Tag: Analytics Training

Q18: Can you please talk about recent developments in higher education on how to train more analysts?

The past couple of years have seen some remarkable developments in higher education in regards to analytics. Just a few years ago there were only a handful of colleges and universities in the U.S. that offered any kind of degree in something akin to data science. However, now you can find dozens of schools offering graduate degrees in analytics and/or data science. These changes in higher ed were preceded by several vocational schools and certificate programs. All in, if you do a google search on data science or analytics degree program you will get 100’s of schools in your results.

Besides the U.S., I have seen a few program in the UK and several in India getting more into analytics education. In the Philippines several schools have already started implementing the CHED (Commission on Higher Education) memo requiring schools to offer a business analytics elective series of classes. We have come a long way in a short time, but what is best for you?

If you are thinking about getting some formal education you will need to determine where you are currently with your analytics skills and where you want to be long term. Because of the crazy growth in the field, it can be pretty hard to tell what is the best bang for your buck.

Without pointing to any specific institution or program, I can give you some broad difference to consider. In a latter blog I will actually review some of the best programs and talk about them in on my blog site.

So here are the differences as I see them:

  1. Accidental Analysts. People who are doing a lot of analytics and have for some time, but have no formal training in analytics. These are accidental analysts who still make up a huge % of people doing analytics every day. For people at this level, going back to school full time to get a formal degree is not generally an option. For people in this bracket short term training programs and certifications in specific tools are the best bet to stay on the cutting edge.
  2. Legitimate Data Scientists. Few and far between, people with both the academic credentials and the business experience to do significant data science generally look upwards to getting a masters or even doctorate in a specialized field from a top school. There are a lot of programs out there to do that, but they tend to be pretty expensive and difficult to get into.
  3. Aspiring Data Scientists. If you are still young in your career and/or not finished with college you can consider getting your undergraduate degree in a related field and then progressing on to post graduate work. This is a recent development that poses an opportunity to those just starting out. In the near future these kinds of analysts will replace the accidental analysts for the most part. That is if there are ever enough.
  4. Part Time Analysts. People who do analytics or are part of a data science team, but have already established a career path in a different discipline. For those like you, training programs and certifications abound. It is pretty easy to find one that fits your unique situation and give you the added data muscle you need in your job.
  5. Managers of Analysts. If you are not really the one doing the heavy data lifting, but have team members that do. You need to be able to understand them, but not all the things they do, then you might be looking for a more generalist overview of analytics. Trying to optimize your analytics business culture and lead big data projects are skills you might want to improve on. There are training programs popping up for this need as well.

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So where does this take higher education? Some schools and programs are very broad based and offer generalist solutions. Others are quite specific and are geared to producing specialists. Knowing which education option is best for you is the challenge.

Higher Education across the globe is evolving to incorporate more analytics and data science into its curriculums. The need is there and is growing at a break neck pace. Where we are now is lights years from where we were two years ago, but where we need to be is far down the road.

More on that next blog post. In the meantime, if you are trying to figure out how to up your analytics game, drop me a note and I’d be happy to help you figure out what path you should take.

Follow Up to Q17: HR Analytics Trends

As a follow up to my last blog, I wanted to share a few more points about HR and Recruitment analytics then time allowed for. So here’s what I left out.

First we are seeing a massive replacement of licensed, traditional HRMS systems taking place. Many large companies either have, our or are looking into replacing the core HR applications. Most where built internally, just store structured data, are difficult to pull data from unless you can write code and are not integrated with other data structures.

The replacements are often vendor managed, cloud based, data storage solutions with end user interfaces that simply finding and analyzing data and often automate much of the reporting. And they can be updated in hours versus minutes, versus the old platforms that could take weeks if not months to update.

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These new platforms are able to provide almost limitless data points, have built in business dashboards and are starting to offer powerful predictive analytics models. The days of many of the old school CRMs and ATSs we are using to manage people data are truly numbered.

Another trend worth mentioning is the efforts cutting edge teams are putting into both candidate and employee engagement. Attempts to “gamify” various part of the employee lifecycle to make data gathering, analysis and sharing more eventful is increasingly common. Its common knowledge that ways to attract and keep the attention of millennials is significantly different then it is for baby boomers or Gen Xers.

