3 Reasons to Embrace Prescriptive Analytics in Healthcare
From flagging an unsafe drug interaction to activating a yearly reminder call for a mammogram, healthcare providers are leveraging patient data for a wide array of healthcare tasks. Yet, a worrying number of healthcare providers struggle to understand which one of the big data analytics methods, prescriptive or predictive, is most effective for their business.
Related Reading: 5 Ways Big Data is Changing the Healthcare Industry
Understanding the difference between prescriptive analytics and predictive analytics is the key to finding the right path to viable and productive solutions for your healthcare industry. This blog discusses why you should consider prescriptive analytics rather than predictive analytics to drive value to your business.
Predictive Analytics: The Ability to Forecast What Might Happen
Predictive analytics has been helpful to healthcare providers as they look for evidence-based methods to minimize unnecessary costs and avoid adverse events, which can be prevented. Predictive analytics aims to detect problems even before they occur using historical patterns and modeling. As the word itself suggests, it predicts. It gives you collated and analyzed data that could serve as raw material for informed decision making.
Related Reading: Data Mining and Predictive Analytics: Know The Difference
However, the healthcare industry demands a more robust infrastructure. It needs access to real-time data that allows quick decision-making both clinically and financially. It also requires medical devices that can provide information on the vitals of a patient up to the nanosecond. Based on the information available for the individual patient, clinical decision support systems should be able to provide an accurate diagnosis and the treatment options available. This must take into consideration the latest advances in medicine available as well. That is where prescriptive analytics comes into the picture.
Prescriptive Analytics: Reveals Actionable Next Steps
Prescriptive analytics takes it a step further by providing actionable next steps. If predictive analytics sheds light on the dark alley, prescriptive analytics reveals the stepping stones that would help map out the course of action to be taken. It empowers you to make more accurate predictions and gives you more options so you can make well-defined split-second decisions, which is critical for the healthcare industry.
According to Research and Markets, the global prescriptive and predictive analytics market is expected to reach $28.71 billion by 2026. The reason for such an increase is because prescriptive analytics has the capacity to analyze, sort and learn from data and build on such data more effectively than any human mind can. Hence, the most outstanding benefit of prescriptive analytics is the outcome of the analysis.
Three Reasons to Consider Prescriptive Analysis
MarketWatch states that Healthcare prescriptive analytics market is poised to grow significantly during the forecast period of 2016-2022. Here are 3 reasons why.
1. Sound Clinical Decision-Making Options
Unlike predictive analytics which stops at predicting an upcoming event, prescriptive analytics empowers healthcare providers with the capability to do something about it, helping them take the best action to mitigate or avoid a negative consequence.
To illustrate, a healthcare service provider might be experiencing an inordinately increased number of hospital-acquired infections. Prescriptive analytics wouldn’t just stop at flagging the anomaly and highlighting who would be the next possible patient with vulnerable vitals. It would also point to the nurse who is responsible for spreading that particular infection to all these patients. It could also prevent similar outbreaks in the future by helping healthcare providers develop a sound antibiotic stewardship program.
2. Sound Clinical Action
Prescriptive analytics doesn’t limit itself to interpreting the evidence. It also allows health care providers to consider recommended actions for each of those predicted outcomes. It carefully links clinical priorities and measurable events such as clinical protocols or cost-effectiveness to ensure that viable solutions are recommended.
To illustrate, a healthcare provider might be able to forecast a patient’s likely return to the hospital in the very next month using predictive analysis. On the other hand, prescriptive analytics would be able to drive decisions regarding the associated cost simulation, pending medication, real-time bed counts, and so on. Or, it could help you decide if you need to adjust order sets for in-home follow-up. It empowers the hospital staff to identify the patient with a greater risk of readmission and take needed action to mitigate such risks.
3. Sound Financial Decisions
Prescriptive analytics has the capability to lower the cost of healthcare from patient bills to the cost of running hospital departments. In other words, it helps in making sound financial and operational decisions, providing short-term and long-term solutions to administrative and financial challenges.
