Article | July 19, 2021
In an era of big data, data health has become a pressing issue when more and more data is being stored and processed. Therefore, preserving the integrity of the collected data is becoming increasingly necessary. Understanding the fundamentals of data integrity and how it works is the first step in safeguarding the data.
Data integrity is essential for the smooth running of a company. If a company’s data is altered, deleted, or changed, and if there is no way of knowing how it can have significant impact on any data-driven business decisions.
Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is the overall accuracy, completeness, and consistency of data. It can be indicated by lack of alteration between two updates of a data record, which means data is unchanged or intact. Data integrity refers to the safety of data regarding regulatory compliance- like GDPR compliance- and security. A collection of processes, rules, and standards implemented during the design phase maintains the safety and security of data.
The information stored in the database will remain secure, complete, and reliable no matter how long it’s been stored; that’s when you know that the integrity of data is safe. A data integrity framework also ensures that no outside forces are harming this data.
This term of data integrity may refer to either the state or a process. As a state, the data integrity framework defines a data set that is valid and accurate. Whereas as a process, it describes measures used to ensure validity and accuracy of data set or all data contained in a database or a construct.
Data integrity can be enforced at both physical and logical levels. Let us understand the fundamentals of data integrity in detail:
Types of Data Integrity
There are two types of data integrity: physical and logical. They are collections of processes and methods that enforce data integrity in both hierarchical and relational databases.
Physical integrity protects the wholeness and accuracy of that data as it’s stored and retrieved. It refers to the process of storage and collection of data most accurately while maintaining the accuracy and reliability of data. The physical level of data integrity includes protecting data against different external forces like power cuts, data breaches, unexpected catastrophes, human-caused damages, and more.
Logical integrity keeps the data unchanged as it’s used in different ways in a relational database. Logical integrity checks data accuracy in a particular context. The logical integrity is compromised when errors from a human operator happen while entering data manually into the database. Other causes for compromised integrity of data include bugs, malware, and transferring data from one site within the database to another in the absence of some fields.
There are four types of logical integrity:
A database has columns, rows, and tables. These elements need to be as numerous as required for the data to be accurate, but no more than necessary. Entity integrity relies on the primary key, the unique values that identify pieces of data, making sure the data is listed just once and not more to avoid a null field in the table. The feature of relational systems that store data in tables can be linked and utilized in different ways.
Referential integrity means a series of processes that ensure storage and uniform use of data. The database structure has rules embedded into them about the usage of foreign keys and ensures only proper changes, additions, or deletions of data occur. These rules can include limitations eliminating duplicate data entry, accurate data guarantee, and disallowance of data entry that doesn’t apply. Foreign keys relate data that can be shared or null. For example, let’s take a data integrity example, employees that share the same work or work in the same department.
Domain Integrity can be defined as a collection of processes ensuring the accuracy of each piece of data in a domain. A domain is a set of acceptable values a column is allowed to contain. It includes constraints that limit the format, type, and amount of data entered. In domain integrity, all values and categories are set. All categories and values in a database are set, including the nulls.
This type of logical integrity involves the user's constraints and rules to fit their specific requirements. The data isn’t always secure with entity, referential, or domain integrity. For example, if an employer creates a column to input corrective actions of the employees, this data would fall under user-defined integrity.
Difference between Data Integrity and Data Security
Often, the terms data security and data integrity get muddled and are used interchangeably. As a result, the term is incorrectly substituted for data integrity, but each term has a significant meaning.
Data integrity and data security play an essential role in the success of each other. Data security means protecting data against unauthorized access or breach and is necessary to ensure data integrity.
Data integrity is the result of successful data security. However, the term only refers to the validity and accuracy of data rather than the actual act of protecting data. Data security is one of the many ways to maintain data integrity. Data security focuses on reducing the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some facets of data security tactics include permissions management, data classification, identity, access management, threat detection, and security analytics.
For modern enterprises, data integrity is necessary for accurate and efficient business processes and to make well-intentioned decisions. Data integrity is critical yet manageable for organizations today by backup and replication processes, database integrity constraints, validation processes, and other system protocols through varied data protection methods.
Threats to Data Integrity
Data integrity can be compromised by human error or any malicious acts. Accidental data alteration during the transfer from one device to another can be compromised. There is an assortment of factors that can affect the integrity of the data stored in databases. Following are a few of the examples:
Data integrity is put in jeopardy when individuals enter information incorrectly, duplicate, or delete data, don’t follow the correct protocols, or make mistakes in implementing procedures to protect data.
A transfer error occurs when data is incorrectly transferred from one location in a database to another. This error also happens when a piece of data is present in the destination table but not in the source table in a relational database.
Bugs and Viruses
Data can be stolen, altered, or deleted by spyware, malware, or any viruses.
