23 min read

Your Complete Guide to Prescriptive Analytics for 2020

Prescriptive Analytics

Prescriptive Analytics

Today, most businesses use big data to understand the future of their businesses and to set goals. While big data might not be as specific as to give you winning lottery numbers, it helps businesses identify problems and understand the reason behind those problems. Businesses can then use data-backed factors to come up with prescriptions to business problems.

What is Prescriptive Analytics?

While predictive analytics inform you of likelihoods and probabilities, prescriptive analytics tell you what to do. Prescriptive analytics use data from descriptive and predictive analytics to create scenarios and identify the most feasible outcomes. For instance, if the head of a marketing team wants to find how many dollars they should put into a marketing channel such as Google ads, predictive analytics show how Google fares against other channels while prescriptive analytics show how many dollars to invest in Google ads. Analysts will use different what-if scenarios to come up with specific answers to business constraints.

Prescriptive analytics is the systematic analysis of data that advises on possible outcomes in actions that are likely to boost the bottom line of a business. The analysis applies simulation and optimization to answer the question, “What should be the next business step?”

Analysts simulate the future under several assumptions to generate different scenarios. When scenario analysis combines with optimization techniques, businesses or analysts are able to create prescriptions for business problems. From the prescriptive analytics definition, this form of systematic analysis seeks to explore possible actions and give suggestions from the results and the data from descriptive and predictive analytics.

Prescriptive analytics relies on data collected by a business and the business rules. The data might be internal, from the operations of a business, or external, collected from customers on social media and other platforms. Business rules refer to business best practices, boundaries, and constraints within a business. Here is a model obtained from Wikipedia that may help you to visualize the idea of prescriptive analytics.

Prescriptive Analytics

Mathematical models applied in Prescriptive analytics:

  • Natural Language Processing
  • Statistics
  • Machine Learning
  • Operations Research

One thing that might complicate prescriptive analytics is the fact that each of the disciplines above has sub-disciplines and variants. For instance, in Operations Research, techniques such as simulation, optimization and decision analysis are applied.

This guide seeks to simplify the concepts of prescriptive analytics.

Prescriptive Analytics Approaches

Prescriptive analytics is divided into two different approaches;

  • Heuristics-based automated decision making
  • Optimization-based decision making

Optimization-Based Decision Making

Optimization has been used for a long time in solving operational problems such as logistics planning. Today, technology makes it easier to model larger, industry-wide problems to offer support for scenario-based analyses, and optimization enables analytics of better decision making.

Today, advanced optimization models bring together financials and value chains, including business constraints, to provide better information than what predictive models offer. The optimization models also ensure there is internal data consistency and helps identify outcomes that may not be feasible.

With the optimization models, analysts are able to carry out unique analyses such as activity-based costing, contribution margin, and pro-forma financial statements. These analyses help users make the most ideal decisions in business. This is an example of one such statement, analyzing the price of t-shirts and the profit margin with the different amounts.

Prescriptive Analytics

(Image credit: Openstax)

Businesses can use optimization to solve business problems involving more than twenty constraints, trade-offs, and objectives. The use of prescriptive analytics allows analysts to sort through different factors to find the route that allows a business to meet most of its objectives.

Optimization uses complex mathematical algorithms that either maximize or minimize objective functions while still considering business realities to produce feasible plans.

Heuristics-Based Decision Making

This is also referred to as rules-based decision making. It involves automatic decisions based on a set or predefined rules. These rules are made by analysts using their knowledge on the business and best practices, and not math.

Unlike optimization, the Heuristics-Based decision making does not offer an answer outside the predetermined rules. Here, analysts apply statistics and simple algorithms to provide prescriptions. For instance:

Prescriptive Analytics

(Image credit: Slideshare)

Prescriptive analytics offers transformational value that support decision making. Unlike other analytics that show you probabilities, prescriptive analytics offers suggestions.


