Crypto News

Crypto exchange Coinbase is pausing its retail staking service in California, New Jersey, South Carolina and Wisconsin after those states said they would require changes to those services while its proceedings against such staking services move forward. Crypto staked before the orders were issued will remain unaffected.

Enterprises are often defined by how they deal with events that are out of their control. For example, how you react to a disruptive technology or cope with a sudden change in the markets can be the difference between success and failure.

Contingency planning is the art of preparing for the unexpected. But where do you start and how do you separate the threats that could do real harm to your business from the ones that aren’t as critical?

Here are some important definitions, best practices and strong examples to help you build contingency plans for whatever your business faces.

What is a contingency plan?

Business contingency plans, also known as “business continuity plans” or “emergency response plans” are action plans to help organizations resume normal business operations after an unintended interruption. Organizations build contingency plans to help them face a variety of threats, including natural disasters, mergers of rival companies, data loss, network breaches and sudden shifts in customer demand.

A good place to start is with a series of “what if” questions that propose various worst-case scenarios you’ll need to have a plan for. For example:

• What if a fire broke out in the warehouse where you keep confidential customer records?
• What if your top three engineers all quit at the same time?
• What if the country where your microprocessors are built was suddenly invaded?

Good contingency plans prioritize the risks an organization faces, delegate responsibility to members of the response teams and increase the likelihood that the company will make a full recovery after a negative event.

Five steps to build a strong contingency plan

1. Make a list of risks and prioritize them according to likelihood and severity.

In the first stage of the contingency planning process, stakeholders brainstorm a list of potential risks the company faces and conduct risk analysis on each one. Team members discuss possible risks, analyze the risk impact of each one and propose courses of action to increase their overall preparedness. You don’t need to create a risk management plan for every threat your company faces, just the ones your decision-makers assess as both highly likely and with a potential impact on normal business processes.

2. Create a business impact analysis (BIA) report

Business impact analysis (BIA) is a crucial step in understanding how the different business functions of an enterprise will respond to unexpected events. One way to do this is to look at how much company revenue is being generated by the business unit at risk. If the BIA indicates that it’s a high percentage, the company will most likely want to prioritize creating a contingency plan for this business risk.

3. Make a plan

For each potential threat your company faces that has both a high likelihood of occurring and a high potential impact on business operations, you can follow these three simple steps to create a plan:

• Identify triggers that will set a plan into action: For example, if a hurricane is approaching, when does the storm trigger your course of action? When it’s 50 miles away? 100 miles? Your teams will need clear guidance so they will know when to start executing the actions they’ve been assigned.
• Design an appropriate response: The threat your organization prepared for has arrived and teams are springing into action. Everyone involved will need clear, accessible instructions, protocols that are easy to follow and a way to communicate with other stakeholders.
• Delegate responsibility clearly and fairly: Like any other initiative, contingency planning requires effective project management to succeed. One proven way to address this is to create a RACI chart. RACI stands for responsible, accountable, consulted and informed, and it is widely used in crisis management to help teams and individuals delegate responsibility and react to crises in real time.

4. Get buy-in from the entire organization—and be realistic about cost

Sometimes it can be hard to justify the importance of putting resources into preparing for something that might never happen. But if the events of these past few years have taught us anything, it’s that having strong contingency plans is invaluable.

Think of the supply chain problems and critical shortages wreaked by the pandemic or the chaos to global supply chains brought about by Russia’s invasion of Ukraine. When it comes to convincing business leaders of the value of having a strong Plan B in place, it’s important to look at the big picture—not just the cost of the plan but the potential costs incurred if no plan is put in place.

5. Test and reassess your plans regularly

Markets and industries are constantly shifting, so the reality that a contingency plan faces when it is triggered might be very different than the one it was created for. Plans should be tested at least once annually, and new risk assessments performed.

Contingency plan examples

Here are some model scenarios that demonstrate how different kinds of businesses would prepare to face risks. The three-step process outlined here can be used to create contingency plans templates for whatever threats your organization faces.

