Entries by John Adler

Emerging Data Risks: More Data = More Risks

The Benefits of Being Frugal With Data

In today’s digital age, enterprises are generating and processing more data than ever before. One estimate stated that 1.7 MB of data was created every second for every person on earth. With the amount of data in the world said to double every two years, we’ve far surpassed that estimate by now.

The massive amounts of data that enterprises have access to can be valuable assets for companies when managed well. We tend to want to save all data since it may be valuable in the future. After all, this data provides insights into customer behavior, shines a light on emerging market trends, and offers opportunities for personalization that most companies would have only dreamed of a few short years ago. On the flip side, data can also be a massive liability for companies if they try to retain too much of it or it is not managed properly.

Emerging Data Risks_1Datensparsamkeit: Keep What You Need, and Nothing More

One approach that we’ve found helps clients successfully frame their approach to data management and retention is to adopt the principle of Datensparsamkeit, which is a German term that roughly translates to “data frugality.” This principle states that organizations should only retain the data they absolutely need and nothing more.

There are a number of benefits to adopting a Datensparsamkeit approach. First, it can help to protect the privacy of individuals. Second, it can reduce the risk of data breaches. Third, it can reduce storage and security costs. By being more selective about the data they retain, organizations can improve their privacy, security, and bottom line.

Emerging Data Risks_2Considerations Around What Data to Keep and What to Discard

One of the key challenges of data management is determining what data to retain. There are a number of factors to consider, including the following:

• Legal requirements: Some data must be retained for legal reasons, such as to comply with regulation or to document business transactions.

• Business needs: Other data may be needed for business purposes, such as to provide customer support or to track product usage.

• Privacy concerns: Organizations must also consider the privacy implications of retaining data. In some cases, it may be necessary to delete data to protect the privacy of individuals.

The “keep-it-all” approach to data management is neither sustainable nor advisable. It’s not financially feasible to retain all of the data that is generated by an enterprise and, even if it were, in many cases the downsides to trying to retain everything far outweigh the upsides. Instead, DMG recommends that organizations take a selective, strategic approach to the data they retain.

Emerging Data Risks_3Putting Datensparsamkeit Into Practice

As you figure out what the most critical data is for your company to retain, here are some additional tips and questions you can ask to help determine what is kept and what gets discarded:

Consider the purpose of the data. What will the data be used for? Is it needed for a specific purpose? To cite one example, do you really need to keep the IP addresses of all website visitors or people who are logged into your site forever? If so, how will you use that data to improve customer experience?

Potentially summarize and delete data outside the value timeframe.  How long is data valuable? At what point is it more of a risk than a benefit? At what point does granular data lose its value, and summarized data is sufficient? Consider how you might update corporate data retention policies and schedules to ensure that data gets purged upon reaching the point where the risks outweigh the benefits.

Evaluate the risk of data breaches. How likely is it that the data could be breached? If the risk is high and there’s personally identifiable information in the data set, what is the benefit to keeping that data?

Consider the cost of storage and security. How much does it cost to store and secure the data? If the cost is high and there’s no obvious use case for the data, why are you paying to store it?

By following these tips and thinking through these questions, organizations can make informed decisions about what data to retain. This will help to protect the privacy of customers, reduce the risk of data breaches, and save money in the long run.

Data management is a complex and challenging task. However, by adopting a Datensparsamkeit approach, organizations can make it easier to manage their data and protect the privacy of individuals.

Need help determining which data is mission critical for your organization? Reach out for a complimentary consultation today. 

Data Quality Metrics and A Data Quality Culture

Data Governance Best Practices

Formalizing and improving your Data Governance framework is one of the soundest ways to ensure that your company does more than just pay lip service to being data-driven. In this post, I’ll cover two Data Governance best practices that can set you well on your way to what I called in a previous post A Minimally Invasive Approach to Formalizing or Improving Data Governance.

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Why You Need to Establish Data Quality Metrics

As we have mentioned previously, Data Governance is largely about modifying peoples’ behavior with relation to data. Metrics drive behavior, and are critical to the establishment and success of Data Governance programs.