Hardware gets compromised when a computer crashes, a server gets down, or problems with any computer malfunctions. Data can be rendered incorrectly or incompletely, limit, or eliminate data access when hardware gets compromised.
Preserving Data Integrity
Companies make decisions based on data. If that data is compromised or incorrect, it could harm that company to a great extent. They routinely make data-driven business decisions, and without data integrity, those decisions can have a significant impact on the company’s goals.
The threats mentioned above highlight a part of data security that can help preserve data integrity. Minimize the risk to your organization by using the following checklist:
Require an input validation when your data set is supplied by a known or an unknown source (an end-user, another application, a malicious user, or any number of other sources). The data should be validated and verified to ensure the correct input.
Verifying data processes haven’t been corrupted is highly critical. Identify key specifications and attributes that are necessary for your organization before you validate the data.
Eliminate Duplicate Data
Sensitive data from a secure database can easily be found on a document, spreadsheet, email, or shared folders where employees can see it without proper access. Therefore, it is sensible to clean up stray data and remove duplicates.
Data backups are a critical process in addition to removing duplicates and ensuring data security. Permanent loss of data can be avoided by backing up all necessary information, and it goes a long way. Back up the data as much as possible as it is critical as organizations may get attacked by ransomware.
Another vital data security practice is access control. Individuals in an organization with any wrong intent can harm the data. Implement a model where users who need access can get access is also a successful form of access control. Sensitive servers should be isolated and bolted to the floor, with individuals with an access key are allowed to use them.
Keep an Audit Trail
In case of a data breach, an audit trail will help you track down your source. In addition, it serves as breadcrumbs to locate and pinpoint the individual and origin of the breach.
Data collection was difficult not too long ago. It is no longer an issue these days. With the amount of data being collected these days, we must maintain the integrity of the data. Organizations can thus make data-driven decisions confidently and take the company ahead in a proper direction.
Frequently Asked Questions
What are integrity rules?
Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution.
What is a data integrity example?
Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are:
• When a user tries to enter a date outside an acceptable range
• When a user tries to enter a phone number in the wrong format
• When a bug in an application attempts to delete the wrong record
What are the principles of data integrity?
The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives.
"name": "What are integrity rules?",
"text": "Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution."
"name": "What is a data integrity example?",
"text": "Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are:
When a user tries to enter a date outside an acceptable range
When a user tries to enter a phone number in the wrong format
When a bug in an application attempts to delete the wrong record"
"name": "What are the principles of data integrity?",
"text": "The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives."
Article | April 6, 2020
Today when we look around, we see how technology has revolutionized our world. It has created amazing elements and resources, putting useful intelligence at our fingertips. With all of these revolutions, technology has also made our lives easier, faster, digital and fun. Perhaps at a point when we are talking about technology, Machine learning and artificial intelligence are increasingly popular buzzwords used in modern terms.Machine Learning has proven to be one of the game changer technological advancements of the past decade. In the increasingly competitive corporate world, Machine learning is enabling companies to fast-track digital transformation and move into an age of automation. Some might even argue that AI/ML is required to stay relevant in some verticals, such as digital payments and fraud detection in banking or product recommendations.To understand what machine learning is, it is important to know the concepts of artificial intelligence (AI). It is defined as a program that exhibits cognitive ability similar to that of a human being. Making computers think like humans and solve problems the way we do is one of the main tenets of artificial intelligence.
Article | April 20, 2020
Achieving organizational success and making data-driven decisions in 2020 requires embracing tech tools like Data Analytics and collecting, storing and analysing data isn’t.The real data-driven, measurable growth, and development come with the establishment of data-driven company culture.In this type of culture company actively uses data resources as a primary asset to make smart decisions and ensure future growth.
Despite the rapid growth of analytic solutions, a recent Gartner survey revealed that almost 75% of organizations thought their analytics maturity had not reached a level that optimized business outcomes. Just like with any endeavor, your organization must have a planned strategy to achieve its analytical goals. Let’s explore ways for overcoming common blockers, and elements used in successful analytics adoption strategies.
Table of Contents:
- AMM: Analytic Maturity Model
- What are the blockers to achieving a strategy-driven analytics?
- What are the adoption strategies to achieve an analytics success?
AMM: Analytic Maturity Model
The Analytic Maturity Model (AMM) evaluates the analytic maturity of an organization.The model identifies the five stages an organization travels through to reach optimization. Organizations must implement the right tools, engage their team in proper training, and provide the management support necessary to generate predictable outcomes with their analytics. Based on the maturity of these processes, the AMM divides
organizations into five maturity levels:
- Organizations that can build reports.
- Organizations that can build and deploy models.
- Organizations that have repeatable processes for building and deploying analytics.
- Organizations that have consistent enterprise-wide processes for analytics.
- Enterprises whose analytics is strategy driven.