Gain the Benefits of Prescriptive Analysis
Prescriptive analytics provides enormous scope and depth as developers improve technologies in the future. It is making truly meaningful advances with regard to the quality and timeliness of patient care and is reducing clinical and financial risks. Are you ready to get on board? Contact us for help.
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6 Ways Cognitive Analytics Contributes To Business Profitability
The ocean of data is deep and seemingly limitless, and its levels are rising exponentially. To stay afloat in the competitive market today and reap good ROI, businesses must learn to connect their data analytics strategy to their business decisions. Here, we are discussing the multiple ways in which cognitive analytics can help your business maximize revenue by converting your data into actionable insights.
What is Cognitive Analytics?
Cognitive analytics is a field of analytics which equips a computerized model to imitate the human thought process, which in turn supports business intelligence and a better decision-making process.
In other words, this branch of analytics draws inferences from the patterns deduced from data provided, and then draws conclusions based on the available knowledge. It then puts the findings back into the knowledge base and thus keeps building a system that gets smarter with time, just like a human mind. These patterns, inferences, and conclusions enable businesses to make smarter decisions and reap increased ROI.
Related Reading: What is Cognitive Computing?
The leading market research firm MarketsandMarkets estimates that by 2022, the cognitive market size will increase by USD 10.95 billion at a Compound Annual Growth Rate (CAGR) of 42.9%.
The report forecasts that many businesses are turning to cognitive analytics to increase their ROI. The challenge is in knowing how to churn data into useful insights that will augment your business growth.
Churn it Right to Get True Insight
Economic Scholar W. E. B. Du Bois once said: “when you have mastered numbers you will in fact no longer be reading numbers, any more than you read words when reading books; you will be reading meanings.” How true that is with cognitive analytics! When you do it right, you gain incredible insights that help you make smarter and better decisions; which in turn will help you reap ROI.
Here are a few ways to effectively apply cognitive analytics in your business:
1. Start small, with a focus on the long term
As businesses are moving from preserving data to sharing it, they also need to learn how to create and capitalize on new opportunities. To achieve your goals, you need to make detailed plans in collaboration with technology experts. Since cognitive technologies generally support individual tasks, scaling up would require integration with processes and existing systems.
Approach cognitive analytics with the focus on modernizing your existing systems. Such an approach minimizes risks, maximizes revenue, solves business issues, and redefines customer experiences.
2. Don’t get hung up on perfection
If you are trying to source data either through commercial or open sources, it is smarter to have specific use cases in mind. Additionally, to drive new insights, it is helpful to have different data sets interconnected. This helps you fill the gaps between data and increases the quality and usability of the data. Clearly, it is not enough to achieve or build up complex data, which could take years. Aim to start with what you already have.
3. Do not hoard data ownership and access
Cognitive analytics can successfully reap ROI when the organization gives data access to as many people as is necessary. This includes all those who are involved in data definition, data verification, data creation, data curation, and data validation. If you want to tap into the true value of data, it is imperative to design an effective data governance policy.
4. Ensure talents matches the task
If you want to get your frontline staff to use the data insights, it isn’t enough to upgrade your software. It is important to integrate insights into everyday workflow. Find ways to eliminate the underlying distrust of analytics among individual members of your staff. Ensure that everyone involved receives the necessary training and analytics literacy. Matching the right talent with the right task ensures that your organization has better insights and can help you make better decisions which leads to higher ROI.
5. Use a secure and reliable hybrid cloud
Having a secure and reliable hybrid cloud enables your organization to link data across multiple cloud environments. It also makes sure that your staff can search and locate data quickly from the cloud.
Related Reading: Why Cloud-First Businesses Should Consider Hyper Hybrid Cloud?
6. Encourage generating counterintuitive insights and new ideas quickly
To keep your business on the cutting edge, your staff should feel free to come up with new ideas and try them out before you finalize or discard an idea. To this end, it is vital to provide the appropriate environment, tools, and technology to your staff. Encourage them to find new features quickly, run correlations and perform the analysis.
Set up Fundamental Building Blocks
We have seen the fundamental building blocks of cognitive analytics.