Dr. Sullivan mentioned that “we are seeing the traditional annual engagement survey is going the way of the dinosaur (slowly however) and a new breed of pulse tools, feedback apps, and anonymous social networking tools has arrived.” It has never been more important to look at not just the enterprise wide health of a company, but that of small communities within the enterprise.

Metrics that measure how engaged an employee once a year is are no longer enough. We can use things like sentiment analysis, text analytics and social media data scrapping to uncover things we would never see in a survey where everyone is pressured to give top scores.

And we really have to get beyond historical data and descriptive analytics to look at current and predictive metrics. We need to quickly know when and why metrics are headed in the wrong direction and measure the impact of our solutions. And this goes for not just current employees, but future ones as well. Candidate satisfaction with the hiring process is often an over looked metric.

We also now have the data and the tools to run predictive models on how, when and why someone may be looking to leave the company. This creates another whole area of HR analytics to look at.

Dr. Sullivan added that “we are seeing tools to predict flight risk, assess high potential job candidates, even find toxic employee behavior – are all in the market today.  While many are not highly proven yet, they all work to a degree, providing great value to any company.”

Now we have, three more trends to consider when it comes to analytics in HR & Recruitment:

  1. Replacement of old internal HR systems with new vendor managed tools
  2. The evolution of employee engagement tools
  3. Predictive analytics modeling

If you are curious about how to get more than just the most basic descriptive analytics out of your business data, then let us sit down and talk about. Finding solutions to replace your old HR systems with more employee engagement options and predictive analytics is not as hard or as expensive as it was a few years ago. Let me show you how getting back on the cutting edge  with your data can be done.

HR & Recruitment Analytics – The recruitment and retention of top talent is the biggest challenge facing just about every organization. DMAIPH is a leading expert in empowering HR & Recruitment teams with analytics techniques to optimize their talent acquisition and management processes. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to learn how to get more analytics in your HR & Recruitment process so you can rise to the top in the ever quickening demand for top talent.

Q17: What are some best practices and technologies used in HR & Recruitment Analytics?

HR and Recruiting Professionals have embraced analytics. It took a while, but the increased need for data and analytics tools –The ability to collect, process and analyze “big data” has become paramount to the people side of the business. In order to gain a competitive edge in the increasingly chaotic global workplace, those who use analytics to gain data-driven insights into recruitment, compensation and other performance centric trends are the ones on the cutting edge.

“In my opinion, 95% of all the work that is done on recruiting metrics ends up being a waste of time, because the work focuses on creating historical tactical metrics never actually used to improve recruiting performance,” says Dr. John Sullivan, an ERE blogger and recruiting metrics expert. He says there are 3 reasons why there are failures and wasted time when it comes to metrics:

  1. Recruiting metrics omit any “big-picture” business impacts
  2. Current recruiting metrics are 100% descriptive and only offer guesses on what is and what will happen.
  3. Once collected, the metrics are reported to “barley interested eyes” who then assign things to a committee whose time spent results in very little measurable impact.

If you are still focused on time to fill and cost per hire, you really are quickly becoming a dinosaur. In addition, the idea of trying bringing in new people while working towards retaining top talent are generally not assigned to the same people. The disconnect between recruiting good people and retaining the good people who have been recruited is a killer to many companies. Both the material and cultural cost of replacing a bad hire isn’t generally looked at.

There are lots of blind spots to what is happening not just internally, but also externally.  Knowing who you are competing against for the same talent and what makes your offer to sign or stay stand out from the crows. None of these points can be analyzed with old school metrics terms and methods.

Dr. Sullivan also recommends six strategic categories of metrics that will help your in not just recruitment but in many other HR initiatives like retention and employee engagement:

  • The positive performance increase added by more productive hires
  • The failure rate of new hires and the damage done by weak hires
  • The losses created by a weak hiring process
  • The opportunity costs of “missed” landable top talent
  • The cost of using excessive hiring manager hours

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If you are looking at metrics like these, and sharing your findings not just with the recruitment team, but the boarder HR team, you can come up with big picture strategies to deal challenges much more effectively. In my own experience, a few other noteworthy trends in HR and Recruitment Analytics to consider include:

  • Disruptive Technology. Giving tools and information to managers and employees directly allows action to happen much quicker and be much more localized in impact. Success means giving the power to the end users so that HR can do more to oversee and manage big picture metrics.
  • Once A Year Is Not Enough. Annual reviews and employee surveys are too old school. Using analytics to gain insights can now be done 24/7. This can really have positive changes on employee engagement without the drawn out and too formal process made uniform to all.
  • Outsource Stuff. In successful companies, many tasks are outsourced to vendors who can do a lot more specialized things then in house generalist staff can do. Its just to much to ask a few people to stay on top of all the things important to the people you rely on. You have to pick and choose what you can keep and what you can outsource.
  • Mobile Apps. Designing apps for mobile first use is the way to go. We too often rely on old school thinking and take web-only or web-first tools and repurpose them for mobile. Times have changed. Mobile first is the way to connect with todays candidates and employees.
  • Look For It On YouTube. Video based learning, recorded by localized subject matter experts is on the cutting edge. The bonuses of learning from someone who is doing it versus traditional corporate trainers and enterprise world eLearning modules is another key to success.
  • Out Of The Box Analytics Tools. Behind the fire wall HR applications are being replaced or augmented by vendor based analytics tools that are more dynamic and expandable. Many can set on top of or replace current tools that are being used to gather, store, analyze and report data. The days when everything has to be designed, developed and maintained by an internal IT team is also going the way of the dodo bird.

So there you have it… becoming an HR and Recruitment Analytics ninja is going to take a lot of new thinking and a lot of letting go of how it worked in the past. Everyone agrees recruiting has never been harder, retention is getting more challenging and the future of finding and retaining talent is looking like a nightmare on the horizon.

If you need some guidance with how to being your HR and/or Recruitment team into the information age, I’m happy to help. One of my favorite things to do is get in a room with HR and Recruitment staff and talk about how to bring the team form the past to the future when it comes to analytics.  Just ask me how.

HR & Recruitment Analytics – The recruitment and retention of top talent is the biggest challenge facing just about every organization. DMAIPH is a leading expert in empowering HR & Recruitment teams with analytics techniques to optimize their talent acquisition and management processes. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to learn how to get more analytics in your HR & Recruitment process so you can rise to the top in the ever quickening demand for top talent.

Hybrid Staffing Solutions – It’s Not Just Outsourcing

One of the challenges of my business is that it is not simple to explain to someone.

We are not a straightforward outsourcing company.

I don’t work with clients who are just looking to save money by sending jobs overseas.

Instead I offer a hybrid staffing solution. And what is that exactly?

First off I specialize in basic analytics. The types of clients we take on have a need for someone to analyze something in order to answer questions and provide solutions. We don’t do traditional customer service, we are not tech support and we don’t take on many advanced analytics projects.

If someone comes to me asking about predictive analytics models, the blending of big data sources or data science, I am happy to consult with them long enough to find a good match with a company who deals in these things. But it’s not what we focus on.

We are good at things like analyzing social media content, public data mining, building and maintaining business dashboards, conducting demographic research and gather competitor data. We use Tableau and Excel. We help people who have systems in place, they just need assistance with maximizing their value. This is what I teach my team.

We are a niche business partnering with companies who have a specific need.

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Our clients are small to medium sized businesses in growth mode. I’ve worked with both small mom and pop businesses and big corporations. They both have limitations to our business model. Our clients all have an analytics centric culture, they just don’t have the resources to optimize and grow the business completely in house.

Another difference between what we do and what traditional outsourcing companies offer is that most of our team is home based. As a general rule, I don’t like the office based model when working with talent from the Philippines. Running an office team in the Philippines is very complicated due to the labor laws and competitive market. It’s us much easier to attract top talent at an affordable price by setting up virtual teams.

In the end, my success has primarily been because we find ways to merge the culture of the client with the team in the Philippines. We don’t look and feel like most outsourced teams, because we integrate the new team into the client’s culture.

So now you have a better idea of why I say it is somewhat of a challenge to explain our model. We offer hybrid staffing solutions… a Philippines based, virtual team set up to mirror the client culture who offer a variety of analytics and back office business services.