For a long time, prescriptive analytics has been used to solve complex problems such as scheduling, staffing, and routing. Such problems were tackled by data scientists and not business leaders. Today, prescriptive analytics is less of an IT and Data Science tool and more of a tool within the business unit. Prescriptive analytics is now a tool for business leaders. So many factors have contributed to the shift in the use of prescriptive analytics in day-to-day business operations: These factors include:

  • The availability of better and more diverse data
  • Availability of prescriptive analytics technological tools where business leaders do not rely on data scientists
  • Business leaders have a list of day-to-day problems that can use advanced optimization tools
  • Most medium and large businesses are using prescriptive analytics and it is becoming a “must-have” and not a “good-addition” tool

It is possible to realize up to 20 times Return on Investment when leaders make the most feasible decisions. While the amount of ROI is dependent on the approach a business leader takes and the type of problem addressed, prescriptive analytics offers better insights and business model improvement than most other forms of analytics. Better yet, prescriptive analytics uses data from all other forms of analytics to deliver data-driven recommendations and suggestions. Below are some of the benefits that business leaders can draw from prescriptive analytics.

Create Solid Plans with Increased Confidence

Business leaders now have more confidence in their plans. By definition, optimization-based plans are feasible, which is why they instill more confidence in business leaders. On the other hand, heuristic-based plans may or may not be feasible – this depends on the nature of the problem and how well the rules were set.

Businesses that use optimization-based decision making have a likelihood of achieving positive results because the operation and financial flow of a business is taken into consideration in the decision making process. Prescriptive analytics gives businesses the understanding of actions needed to see a plan through and the ability to deliver the plan. When a manager presents a plan with confidence, they gain respect and are able to implement further changes in the business.

Improved Business Performance

Prescriptive analytics delivers actionable insights that help business leaders better the operational and financial performance of a business. When applied on operations that previously relied on intuition or other unreliable tools such as Excel, Prescriptive analytics is able to streamline operations.

The impacts on businesses include:

  • Improved effectiveness in achieving business objectives
  • Increases business operation efficiency – that is, a business does more with the resources available
  • Maximize returns from business investments, for instance, by optimizing the allocation of resources to different investment channels

Streamline the Decision-Making Process

Difficult decisions can take weeks or even months. Sometimes, businesses rely on external consultants, and this might cost the company a lot of money. Minor decisions made weekly are never given the consideration and time that significant decisions are given. Again, these weekly decisions may not present enough time for the business leaders to carry out the analyses. Prescriptive analytics enhances organizational knowledge on the different functions of business impact on each other, and by so doing, recommend a path that increases a business’ ability to evaluate different what-if scenarios to deliver a faster decision-making process.

Reduce Investment Risk

Risks come in the form of finances or operations. These risks may not mirror how businesses operate. Prescriptive analytics assist business leaders in identifying and quantifying the risks that come with both short and long term decision-making processes. This way, leaders can develop risk mitigation strategies.

Increase Returns on Existing Investments

Prescriptive analytics gives insights on how to leverage existing investment in tools such as Electronic Resource Planning, ERP, Software to offer new data on these investments. Because prescriptive analytics shows the best path forward, employees have a chance to make an impact on the progress of a business and climb the career ladder.

Address Business Planning Challenges 

Prescriptive analytics helps business leaders find solutions to complex challenges that other forms of analytics do not provide.

How Prescriptive Analytics Work

There are two categories of algorithms applied in Prescriptive analytics:

  • Heuristic algorithms (rules-based)
  • Exact algorithms

Heuristic algorithms do not offer specific answers, but when well-designed, they offer a shorter route to finding feasible solutions in a short amount of time. Exact algorithms, on the other hand, guarantee specific answers, but a business needs time to come up with a solution, especially if the problem is sizable. Exact algorithms are also referred to as optimization. For optimization to offer specific answers to specific business problems, proven scientific techniques have to be applied. The rule-based approach, heuristics approach, does not need the same mathematical proof. For the heuristics approach, it is not possible to tell if you can provide the best answer in some scenarios.