Besides transforming the execution of core business functions, it is also changing the nature of competition. To achieve the true purpose of cognitive analytics, that is, churning data into insights, you must strengthen your business’s fundamental building blocks.
Stay ahead of your competition by utilizing Fingent’s analytics and visualization services. Develop the ability to read not just data or numbers, but to infer insights, and use them to increase your return on investment. Take the next step in analyzing your big data by connecting with our expert.
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Data Mining Vs Predictive Analytics: Learn The Difference & Benefits
With big data becoming the lifeblood of organizations and businesses, data mining and predictive analytics have gained wider recognition. Both are different ways of extracting useful information from the massive stores of data collected every day. Often thought to be synonyms, data mining and predictive analytics are two distinct analytics methodologies with their own unique benefits.
This blog examines the differences between data mining and predictive analytics.
Difference Between Data Mining and Predictive Analytics
Data mining and predictive analytics differ from each other in several aspects, as mentioned below:
Data mining is a technical process by which consistent patterns are identified, explored, sorted, and organized. It can be compared to organizing or arranging a large store in such a way that a sales executive can easily find a product in no time. Various reports state that by 2020 the world is poised to witness a data explosion. Therefore, data mining is a strategic practice that is necessary for successful businesses. It helps marketers create new opportunities with the potential for rich dividends for their businesses.
Predictive analytics is the process by which information is extracted from existing data sets for determining patterns and predicting the forthcoming trends or outcomes. It uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In other words, the aim of predictive analytics is to forecast what will happen based on what has happened.
Techniques and Tools
Although there are many techniques in vogue, data mining uses four major techniques to mine data. They are regression, association rule discovery, classification, and clustering. These techniques require the use of appropriate tools that have features like data cleansing, clustering, and filtering. Python and R are the two commonly used programming languages in data mining.
Unlike data analytics, which uses statistics, predictive analytics uses business knowledge to predict future business outcomes or market trends. Predictive analytics uses various software technologies such as Artificial Intelligence and Machine Learning to analyze the available data and forecast the outcomes.
Data mining is used to provide two primary advantages: to give businesses the predictive power to estimate the unknown or future values and to provide businesses the descriptive power by finding interesting patterns in the data.
Predictive analytics are used to collect and predict future results and trends. Although it will not tell businesses what will happen in the future, it helps them get to know their individual consumers and understand the trends they follow. This, in turn, helps marketers take necessary, action at the right time, which in turn has a bearing on the future.
Data mining can be broken down into three steps. Exploration, wherein the data is prepared by collecting and cleaning the data. Model Building or Pattern Identification by which the same dataset is applied to different models, thus enabling the businesses to make the best choice. Finally, Deployment is a step where the selected data model is applied to predict results.
Predictive analytics focuses on the online behavior of a customer. It uses various models for training. With the use of sample data, the model could be trained to analyze the latest dataset and gauge its behavior. That knowledge could be further used to predict the behavior of the customer.
Data mining is generally executed by engineers with a strong mathematical background, statisticians, and machine learning experts.
Predictive analytics is largely used by business analysts and other domain experts who are capable of analyzing and interpreting patterns that are discovered by the machines.
Data mining enables marketers to understand the data. As a result, they are able to understand customer segments, purchase patterns, behavior analytics and so on.
Predictive analytics helps a business to determine and predict their customers’ next move. It also helps in predicting customer churn rate and the stock required of a certain product. Additionally, predictive analytics enable marketers to offer hyper-personalized deals by estimating how many new subscriptions they would gain as a result of a certain discount, or what kind of products do their customers seek as a complement to the main product they bought from the seller.
Related Reading: Using Predictive Analytics For Individualization in Retail
Effect of Data Mining and Predictive Analytics on the Future
The global predictive analytics market is estimated to reach 10.95 billion by 2022. We are now in a period of constant growth, where businesses have already started using data mining and predictive analytics sift through the available data for searching patterns, making predictions and implementing decisions that will impact their business.
