DQLabs, Inc.

Introduction

In recent years the technology has become prominent, both at work and at home. Machine learning (ML) and Artificial Intelligence (AI) are evolving quickly today. Almost everyone will have some interaction with a form of AI daily. Some common examples include Siri, Google Maps, Netflix, and Social media (Facebook/Snapchat).AI and ML have popularly used buzzwords right now, often used interchangeably. Most experimentation has been geared to finding specific solutions to specific problems. Artificial Intelligence (AI) is an application in which a machine can perform human-like tasks. At the same time, Machine Learning (ML) is a system that can automatically learn and improve from experience without being directly programmed.

Data quality refers to how relevant information is for use. If information isn’t suitable, you won’t be able to make the right decisions. Data quality is determined by several factors, including; accuracy, completeness, reliability, relevance, and timeliness. If there’s a missing factor or is lower than other factors, your data quality won’t be very high. Read more about what is data quality and why is it important.

Increased data volumes have put companies under pressure to manage and control their data assets systematically. Also, standard data management practices lack sufficient scalability and cannot manage ever-increasing data volumes. Companies, therefore, need to rethink their data management. The good news is that substantial progress in artificial intelligence (AI) and machine learning (ML) through entities such as DQLabs.ai – AI/ML augmented data quality management platform, can support you in your data management activities.

How has AI and ML transformed quality management?

Automatic data capture

Besides data predictions, AI helps improve data quality by automating data entry through executing intelligent capture. This ensures all the valuable information is captured, and there are no gaps in the system.

Recognize duplicate records

Twofold entries of data can lead to outdated records that result in bad data quality. AI helps eliminate duplicate records in an organization’s database and keeps precise gold keys in the database. It is hard to identify and remove recurring entries in a big company’s repository without implementing sophisticated mechanisms. An organization can combat this by having intelligent systems that can detect and remove duplicate keys.

Detect anomalies

A small human mistake can drastically affect the utility and the quality of data in a CRM. An AI-enabled system removes defects in a system. Data quality can also be enhanced through the implementation of machine learning-based anomalies.

Third-party data inclusion

Apart from correcting and maintaining data integrity, AI can improve data quality by adding to it. Third-party organizations and governmental units can significantly add value to the quality of a management system and MDM platforms by presenting better and more complete data, contributing to precise decision making. AI makes suggestions on what to fetch from a particular set of data and the building connections in the data. When a company has detailed and clean data in one place, it has a higher chance of making informed decisions.

Fill data gaps

While many automation systems can cleanse data based on explicit programming rules, it’s almost impossible for them to fill in missing data gaps without manual intervention or plugging in additional data source feeds. However, machine learning can make calculated assessments on missing data based on its reading of the situation.

Assess relevance

On the other end of the scope of missing data, organizations often accumulate a large amount of redundant data over the years that do not have any use in a business context. Using machine learning, the system can self-teach on the data points required and those not needed. Analysis of this kind can help revamp the process and, eventually, make it simpler.

Match and validate data

Coming up with rules to match data collected from various sources can be a time-consuming process. As the number of births increases, this becomes increasingly more challenging. ML models can be trained to learn the rules and predict matches for new data. There is no restriction to the volume of data, and as a matter of fact, more data works favorably in fine-tuning the model.

The cost of bad data

Bad data can prove to be quite expensive for companies. Attempts to quantify the financial impact have resulted in some shocking numbers. It’s also important to remember that decisions based on flawed data can lead to severe consequences in some cases. Machine learning algorithms can flag some of these situations before they get too far. Financial companies use them to identify forged transactions. It’s estimated that ML models can result in a $12 billion savings for card issuers and banks.

Conclusion

Most businesses look for fast analytics with high-quality insights to deliver real-time benefits based on fast decisions. They consider this a high priority and means of competitive advantage. To enable this, there is an opportunity for organizations to fine-tune and enhance the current data quality approach using ML techniques. Many leading data quality tools and solution providers have tried out ML territory in expectation of increasing the effectiveness of their solutions. Thus, it has the chance of being a game-changer for businesses in pursuit of improved data quality. Although the current intake level of the use of ML for data quality assessment and enhancement is low, it has promising prospects to churn large data sets and enhance data quality.

Data is the driving force of every organization in the modern world. As  organizations continue to collect more and more data, the need to manage the quality of the data becomes more prominent each day. Data Quality Management can be defined as a set of practices undertaken by a data manager or a data organization to maintain high quality information. These set of practices are undertaken throughout the process of handling data; from acquiring it, implementation, distribution, and analysis.

This article outlines what DQM entails, its importance, and the metrics used to assess data quality measures.