Most Data Governance programs are concerned about Data Quality, so let’s delve a little deeper into Data Quality metrics. Data quality metrics are important to measure the effectiveness of your Data Governance program and ensure that data quality and integrity are maintained. By establishing data quality metrics, you can make adjustments as needed to ensure that data quality is maintained.

These are some common data quality metrics that DMG frequently helps companies measure:

Completeness measures the percentage of required data fields that are populated in a dataset. A company might measure the completeness of customer records to ensure that all required fields are present.

Accuracy measures the degree to which data reflects the actual state of the real-world object or event it represents. You likely want to measure the accuracy of sales data, for example, to ensure that it reflects the actual sales transactions that occurred.

Consistency measures the degree to which data is consistent across different sources or systems. A company could measure the consistency of product data across different sales channels to ensure that customers receive consistent and accurate product information.

Timeliness measures the degree to which data is up-to-date and reflects the current state of the business. The timeliness of financial data to ensure that reports and analyses are based on the most recent data available would be an example of such a measure.

Validity measures the degree to which data conforms to predefined business rules and standards. You could measure the validity of customer data to ensure that it conforms to established data quality standards.

Relevance measures the degree to which data is useful and applicable for the intended purpose, like the relevance of marketing data to ensure that it is applicable to the target audience and can be used to make effective marketing decisions.

Duplication measures the number of duplicate records in a dataset. Duplicate data can cause inaccuracies and inconsistencies in data analyses and reporting.

Integrity measures the degree to which data is complete, accurate, and consistent over time. A company might measure the integrity of employee data to ensure that it remains accurate and up-to-date throughout the employee lifecycle.

Automated data quality checks can be implemented to run in the background so they identify any issues with data quality in real-time. DMG recommends at minimum setting up rules that check for missing data, inconsistent data, and duplicate records. If and when these issues can be caught early, they can be flagged for troubleshooting before they’re allowed to permeate and erode data quality.


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Develop a Data Quality Culture

Developing a data quality culture is perhaps the most important aspect of ensuring data quality and integrity. A data quality culture is one in which all members of the workforce understand the importance of data quality and are committed to maintaining it.

Just a few of the ways you can do this include establishing data quality goals, objectives, and metrics; providing regular feedback to your team on data quality metrics and measurable impact your Data Governance efforts are having on the business; and recognizing and rewarding individuals or teams that contribute to maintaining data quality. By establishing a data quality culture, you can ensure that data quality and integrity become tightly intertwined with your overall approach to Data Governance.

Capturing the Benefits of Data Governance Doesn’t Have to Be Painful

Implementing a robust Data Governance program at the enterprise level can be challenging, but there are a number of strategies you can take to ensure you minimize the additional strain placed on your team. This is an area where we have successfully led numerous transformations for enterprise clients to help them go from questioning their data to having their data help answer some of their most vexing questions.

If you haven’t read the first 2 posts in this series yet, you can read ‘A Minimally Invasive Approach to Formalizing or Improving Data Governance’ here and ‘Automated Data Quality Checks and Standardized Data Formats’ here. You can learn more about our lean approach to Data Governance and schedule a complimentary consultation today if your organization would like outside expertise formalizing or improving your existing Data Governance approach.   

Automated Data Quality Checks and Standardized Data Formats

Data Governance Best Practices

Unlocking the power of accurate and reliable data is no longer a daunting task. Companies that are able to do so will be well-positioned for the future, as they use data to enhance customer experience, increase customer retention, and drive innovation.   

Data Governance plays a prominent role in fulfilling the promise of doing more with your data. Companies that take advantage of revolutionary advancements in areas like automated data quality checks, paired with best practices like standardized data formats, will be well on their way to having actionable data that informs mission-critical business decisions and opens up opportunities for growth.  


Why You Should Implement Automated Data Quality Checks

Data Quality is a typical priority for most Data Governance programs. Automated data quality checks are an effective way to ensure that data quality is maintained throughout your organization without adding significant additional demands on your workforce. They provide the following benefits to organizations that are looking to elevate their Data Governance efforts:

1. Improved data accuracy: Automated data quality checks can help companies identify and correct errors in data quickly, improving data accuracy and reducing the risk of making decisions based on incorrect data.

