What Is Data Quality Steward?
​Several companies mentioned the requirement for combining data scientist skills with traditional data management features. Solid knowledge of data architectures, metadata, data quality and correction methods, Data Quality Stewardship and administration, master data management hubs, matching algorithms, and a host of other data-specific topics are essential for firms seeking big data as a long-term strategic differentiator.

​A Data Quality Steward is responsible for the management and proficiency of data stored in an organization. Big organizations expect Data Quality Stewards to handle all things related to data processing expertly, data policies, data guidelines, and administer the organization’s valuable information in compliance with policy and regulatory obligations.

Data Quality Stewards are ultimately responsible for establishing data-quality metrics and requirements, including defining the values, ranges, and parameters acceptable for each data element. They also work with the team to develop procedures for detecting and correcting data-quality issues and collaborating to establish policies, procedures, and internal controls affecting data quality.

Also, Data Quality Stewards engage in the ongoing and detailed evaluation of data quality, identifying anomalies and discrepancies, and contributing expertise to understand the root cause and implement corrective measures.

Preparation Tips of SAS Data Quality Steward Certification
1. Review All SAS Data Quality Steward Exam Objectives
The moment you begin your preparation for the Data Quality Steward Certification exam, the first thing one must do is to visit the SAS official Website. And make positive to review all the exam objectives. The moment you review all the exam objectives, you will configure more about the examination and increase your morale. Since this step sets the whole foundation of the exam preparation. So, tell yourself to go through with the most trusted website first and then move on to other learning resources.

2. Try Training
When it comes to training, none can beat SAS itself. SAS provides many great training courses. These are the on-demand training and instructor-led courses. This will give you up-to-date information regarding the same that too at your comfort.

3. Try Online Forum or Study Group
Online forums or study groups can turn out to be an excellent method to prepare for the Data Quality Steward Certification exam. Therefore, feel free to join study groups or online forums. Moreover, you can ask a question related to the topic you are having difficulty with.

However, it is not compulsory to join as it is just very subjective. Not to mention, these study groups aid you stay motivated, seeing so many individuals walking the same path as yours.

4. Hands-on Experience
This exam is created so that it proves the technical skills compared with the job role. Take it from us; this by far is the best preparation for the exam.

5. Free Study Resources Are Out There
The internet is cluttered with so many resources, but you must know which means to rely upon. Both formal and informal training can become the icing on your cake. Above all, do not forget self-study as it is the most important one.

6. Schedule Practice Tests
Schedule SAS Data Quality Steward Practice tests well in progress of your scheduled exam date. Set a deadline for yourself and check what you have seen in between that time. You will discover the areas where you most need to improve.
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The last couple of weeks before the SAS exam should be dedicated entirely to solving questions. Try to find new questions by online mock test programs as you can find. This should be the point when you have completely taken the concepts of networking that will be on your A00-262 certification.

7. Refresh Your Knowledge Right Before the SAS Data Quality Steward Exam
We recommend re-reading the books again before your scheduled exam date. This will encourage you to refresh your memory and help you identify anything you might have skipped over the first time.

Wrapping It Up!
SAS Data Quality Steward is an exciting yet challenging field. But it needs a lot of hard work, knowledge, certifications, and experience. It is not easy to get a relevant job in this industry. If you are lucky enough to get a job as a Data Quality Steward, keep yourself updated on new trends and developments.

Also, continue developing your networking skills, work on as many different projects as possible, and build a connection with data scientists and vendors to boost your career opportunities and stay competitive. 

​What Is Predictive Modeler?

​​In short, a Predictive Modeler is a statistical technique applying machine learning and data mining to predict and forecast likely future issues with the help of historical and existing data. It operates by analyzing current and historical data and projecting what it learns on a model made to forecast likely outcomes.

Predictive Modeler can be used to predict just about anything, from TV ratings and a customer’s next purchase to credit risks and corporate earnings. A predictive model is not fixed; it is approved or revised regularly to incorporate changes in the underlying data. In other words, it is not a one-and-done prediction.
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Predictive models make opinions based on what has happened in the past and what is occurring now. If incoming, new data reveals changes in what is occurring now, the likely future outcome's impact must be recalculated, too. A software company could model historical sales data next marketing expenditures across multiple regions to create a future revenue model based on the marketing spend.

Most predictive models work fast and often perform their calculations in real-time. That’s why banks and retailers can, for example, calculate the risk of an online contract or credit card application and accept or decline the offer almost immediately based on that prediction.

Some predictive models are more complicated, such as those used in computational biology and quantum computing; the resulting outputs take longer to measure than a credit card application but are done much more quickly than was likely in the past thanks to advances in technological capabilities, including computing power.

What Are the Benefits of Predictive Modeler?

As the industry goes towards digitalization, manufacturers face more significant pressure to have an aggressive edge, and many begin asking why Predictive Modeler?

Predictive Modelers are essential for applications such as predictive quality and predictive performance that allow manufacturers to recognize problems at their very earliest stages, so they can be dealt with before issues start to unfold.

With return on investment a key driver for the industry, Predictive Modeler can deliver quick wins, with many factories seeing average cost savings and optimization opportunities after just a few months.

1. Identify Patterns to Predict Performance

Predictive Modeler sifts through vast amounts of historical data much more quickly and accurately than a human can. Machine learning technologies can identify repeated patterns and other relationship variables, such as when we turn these settings, we improve production by 10% without sacrificing the first-pass yield. AI and machine learning can search for patterns and explore different combinations that support your organization, identify potential efficiency improvements, predict issues, and decrease waste.

2. Improve Operations in Real-Time

A Predictive Modeler delivers near real-time insights by comparing data from historical production runs with live production activity. These comparisons, which convert to predictive and prescriptive analytics, drive recommendations and alerts to improve real-time operations.

3. Decrease Costs

Quality failures can translate into actual losses in the product, overhead labor, and time. Predictive Modeler can enable factories to identify quality failures and take corrective action faster to minimize impact and decrease waste costs. Prescriptive analytics can further these cost savings by allowing you to replicate your most efficient runs more consistently. Additionally, Predictive Modeler and condition-based monitoring can help factories reduce unplanned downtime and lost productivity by notifying plants of potential equipment problems.

4. Optimize to Perfection

Manufacturers have been accustomed to Lean Principles for decades. These best practices sought to help manufacturers achieve maximum production efficiency with minimum waste. Predictive Modeler ultimately provides manufacturers with real-world data to help them optimize their processes reach perfect perfection.

Earn Predictive Modeler Certification

A predictive Modeler is an attractive option because it brings a ton of value to any organization. However, it is also on the higher end of the complexity spectrum than some other business analytic options.
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A predictive Modeler is an attractive option because it brings a ton of value to any organization. However, it is also on the higher end of the complexity spectrum than some other business analytic options.

​The complexity in utilizing the Predictive Modeler predominantly manifests in the number of steps required and the expertise needed for some of the steps. This course is designed to reduce and eliminate these issues.

The SAS Certified Predictive Modeler Using SAS Enterprise Miner 14 credential is globally recognized for validating SAS Predictive Modeler knowledge. With the Certification credential, you stand out and prove that you have the SAS Predictive Modeler knowledge to distinguish within the organization.

During this performance-based examination, the candidates will use the SAS Enterprise Miner to perform the examination tasks. The candidate must have a firm understanding and mastery of Predictive Modeler's functionalities in SAS Enterprise Miner.