Why you need Data Quality Management for your business

The proliferation of data in the digital age has presented a real challenge – data crisis. The data crisis entails low quality data in its volumes that makes it hard for the businesses to make sense out of it, and in some instances, unusable. DQM has thereby come forth and has become an important process used to make sense out of data. It aims at helping organizations point out errors in their data which need to be resolved. It also aims at assessing if the data in their systems is accurate to serve the intended purpose.

Let us outline four reasons why you need Data Quality Management;

Better functioning business

All the basic operations of a business are managed quickly and efficiently when the data has been managed properly. High quality data enhances decision making at all levels of operations and management.

Efficient use of resources

Low quality data in an organization means resources including finances are used inefficiently. When businesses maintain data quality through DQM practices saves them from wastage of resources leading to bigger and better results.

Competitive advantage

Reputation precedes every business. A business with a good reputation gains a higher competitive advantage over others. High quality data ensures that a business maintains a high reputation. Low quality data has been proven to bring about distrust from customers, leading to their dissatisfaction in a business’ products and services.

Good business leads

Creating a marketing campaign from erroneous data where the targeted customers do not exist, makes no sense. When the leads are from poor quality data, then there is no point targeting them with campaigns. Accurate customer data brings about better conversion from a better reach. Good data management initiatives therefore, must be practiced.

What are the key features of Data Quality Management?

A good DQM makes use of a system that has various features that will help in improving the trustworthiness of organizational data. Let us outline the various features of a good DQM;

Data cleansing corrects unknown data types, duplicate records, as well as substandard data representations. Data cleansing ensures that data standardization rules that are needed to enable analysis and insights from your data sets are followed. The data cleansing process also establishes hierarchies and makes data customizable to fit an organization’s unique data requirements.

Data profiling is the process of monitoring and cleansing data. Data profiling is used to;

Validate available data against the standard statistical measures,

Create data relationships

Verify the available data against matching descriptions

The data profiling process establishes trends that help in discovering, understanding and exposing inconsistencies in the data, for any corrections and adjustments.

What are the metrics that measure Data Quality?

Data quality metrics are very important in assessing the efforts made to increase the quality of your data. Data quality metrics must be top-notch and must be clearly defined. In the data quality metrics, be sure to look out for; accuracy, consistency, completeness, integrity, and timeliness. Let us discuss different categories of data quality metrics and what they hold in;

Accuracy

Data accuracy refers to the degree to which the said data accurately reflects an event or object that is described.

Completeness

Data is considered to be complete when it fulfills certain expectations of comprehensiveness in an organization. Data completeness indicates if there is enough of it that can draw meaningful conclusions.

Consistency

Data consistency simply specifies that two data values retrieved from multiple and separate data sets should in no way conflict with each other. However, data consistency does not necessarily imply that the data is correct.

Integrity

Also referred to as data validation, data integrity refers to structurally testing data to ensure compliance with an organization’s data procedures. Such data shows that it has no unintended errors, and that it corresponds to its appropriate data types.

Timeliness

When your data isn’t ready when users need it, it fails to fulfill the data quality dimension of timeliness.

Some examples of data metrics that help an organization to measure data quality efforts include;

The ratio of data to errors

This data metric allows tracking of the number of known errors within a data set corresponding to the actual size of the data set.

Number of empty values

This metric counts the number of times there is an empty field within a data set. Empty values usually indicate missing information or information recorded in the wrong field.

Data time-to-value

This metric evaluates how long it takes to gain meaningful insights from a data set.

Data transformation error rate

This metric will track how often a data transformation operation will fail.

Data storage costs

If an organization stores data without using it, this could be an indication that the data is of low quality. Conversely, if the organization’s data storage costs decline while the data operations stay the same or continue to grow, the quality of the data is most likely improving.

Summary

While it may look like it is a real pain to maintain high quality data, some organizations also feel like Data Quality Management is a huge hassle. This means if your organization is the one that takes the lead in making its data sound, it will automatically gain a competitive advantage in its industry.

In today’s world, we have been doing more of the traditional data management practices. This is a process of connecting people, processes, and technologies by creating governance foundations, going into data stewardship, standardizing and setting policies, execution of master data management, data quality, and with a feedback loop. The problem is that it takes a lot of time and cost and most of the time the value is not generated.

DQLabs, however, takes a paradigm shift from this traditional approach and focuses on,

Self-service automation

Support all types of users

Automate first as much as one could

DQLabs.ai can be described as an augmented data quality platform that manages an entire data quality life cycle. With the use of ML and self-learning capabilities, DQLabs helps organizations measure, monitor, remediate and improve data quality across any type of data.