2. Better compliance: Companies that employ automated data quality checks ensure that they are meeting regulatory requirements and industry standards, reducing the risk of penalties or fines for non-compliance.

3. Greater transparency: Automated data quality checks can help companies build trust with stakeholders by providing a clear audit trail of data changes and updates, improving transparency and accountability.

4. Cost savings and risk mitigation: By identifying and correcting errors in data early, companies can avoid the costs associated with correcting data quality issues downstream, such as lost productivity, reputational damage, or customer churn.

5. Improved decision-making: Automated data quality checks can help companies make better-informed decisions by ensuring that the data used to inform those decisions is accurate and reliable.

Automated data quality checks can be implemented to run in the background so they identify any issues with data quality in real-time. DMG recommends at minimum setting up rules that check for missing data, inconsistent data, and duplicate records. If and when these issues can be caught early, they can be flagged for troubleshooting before they’re allowed to permeate and erode data quality.


Why You Should Standardize Data Terminology, and How You Can Get Started

Standardizing data terminology is another way to ensure data quality and integrity without adding significant additional demands on your workforce. Some examples of data terminology are: product identifier, states, colors, amortization value, cost of goods, etc. These are largely based on the industry and business that you are in, and which data is considered to be of high value to the organization.

By standardizing the terminology associated with data formats (for example), you can ensure that data is consistent across different data sources and that it can be easily integrated between systems and analyzed. Standardizing data formats can also help reduce errors and inconsistencies in data by ensuring that all data is entered or captured in a consistent and uniform manner.

Here are some examples of how to standardize data formats in a Data Governance model, along with some of the benefits of each:

Use of data dictionaries: A data dictionary is a centralized repository that defines the names, definitions, and formats of data elements used in an organization. By establishing a common data dictionary, all data elements can be consistently defined and documented, reducing ambiguity and improving understanding and use of the data.

Data validation rules: Validation rules are used to check data values against predefined criteria to ensure that data is accurate and consistent. They ensure that data can be standardized to a common format and structure.

Data modeling: Data modeling is the process of creating a conceptual or logical model of data, which defines the relationships between data elements and the rules governing their use. Taking this step means data can be structured consistently across different systems and applications.

Data transformation and mapping: Data transformation and mapping tools can be used to convert data from one format to another, while preserving the meaning and integrity of the data. By standardizing data transformation and mapping processes, data can be easily moved and shared across different systems and applications.

Master data management: Master data management (MDM) is a set of processes and tools used to manage the most important data elements in an organization, such as customer, product, or location data. MDM empowers organizations to ensure that critical data is accurate, consistent, and accessible across the enterprise.

Use of data exchange standards: Data exchange standards, such as XML, JSON, or CSV, or industry data standards (such as MISMO in mortgage banking) can be used to define a common format for exchanging data between different systems and applications. This allows for data to be easily shared and integrated across different platforms, reducing data silos and improving data quality.

In addition to all of the above, standardizing data formats also makes it easier to implement the previous best practice of automated data quality checks.

And remember that data formats are just one type of data terminology. There are many others out there. That is a larger subject, however, and one that we may revisit in a future post.

Let’s Discuss Data Quality Metrics and A Data Quality Culture

Establishing or improving your Data Governance framework can feel like an overwhelming challenge, especially with the deluge of data and systems that even small to medium sized businesses have to manage. The good news is that it has never been easier to automate data quality checks, and standardizing your data terminology and data formats can go a long way toward helping you do so.

The first post in this series looked at how organizations can take a minimally invasive approach to formalizing or improving their data governance efforts. In the third and final post of the series, we’ll cover the importance of choosing the right data quality metrics and establishing a data quality culture where everyone is invested in making sure your Data Governance efforts bear fruit.

You can learn more about our lean approach to Data Governance and schedule a complimentary consultation today if your organization would like outside expertise formalizing or improving your existing data governance approach.

