Data Quality Management: Best Practices and Processes

Data is important, but the quality of the data collected is more important. With the data collected, you can make business decisions, which is why it’s important to pay attention to your data quality. You should measure the quality of your data based on consistency, accuracy, completeness, timeliness, uniqueness, and so on. Here we will discuss the best practices and stages of data quality management.

Best practices of data quality management

Data quality management is a process that involves rational step-by-step execution. These steps normalize the data management practices which are used to incorporate data quality techniques into the business.

The best practices include:

• Prioritize data quality

Low data quality can cause a lot of issues. The first step is to ensure that your employees understand. Then, you create an enterprise-wide data strategy. Thirdly, design user roles with clear privileges and liabilities. Fourthly, establish a data quality management process and finally, have a dashboard to monitor the status quo. Incorporating all of this into your business process will help prioritize data quality management.

• Data entry automation

Manual data entry is one of the root causes of poor data quality. Human errors are sometimes inevitable, but automating data entry processes can help reduce it. Implementing data entry automation will help increase data quality.

• Preclude duplicates

As you should know, prevention is better than resolution. Precluding duplicates is a better option for improving data quality. Implementing duplicate detection rules and regular cleaning will help identify similar entries that already exist in the database. Then, you can ban creating another one or merge the entries.

Data quality management process

Data quality management revolves around certifying that the data is relevant, reliant, and accurate. It is a process aimed at accomplishing and preserving high data quality. Its main stages involve:

1. Gather data and establish data quality rules

After collecting and analyzing data, tables need to be created in database design. Then you scrutinize what data will be held in each table and what fields will be integrated into each table. In a situation whereby there are massive amounts of data in existence, you have to determine what is relevant to keep and what will be withheld in each table of the database.

2. Assess the quality of data

The business/technical rules that have been created and defined should be checked. The development of quality rules is essential for the success of the data quality management process. You should enforce these rules to make sure they will find and stop compromised data from corrupting the whole set.

3. Resolve data quality issues

At this stage, data quality rules should be reviewed again. The review process will help determine if the rules need to be modified or updated, and it will help resolve data quality issues. Once the data quality issue is resolved, vital business processes and functions should proceed more efficiently and accurately.

4. Monitor and control data

Data quality management is a continuous process that involves regular review of data quality rules. Monitoring and controlling data is very important in this time of constant change within the business environment.

Data quality can be ensured by engaging in effective data management tools. You have to consider a quality management solution that closely aligns with your unique business objectives. Data quality management involves many aspects and most often requires professional assistance. At ISU Corp we are always ready to help, contact us today!

The Differences between Data Mining and Predictive Analytics

Data has become an important part of businesses and it gives businesses an advantage over the competition when used appropriately. Data Mining and Predictive Analytics have gotten wider recognition.  

Data Mining and Predictive Analytics use data to discover information and provide the best solutions. Both are commonly linked to explain how data is processed, however, there are substantial differences between them. The difference between Data Mining and Predictive Analytics is that the latter studies the data and the former answers. In essence, they are two different analytical methods with their exclusive benefits.

In this blog, we will examine the differences, as well as the benefits of Data Mining and Predictive Analytics.  

Let’s dive in!

Definitions

Data Mining is the technical process of extracting usable data from a bigger set of any raw data to identify patterns and establish relationships. With data mining, you can identify, investigate, sort, and organize consistent patterns. This is a strategic practice that is essential for successful businesses. Data sources can include databases, data warehouses, and the web.

Predictive Analytics is a valuable tool for forecasting. Predictive Analytics describes a range of analytical and statistical techniques used for determining patterns that may be used to predict future trends, behaviours or outcomes. In other words, Predictive Analytics aims to forecast future events.

Techniques and Tools

There are several innovative and intuitive Data Mining techniques, which include classification, sequential patterns, clustering, regression, outer detection, and association rule discovery. Data cleansing, clustering, and filtering are features a Data Mining tool should have. Two frequently used programming languages in data mining are R and Python.

With the advancement in technology, business users can use a more user-friendly tool to forecast business outcome or market trends. Software technologies such as Machine Learning and Artificial Intelligence are Predictive Analytics tools to examine the available data and predict the outcomes.

The Objective

The two main objectives of Data Mining is to offer businesses information by retrieving interesting patterns in the data and to provide businesses with predictive power to assess future values or outcomes.