Maybe it is not as hard to explain after all.

nalytics Outsourcing – DMAIPH has successful set up Filipino analytics teams for over a dozen U.S. based businesses. Offering both virtual and office based teams that specialize in problem solving using data, new technology and analytics techniques is our strength. Finding and empowering analytics talent is increasingly challenging, but we have it down to a science. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to learn more about how to set up an analytics-centric team in the Philippines.

Q13: A lot of us want to know what is business intelligence and how does it add value to analytics?

Per Wikipedia, Business Intelligence (BI) is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data.

BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting. BI can be used to support a wide range of business decisions ranging from operational to strategic as well as both basic operating decisions include product positioning or pricing and strategic business decisions include priorities, goals and directions at the broadest level.

The CHED memo breaks business intelligence into four phases:

  1. Data Gathering. Business analysts need to identify the appropriate data-gathering technique by conducting research. Once you have identified the right data, it needs to be captured. This process is the same as the identify process.
  2. Data Storing. A general term for archiving data in electromagnetic or other forms for use by a computer or device. There is a common distinction between forms of physical data storage is between random access memory (RAM) and associated formats, and secondary data storage on external drives. This process is akin to the first part of the inventory process.
  3. Data Analysis. The process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data is the analysis phase. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names. We need to have a data analysis to improve the company’s performance. This process is the 2nd half of the inventory process.
  4. Data Access. Data Access refers to software and activities related to storing, retrieving, or acting on data housed in a database or other repository. Two fundamental types of data access exist: sequential access (as in magnetic tape, for example) Data access crucially involves authorization to access different data repositories. Data access can help distinguish the abilities of administrators and users.

That is a good starting point to understanding the concept. The memo breaks down the data analysis process into 4 parts to show how important the structure or data lake your data is stored is as important as the data itself.

Business Intelligence tools all work based on the premise that you have structured data neatly stored in tables with header rows and columns of data. More advanced BI tools can handle unstructured data, but for the most part they are all built to pull data from structured environments. BI Tools are like a fish or depth finder to help you access your data from the data lake quicker and with more efficiency.

Another important point to note is that business intelligence and business analytics are sometimes used interchangeably, but there are different.

From my perspective, the term business intelligence refers to collecting business data to find information primarily through asking questions, reporting, and online analytical processes.

Business analytics, on the other hand, uses statistical and quantitative tools for explanatory and predictive modeling. In this definition, business analytics can be seen as the subset of an enterprise wide BI strategy focusing on statistics, prediction, and optimization. The CHED memo is more closely aligned to that division as well as the primary focus is on the storage of data and the use of modeling.

As for myself, I worked with business intelligence software and methodologies with Wells Fargo long before I had even heard of the term BI.

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I want to leave you with on tip. If you are fairly new to the concept of business intelligence tools I suggest you download Tableau Public. It is very easy to learn, there is a very active user community to learn from and best of all it’s free.

So check it out.

Q12: Next please explain when and how we can use prescriptive analytics?

Prescriptive analytics goes one step further and finds the best course of action for a given situation. Its primary goal is to enhance decision-making by giving multiple outcomes based on multiple variables.   The analogy of how doctors prescribe medicine to patients based on a wide range of variables in a patient’s health, using an equally wide range of treatment options.

“Prescriptive tells you the best way to get to where you want to be,” says Anne Robinson, director of supply chain analytics at Verizon Wireless and a past president of INFORMS, a society for analytics and operations research professionals.  “If you want to differentiate yourself, the next step is the prescriptive tool box.

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Predictive analytics answers the question what will happen. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option.

Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics allows us to handle blended data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead. It also allows to take advantage of this predicted future without compromising other priorities.

In addition, most prescriptive analytics efforts require a predictive model with two additional components: actionable data and a feedback system that tracks the outcome produced by the action taken. It is simply too much data and too many outcomes to track if you haven’t invested in the right people and the right technology.

To really be impactful, this type of analytics also requires more data integration then the other types. “Data scientists typically spend about three-quarters of their time preparing data sets and only a quarter running analysis”, says Forrester Research analyst Mike Gualtieri. The need to not only blend and integrate data, but to constantly be looking at ways to keep the good and toss out the bad.

There is also a lot of discussion ongoing about the role prescriptive analytics actually replacing human decision-making. Advances in machine learning have gotten to a point where many routine business decisions can be made automatically.