A Minimally Invasive Approach to Formalizing or Improving Data Governance

Since the days of Moneyball, many leaders have pushed their organizations to become increasingly data-driven. What few of these leaders fully grasp, however, is the additional workload this kind of fundamental shift places on existing staff. This is especially true when it comes to creating a high-functioning Data Governance model, which relies heavily on data quality and integrity to serve as the bedrock upon which business decisions can be made.

We frequently work with clients to establish strategies, approaches, and methods to ensure that they are  operating with data of sufficient quality and integrity to establish a data-driven culture. Fortunately, there are a number of ways you can do so without adding substantial workload to your organization.

Many large organizations have a standard architecture approach which will need to be considered as part of Data Governance adoption. For example, some use The Open Group Architecture Framework (TOGAF) which is a high-level approach to design. It is typically modeled at four levels: Business, Application, Data, and Technology. If you are familiar with TOGAF or other frameworks, you will likely recognize these levels in the approach described here.

In this series of three blog posts, we will explore minimally invasive ways to ensure data quality and integrity in your Data Governance model without adding significant additional demands on your workforce.

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A Data Governance Strategy is a Must

One of the first steps in ensuring data quality and integrity is to have a clear Data Governance strategy in place. This strategy should outline the goals and objectives of your Data Governance program and provide a roadmap for achieving them.

Typical goals and objectives of a Data Governance program include:

Data quality: Ensuring that data is accurate, complete, and consistent across different systems and departments.

Data security: Protecting data from unauthorized access, use, and disclosure.

Data privacy: Ensuring that data is collected, processed, and used in compliance with privacy laws and regulations.

Data management: Developing policies and procedures for the creation, use, storage, and disposal of data.

Data integration: Facilitating the integration of data from different sources and systems to improve decision-making and operational efficiency.

Data standardization: Establishing common standards and definitions for data elements to improve consistency and reduce errors.

Data accessibility: Ensuring that data is available to authorized users when and where it is needed.

Data Governance management and oversight: Monitoring compliance with Data Governance policies and procedures, and identifying areas for improvement.

Data Governance education: Providing education and training to employees on the importance of Data Governance and their roles and responsibilities in maintaining data quality, security, and privacy.

Data Governance culture: Creating a culture of Data Governance throughout the organization to ensure that data is valued and managed as a strategic asset.

A Data Governance strategy will help you identify the data sources that are most critical to your business, determine the level of data quality required for each source, and establish processes for ensuring that data quality is maintained.

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Measuring Business Value – Quantifying Success and Improvement

One of the significant challenges in standing up a data governance program is the daunting amount of data that organizations today have. It seems overwhelming…so where do you start?

In our experience there is some set of data that has more business value than other data. Sometimes we call this critical data. Critical data elements might include: key transactional data, financial data, or regulated data (e.g. PII, HIPAA, etc). Part of the Data Governance strategy and charter will define the critical data and areas that the program needs to impact. The success metrics need to be aligned with the strategy, and this may include metrics for the handling, quality, and security of these data elements.

Additionally, the Data Governance program itself will have metrics associated with it. These may include key milestones such as when the Data Governance Charter and Policy were approved, how many systems are in the adoption phase of Data Governance, and when the initial set of critical data were identified.

The bottom line is that Metrics Drive Behavior, and since Data Governance is largely about modifying the organization’s behavior regarding data, we must have a set of initial (and improving) metrics to understand the impact of the Data Governance program and the value to the organization.

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Data Governance Tools are Key To Enabling and Accelerating Efforts

Once a Data Governance strategy is in place, you’ll want to evaluate which tools and technologies are needed to delivery on your strategy and roadmap. Technology is undoubtedly a cornerstone of getting your Data Governance efforts firing on all cylinders without placing huge additional demands on your team members.

There are a wide variety of tools that DMG has used to help enterprises elevate their Data Governance efforts. These include:

• Data Quality Management tools like Informatica Data Quality and Talend Data Quality; and AI tools such as DataRobot, Trifacta, and DataWrangler by Google Cloud

• BI tools like Qlik, Tableau, and PowerBI

• ETL tools like Talend, Microsoft SSIS, and Oracle Data Integrator

• Data Governance tools like Collibra, Alation, and BigID

• Cloud-based data quality services like Microsoft Azure Data Factory and AWS Data Quality Services.