A Prescriptive analytics solution relies on heuristics or optimization. Sometimes, the two solutions can be applied simultaneously, but this is less common. There is no approach that is better than the other; business leaders need to identify the most appropriate approach, so they know where to apply either of the approaches.

Determining the Best Approach in Prescriptive Analytics

When considering the approach to use, consider the following factors:

  • Nature of Problem: Some problems fit better in heuristics than they do in optimization as seen later on this guide.
  • Complexity of the Problem: Well-known problems might be challenging to solve using optimization. In some cases, finding an answer fast is more important, and so, a rules-based approach is better than optimization.
  • Urgency: If you need an answer today, use a heuristic approach. If you are willing to wait for the answer, an optimization approach might be the better choice.
  • Frequency: If you have to make a decision many times a day, you may not have the time for an optimization approach, and a heuristic approach, therefore, comes in handy.


Heuristics are rules related to the problem at hand. If you can narrowly define a problem, or if the problem is operational, you can apply heuristics. Again, these rules are applicable when a decision has to be made hundreds of times every day.

While heuristics may not give you an exact answer, they still apply specialized techniques that take advantage of specific aspects of a problem. Analysts can develop a set of mathematical functions such as f(x) = y, or a set of scenarios such as “If this…then…”; and sometimes both.

Imagine walking in a new neighborhood looking for a building you have never seen but only heard of. The person who sent you gave you simple instructions such as “head east until you come to a junction with a large water fountain.” So you start walking. Because you may not have a map and you do not have GPS or precise instructions that include time and distances, you need to rely on rules – you can depend on your knowledge of traffic and your intuition to find the fountain. Here, you cannot take the shortest route, because you do not know what lay ahead. You might end up walking for ten more minutes than if you knew the exact location of the building. Without additional details, you might not even get to the building. This scenario mirrors a heuristics-based problem.

One of the tools you can use to make business decisions is Excel. This tool uses features like, IF functions to make a hypothesis about a feasible answer. When you enter values, a solution appears immediately. Unless you use an optimization approach, there is no telling whether the answer returned is the best.

Some problems and decisions are better suited to heuristics than optimization. Some of these problems and scenarios include:

  • Purchase: The purchasing of raw materials, say when a business wants to purchase from the cheapest supplier first.
  • Allocation: Allocation of resources, for instance, allocate to line one, then two, onwards regardless of the cost of operation.
  • Marketing: Marketing, for instance, offering customers discounts and promotions based on prior purchase or any other rule.
  • Demand Fulfillment: Demand fulfillment, for example, a rule to meet the demands of tier 1 customers while other customers wait.

The Pros and Cons of Heuristics

The approach is ideal for decision automation, as it provides solutions immediately. Business leaders can use heuristics for complicated business problems, such as scheduling. Unlike optimization, heuristics are easier to implement and may only require the tools a business already has.

Even with the benefits above, the approach still offers minimal benefits for holistic decision making. Worse yet, the answers are “good enough”, but not guaranteed, seeing that the heuristics do not analyze every scenario. If the instructions are not clear, the approach will not find the right solution.

When used for strategic or tactical decision making, the plans might be infeasible. This is especially so because the rules become obsolete.


Optimization applies mathematical modeling and algorithms to find the most feasible solution to a business problem. You have to start defining the problem by writing a math equation on a business modeling platform. After a model is created, it goes through a highly specialized algorithm that solves the problem.

