When was the last time you did a needs analysis? When was the last time you should have done a needs analysis?

ATD released some interesting data recently. Out of the organizations surveyed, which employed digital learning services, only 56% said they did needs analysis for learning projects, 37% said they did not, and the remaining 7% were not sure. For organizations that did needs analysis, the following table shows, top to bottom, the most frequently used methods of data gathering, compared to what they perceived as the most effective methods of data gathering.

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Surveys are quick and easy, but they are also among the least effective methods. What’s striking is that the most effective methods were the ones that involve personal contact and relationship building, not only with SMEs, but more importantly with potential learners. What skill gaps do they perceive? What additional job support is needed from their perspective? Interviews and focus groups are obviously great, but don’t underestimate the usefulness of observation, especially when it’s framed as shadowing or “can I tag along with you on this job and ask a few questions?” Surveys are typically anonymous, which is often seen as a plus, but there is a downside too in that they can often raise suspicion. “Why are you asking all these questions?” “Is the company getting ready to reorganize and lay us off?” “What does management want to hear that will protect my job?” The methods that involve personal interaction help build trust and generate honest feedback.

When it comes to gathering data for a needs analysis, we typically draw on a number of sources. Of the organizations surveyed, 78% said they used SMEs, 71% used learners’ managers, 69% used learners, 60% used senior leaders, and 56% used previous training evaluation results. As I mentioned above, it’s really important to involve the learners, and almost a third of the time, their voice goes unheard. They typically want to help fix the problem because they feel the consequences of the problem.

So why don’t more organizations conduct needs analyses for training projects? Those surveyed cited lots of reasons including lack of time, lack of resources, getting buy-in from senior leaders, and difficulty in convincing others to do a needs analysis. The overwhelming number one response, however, was “stakeholders believe they already know the needs.” Have you ever run into that one before? I certainly have. It’s tough to convince stakeholders that they might not know the extent of the needs, or that they are trying to solve a non-training problem by throwing training at it. (Are you familiar with Gilbert’s Six Boxes? That’s a great tool for showing a client what problems can be solved with training and what problems require adjusting other job aspects.)

Mastech Digital is a leading digital transformation services company with a robust practice dedicated to the intricacies of digital learning services. When we scope projects, we often propose starting with a short analysis phase to validate the “needs analysis” that was already done.  Sometimes that’s as little as 20-40 hours, depending on the project. It’s a relatively small investment for the client, but it allows us to confirm what the stakeholders believed or discover the extent of the actual needs and adjust the project scope accordingly.

 

Author James Wallace 
James Wallace
Consultant Manager



Prescriptive analytics is a branch of data analytics that uses prescient (predictive) models to propose actions for ideal results. Prescriptive analytics is associated with both prescient and descriptive analytics. While prescient analytics figures what might happen, and descriptive analytics provides data into what has happened, prescriptive analytics helps provide the best results among different choices, given the known parameters. Prescriptive analytics stretches out past prescient analytics by indicating both the activities important to accomplish anticipated results, and the interrelated impacts of every choice.

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Prescriptive analytics endeavors to evaluate the impact of future choices so as to exhort conceivable results before the choices are actually made. Prescriptive analytics predicts what will happen, as well as why something will happen, giving recommendations with respect to actions that will make a favorable position of the predictions.

Prescriptive analytics utilizes recent advancements such as machine learning and artificial intelligence to comprehend what the effect is of future choices and utilizes those situations to determine the best results. Advancements in the computing speed and the development of complex mathematical algorithms connected to informational indexes have made prescriptive analysis conceivable. Specific procedures used in prescriptive analytics include streamlining, simulation, game theory, and decision-analysis methods.

Application of Prescriptive Analytics

Prescriptive analytics is utilized in situations where there are an excessive number of choices, factors, requirements, and information for the human mind to proficiently assess without technology assistance. It is likewise utilized when testing in the real world would be prohibitively costly, excessively hazardous, severely time-consuming.

In a perfect state, any business issue would experience every one of these stages (Descriptive, Predictive, and Prescriptive) to ascertain a solution. In any case, there are certain business issues that absolutely require prescriptive analytics, such as:

  • Supply Chain Optimization
  • Logistics is the biggest consumer of this form of analytics. It utilizes network flow modeling to resolve transshipment issues, shortest path issues, maximal flow issues, transportation/assignment problems, and generalized network flow problems. It involves minimizing the cost of risk while getting from source to destination.
  • Operations Management
  • It uses linear and, more often, nonlinear programming to outline and control the process of generation and redesigning business activities in the production of goods or services. It includes minimizing throughput or maximum output of the whole task/process.
  • Inventory Management
  • Here, it’s all about setting generation levels and stock levels to meet forecasted demand at sales locations. How much should each plant supply to each distribution center? Which stockrooms should serve which sales locations? The list goes on.
  • Price Optimization
  • Organizations can be confident in making pricing decisions because prescriptive analytics helps organizations identify and understand patterns and insights.