Both approaches enable marketers to make informed decisions by increasing productivity, reducing costs, saving resources, detecting frauds, and yielding faster results. To make the best use of data mining and predictive analytics, you need the right guidance and the best expertise. Talk to our experts and find out how Fingent can help your business scale up with the power of data. Get on your way to a digital-first future with Fingent.
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Step-by-Step Guide To Using Tableau In Data Visualization
Organizations now have access to more data than before. Earlier, data were not considered important enough and remained severely underutilized. Today, we witness a complete reversal, as data have become a pivotal element in innumerable processes governing an organization’s functioning. This value is not simply a result of procuring data on unclassified stacks, but assimilating them and gathering the needed insights that potentially bring about transformation.
Insights from data are made discernible and are put into plain view via dashboards. They bring into place a new way to understand complex data sets by projecting the values into visual forms like charts, graphs, bars, lines, dots, etc., known as data visualization. The process collates data in varied forms from a broader web of sources to unravel insights. To assist with this is a plethora of business intelligence tools that companies can utilize to visualize their data sets.
Tableau has emerged as a leader among business intelligence tools and stood out from other BI platforms, chiefly because of its powerful, interactive visualization dashboards that discern and quantifies complex data sets into easily understandable visual forms. Being a market-leading business intelligence platform, Tableau aids individuals, teams and organizations visualize and analyze their data. Its interactive dashboard allows analysts to engage with live data sets to get a better overview of the results.
Owing to its innovative and embedded analytics platform, Tableau has been featured as the best in its category by the global market research firm Gartner. Accordingly, Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms mentioned Tableau as a leader, consecutively for the sixth time based on its customer-focused innovation and real-world value in helping data-driven enterprises solve their business challenges right away.
Related Reading – Five Questions to Ask While Considering Data Visualization Tools
Fig. 1 – Gartner Magic Quadrant for Analytics and Business Intelligence Platforms, Source – Gartner
Tableau combines laser-focus, efficiency and feature-rich elements that determine how people see and understand data. All this comes integrated into a robust and scalable platform, that lets you harness data needed to run even the world’s largest organizations. What Tableau brings to the table is an interactive experience, where we can directly control or modify the data sets in real time to extract intelligent insights instead of just viewing them on the dashboard.
The worksheet, dashboards and layout containers constitute the three main elements of Tableau. The powerful dashboards integrated with Tableau remain one of its core components. It essentially simplifies the process of quantifying data through features like drag-and-drop and side-by-side comparisons. Such an approach towards understanding data allows more transparency in business processes, which lets you closely monitor, evaluate and forecast performance levels.
Together all the worksheets when combined together form the basis for a dashboard. Every single worksheet contains visualizations of data obtained from a range of sources, which can be grouped together to create a single dashboard. You can even include several dashboard objects that enhance the interactivity and visual appeal. Layout containers, whether horizontal or vertical perform the role of clustering objects together that helps change how the dashboard responds in accordance with user navigation.
Tableau’s dashboards also take visualization a cut above through its several built-in features like story points and device designer. They bring into the picture an overview of all the metrics and KPIs that define a process that helps with forecasting. Later on, a business can utilize these data to put up a comparison with the previous values and ascertain the effect of each action as well as find ways to improve them.
How to Use Tableau?
You just need to follow the below 3-step mantra to use Tableau:
- Connect to data
- Play around with the UI
- Create visualizations
1. Connect to Data
Connect to your data is the initial thing to do while starting to use Tableau. Connections mainly come in two types – to a local file or a server. Tableau can connect to almost any type of data server. Listed below are some of the popular databases that Tableau can connect:
2. Play around with UI
Once we import the dataset, a “Go to Worksheet” option is displayed next to the Data Source Tab at the bottom portion of the screen. A worksheet is a place where we create all of the graphs, so click on that tab to reach the following screen:
Fig. 3 – Show me option in Tableau
3. Create Visualizations
Choosing the right visualization techniques for conveying insights in the most effective way is a challenging task. The below table will give you a brief idea about opting the preferred visualization method:
Insights from Using Tableau – A Typical Use Case
The working of Tableau is best understood through an example where there are different data types, each of which has the potential to reveal valuable business insights. Fingent helped one of their clients drive important business decisions by visualizing their secured and dynamic data based on their requirements.