This article helps you understand how DQLabs performs data quality monitoring continuously, not just once. There are three different metrics that we capture by using a lot of other processing procedures automatically. There are three levels of measurement we do;

Data quality scores are the standard data quality indicators used to record the quality attributes of the data. Usually, most products are validated by these data quality rules or by tying different rules to different sizes and then bringing a score. DQLabs does that. However, the main difference is we don’t expect the users to manage or create any of these rules. DQLabs platform does it all automatically by a semantic classification and discovery of the data within your data sets.     For example, if you have a number data, is it a phone number, social     security number, or license number? Those are the questions we ask, and we use different types of technologies around that to identify that. Once we recognize, we automatically create all these checks we need to do across these dimensions and calculate this. We also do subjective measurements. Subjective dimensions are usually collected by customer satisfaction service or input from different users, functional stakeholders, etc., usually in a traditional world. At DQLabs, however, we have a collaborative portal that users within your organizations use. We track every type of usage that happens within that portal in terms of viewing, adding favorites, a conversation that goes across, or any remediation of data quality issues that occurs within that particular data set. That allows    this subjective way of measuring data quality metrics, so that’s a     measurement at one snapshot, but this is also done continuously.

Impact Score; we not only measure and give how many records are bad based on those checks, but we also take it to the next level of how much we can convert automatically. This is important because we no longer find insufficient data and provide tools to remedy it. We then take it to the next level of how much difference we can make automatically. This is critical in the world of data preparation, data science, engineering, or data engineering because you’re not doing it manually. It is a seamless process and measures how much of an impact we are making. This ensures you understand the bad records using a quality data score,     and you can measure what percentage of those bad records can be turned into good records. For example, if the data quality score depicts the accuracy score to be 60%, the impact score can automatically determine how much of the 40% bad score can be converted into a good score. This will happen for all the data quality checks such as completeness, consistency and accessibility in a continuous way.

The third level of scoring is called a drift level. This is primarily identifying the volatility of the data. An example of a drift level is a stock market price for a stock ticker. The cost can go up and down, and sometimes based on the data collection, it could be a system outage that may be causing a bad record or macro factors such as economic factors, which may be beyond your organization’s control. We have created another set of scores to measure the volatility of the data, and based on the strip level, which can go from none to low to medium to high. All this is done automatically out of DQLabs this is done using the statistical trending benchmarking and then using different AI/ML based algorithms etc.

Watch our on-demand webinar to learn more about the use of advanced algorithms to identify data quality issues not just once but continuously.

In conclusion, the idea of continuous data quality monitoring is to prioritize data quality first, and then move on to the process of discovering all of these metrics right away. This enables greater automation, increased ROI for organizations, and enhanced customer experiences by providing them with trustworthy data and business insights in minutes.

Data quality is critical in any web scraping or data integration project. Data-driven businesses rely on customer data, it helps their products, provides valuable insights, and drives new ideas. If organizations expand its data collection, it becomes more vulnerable to data quality issues. Insufficient quality data, such as inaccurate, missing, or inconsistent data, provides a lousy foundation for decision-making. The only way to maintain high-quality data is by implementing quality checks at every step of your data pipeline.

ETL (Extract Transform Load) is the process of extracting, transforming, and loading the data. It defines what, when, and how data gets from your source to your readable database. Data quality relies on implementing a system from the early stage of extraction to the final loading of your data into readable databases.

Data quality ETL procedure:

Extract: Scheduling, maintaining, and monitoring are all critical aspects to ensure your data is up to date. You know what your information is at the extracting phase, and you should implement scripts that will look at its quality. This gives the system more time to troubleshoot closer to the source, and you can intervene before the data is changed.

Transform: Transformation is when most of the quality checks are done. No matter what is used, it should at least perform the following tasks

- Data Profiling - Data cleansing and matching - Data enrichment - Data normalization and validation

Load: At this point, you know your data. It’s been changed to fit your needs and, if your quality check system is efficient, the data that reaches you is reliable. This way, you avoid overloading your database or data warehouse with unreliable or lousy quality data, and you ensure that the results have been validated.

What is high-quality data?

Accuracy data: The accuracy of data refers to the extent to which data is considered to be true, can be relied on and is error-free.

Complete data: Data is considered to be complete when it fulfills certain expectations of comprehensiveness in an organization.

Consistency: Consistency simply specifies that two data values retrieved from multiple and separate data sets should in no way conflict with each other.

Timeliness: Timeliness refers to how recent the event the data represents took place. Data that reflect events that happened recently are likely to show the reality. Using outdated data can lead to inaccurate results and taking actions that don’t remember the present fact.