READ MORE:EFFECTIVE STRATEGIES TO DEMOCRATIZE DATA SCIENCE IN YOUR ORGANIZATION
What are the blockers to achieving a strategy-driven analytics?
- Missing an Analytics Strategy
- Analytics is not for everyone
- Data quality presents unique challenges
- Siloed Data
- Changing the culture
What are the adoption strategies to achieve analytic success?
• Have you got a plan to achieve analytic success?
The strategy begins with business intelligence and moves toward advanced analytics. The approach differs based on the AMM level. The plan may address the strategy for a single year, or it may span 3 or more years. It ideally has milestones for what the team will do. When forming an analytics strategy, it can be expensive and time consuming at the outset. While organizations are encouraged to seek projects that can generate quick wins, the truth is that it may be months before any actionable results are available. During this period, the management team is frantically diverting resources from other high-profile projects. If funds are tight, this situation alone may cause friction. It may not be apparent to everyone how the changes are expected to help. Here are the elements of a successful analytics strategy:
• Keep the focus tied to tangible business outcomes
The strategy must support business goals first. With as few words as possible, your plan should outline what you intend to achieve, how to complete it, and a target date for completion of the plan. Companies may fail at this step because they mistake implementing a tool for having a strategy. To keep it relevant, tie it to customer-focused goals. The strategy must dig below the surface with the questions that it asks. Instead of asking surface questions such as “How can we save money?”, instead ask, “How can we improve the quality of the outcomes for our customers?” or “What would improve the productivity of each worker?” These questions are more specific and will get the results the business wants. You may need to use actual business cases from your organization to think through the questions.
• Select modern, multi-purpose tools
The organization should be looking for an enterprise tool that supports integrating data from various databases, spreadsheets, or even external web based sources. Typically, organizations may have their data stored across multiple databases such as Salesforce, Oracle, and even Microsoft Access. The organization can move ahead quicker when access to the relevant data is in a single repository. With the data combined, the analysts have a specific location to find reports and dashboards. The interface needs to be robust enough to show the data from multiple points of view. It should also allow future enhancements, such as when the organization makes the jump into data science.
Incorta’s Data Analytics platform simplifies and processes data to provide meaningful information at speed that helps make informed decisions.
Incorta is special in that it allows business users to ask the same complex and meaningful questions of their data that typically require many IT people and data scientist to get the answers they need to improve their line of business. At the digital pace of business today, that can mean millions of dollars for business leaders in finance, supply chain or even marketing. Speed is a key differentiator for Incorta in that rarely has anyone been able to query billions of rows of data in seconds for a line of business owner.
- Tara Ryan, CMO, Incorta
Technology implementations take time. That should not stop you from starting in small areas of the company to look for quick wins. Typically, the customer-facing processes have areas where it is easier to collect data and show opportunities for improvement.
• Ensure staff readiness
If your current organization is not data literate, then you will need resources who understand how to analyze and use data for process improvement. It is possible that you can make data available and the workers still not realize what they can do with it. The senior leadership may also need training about how to use data and what data analytics makes possible.
• Start Small to Control Costs and Show Potential
If the leadership team questions the expense, consider doing a proof of concept that focuses on the tools and data being integrated quickly and efficiently to show measurable success. The business may favor specific projects or initiatives to move the company forward over long-term enterprise transformations (Bean & Davenport, 2019). Keeping the project goals precise and directed helps control costs and improve the business. As said earlier, the strategy needs to answer deeper business questions. Consider other ways to introduce analytics into the business. Use initiatives that target smaller areas of the company to build competencies. Provide an analytics sandbox with access to tools and training to encourage other non-analytics workers (or citizen data scientists) to play with the data. One company formed a SWAT team, including individuals from across the organization. The smaller team with various domain experience was better able to drive results. There are also other approaches to use – the key is to show immediate and desirable results that align with organizational goals.
• Treating the poor data quality
What can you do about poor data quality at your company? Several solutions that can help to improve productivity and reduce the financial impact of poor data quality in your organization include:
• Create a team to set the proper objectives
Create a team who owns the data quality process. This is important to prove to yourself and to anyone with whom you are conversing about data that you are serious about data quality. The size of the team is not as important as the membership from the parts of the organization that have the right impact and knowledge in the process. When the team is set, make sure that they create a set of goals and objectives for data quality. To gauge performance, you need a set of metrics to measure the performance. After you create the proper team to govern your data quality, ensure that the team focuses on the data you need first. Everyone knows the rules of "good data in, good data out" and "bad data in, bad data out." To put this to work, make sure that your team knows the relevant business questions that are in progress across various data projects to make sure that they focus on the data that supports those business questions.
• Focus on the data you need now as the highest priority
Once you do that, you can look at the potential data quality issues associated with each of the relevant downstream business questions and put the proper processes and data quality routines in place to ensure that poor data quality has a low probability of Successful Analytics Adoption Strategies, continuing to affect that data. As you decide which data to focus on, remember that the key for innovators across industries is that the size of the data isn’t the most critical factor — having the right data is (Wessel, 2016).
