow of various departments:

  • Build an effective schedule that will avoid extreme workload and avoid needless downtime
  • Manage personnel allocation
  • Predict supply chain demands and refill/maintenance schedule

All this can help to flatten the bell curve and even out the workflow of each department (unless we’re talking about ER, where the flow is pretty much unpredictable.)

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Supply Chain Management

Supply chain management is an important integral part of the healthcare workflow. Predictive analytics algorithms in hospital analytics can solve a few issues here:

  • Provide a more in-depth view into the state of the market and its possibilities
  • Give hospital administrative operations management team an opportunity to cut healthcare costs and use supply chain budget more effectively
  • Can help to better utilize the supply chain according to the demands of the healthcare operation

In other words, Predictive Analytics puts things into perspective. A combination of the current trends and history can show what the optimal decision can be in the current situation. 

It is a variation of e-commerce market basket analysis with additional inventory management tools. Predictions are based on associations between the items and their consumption and the results can streamline the workflow. As a result, you get a much more cost-effective operation and much less headache.

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InConclusion – Actionable Insights

The Healthcare industry is bound by the need for the right decision making and the key to this is understanding what the future holds.

Predictive analytics with its handy sets of predictions and estimates provide a competitive advantage and lets you think through the course of action a couple of steps ahead. Predictive analytics help to act instead of reacting. 

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Case Study: Cross-Platform Data Analytics with Data Warehouse

The nature of enterprise companies is that they are reliant on the “big picture” – an overarching understanding of the things happening on the market in general as well as in the context of a particular product.

Data Analytics is the way of visualizing the information with a handy set of tools, which show how things are moving along. As such, it is an indispensable element of the decision-making process.

Understanding the big picture binds every source of information together in one beautiful knot and presents a distinct vision of past, present, and possible future.

In one way or another the big picture affects everything:

  • Day-to-day operations
  • Long-term planning
  • Strategic decisions

Big picture view is especially important when your company’s got more than one product, and the overall analytics toolbox is scattered.

One of our clients needed a custom big data analytics system, and that was the task set before the APP Solutions’ team of developers and PMs.

ECO: Project Setup

The client had several websites and applications with a similar business purpose. The analytics for each product were separate, so it took considerable time and effort to combine and assess into the plain overarching view.

The dispersion of the analytics caused several issues:

  • The information about the users was inconsistent throughout the product line;
  • There was no real understanding of how the target audiences of each product overlap.  

There was a need for a solution that will gather information from different sources and unify them in one system.

Our Solution – Cross-Platform Data Analytics System

Since there were several distinct sources of information at play, which were all part of one company, it made sense to construct a nexus point where all the information would come together. This kind of system is called cross-platform analytics or embedded analytics.

Overall system requirements were: 

  • It has to be an easily-scalable system
  • It can handle big data streams
  • It can produce high-quality data analytics coming from multiple sources. 

In this configuration, the proposed system consists of two parts:

  • Individual product infrastructure – where data is accumulated;
  • Data Warehouse infrastructure – where information is processed, stored, and visualized.

Combined information streams would present the big picture of product performance and the audience overlap.

The Development Process Step by Step

Step 1: Designing the Data Warehouse

Data Warehouse is the centerpiece of the data analytics operation. It is the place where everything comes together and gets presented in an understandable form.

The mains requirements for the warehouse were:

  • Ability to process a large amount of data in a real-time mode
  • Ability to present data analytics results in a comprehensive form.

Because of that, we needed to figure out a streamlined dataflow that will operate without much of a fuss.

There are lots of data coming in different types of user-related events:

  • clicks,
  • conversions,
  • refunds
  • other input information.

In addition to storing information, we needed to tie it with the analytics system, which required synchronization of the system elements (individual products) for ever-relevant analytics.

We decided to go with the Cloud Infrastructure for its resource management tools and autoscaling features. It made the system capable of sustaining a massive workload without skipping a beat.

Step 2: Refining Data Processing Workflow

The accuracy of data and its relevance are critical indicators of the system working correctly. The project needed a fine-tuned system of data processing with an emphasis on providing a broad scope of results in minimal time.

The key criteria were:

  • User profile with relevant info and updates
  • Event history with a layout on different products and platforms

The system was thoroughly tested to ensure the accuracy of results and efficiency of the processing.

  • We used BigQuery’s SQL to give data a proper interface.
  • Google Data Studio and Tableau are used to visualize data in a convenient form due to its flexibility and accessibility.

Step 3: Fine-Tuning Data Gathering Sequence

Before any analytics could happen – there is data gathering to be done, and it should be handled with care. The thing is – there should be a fine-tuned sequence in the data gathering operation so that everything else could work properly.

To collect data from various products, we have developed a piece of javascript code that gathers data from different sources. It sends data over for processing and subsequent visualization in Google Data Studio and Tableau.

This approach is not resource-demanding and highly efficient for the cause, which makes the solution cost-effective.

The whole operation looks like this:

  1. Client-side Data is gathered by JavaScript tag
  2. Another part of the data is submitted by individual products server-to-server
  3. The information is sent to the custom analytics server API which publishes it to the events stream
  4. Data processing application pulls events from the events stream and performs logical operations on data
  5. Data processing app stores resulting data into BigQuery

Step 4: Cross-Platform Customer/User Synchronization

The central purpose of the system was to show an audience overlap between various products.

Our solution was to apply a cross-platform user profiling based on the digital footprint. That gives the system a unified view of the customer – synchronized across the entire product line.

The solution includes the following operations:

  • Identification of the user credentials
  • Credential matching over profiles on different platforms.
  • After that – the profiles were then merged into a unified profile that was gathered data across the board
  • Retrospective analysis – to analyze the user activity on different products, compare profiles, and merge the data if there are any significant commonalities.

Step 5: Maintaining Scalability

The number one priority of any big data-related operation can scale according to the required workload.

Data processing is a kind of operation that requires significant resources to be appropriately performed. It needs speed (approx 25GB/h) and efficiency to be genuinely useful in serving its cause.

The system requirements included:

  • Being capable of processing large quantities of data at the required timeframe
  • Being capable of easily integrating new elements
  • Being open to a continuous evolution

To provide the best possible environment for scalability – we have used the Google Cloud Platform. Its autoscaling features secure smooth and reliable data processing operations.

To keep the data processing workflow uninterrupted no matter the workload, we used Apache Beam.

Tech Stack

  • Google cloud platform
  • Cloud Pub/Sub
  • Cloud Dataflow
  • Apache Beam
  • Java
  • BigQuery
  • Cloud Storage
  • Google Data Studio
  • Tableau

Project Team

Any project would not be complete without the team 

  • Project Manager
  • Developer
  • System Architect
  • DevOps + CloudOps


This project can be considered as a big milestone for our team. Over the years we have worked on different aspects of a big data operation and developed many projects that involved data processing and analytics. However, this project gave a chance to create an entire system from the ground up, integrate it with the existing infrastructure, and bring it all to a completely new level.

During the development of this project, we have utilized more streamlined workflows that allowed us to make the complete turnaround much faster. Because of that, we have to manage to deploy an operating prototype of the system ahead of the planned date and dedicated more time to its testing and refinement.

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