Validity: This refers to how the data is collected rather than the information itself. Information is valid if it is suitable for the correct type and falls within the appropriate range.

Relevancy: The data you collect should also be helpful for the initiatives you plan to apply it for. Even if the information you get has all the other characteristics of quality data, it’s not helpful to you if it’s not relevant to your goals.

Conclusion:

Having a robust ETL tool supported by a great scraper is crucial to any data aggregation project. But to ensure that the results meet your needs, you also need to make sure you have a quality check system in place. At DQLabs, we try to eliminate the traditional ETL approach and manage everything through a simple frontend interface by providing the data source access parameters.

The value of your data depends on how well you profile it. Today, only about 3% of data meets quality standards. That means poorly managed data is costing companies millions of dollars in wasted time, money, and untapped potential. Data profiling helps your team organize and analyze your data to yield its maximum value and give you a clear, competitive advantage in the marketplace. What is data profiling? Data profiling is assessing the quality and structure of data sources, so you have a complete, 100 % accurate picture of your data. Data profiling verifies that data columns are populated with the types of data you expect. If a profile reveals problems in data, you can define steps in your data quality project to fix those problems. Data profiling promotes good data governance.

Types of data profiling:

Structure discovery - Confirming that data is consistent and formatted correctly, and performing mathematical checks on the data. Structure discovery aids in understanding how well data is structured.

Content discovery - Checking individual data records to discover errors. It identifies which specific rows in a table contain issues and which systemic problems occur in the data.

Relationship discovery - Identifying how parts of the data are interrelated. For example, critical relationships between database tables, references between cells, or tables in a spreadsheet. Understanding relationships is crucial to reusing data; related data sources should be united into one or imported in a way that preserves relationships.

Why is data profiling important?

- Better data quality and credibility

- Predictive decision making

- Proactive crisis management

- Organized sorting

Data profiling challenges It is often tricky due to the utter volume of data you’ll need to profile. This is majorly true if you are looking at a legacy system. A legacy system may have years of older data with thousands of errors.

If you manually perform your data profiling, you’ll need an expert to run a number of queries and go through the results to gain meaningful insights about your data, which can utilize precious resources. Additionally, you might only check a subset of your overall data because it is too time-consuming to go through the entire data set.

Conclusion

As more companies store enormous amounts of data in the cloud, the need for effective data profiling is more important than ever. Cloud-based data lakes already allow companies to store petabytes of data. The Internet of Things is expanding our capacity for data by collecting vast amounts of information from an ever-evolving range of sources, including our homes, what we wear, and the technologies we use.

Data mesh is an architectural paradigm that unveils analytical data at scale, rapidly releasing access to an increasing number of distributed domain data sets for a proliferation of consumption scenarios such as machine learning, analytics, or data-intensive applications across the organization. It addresses the standard failure modes of the traditional centralized data lake or data platform architecture, shifting from the centralized paradigm of a lake, or its predecessor, the data warehouse.

Data mesh shifts to a paradigm that draws from modern distributed architecture: considering domains as the first-class concern, applying platform thinking to create a self-serve data infrastructure, treating data as a product, and implementing open standardization to enable an ecosystem of interoperable distributed data products. Data Mesh acquisition needs a very high level of automation regarding infrastructure provisioning, realizing the self-service infrastructure. Every Data Product team should manage to provide what it needs autonomously.

A critical point that makes a data mesh platform successful is the federated computational governance, which provides interoperability via global standardization. The “federated computational governance” is a group of data product owners with the challenging task of making rules and simplifying the conformity to such regulations. What is decided by the “federated computational governance” should follow DevOps and Infrastructure as Code conduct.

With the help of a centralized data warehouse, data mesh solves these challenges;

Lack of ownership

Lack of quality: Poor data quality, thus enabling the infrastructure team to know the data they are handling

Organizational scaling: Scaling of a business or organization, thus enabling the central team to become the center point.

Data infrastructure is the other makeup of a data mesh. Data infrastructure entails the provision of access control to data, its storage, a pipeline, and a data catalog. The main goal of the data infrastructure is to avert any duplication of data in an organization. Every data product team focuses on building its own data products faster and independently. This way, the data infrastructure platform is compatible with different data domain types.

Why use a data mesh?

Allowing greater autonomy and flexibility for data owners, facilitating greater data experimentation and innovation while lessening the burden on data teams to field the needs of every data consumer through a single pipeline.

Data meshes’ self-serve infrastructure-as-a-platform provides data teams with a universal, domain-agnostic, and often automated approach to data standardization, data product lineage, data product monitoring, alerting, logging, and data quality metrics.