An optimization problem has three parts:

  • Questions: These are the problems the business is facing and whose answers are needed. Complex problems might present millions of issues and decisions. These questions might relate to the amount of raw materials to buy, the number of hours to dedicate to production lines, number of products to sell to certain markets, amount of investment to make to specific marketing channels, and so on.
  • Data: The data available, also referred to as coefficients, refer to the uniqueness of the problem and what the algorithm has to work with. The data can include prices and yields. For instance, from the above questions, data can be in terms of the cost of each ton of raw materials, the cost of running a production line, the market size of different markets, and the cost of investment in different marketing channels.
  • Business Constraints: These are also referred to as business realities or restrictions. They include company policies and physical laws. Questions on this section might include the availability of raw materials, the working capacity of a production line, and the demand in a given market.

To get the most feasible answer, an objective to either minimize or maximize a metric should be entered. Metrics include profits, costs, volume, personnel utilization, and more. Users can also include the level of specificity needed and how long they are willing to wait for the solution. From the data provided, the algorithm runs the answer.

With optimization, you can address the following problems:

  • Transportation
  • Determining when to replace equipment
  • Assigning staff to different equipment
  • Gasoline blending

The availability of advanced technological tools enables businesses to tackle cross-functional problems for valuable applications such as:

  • Customer profitability
  • Pricing
  • Asset investment planning
  • Creation of product mixes, blends, and substitutes
  • Workforce planning
  • Training
  • Commodity trading

Different industries have different ways of applying optimization. The following is one example of an optimization model.

Prescriptive Analytics

(Image credit: DZone Big Data)

Pros and Cons of Optimization

Unlike heuristics, optimization provides the most feasible solutions with opportunity values. This way, business leaders can make the complex decisions to bring better yields.

The technique is proven and has been in use for decades. Even better, there are so many tools to help with algorithms.

The main challenge with optimization is that it takes time to give the best answer. If your business uses traditional software packages, you might need specialized skills to write mathematical equations.

Picking the Right Approach

You need to understand how both heuristics and optimization work to choose the right approach. The technologies and the approach you choose should accord you the flexibility to perform what-if scenario analyses. Consider all the decisions you will make after the current decision. Again, consider the availability of data and the definition of the problem. Where the problem is well-defined, optimization works better.


When you launch an initiative, standard skills and roles such as project manager, initiative lead, and steering committee are important. However, special skills to deploy prescriptive analytics are also needed in the following fields:

Model Configuration

A qualified business analyst should be able to create prescriptive analytics models from the date provided. With so many prescriptive analytics tools today, there is no need for a data scientist or an operations research specialist. A business analyst who has worked with complex excel sheets should be able to configure models. Anyone in the field of engineering, economics, accounting, math, actuarial, and business operations management can do it.

Problem Definition

Problem definition requires someone who understands the business problem at hand. The expert can be an experienced practitioner who can describe the problem in detail. The professional can be a planner or an analyst.

Application Design

An application designer embeds the prescriptive analytics model in an application that integrates with the business planning process. The role of the designer will include data integration, data management, and workflow configuration. This can be done by a solution architect, but if the User Interface requires special skills, a business intelligence expert can be enlisted.


A finance professional ensures that the prescriptive analytics models represent the correct financials as seen on financial statements including cash flow, cost accounting, and marginal contribution among others.

Prescriptive Analytics Tools

When shopping for prescriptive analytics tool, there are two primary technological approaches:

  • Packaged applications
  • Optimization platforms

A packaged application is a fully-functional program that you buy and install or subscribe to and then configure without any additional coding. The program offers both heuristic and optimization-based answers. You might need to do some configurations such as choosing which module to install. Some applications also allow you to customize them to meet specific requirements.

In most cases, packaged applications target well-defined business areas or niches. The vendor creates an application that already works for the problem you need to solve, the data you have, and the solution you are looking for. An example of these applications includes Llmasoft’s Supply Chain Guru, which deals with network optimization and JDA’s Manufacturing Planning, which deals with manufacturing processes.

Unlike a packaged application that comes ready-to-use out of the box, an optimization platform helps you create applications and might require a lot of customization. Most of the platforms run on Linux or Windows and you need a license to run one. While a packaged application helps you manage inventory, an optimization platform lets you create applications for inventory, demand, capacity, and any other use.