A network provider facing a massive outage

What if your core business was so critical to your customers that downtime of even just a few hours could result in millions of dollars in lost revenue? Many internet and cellular networks face this challenge every year. Here’s an example of a contingency plan that would help them prepare to face this problem:

1. Assess the severity and likelihood of the risk: A recent study by Open Gear showed that only 9% of global organizations avoid network outages in an average quarter. Coupled with what is known about these attacks—that they can cause millions of dollars in damage and take an immeasurable toll on business reputation—this risk would have to be considered both highly likely and highly severe in terms of the potential damage it could do to the company.
2. Identify the trigger that will set your plan in action: In this example, what signs should decision-makers have watched for to know when a likely outage was beginning? These might include security breaches, looming natural disasters or any other event that has preceded outages in the past.
3. Create the right response: The organization’s leaders will want to determine a reasonable recovery time objective (RTO) and recovery point objective (RPO) for each service and data category their company faces. RTO is usually measured with a simple time metric, such as days, hours or minutes. RPO is a bit more complicated as it involves determining the minimum/maximum age of files that can be recovered quickly from backup systems in order to restore the network to normal operations.  

A food distribution company coping with an unexpected shortage

If your core business has complex supply chains that run through different regions and countries, monitoring geopolitical conditions in those places will be critical to maintaining the health of your business operations. In this example, we’ll look at a food distributor preparing to face a shortage of a much-needed ingredient due to volatility in a region that’s critical to its supply chain:

1. Assess the severity and likelihood of the risk: The company’s leaders have been following the news in the region where they source the ingredient and are concerned about the possibility of political unrest. Since they need this ingredient to make one of their best-selling products, both the likelihood and potential severity of this risk are rated as high.
2. Identify the trigger that will set your plan in action: War breaks out in the region, shutting down all ports of entry/exit and severely restricting transport within the country via air, roads and railroads. Transportation of their ingredient will be impossible until stability returns to the region.
3. Create the right response: The company’s business leaders create a two-pronged contingency plan to help them face this problem. First, they proactively search for alternate suppliers of this ingredient in regions that aren’t so prone to volatility. These suppliers may cost more and take time to switch to, but when the overall cost of a general production disruption that would come about in the event of war is factored in, the cost is worth it. Second, they look for an alternative to this ingredient that they can use in their product.

A social network experiencing a customer data breach

The managers of a large social network know of a cybersecurity risk in their app that they are working to fix. In the event that they’re hacked before they fix it, they are likely to lose confidential customer data:

1. Assess the severity and likelihood of risk: They rate the likelihood of this event as high, since, as a social network, they are a frequent target of attacks. They also rate the potential severity of damage to the company as high since any loss of confidential customer data will expose them to lawsuits.
2. Identify the trigger that will set your plan in action: Engineers make the social network’s leadership aware that an attack has been detected and that their customer’s confidential information has been compromised.
3. Create the right response: The network contracts with a special response team to come to their aid in the event of an attack and help them secure their information systems and restore app functionality. They also change their IT infrastructure to make customer data more secure. Lastly, they work with a reputable PR firm to prepare a plan for outreach and messaging to reassure customers in the event that their personal information is compromised.

The value of contingency planning 

When business operations are disrupted by a negative event, good contingency planning gives an organization’s response structure and discipline. During a crisis, decision-makers and employees often feel overwhelmed by the pile-up of events beyond their control, and having a thorough backup plan helps reestablish confidence and return operations to normal.  

Here are a few benefits organizations can expect from strong contingency plans:

• Improved recovery times: Businesses with good plans in place recover faster from a disruptive event than companies that haven’t prepared.  
• Reduced costs—financial and reputational: Good contingency plans minimize both financial and reputational damage to a company. For example, while a data breach at a social network that compromises customer information could result in lawsuits, it could also cause long-term damage if customers decide to leave the network because they no longer trust the company to keep their personal information safe.
• Greater confidence and morale: Many organizations use contingency plans to show employees, shareholders and customers that they’ve thought through every possible eventuality that might befall their company, giving them confidence that the company has their interests in mind.

Contingency plan solutions

IBM Maximo Application Suite is an integrated cloud-based solution that helps businesses respond quickly to changing conditions. By combining the power of artificial intelligence (AI), Internet of Things (IoT) and advanced analytics, it enables organizations to maximize the performance of their most valuable assets, lengthen their lifespans and minimize costs and downtime.