We are currently seeing a lot of buzz in the industry about how far can an automated predictive analytics solution take us in freeing up time and resources. Currently we are finding ways to spend less time data blending and integrating and more analyzing and taking action. But soon it may be the whole analytics process that is managed by artificial intelligence.

Prescriptive analytics is the way of the future for those with the resources to apply it. However, for those who do not have those resources, prescriptive analytics is out of reach. This to me is a huge challenge for the analytics industry to solve.

The 3 Parts of Me: BPO Elite, DMAIPH and Sonic Analytics

A little about me. I oversee three small companies that specialize in analytics. I am not actively trying to sell you my services, but do hope that if you ever have a need for tailor made analytics solutions, you remember me.

BPO Elite is a consulting business that matches up companies in the U.S. with talent in the Philippines to do a variety of basic analytics and back office work. We DO NOT deal with companies looking just to send jobs overseas, focusing only on partners who need to add flexibility and depth to the talent pool. We have helped over a dozen companies find the right solution for their business to date.

DMAIPH is a company designed to deliver analytics training and support to colleges and universities looking to add more analytics centric courses and materials to their curriculum. To date, I have consulted with over a dozen of the top schools in the Philippines as well as working with student interns from UC Berkley, San Diego State and Diablo Valley College. My interns have helped a number of small business with basic analytics projects. I also blog about my love for analytics and how I teach it.

Sonic Analytics is a training business that focuses on corporate trainings in analytics related topics. Based on my experience as a senior analytics consultant with Wells Fargo Bank and in teaching analytics to college students in both the U.S. and the Philippines, I have come up with a very effective way to help professionals get a better handle on the analytics culture in their business. I have delivered trainings to thousands of people over the past few years, helping them learn how to make more data-driven decisions.

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Each company represents one of the key components of my dream to bring better analytics to as many businesses as possible.

 

Q11: Can you next describe how to best use predictive analytics?

A look at how predictive analytics is used to help drive decision-making starts with a basic need to improve things. Someone once told me that despite all the advanced technology in our phones, cars, homes, workplaces… the world is a remarkably inefficient, wasteful place.

Two blogs ago, I defined predictive analytics as a process that takes data and extrapolates patterns to predict likely outcomes. Past, Present, Past Present, Future… the goal being too provided educated guesses on what is most likely to happen next. The primary use of predictive analytics is to predict outcomes using models that will mitigate risk and eliminate choices based on unlikely outcomes.

For anyone who is familiar with Lean or Six Sigma, there is a lot in common with predictive analytics and process improvement methodologies.  We take historical performance data and combine it with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. Once we see where we think things might go wrong, we make changes to prevent or at least mitigate the future.

Predictive analytics is used most extensively in places where you want to know the future like sales, marketing, and finance. To do this you need to build models. Models are not always simple and often take someone with both business experience and professional training in certain coding or programming languages.

In the hands of a good analyst, predictive analytics helps a business continually reinvent itself based not just on what happen, but what is likely to happen.

This allows a wide range of organizational activities to be improved by predicting the behaviors and outcomes of people, the futures of individual customers, debtors, patients, criminal suspects, employees, and voters. It’s that generality that makes this technology so awesome.

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Business that have good predictive analytics are much more likely to be successful over the long term. When you look at businesses that fail, its generally because they didn’t have an eye of the future.

If you are wondering how to take your descriptive analytics to the next level and start getting more into predictive analytics, let me know. I can help you figure out to starting using something besides the magic 8 ball to predict what lies ahead.

Q10: Please talk about how, when and why we use should descriptive analytics?

Going back to our previous definition of descriptive analytics, it is used to answer questions about what has happened in a business. It is primary use is to look at the current business situation with an eye towards looking for cause and effect. It helps one to understand how to manage in the present based on what happened in the past.

The vast majority that have attended my trainings on analytics, are looking for help with descriptive analytics challenges. Using unstructured big data for predictive analytics modeling is not really something they are concerned with.

I have found that people who are really engaged with analytics are very driven to self-educate. They are driven by curiosity to make use of cutting edge stuff to tackle bigger and bigger challenges. For data scientists and really good analysts, descriptive analytics is easy and kinda boring.

But that is a small percentage of people who use analytics every day.  To most of my attendees, its more about how to cut down on the time it takes for them to prepare the reports they have to make and how to make them more useful to their bosses. That’s where most of my descriptive analytics training has an impact.