An optimization platform might require you to create the math. Examples of such platforms include IBM’s CPLEX Optimization Studio and Advanced Interactive Multidimensional Modeling System, AIMMS.

The packaged applications and the optimization platforms use specialized algorithms, known as optimization solvers, to find feasible solutions to business problems. Some of the leading solvers on the market include Gurobi, FICO’s Xpress, and IBM’s CPLEX. In a packaged application, solvers are not visible on the user interface, but all platforms need to interact with solvers explicitly.

The figure below highlights the difference between the two approaches. The two boxes on the left comprise some of the vendors who sell optimization modeling platforms and solvers. The box on the right lists a few primary business-related subject areas served by packaged application software.

Common Optimization Modeling Platforms

  • AMPL
  • FOCI
  • SAS
  • River Logic
  • IBM
  • Portfolio Decisions

Popular Optimization Solvers

  • Conopt
  • FICO Xpress
  • Gurobi
  • Knitro
  • SAS/OR

Optimization Applications

  • Marketing
  • Pricing
  • Logistics
  • Supply Chain
  • Government
  • Strategic planning
  • Operations

Note that an optimization platform and solvers allow you to create applications. On the other hand, packaged applications come ready-to-use where the vendor has done everything for you. You need to understand the pros and cons of each as explained below.

Packaged Applications Defined Further

Most prescriptive analytics packaged applications are cloud-based. The main advantage of the Software as a Service, SaaS, and Platform as a Service, PaaS, is that you only pay for the hardware and Software you use while the vendor does the maintenance. Again, as a customer, you have immediate access to the latest updates.

With a packaged application, only simple configuration, training, integration, and support are needed to start. Configuration includes setting up a user account and defining the data sources. Integration might include using vendor-provided templates to upload data files.

Most applications connect directly to data sources outside the system and to other programs. This is a good thing if you have data outside the business data bank. However, it can be an issue if your business does not allow outside data through the firewall.

If an application requires you to write math equations, then it doesn’t meet the prescriptive analytics definition of a packaged application. A good packaged application doesn’t require you to write math. Again, the vendor will never expose their proprietary code.

Popular Packaged Solutions 

Pros and Cons of Packaged Applications

If a problem is well defined, say product pricing, a packaged application comes in handy. It is also great for large industries with many companies such as the retail industry.

Because most of the packaged applications are web-based, they do not require any installation and will be up and running in a few hours. Not only are they easy to start, but they also do not require you to write math equations, and it uses limited company resources.

However, these applications also have a few setbacks. There are fewer applications for small industries and for complex problems. Again, these applications might not meet all your requirements and you rely on the vendor to fix bugs.

Optimization Platforms

Like earlier mentioned, optimization platforms consist of a modelling platform and a solver. A modelling platform helps you write the mathematics you need to define the problem. You can write mathematics by coding or via drag-and-drop functionality. After entering the math, solvers find the exact solution you need.

Program names come with words such as developer, studio, modeler, toolbox or similar variations. These programs work on Windows and Linux operating systems. You also need a license, which can be tied to a user, machine, or an organization. Today, there are at least 50 platforms on the market.

Popular optimization platforms today:

  • AIMMS – modeling language and development environment
  • AMPL – algebraic modeling language
  • COINOR – open source modeling language
  • FICO Xpress – modeling development and solver
  • Frontline – a solver for Excel
  • GAMS – modeling development for mathematical programming
  • Gurobi – solver with many modeling languages
  • IBM CPLEX – algebraic modeling language
  • LINDO –algebraic modeling language
  • Mathematica – used for symbolic computational mathematics
  • MATLAB – optimization toolbox with many algorithms and techniques
  • MOSEK – solver for large scale problems
  • River Logic – Code-free, drag and drop modeling development

Pros and Cons of Optimization Platforms and Solvers

Optimization comes in handy if you need a customized solution for a specialized company or to solve complex problems. It is also an ideal choice when your company’s IT policy requires everything behind the company’s firewall. Most of the vendors above have been in existence for more than 30 years.

