7 Tips for Successful Software Adoption

So, you have decided to adopt a new software (CRM system, timesheet tracker, marketing hub) to make your organization more effective and productive – Congrats! Often organizations only have an implementation plan and skip the adoption process. And as a result, they end up running into trouble because of the low user adoption rate and a lot of challenges during the new software being implemented.

The real challenge is in the adoption of the software by employees and its integration into business processes and corporate culture. Here, we have provided seven tips to help you have a smooth transition into your new software.

Let’s get started!

Choose the Right Software

The goal here is not to choose the best technology, the most efficient, or the one that invokes. You need to consider your business needs, don’t just weigh the name of the vendor and price. Your choice of software should be the one that will help your business achieve its strategic goals.

To help with the adoption process, your software should not require major changes in your business processes, but adapt to them.  Additionally, the right software should be simple, insightful, and useful. People are more likely to adopt new software if they see how it assists them in accomplishing their goals and objectives.

Ensure Your Support Teams Are Part of the Process

We cannot stress this enough, involve your support team in your software adoption process. They can provide their expert advice to those that will be using/implementing this software. There will be consequences if you are implementing new software in your organization without suitable support teams.

Get Your Team Excited!

How do you get your team excited? Simply communicate. You cannot just spring up change on people, even if they are your employees. The employees need to understand the benefit of the software, not only for your organization, but for themselves. You need to communicate your goals and explain how the software is a way of achieving them.  

How do you do this? Here’s a tip:

Reveal the changes that will be taking place at the weekly team meetings and host lunch meetings. Informing your employees about your decision and explaining how it will make their jobs easier will help them comprehend why the changes are occurring. You should help them recognize the new software as a tool to make their job easier, not a game-changer.

And because communication goes both ways, you should answer their questions, as well as clear up all misunderstandings. You should help your employees feel like they are a part of the decision-making process. Give room for feedback!

Find and Involve Internal Champions

The champion we are referring to is an employee open to feedback, always prepared to adjust, to correct or resolve a problem. An exceptional communicator that can convey the positive aspects of a change. The champion will also become a resource others can turn to when needed.

These champions will be happy to adopt the new software into their daily routine and their enthusiasm for the new software can be used to inspire reluctant employees. Make your champions understand the reasons for your option so they can disseminate that information as part of their discussion with other employees in your organization.

Hold Training Events

One of the major risks of user adoption is the lack of sufficient and customized training. Training is a good way to increase adoption. Training is also a good way to decrease resistance and answer your employees’ most important questions. You can also clear confusion and walk them through a practical application that will reinforce the software’s benefits.

Since we all don’t learn and adapt at the same pace, it is important to have your internal champions at the training events so that your employees know who they can approach with their questions or concerns about the software.

Market Internally

Just like you market a new product to your customer, you should create a marketing plan and a promotional strategy for your internal end-users to help with the adoption process. This can help create enthusiasm around the software adoption process without too many protests.

You can utilize contests, giveaways, and posters to help you build the drive you need to launch the project.

Highlight Progress and Celebrate Victories

You have to constantly encourage and positively reinforce the use of the software to facilitate the adoption process. To do this, you can set measurable and achievable goals in the adoption process. Recognizing your employee’s effort is a way to motivate and encourage them. Celebrating victories and progress ensures that you create a positive state of mind around the software to stimulate its adoption.

What’s Next? Make it People First!

Keep in mind that new software adoption is not easy, but it doesn’t have to be painful. Put together a plan to find and address implementation problems early and gain the commitment of employees to drive engagement and enhance efficiency. Taking the right steps and having the right outlook is important to improving user adoption – enabling you to maximize your return on investment.

You don’t want your software to be another expensive tool that no one uses effectively.

Get in touch with us today to discuss how we can help you with your new software adoption needs.

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!