There are many other tools and methods available. These are representative examples. DMG is partners with or certified in a number of Data Governance tools and technologies. We regularly assist customers with optimization of their existing tools and selection and implementation of new tools based on their Data Governance strategy.

Let’s Consider Implementation Best Practices

Once a Data Governance strategy, success metrics, and the requisite Data Governance tools are in place, it’s time to move on to the most rewarding parts of your Data Governance efforts: implementation, execution, and iteration. In our next two posts, we’ll look at best practices we guide customers through to ensure their Data Governance efforts are successful with minimal impact to their teams’ workload. 

You can learn more about our lean approach to Data Governance and schedule a complimentary consultation today if your organization would like outside expertise formalizing or improving your existing data governance approach.

5 Keys to Establishing a Successful Data Governance Program

Data Management Group (DMG) works regularly with clients who are interested in establishing a Data Governance program, but may not know exactly what that entails.  Data Governance is a powerful approach to aligning interests in, and furthering improvements in Data Quality and Accountability.

It’s worth mentioning that Data Governance is the foundation of Data Management, and it plays a critical role in ensuring that data is managed in a way that supports the goals and objectives of an organization. As businesses continue to rely on data to make decisions, Data Governance has become a vital component of the modern business landscape.

What is Data Governance?

Data Governance refers to the overall management of the availability, usability, integrity, and security of the data used in an organization. It includes the processes, policies, standards, and technologies that are used to ensure that data is managed effectively across the enterprise. The importance of Data Governance in today’s business landscape cannot be overstated, as it provides a framework for organizations to manage data effectively, efficiently, and in a compliant manner.

The Benefits of Data Governance 

One of the primary benefits of Data Governance is that it helps organizations make better decisions. By ensuring that data is accurate, reliable, and timely, Data Governance enables organizations to make informed decisions based on reliable information. This is particularly important in today’s fast-paced business environment, where decisions need to be made quickly and based on the most up-to-date information available.

Another key benefit of Data Governance is that it helps organizations manage risk. As data becomes more valuable and more vulnerable to cyber-attacks, Data Governance provides a framework for managing data security and privacy risks. By establishing clear policies and procedures for managing data, organizations can reduce the risk of data breaches and ensure that data is used in a compliant manner.

In addition to managing risk, Data Governance can also help organizations comply with regulatory requirements. Many industries, like financial services and healthcare, are subject to strict regulations that govern how data should be managed, stored, and used. Looking beyond specific industries, consumer protection laws like GDPR and the California Consumer Privacy Act (CCPA) also have vast ramifications for businesses of all shapes and sizes. Data Governance provides a framework for ensuring that organizations comply with these regulations, reducing the risk of fines and other penalties.

Data Governance is also essential for managing data quality. Poor data quality can lead to costly errors, lost revenue, and damaged reputation. Industry estimates show that more than $3 trillion dollars was lost annually due to bad data all the way back in 2016. With the amount of data in the world estimated to be doubling every two years, the annual dollar amount lost due to bad data is undoubtedly even more staggering today.

Data Governance ensures that data is accurate, complete, and consistent, improving the overall quality of the data used in an organization. This, in turn, can lead to improved decision-making and better business outcomes.

5 Keys to Establishing a Successful Data Governance Program

Data Governance can be complex and difficult to implement. However, with the right approach, organizations can overcome these complexities and reap the benefits of an effective Data Governance program. Here are some key steps to establishing a successful Data Governance program:

1. Define Data Governance goals and objectives. Without clear goals and objectives, Data Governance programs will flounder. Goals and objectives will help ensure that the program is aligned with the needs of the organization — data quality, data security, data integrity, data access — and that the right resources are allocated to achieve these goals.
2. Define Data Governance roles and responsibilities. Data Governance requires a team effort, and it’s important to define clear roles and responsibilities for everyone involved. These include defining the roles of the Data Governance team, data stewards, and other stakeholders.
3. Develop Data policies and procedures. Once roles and responsibilities are defined, it’s essential to develop clear policies and procedures for managing data. This includes defining data standards, data quality requirements, and data security and privacy policies.
4. Establish Data Quality and Governance metrics and measurement. Data Governance requires ongoing monitoring and measurement to ensure that it’s effective. Establishing clear metrics and measurement criteria will help ensure that the program is achieving its goals and identify areas for improvement.
5. Engage Data Governance stakeholders. Effective Data Governance requires the engagement of all stakeholders. This includes executive sponsors, business users, and IT staff. Engaging stakeholders early in the process will help ensure that the program is aligned with the needs of the organization and that everyone is invested in its success.