Learn more about IBM Maximo Application Suite

In short, yes. When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality uses those criteria to measure the level of data integrity and, in turn, its reliability and applicability for its intended use. Data quality and integrity are vital to a data-driven organization that employs analytics for business decisions, offers self-service data access for internal stakeholders and provides data offerings to customers.

Data integrity

To achieve a high level of data integrity, an organization implements processes, rules and standards that govern how data is collected, stored, accessed, edited and used. These processes, rules and standards work in tandem to:

• Validate data and input
• Remove duplicate data
• Provide data backups and ensure business continuity
• Safeguard data via access controls
• Maintain an audit trail for accountability and compliance

An organization can use any number of tools and private or public cloud environments throughout the data lifecycle to maintain data integrity through something known as data governance. This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches.

The benefits of data integrity

An organization with a high level of data integrity can:

• Increase the likelihood and speed of data recoverability in the event of a breach or unplanned downtime
• Protect against unauthorized access and data modification
• Achieve and maintain compliance more effectively

Good data integrity can also improve business decision outcomes by increasing the accuracy of an organization’s analytics. The more complete, accurate and consistent a dataset is, the more informed business intelligence and business processes become. As a result, leaders are better equipped to set and achieve goals that benefit their organization and drive employee and consumer confidence.

Data science tasks such as machine learning also greatly benefit from good data integrity. When an underlying machine learning model is being trained on data records that are trustworthy and accurate, the better that model will be at making business predictions or automating tasks.

The different types of data integrity

There are two main categories of data integrity: Physical data integrity and logical data integrity.

Physical data integrity is the protection of data wholeness (meaning the data isn’t missing important information), accessibility and accuracy while data is stored or in transit. Natural disasters, power outages, human error and cyberattacks pose risks to the physical integrity of data.

Logical data integrity refers to the protection of data consistency and completeness while it’s being accessed by different stakeholders and applications across departments, disciplines, and locations. Logical data integrity is achieved by:

• Preventing duplication (entity integrity)
• Dictating how data is stored and used (referential integrity)
• Preserving data in an acceptable format (domain integrity)
• Ensuring data meets an organization’s unique or industry-specific needs (user-defined integrity)

How data integrity differs from data security

Data security is a subcomponent of data integrity and refers to the measures taken to prevent unauthorized data access or manipulation. Effective data security protocols and tools contribute to strong data integrity. In other words, data security is the means while data integrity is the goal. Data recoverability — in the event of a breach, attack, power outage or service interruption — falls under the realm of data security.

The consequences of poor data integrity

Human errors, transfer errors, malicious acts, insufficient security and hardware malfunctions all contribute to “bad data,” which negatively impacts an organization’s data integrity. An organization contending with one or more of these issues risks experiencing:

Poor data quality

Low-quality data leads to poor decision-making because of inaccurate and uninformed analytics. Reduced data quality can result in productivity losses, revenue decline and reputational damage.

Insufficient data security

Data that isn’t properly secured is at an increased risk of a data breach or being lost to a natural disaster or other unplanned event. And without proper insight and control over data security, an organization can more easily fall out of compliance with local, regional, and global regulations, such as the European Union’s General Data Protection Regulation.

Data quality

Data quality is essentially the measure of data integrity. A dataset’s accuracy, completeness, consistency, validity, uniqueness, and timeliness are the data quality measures organizations employ to determine the data’s usefulness and effectiveness for a given business use case.

How to determine data quality

Data quality analysts will assess a dataset using dimensions listed above and assign an overall score. When data ranks high across every dimension, it is considered high-quality data that is reliable and trustworthy for the intended use case or application. To measure and maintain high-quality data, organizations use data quality rules, also known as data validation rules, to ensure datasets meet criteria as defined by the organization.

The benefits of good data quality

Improved efficiency

Business users and data scientists don’t have to waste time locating or formatting data across disparate systems. Instead, they can readily access and analyze datasets with greater confidence. Additional time is saved that would have otherwise been wasted on acting on incomplete or inaccurate data.

Increased data value

Because data is formatted consistently and contextualized for the user or application, organizations can derive value from data that may have otherwise been discarded or ignored.