The objective of Predictive Analytics is to go beyond identifying and understanding what has happened, to offer the best estimation of what will happen in the future. And it helps businesses to get to know their consumers and understand the trends they follow. Although, it will not give an exact picture of what will happen in the future, Predictive Analytics can help businesses mitigate future risks. Businesses will be able to take necessary action at the right time.

Functionality

Data Mining has three phases which are:

a) Exploration – this phase includes business understanding, data understanding, and data preparation. Here, the appropriate data is collected, cleaned, and integrated from multiple sources.

b) Model Building or Pattern Identification – this phase involves modelling and evaluating the data. Here, the same dataset is applied to different models, and the most fitting with the business requirement needs will be chosen and evaluated.

c) Deployment – in this phase an implementation plan is made, strategies to support and monitor the results for its effectiveness are formed, review to check for repetition and finally, the selected data model is applied to predict results. 

Predictive Analytics utilizes various models to analyze and predict a customer’s behaviour. Models can be prepared to analyze the most recent dataset and examine their behaviour. 

Talent

Engineers with a strong mathematical background, machine learning experts, and statisticians’ experts commonly use Data Mining techniques. 

Predictive Analytics is usually employed by business analysts and other field whizzes who are skilled in evaluating and decoding patterns located by machines. 

Benefits  

Data Mining helps businesses to get the information required to make better decisions. As a result, you can add value to your business by better understanding customer segments, purchase patterns, and behaviour analytics. 

Predictive Analytics helps a business to improve efficiency, as well as gain an advantage over the competition. It allows you to tap into valuable information that already exists to provide more insights. Additionally, Predictive Analytics helps to considerably reduce risk and meet consumers’/customers’ expectations.

So, what is the future of Data Mining and Predictive Analytics?

The world of business moves briskly, and technology moves even faster. We are in an era of constant growth, where businesses are using available data for investigating patterns, forecasting outcomes, and executing decisions that will influence their business.

Data Mining and Predictive Analytics are some of the tools that can help businesses to make informed decisions by cutting costs, discovering fraud, saving resources, intensifying productivity, and producing effective results. The future of Data Mining and Predictive Analytics seems bright.

You need the right knowledge or expertise to help you make the best out of Data Mining and Predictive Analysis. Get in touch with us to learn more!

6 Ways to Ensure Your Nonprofit is Secure

There’s a saying that goes something like “why do bad things happen to good people?”. What we are referring to here is the constant security issues for nonprofit organizations. Which is why security should be one of the main priorities for any and all nonprofits! 

In the past, nonprofits didn’t have the monetary funds to allocate towards securing the organization from hackers or other outside threats (which will be discussed later in detail). However, with more traction must come more security. 

In this blog, we’ll look at 6 different steps your nonprofit organization can take to build a strong security system, that way you’re able to stress less and focus on your mission. 

Securing your Nonprofit Organization

As cybersecurity breaches tend to be more common these days, nonprofits must change the way in which their security functions. Compromised data, breaches, and cyber attacks are grave occurrences within today’s technological world and unfortunately, the main targets tend to be nonprofits. 

The last thing your organization needs is to be vulnerable and exposed. You’re able to eliminate this risk by putting security higher on your list of priorities.

How can you be sure your organization is taking the right precautions? Take a look below at the 6 steps to secure your network. 

6 Steps to Secure Your Nonprofit 

1.  Form a security solution to ensure you’re able to detect a breach and respond quickly.

2.  Be aware of how and where your staff, board members, and whoever else is part of your organization are accessing the nonprofit’s data. You need to secure the endpoints with multiple authentications and management solutions. 

3.  Use an “assume breach” method for your security; assume that a breach is about to occur - this reduces the amount of time it takes to recover. 

4.  Move your nonprofit to a secure cloud server; this comes with great benefits! 

5.  Watch out for Shadow IT (e.g. Dropbox or Google Drive which are in your network without authorization). Members of your staff may have downloaded such applications to ease their workload, but without your knowledge, it can put your entire organization at risk. 

6.  Balance your employee’s productivity with your organization’s security. Ensure you’re protecting your data, regardless of where or who it is shared with. 

Microsoft 365 Business is a beneficial tool as it integrates collaboration and productivity resources! 

Do you want to increase your organization’s security? 

If after reading those steps, you fear your organization may be lacking in cybersecurity and are unaware of what security measures should be taken, then consider ISU Corp for your security needs! 

We are able to guide you on proper security measures and offer great security services to ensure your organization is able to accomplish your mission. Reach out to us today to learn more.