The programs require an OR expert who will write math. They also need another expert to build a user-interface.

Which Technology to Purchase?

The technology you choose should allow the company to meet its objectives while mitigating risks. It is essential to consider both the problem at hand and the longer-term vision of the business. You need to consider the value that prescriptive analytics will bring to the planning and decision-making process of a company. Some of the key features to consider should include modeling, user interface, data management, and architecture.


The modeling platform or the packaged application should meet the needs of a company. Here, you need to consider the key constraints that you need to use to get a feasible solution. Consider whether you will use ratios, blending, social ordered sets, conditional minimums, and financial ratios. You also need to pay attention to the flexibility of the platform for future changes. The technology should also support business financials as inputs and outputs.

Always choose a program that is easy for personnel to learn, configure, and implement. A drag and drop functionality is easier for the in-house business analyst.

User Interface

An intuitive user interface allows different stakeholders in a business to interact with the program. The UI should support entering information and visualizing the inputs. It should also allow a user to define, configure, and run different what-if scenarios. The reports should support the right visualization, and the UI should be configurable without the need for programming.

Data Management

Prescriptive analytics requires a lot of data, and the data needs cleaning and approval before use. When shopping, you need a platform with tools to bring in data, transform the data, and run checks to identify missing links. The platform should also support workflow where one user receives the data, another reviews it or edits it, and another approves it. Lastly, consider the compatibility of the platform with applications.


Some of the questions you need to ask here include: Does it offer cloud-based? Do you need time to install? How much scalability does it provide? Again, ensure the vendor has put in high-security measures.


To understand the main difference in Prescriptive analytics vs. Predictive analytics, picture the manager in a packaged goods company (let’s call her Davina) and a business analyst (let’s call him Ed).

Davina goes to Ed and asks him to create dashboards that collect data to answer questions that could help in decision making. If the manager, Davina, struggles with her budget, Ed can compile dashboards that help her track her spend and send her notifications whenever she nears her monthly budget. For Ed to create the dashboard for Davina, he needs to create a report that updates her expenditure on promotions, ads, trade shows, and any other categories of spending. Besides the notifications, Ed also creates charts and visuals that show Davina’s real-time spending data. This is an example of Business Intelligence, which forms the basis for business analytics.

Predictive Analytics

From the above example, Davina can stick to a budget, following dashboards that Ed creates. Through Business Intelligence, Davina can tell the campaigns, channel initiatives, and lead personas that have driven the business’s highest revenue. However, BI is still not enough to streamline the company as the insights received do not offer the full picture besides tracking expenditure.

After tracking her spending, issues such as customer churn still affect the business. For descriptive analytics, Ed, the business analyst, needs to collect marketing and sales data. The data comprises information on deal closures, losses, website management, social media engagement, customer behavior, campaign information, and brand engagement.

Using the data collected, Ed can create predictive modeling to predict the likelihood of outcomes. To do that, the analyst uses regression analysis, pattern matching, multivariate statistics, and forecasting. The predictions will help Davina improve her marketing performance.

With predictive analytics, business leaders predict the likelihood of customer churn and step in before the damage occurs. With a reduced churn rate, business profit margins rise. This is an example of predictive analytics.

Where Does Prescriptive Analytics Come In?

Even with predictive analytics, there are still some questions left unanswered. Descriptive analytics show the channels that bring in the most revenue and help predict outcomes, but the analytics do not tell you what to do.

For instance, if you understand that marketing channel A brings the most revenue, how much money should you spend on this channel to have the most returns? Those in marketing might need to understand the exact dollar to allocate for optimal return on investment.