Improved collaboration and better decision-making

High-quality data eliminates incongruency across systems and departments and ensures consistent data across processes and procedures. Collaboration and decision-making among stakeholders are improved because they all rely on the same data.

Reduced costs and improved regulatory compliance

High-quality data is easy to locate and access. Because there is no need to re-create or track down datasets, labor costs are reduced, and manual data entry errors become less likely. And because high-quality data is easy to store in the correct environment as well as collect and compile in mandatory reports, an organization can better ensure compliance and avoid regulatory penalties.

Improved employee and customer experiences

High-quality data provides more accurate, in-depth insights an organization can use to provide a more personalized and impactful experience for employees and customers.

The six dimensions of data quality

To determine data quality and assign an overall score, analysts evaluate a dataset using these six dimensions, also known as data characteristics:

1. Accuracy: Is the data provably correct and does it reflect real-world knowledge?
2. Completeness: Does the data comprise all relevant and available information? Are there missing data elements or blank fields?
3. Consistency: Do corresponding data values match across locations and environments?
4. Validity: Is data being collected in the correct format for its intended use?
5. Uniqueness: Is data duplicated or overlapping with other data?
6. Timeliness: Is data up to date and readily available when needed?

The higher a dataset scores in each of these dimensions, the greater its overall score. A high overall score indicates that a dataset is reliable, easily accessible, and relevant.

How to improve data quality

Some common methods and initiatives organizations use to improve data quality include:

Data profiling

Data profiling, also known as data quality assessment, is the process of auditing an organization’s data in its current state. This is done to uncover errors, inaccuracies, gaps, inconsistent data, duplications, and accessibility barriers. Any number of data quality tools can be used to profile datasets and detect data anomalies that need correction.

Data cleansing

Data cleansing is the process of remediating the data quality issues and inconsistencies discovered during data profiling. This includes the deduplication of datasets, so that multiple data entries don’t unintentionally exist in multiple locations.

Data standardization

This is the process of conforming disparate data assets and unstructured big data into a consistent format that ensures data is complete and ready for use, regardless of data source. To standardize data, business rules are applied to ensure datasets conform to an organization’s standards and needs.

Geocoding

Geocoding is the process of adding location metadata to an organization’s datasets. By tagging data with geographical coordinates to track where it originated from, where it has been and where it resides, an organization can ensure national and global geographic data standards are being met. For example, geographic metadata can help an organization ensure that its management of customer data stays compliant with GDPR.

Matching or linking

This is the method of identifying, merging, and resolving duplicate or redundant data.

Data quality monitoring

Maintaining good data quality requires continuous data quality management. Data quality monitoring is the practice of revisiting previously scored datasets and reevaluating them based on the six dimensions of data quality. Many data analysts use a data quality dashboard to visualize and track data quality KPIs.

Batch and real-time validation

This is the deployment of data validation rules across all applications and data types at scale to ensure all datasets adhere to specific standards. This can be done periodically as a batch process, or continuously in real time through processes like change data capture.

Master data management

Master data management (MDM) is the act of creating and maintaining an organization-wide centralized data registry where all data is cataloged and tracked. This gives the organization a single location to quickly view and assess its datasets regardless of where that data resides or its type. For example, customer data, supply chain information and marketing data would all reside in an MDM environment.

Data integrity, data quality and IBM

IBM offers a wide range of integrated data quality and governance capabilities including data profiling, data cleansing, data monitoring, data matching and data enrichment to ensure data consumers have access to trusted, high-quality data. IBM’s data governance solution helps organizations establish an automated, metadata-driven foundation that assigns data quality scores to assets and improves curation via out-of-the-box automation rules to simplify data quality management.

With data observability capabilities, IBM can help organizations detect and resolve issues within data pipelines faster. The partnership with Manta for automated data lineage capabilities enables IBM to help clients find, track and prevent issues closer to the source.

Learn more about designing the right data architecture to elevate your data quality here.

“Despite market volatility, a fall in digital asset prices and the collapse of a number of crypto businesses, investment in crypto-assets is expected to remain strong in 2023,” Jon Garvey, PwC United States’ global financial services leader, said. “Traditional hedge funds, committed to the market in the longer term, are not only increasing their crypto-assets under management, but also maintaining – if not increasing – the amount of capital deployed in the ecosystem.”