Data Governance at Data Management Group

Data Management Group works every day with companies that need a framework for managing their data, as a strategic approach to Data Governance is essential in today’s business landscape. We provide companies with a framework for managing their data effectively, efficiently, and in a compliant manner. This leads to improved decision-making, reduced risk, and improved data quality. While Data Governance can be challenging to implement, organizations that are successful at creating Data Governance programs stand to improve data quality and business planning, increase security, and mitigate risk. 


Why Data Security Continues to be Job #1 For Financial Services Companies

Data security is paramount when you’re dealing with customers’ sensitive financial information. A recent Verizon research study showed that nearly 90% of all data breaches are financially motivated. And this timeline of 200+ serious cyber incidents involving financial institutions since 2007 barely scratches the surface of the actual number of incidents. 

Simply put, if you’re in the Financial Services space, data security is at the top of your list of concerns.

Data security has always been a significant focus area, and it became a pressing issue for one of our customers late last year when a vulnerability in the commonly-used Apache Log4j software library was identified. This client wanted to do everything in their power to ensure that their customer data and employee data was secure.

In a matter of weeks, we worked with them to ensure that all of their 1,000+ employees’ devices — whether laptop, desktop, or mobile phone — were fortified against security vulnerabilities, including unpatched software and phishing attacks.

What’s at Stake? Only the Fate of Your Company

The consequences can be earth-shattering for companies that don’t comply with regulations to protect consumer data quickly enough.

Equifax, to take one prominent example, was forced to pay $700 million in 2019 to settle lawsuits after they failed to take steps to protect consumer data by patching a known vulnerability in their database. The resulting breach exposed the personal information of 147 million Equifax customers. In addition to the hefty fines, the incident caused untold, ongoing amounts of damage to Equifax’s reputation. Equifax was still being publicly flogged for the incident five years after the breach in a press release from the FTC on the Log4j vulnerability.

As Equifax can attest, there is such a thing as bad publicity when it comes to data security. Here are a few ways we advise customers on how to ensure their data stays secure so they can stay out of the headlines.

Protect Data Anywhere & Everywhere, No Matter the Device

As the working world has increasingly gone remote, the need to protect data no matter where it lives and how it’s accessed has also become a top priority for many companies. It doesn’t matter where the data is stored — public cloud, private cloud, or on-prem — and it doesn’t matter where it’s accessed or what device it’s accessed on; the needs remain the same. Threat protection, threat detection, and rapid incident response are not just nice-to-haves, they are must-haves.

Fortunately, technology providers like Microsoft have robust security products already in-market that can help companies handle these areas and remediate any incidents quickly. If your organization has Microsoft 365, you have a full set of data security tools at your fingertips.

In the Log4j example referenced above, we had already installed and configured Microsoft Defender for Endpoint, so we leveraged them to run vulnerability assessments for all employee devices, and we used Microsoft Endpoint Manager for remediation.

Defender for Endpoint identified vulnerable devices and software shortly after the Log4j vulnerability became public. Within a matter of weeks, the company’s remediation efforts had reduced the number of vulnerable devices from 200 to 50. In under 8 weeks, there were 0 devices that had security vulnerabilities.

We accomplished a lot using Endpoint Manager. Endpoint Manager allows IT/security teams to easily manage settings and deploy patches for devices across most platforms. This enabled the security team to rapidly whittle down the devices with vulnerabilities all the way to none. The device landscape that Endpoint Manager can be used on includes iOS and Android phones and tablets, Windows desktops and laptops, and Apple macOS machines.