When precise answers on what to do are needed, prescriptive analytics come in. Business analysts collect data on business processes, rules, objectives, preferences, constraints, policies, boundaries, best practices, revenue, and costs. Using the data and a set of algorithms, analysts are able to find the way forward. Prescriptive analytics tells you what to do – you enter the math to define a problem and a set of algorithms find the most feasible solution. Here is a great image that helps to understand how these two analytical tools work together.

Prescriptive Analytics

(Image credit: SMatstraffic)

Implementing the Prescriptive Analytics Initiative

Once you understand what is prescriptive analytics and the tools available, you can now implement an initiative. When doing so, you need to:

  • Establish your vision
  • List the critical process steps
  • Ensure you have the right skills

Your vision is the overall opportunity that prescriptive analytics presents. The vision includes how the business seeks to make its decisions now and in the future, how these decisions will affect business performance, and a roadmap of the steps to follow. The roadmap contains key steps that the organization needs to follow to achieve its objectives. Education should be part of the vision as prescriptive analytics is seen as a niche that only Operations Research PhDs understand.

The key processes from evaluation to implementation include:

  • Team assignment – find a team
  • Value discovery – interviews and workshops to define the problem
  • Proof of concept, POC – identify a subset of problems to run a proof of concept
  • Implementation – this is the design and model building process
  • Data management and workflow management, user interface development, technical testing, and user acceptance training
  • Expansion – leverage the initial win and identify the next areas of problems


Prescriptive analytics by definition shows that it is a set of analyses that seeks to answer the question; What to do? How much to invest? What to do first? While other analytics give you so much data to help you make the right decision, prescriptive analytics points you to the exact direction of the decision.

Prescriptive analytics is either heuristics-based or optimization-based. Heuristics based analytics uses a set of what-if rules to prescribe solutions. These solutions may not be feasible. Optimization-based prescriptive analytics applied math and algorithms to find the most feasible solution. There are many tools to use, but they all fall into two categories; modeling platforms and packaged applications. Packaged applications are ready-to-use out of the box and only require a few configurations and settings while modeling platforms help you create applications.

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Many companies offer training courses in analytics, including prescriptive analytics. One company is Digitaldefynd, which is available as an e-learning, online course.

Prescriptive analytics enables healthcare administrators or analysts optimize business outcomes by recommending the best course of action for patients, hospitals, clinics or providers. They also enable comparison of multiple “what if” scenarios to assess the impact of choosing one action over another.

Prescriptive Analytics allows businesses or analysts to make more informed decisions by combining big data, machine learning, and business rules to project outcomes that drive the most efficacious business decisions.

Roughly, to implement a prescriptive analysis model into your business, start with these 5 steps:

  1. Define the business result you want to achieve.
  2. Collect relevant data from all available sources.
  3. Improve the quality and consistency of data using data cleansing techniques.
  4. Choose predictive analytics solutions or build your own models to test the data.
  5. Evaluate and validate the predictive model to ensure accuracy.

A prescriptive analytics tool offers insights that can lead to data-driven and proven decisions. Users can get information from relevant apps or databases, combine that data and then visualize projected outcomes.

According to IBM, “IBM Decision Optimization is a family of prescriptive analytics offerings that helps organizations solve their toughest decision-making problems by providing tools to convert business problems to optimization models. IBM Decision Optimization provides powerful optimization engines that help solve a variety of optimization models.”

Prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters.

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Daniel Urmann

Author Bio:

Daniel Urmann is the co-founder of Diib.com. Over the past 17 years Daniel has helped thousands of business grow online through SEO, social media, and paid advertising. Today, Diib helps over 150,000 business globally grow online with their SaaS offerings. Daniel’s interest include SMB analytics, big data, predictive analytics, enterprise and SMB search engine optimization (SEO), CRO optimization, social media advertising, A/B testing, programatic and geo-targeting, PPC, and e-commerce. He holds a Master of Business Administration (MBA) focused in Finance and E-commerce from Cornell University – S.C. Johnson Graduate School of Management.

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