How to make a better report? How to build and maintain a simple business dashboard? How to have more impactful power point slides. How to streamline the reporting process? This is one way to look at descriptive analytics… its not just taking historical data and using it for reports, but also how to make the reports better.

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So how can we use descriptive analytics? Well, we probably already are. Inventory control, payroll, performance management, quality assurance, sales reports, marketing results… all use forms of descriptive analytics. They take what happen, they look at it and then they make decisions.

For the most part this can and is done in Excel. If you want to supercharge what you do in Excel, then you can use a business intelligence tool to build dashboards and publish dynamic reports. This is where most people doing reports need help. How to better visualize the data so it has more power and how to use BI tools to do things faster than can be done in Excel.

In many, many companies a lot of time and energy has been devoted to building reporting tools in house. And this is generally the problem. The reports are static and hard to change. If you are in a company like this, then descriptive analytics can be a bear.

To make the most of it, I suggest using free tools like Tableau Public, which is free, to demonstrate new ways to analyze and report data, to get the boss interested in updating the way you company reports.

Another big challenge facing analysts doing mostly descriptive analytics in the form of reporting, is blending data. Taking data from different data sources and combining them. This can often be very manual and general done in excel if you company hasn’t invested in a way to centrally store enterprise wide data and make it easily accessible. There are some applications out there that can help you with this, Alteryx and Qlikview being ones I have used and they both have a free demo.

If you are already doing predictive analytics, then you probably have your descriptive analytics figured out.

So, if you need help super charging your reporting, are looking to get started using business intelligence and data blending tools, and/or need to build a business case to invest more into analytics, let me know. I’m happy to help you come up with a much better way to build reports that have real impact and don’t take up all your time.

 

Prelude to Q10: Understanding the 3 different types of analytics.

The analytics efforts in a business are generally divided into 3 types; descriptive, predictive and prescriptive analytics.

A simple definition of descriptive analytics is that it is used to answer questions about what has happened in a business. It is primary use is to look at the current business situation with an eye towards looking for cause and effect. It helps one to understand how to manage in the present based on what happened in the past.

Per the Commission on Higher Education (CHED), descriptive analytics make use of current transactions to enable managers to visualize how the company is performing. When teaching the concept, it is generally focused on analysis and reporting to guide decision-making.

Most businesses use mostly descriptive analytics in their analysis, reporting and decision-making.

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Have to apologize to whoever made this image,  I dont know the source, but you have my thanks for making it. 

As you can see in the image, predictive analytics takes data and extrapolates patterns to predict likely outcomes. Past, Present, Past Present, Future… the goal being too provided educated guesses on what is most likely to happen next. The primary use of predictive analytics is to predict outcomes using models that will mitigate risk and eliminate choices based on unlikely outcomes.

Per CHED, Predictive analytics allows voluminous data to be used for prediction, classification and association making it very useful tool for projections, forecasts, and correlations. Most lessons around predictive analytics involve data modeling and require a much higher degree of skill then descriptive analytics.

In general, predictive analytics is used by large companies in data-rich industries. Up until recently there were very few tools available to smaller businesses to add this type of analytics to their decision-making.

Prescriptive analytics goes one step further and finds the best course of action for a given situation. Its primary goal is to enhance decision-making by giving multiple outcomes based on multiple variables.   The analogy of how doctors prescribe medicine to patients based on a wide range of variables in a patient’s health, using an equally wide range of treatment options.

Per CHED, Prescriptive Analytics help organizations develop insights to make decisions from the current data that maximizes the organization goals.  Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Largely, instruction take the model building found in predictive analytics and supercharges it with more data, more choices and more outcomes.

Prescriptive analytics is fairly new and just now gaining widespread use in the corporate world. There are not many tools available that are cheap or easy to use. Generally, you find data scientists assigned to prescriptive analytics projects. It also take us closer to some decision-making in a business being completely automated. With enough data on hand, using machine learning to analyze the data, we are starting to see artificial intelligence at play with prescriptive analytics. It is a pretty exciting time.

Its important to keep in mind that to really be good at predictive and prescriptive analytics you need both the high tech tools and the training/experience to use them effectively.