Reign in Application Sprawl to Limit Attacks that Slip Through the Cracks

There’s great value in having one tool to rule them all, or at least as few tools as possible, when it comes to your company’s data security. The splintered market of cybersecurity tools and technologies can actually have the opposite effect of what’s intended.

Fragmented best of breed approaches can make companies less secure, while also costing them significantly more in ongoing licensing costs.

A recent study cited by CIO Dive found that security departments use, on average, 78 apps. At the same time, 75% of IT leaders said that security was their top concern regarding app sprawl. If you’re using anywhere close to 78 security apps, it’s not hard to envision a scenario where a cyberattack could slip through your defenses undetected because, well, doesn’t one of the other security apps have that angle covered?

How Can We Help Secure Your Data?

If you take an integrated approach to data security, limiting the tools you use to the most discrete set possible, you’ll be well on your way to ensuring your customer data remains out of sight of prying eyes.

Taking this approach had the following benefits for our client:

    • Provided a clearer picture of the overall health of their systems and data security efforts
    • Eliminated vulnerabilities that could have had major negative financial and reputational impact
    • Helped simplify and streamline their data security suite of tools, while also training them on underutilized capabilities of MS 365
    • Allowed them to reduce ongoing costs by removing dependencies on extraneous third-party apps

If you feel like your data security efforts could use a boost in one or all of these areas, please don’t hesitate to schedule a free consultation with our team.

Securing Your Mobile Tech Stack

Mobile development platforms and languages evolve so quickly these days that they seem to age in dog years. Today’s technology choice du jour may very well be tomorrow’s albatross around your development team’s neck.

Keeping your mobile development tech stack up to date and secure in this environment is a challenge for even the most Agile organizations and tech-savvy leaders. It was no surprise, then, when a customer in the FinTech industry recently came to DMG looking for a recommendation on how to move their mobile efforts beyond the Xamarin platform. 

The Impetus for Platform Change

The company’s leaders knew they needed to modernize their mobile tech stack to provide their customers with the best possible mobile experience. Mobile has clearly been a key consideration for companies large and small for years, but the pandemic has only served to heighten consumer expectations around what they can and can’t do on their mobile devices.

Increasingly, the answer is that consumers want to do it all via mobile. Perhaps most important of all for this customer, consumers don’t mind using mobile apps to track their overall financial health and plan their biggest purchases, like new homes and new cars.

Consumers’ growing comfort with mobile to manage financial wellness was a trend our customer recognized and wanted to have the ability to capitalize on, but they knew they needed to present the best possible mobile experience to users to compete with the Mints and consumer banking apps of the world. It was becoming more and more apparent to them that a mobile experience commensurate with the high consumer expectations for mobile apps simply wouldn’t be possible with Xamarin.  

Why Xamarin, Specifically, Needed to Be Replaced

Xamarin is a .NET application development platform that allows you to write cross-platform apps that can run on any device. Unfortunately for organizations that use it, Microsoft deprecated support for a key component of Xamarin, Xamarin.Forms, in November 2021.

Combined with Xamarin’s inherent limitations on access to device-specific capabilities like GPS, the deprecation was consequential enough that it made the customer’s tough decision — to move away from Xamarin as their app development platform and on to something else — an easy one.  

What would the right technology choice be? That’s where we came in to help. 

Evaluating Options, and Landing on MAUI (Blazor)

DMG worked with the customer to develop a robust analysis on potential Xamarin replacements that was based on the following criteria: 

  • Business needs and other technologies used in their tech stack 
  • Features they wanted to include in their mobile apps that were unable to with Xamarin
  • Technology licensing costs
  • Ramp time for existing team to learn a new app development platform

Ultimately, we ended up selecting MAUI Blazor and helping them make a quick, seamless transition to what is essentially Microsoft’s replacement for Xamarin.

The biggest benefit for our customer is that their dev team can still write one set of code and have it run anywhere, but they can now also tap into mobile device-specific functionality. What’s more, their mobile tech stack has them well prepared for the future based on where Microsoft will be spending their R&D dollars in the mobile development space. Last but not least, our customer is delivering an improved mobile experience to their customers that puts them on par with the competition and makes their existing customers more likely to remain so.