Business need: Being part of an extremely dynamic industry, tracking the slightest changes in their ticket status is of the highest priority for the client. They needed a solution that will enable them to react quickly to varying stages of their tickets and to reduce damage. A solution that would be able to generate hassle-free, ad-hoc & secured reports for delivering accurate data visualization.
Solution: As per the requirements gathered, several dashboards and reports were designed for various levels by connecting to the Microsoft SQL server database. Refer to the screenshot below to understand how our client made use of Tableau to derive insights.
Member Dashboard Created For Fingent Clients
Member Dashboard consists of two tabs: Live tickets and Historic ticket. This dashboard visualizes the history data to derive insights for making better business decisions.
Fig. 4 – Member Dashboard
In this Dashboard, members can do a lot of analysis like the total number of different kinds of tickets, Ticket status vs County, cross tab with country wise ticket details and more. Interactive filters help the members to drill down into the dashboards with their needs for creating different subreports.
When members click on the live ticket icon navigation arrows, they are redirected to live tickets dashboard for the members.
Members view contains information formatted to aid facility operators responsible for locating. These would show at a high level how the user and its company are performing.
Fig. 5 – Live Tickets Dashboard
This dashboard view revolves around the person/entity who created the ticket. Excavator dashboard clearly helps track the lifecycle of tickets that are pending, need more attention, requires a positive response from the excavator, and those that passed the due time, etc. This dashboard also drills down the flexibility in each stage of the life cycle for better analysis and quick actions.
Fig. 6 – Excavator Dashboard
Damage Dashboard for Members and Excavator
This dashboard gives insights into the total damages occurred during the excavation in a plain and understandable way. Damage rate vs date, facility damaged over the years, country vs damages, damages by course, top excavation types causing damages, etc., are all covered in this dashboard.
Fig. 7 – Damage Dashboard
The intention here is to give you an overview of what are the various business problems and questions, which can be answered using data visualization in Tableau and how it helped the client to drive business insights from the bulk amount of data in a synchronized way.
Related Reading – Power BI Or Tableau: The Better Choice for your Business
With more businesses going data-driven, the onus is on adopting a wider strategy for keeping data at the very center. Distilling insights from a mix of data obtained from a variety of sources is made easy with intuitive and easy to use BI tools like Tableau. The interactive dashboards in Tableau give rise to a new way to look at data by visualizing them, which refines our understanding of every single metric or KPI that is being displayed. Better accessibility into the dashboards and visualized data even from mobile devices further add to its flexibility.
Tableau comes inbuilt with some powerful BI tools that can do a host of other things alongside data visualization such as data stories, data analysis of workbooks, etc. With it comes the end result of churning out intelligent insights teeming with potential to bring about a transformation into the existing business processes. Besides, there is minimal effort required to start learning and using Tableau owing to its simplicity, easy navigation and other features like drag and drop interface. To sum up, Tableau fully redefines data visualization by helping companies leverage data for creating insights that drive business value. To know how your business can benefit from Tableau, get in touch with our tech experts now!
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A Comparison Between Tableau and Power BI: The Most Powerful Leaders In The BI Market.
Business Intelligence or BI tools are a precursor of the world-altering digital technology in this modern technology landscape. Analytics plays a key role in determining which Business Intelligence tool is a better choice. This is because the more flexible the analytics platform offered by a specific BI tool is, the more it provides businesses to customize applications that need updates. Let’s take a deeper look at how Power BI is different from Tableau and which technology promises a better future for your business.
Tableau And Power BI
Tableau was the first and foremost to come into the market. Though both Tableau and Power BI are well-known to be able to execute fine enough, Power BI has an advantage of making itself accessible to even the no-techy users, making it possess a higher adoption rate than Tableau.
On the other hand, Power BI is ranked higher on one of the key characteristics in terms of its Data Visualization, according to Gartner’s Magic Quadrant.