Benefits

The benefits of including prescriptive analytics as part of your analytics hub can be outlined as follows:

  • Anticipates what will happen, when something will happen, and why it will happen
  • Suggests choice alternatives to exploit a future opportunity
  • Mitigates a future hazard
  • Ingests new data to re-predict and re-recommend, thus automatically improving forecast precision and endorsing better decision options without compromising other priorities
  • Automatically and reliably processes up-to-date data to improve the accuracy and efficiency of predictions, and to give better decision choices
  • Predicts various prospects and enables organizations to evaluate various conceivable results in light of their activities

Conclusion

Prescriptive analytics is the prospective of Big Data, but it is as yet far away before it will be common language. The potential is colossal; however, it requires gigantic measures of information to be able to make correct decisions. Only a handful of organizations and industries have that measure of information and informational indexes to make something useful out of prescriptive analytics. However, in 5-10 years, prescriptive analytics will be as typical as business intelligence today. Gartner gauges the prescriptive analytics software market will reach $1.1 billion by 2019.

Mastech InfoTrellis – a pioneering data and analytics company – is a leader in the implementation and use of advanced analytics applications across a wide array of industries.

Get in touch today to learn more about how you can take advantage of this new wave of analytics and gain actionable insights through a relevant, powerful analytics hub. Improve your organization’s decision-making capabilities today.

 

Garima_Jain
Garima Jain
Technical Consultant, Mastech InfoTrellis



Imagine being an athlete coming off a big loss. While you’re just about to hang your head in defeat and try to forget what happened, you instead realize your jerseys, your shoes, your stadium, your entire team office are all equipped with the latest technologies, tracking your every move, measuring the efficiency of each play and player. You and your team are in gear to change the outcome of the next match. Even fans – both at home and at the venue – are in a position to bring a positive outlook to current and upcoming games! Digital business transformation in sports is now extending the reach and power of modern athletics from the physical realm to the digital.

The analytics behind athlete efficiency, popularized by the book, Moneyball, has quickly picked up steam in most sports today. The physical, mental, and social factors affecting an athlete can all be aggregated, processed, and analyzed to help team staff make more informed recruiting decisions, and to help athletes up their game, both on and off the field. And adding to the effect of watching game film or video recaps, even coaches and trainers can utilize advanced analytics, or even wearable tech on the athlete, to understand what the most optimal path to success is, utilizing those insights to help their students be ready to perform at the next level.

But these new-age technologies are not just for the athlete. Thanks to the rise of smartphones and other portable devices, the fan experience has become just as integral to an athlete’s success as the athlete’s own performance. Immersive, in-venue experiences using augmented or virtual reality allow fans to feel like they’re actually part of the game, playing with their favorite athletes. Stadiums are even utilizing new networking and connectivity technologies to ensure each fan is connected to the team, the game, the stadium itself, and every other fan, inside and outside the stadium. In today’s day and age, a fan’s cheer is amplified tremendously, not only by voice, but by the click of a few buttons too – a huge contributing factor to a team’s morale and results.

From the grounds of the stadium to the comfort of your living room, many of the same immersive experiences had at a venue can enhance the engagement of viewers at home as well, across a plethora of media, including mobile, computer, and television. Moving past the reach of traditional TV ads, digital analytics on personalized content online can inform marketers of what excites a viewer, and when and where, helping create tailored engagement initiatives that keep a fan glued to their screen and cheering for their team. In a world where data is as dominant as the best player, utilizing this data can create new revenue streams and unlock championship potential.

New technologies have been rapidly transforming businesses across industries of all sorts. The sports industry is no different. And for as long as innovation is alive and well, digital business transformation will continue to alter and enhance business processes. Sports franchises and businesses engaged in the sports world need not only employ digital talent but set up the right processes and acquire the right technologies to create that all-encompassing winning experience.

For over 30 years, Mastech Digital – a leading Digital Transformation Services company and a premier Data & Analytics Services provider in North America – has been helping SMEs to Fortune 500s tackle digital business transformation and enhance the value they deliver to their customers. In short, we’ve been building winning teams with one of the best records in the industry.

The clock is running. Who’s your pick?

 

Account Executive Vincent Moses
Vincent Moses
Senior Account Executive