• Automate the process of data quality when data volumes grow too large
When data volumes become unwieldy and difficult to manage the quality, automate the process. Many data quality tools in the market do a good job of removing the manual effort from the process. Open source options include Talend and DataCleaner. Commercial products include offerings from DataFlux, Informatica, Alteryx and Software AG. As you search for the right tool for you and your team, beware that although the tools help with the organization and automation, the right processes and knowledge of your company's data are paramount to success.
• Make the process of data quality repeatable
It needs regular care and feeding. Remember that the process is not a one-time activity. It needs regular care and feeding. While good data quality can save you a lot of time, energy, and money downstream, it does take time, investment, and practice to do well. As you improve the quality of your data and the processes around that quality, you will want to look for other opportunities to avoid data quality mishaps.
• Beware of data that lives in separate databases
When data is stored in different databases, there can be issues with different terms being used for the same subject. The good news is that if you have followed the former solutions, you should have more time to invest in looking for the best cases. As always, look for the opportunities with the biggest bang for the buck first. You don't want to be answering questions from the steering committee about why you are looking for differences between "HR" and "Hr" if you haven't solved bigger issues like knowing the difference between "Human Resources" and "Resources," for example.
• De-Siloing Data
The solution to removing data silos typically isn’t some neatly packaged, off-the-shelf product. Attempts to quickly create a data lake by simply pouring all the siloed data together can result in an unusable mess, turning more into a data swamp. This is a process that must be done carefully to avoid confusion, liability, and error.
Try to identify high-value opportunities and find the various data stores required to execute those projects. Working with various business groups to find business problems that are well-suited to data science solutions and then gathering the necessary data from the various data stores can lead to high-visibility successes.
As value is proved from joining disparate data sources together to create new insights, it will be easier to get buy-in from upper levels to invest time and money into consolidating key data stores. In the first efforts, getting data from different areas may be akin to pulling teeth, but as with most things in life, the more you do it, the easier it gets.
Once the wheels get moving on a few of these integration projects, make wide-scale integration the new focus. Many organizations at this stage appoint a Chief Analytics Officer (CAO) who helps increase collaboration between the IT and business units ensuring their priorities are aligned. As you work to integrate the data, make sure that you don’t inadvertently create a new “analytics silo.” The final aim here is an integrated platform for your enterprise data.
• Education is essential
When nearly 45% of workers generally prefer status quo over innovation, how do you encourage an organization to move forward? If the workers are not engaged or see the program as merely just the latest management trend, it may be tricky to convince them. Larger organizations may have a culture that is slow to change due to their size or outside forces.
There’s also a culture shift required - moving from experience and knee-jerk reactions to immersion and exploration of rich insights and situational awareness.
- Walter Storm, the Chief Data Scientist, Lockheed Martin
Companies spend a year talking about an approved analytics tool before moving forward. The employees had time to consider the change and to understand the new skill sets needed. Once the entire team embraced the change, the organization moved forward swiftly to convert existing data and reports into the new tool. In the end, the corporation is more successful, and the employees are still in alignment with the corporate strategy.
If using data to support decisions is a foreign concept to the organization, it’s a smart idea to ensure the managers and workers have similar training. This training may involve everything from basic data literacy to selecting the right data for management presentations. However, it cannot stop at the training; the leaders must then ask for the data to move forward with requests that will support conclusions that will be used to make critical decisions across the business.
These methods make it easier to sell the idea and keep the organization’s analytic strategy moving forward. Once senior leadership uses data to make decisions, everyone else will follow their lead. It is that simple.
The analytics maturity model serves as a useful framework for understanding where your organization currently stands regarding strategy, progress, and skill sets.
Advancing along the various levels of the model will become increasingly imperative as early adopters of advanced analytics gain a competitive edge in their respective industries. Delay or failure to design and incorporate a clearly defined analytics strategy into an organization’s existing plan will likely result in a significant missed opportunity.
READ MORE:BIG DATA ANALYTICS STRATEGIES ARE MATURING QUICKLY IN HEALTHCARE
Article | February 17, 2020
In recent years, artificial intelligence research and applications have accelerated at a rapid speed. Simply saying your organization will incorporate AI isn’t as specific as it once was. There are diverse implementation options for AI, Machine Learning, and Deep Learning, and within each of them, a series of different algorithms you can leverage to improve operations and establish a competitive edge. Algorithms are utilized across almost every industry. For example, to power the recommendation engines in all media platforms, the chatbots that support customer service efforts at scale, and the self-driving vehicles being tested by the world’s largest automotive and technology companies. Because of how diverse AI has become and the many ways in which it works with data, companies must carefully evaluate what will work best for them.