However, Microsoft’s Power BI has the most user-friendly features in terms of ‘completeness of vision’ or ‘Data Visualization’ capability and has been embedded within Office 365. But Tableau offers advanced functionality and it is best considered for power users.
So to choose a BI tool that is the best fit for your business, it is important to first learn about the analysis needs. In the recent decade, Power-BI and Tableau have emerged as the two powerful BI tools.
Let us look at how companies can choose the best for their business from the following key factors:
Cost of Tableau is on the higher side when it comes to larger enterprises. The primary reason for this premium cost is the need to build data warehousing. Thus, it is advisable for a startup to choose Power BI initially and then consider Tableau when required.
The professional version of Power BI costs you less than 10$ whereas, on the other end, Tableau would cost you more than 35$ per month per user.
Power BI supports Predictive Modelling and Reporting when on the other side, Tableau opts for Data Visualization.
With Power BI, we can create visualizations by queries and natural language. Say, for instance, Cortana PDA (Personal Digital Assistant). Power BI is said to place a 3500 limit when it comes to conducting analysis on data sets.
Tableau can be the best choice when it comes to Data Visualization. With a user-friendly dashboard, Tableau allows an in-depth data analysis. As compared with Power BI, Tableau offers more visualization flexibility.
With Tableau, we are able to create 24 different types of basic visualizations. This includes heat maps and line charts.
The functionality associated with Tableau with respect to Data Searching is on the higher side than when compared to that of Power BI.
Tableau tends to answer more queries from users as compared to Power BI.
Large Data Handling Capacities
In case of processing large chunks of data, the capacity of Tableau is over and above that of Power BI.
Power Bi handles data via import functionality and hence is slower to process large volumes of data as compared to Tableau that makes use of direct connections for the same purpose.
Tableau offers, convenience for data connectors. For example, OLAP (OnLine Analytical Processing), cloud and also big data options such as Hadoop and NoSQL. Tableau can automatically determine the relationships of data that users add from various data sources. It also provides for the creation and modification of data links manually as per the company policies.
Power BI, on the other hand, can connect to user’s external sources such as SAP HANA, MySQL and JSON. It helps users connect to third-party databases and online services like Salesforce.
Thus, if connecting to a specific data house is your business requirement, Tableau is the best choice as Power BI is integrated with Microsoft’s Azure cloud platform.
Power BI is a SAAS model. Tableau, on the other hand, is available both on cloud and on-premises options. The deployment options for Power BI is lower as some business policies do not allow for SAAS deployment. Thus, in case of flexible deployment capacity, Tableau is considered the better option here, even though it is on the higher-end when the cost factor is considered.
The user interface of Tableau allows for the creation of a customized dashboard. On the other hand, Power BI has an interface that is easy to use and intuitive. So, if easy to use is your major requisite, Power BI is the choice for your business.
Programming Tools Support
Though both Power BI and Tableau run smoothly with programming languages, Tableau can be integrated better with the R language rather than Power BI. R language provides a wide range of tools used to capture the right model of your data.
Power BI, on the other hand, also can be connected to the R language, but by using Microsoft Revolution analytics and it is made available only for Enterprise users.
Product and Customer Support
Tableau emerged in an early stage than Power BI and hence has a smaller community when compared to Tableau. The knowledge base of Tableau has three subscription categories, namely Desktop, Online, and Server.
On the other hand, Power BI offers a support functionality that is limited to users with a free account, allowing only it’s premium and pro users for faster support.
This ultimately depends on whether you want to pay the full cost up front. If yes, then Tableau should be your first choice.
If we could put it this way, Power BI can be your best choice if you are a common stakeholder because of its intuitive drag and drop features, for which a data analyst’s experience is not crucial. Tableau can win if your choice is speed and if you have the capital to support.
Related Reading: Find how SAP HANA is becoming the game changer.
In a nutshell, both Power BI and Tableau have different functionalities which depend on the variant business requirements. The best BI tool for your business can be selected only depending on the business requirements. With the help of expert IT consultants, you can make the right choice for your business. Contact Fingent today!