Provides a competitive edge compared to traditional data architectures, which are often hamstrung by the lack of data standardization between investors and consumers.

Conclusion

Gartner says a data fabric is a custom-made design that provides reusable data services, pipelines, semantic tiers, or APIs via a combination of data integration approaches in an orchestrated fashion. It can be made better by adding dynamic schema recognition or even cost-based optimization approaches. As a data fabric becomes increasingly involved or even introduces ML capabilities, it changes from a data fabric into a data mesh network.

Data fabric is a designed approach, mostly inclined toward use cases and locations on either “side” of a thread. The threads can cross and do handoffs in the centre or even reuse their parts, but they are not built dynamically. They are just highly reusable, normal services.

Characteristics of Data Fabric

Unified data access: a single, cohesive way to access data from multiple sources Consolidated data protection: a consistent approach to data back-up, security, and recovery wherever the data is generated and stored Centralized service level management: a single way of measuring and monitoring service levels related to responsiveness, availability, reliability of data. Cloud mobility and portability: supporting the idea of a true hybrid cloud by minimizing the friction caused by collating and analyzing data from different cloud providers and apps. Infrastructure resilience: by separating data management from specific technologies and putting it in a single, dedicated environment, a data fabric creates a more resilient system where emerging technologies or new data sources can be connected with minimal disruption.

Data is an essential topic in today’s business world. Every business owner wants to talk about innovative ideas and the value that can flow from data. The data regarding markets, customers, agencies, other companies, and publishers are considered to be valuable resources. Statistics and data are only useful if they are of high quality.

The definition of data quality is so broad that it helps companies with different markets and missions to understand whether their data meets the standards. There are some major benefits of Data Quality that will help you to recognize the true values of high-quality data. Good data requires data governance, strict data management, accurate data collection, and careful design of control programs. For all quality issues, it is much easier and less costly to prevent data issues from happening. You can say that data quality is the key to being successful.

Gartner describes Data Ops as “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.” Data Ops is about reorienting data management to be about value creation. The Data Ops mentality stresses cross-functional collaboration in data management, learning by doing, rapid deployment, and building on what works.

Gartner recommends three approaches to DataOps based on how an organization consumes data. They are,

Utility Value Proposition

By treating data as a utility that focuses on removing silos and manual effort when accessing and managing data. As such, data and analytics are readily available to all key roles. Because there are many relevant roles and not a single owner of the data, assign a data product manager to ensure data consumers’ needs are being met.

Enabler Value Proposition

For this value proposition, data and analytics support specific use cases such as fraud detection, analysis of supply chain optimization, or inter-enterprise data sharing. Product serving their use case.

According to Gartner, the enabler value proposition works best for teams supporting specific business use cases. “DataOps must focus on early and frequent collaboration with the business unit stakeholders who are the customers for a specific product serving their use case.”

Collaboration is a key benefit of DataOps that we’ve explored extensively.

Our DataOps Platform has functionality that will enable you to report on data team productivity and efficiency.

Driver Value Proposition

Use data and analytics to create new products and services, generate new revenue streams or enter new markets. For example, an idea for a new connected product emerges from your lab and must evolve into a production quality product for use by your customers. Use DataOps to link “Can we do this?” to “How do we provide an optimized, governed data-driven product to our consumers?”

Gartner explains that this is “the proposition that causes intractable challenges relating to data governance and the promotion of new discoveries into production.”

Conclusion

As the increase in organizations and the underlying tech stacks powering them become more complicated, it’s important for DevOps teams to maintain a constant pulse on the health of their systems. With the rise of data downtime and the increasing complexity of the data stack, observability has emerged as a critical concern for data teams, too.

Data Observability Pillars

Freshness: Is the data recent and Up to date? When was the last time it was generated? Are there any gaps in data?

Distribution: Is the data within accepted ranges? How healthy is my data at the field level?

Volume: It refers to the completeness of your data tables and offers insights into the health of your data sources

Schema: Changes in the organization of your data, monitoring who makes changes to these tables and when is foundational to understanding the health of your data ecosystem.

Lineage: It helps us tell a story about the health of your data, what is affected upstream and downstream? How do my data sources depend on one another?

Benefits:

Observability is a growing trend in the DevOps software development methodology due to its many benefits. This is because it enables to;

Collect, explore, alert, and correlate all telemetry data type

Accelerate time to market

Ensure uptime and performance

Troubleshoot and resolve issues faster

Gain greater operating efficiency and produce high-quality software at scale

Understand the real-time fluctuations of your digital business performance

Optimize investments

Build a culture of innovation

Metadata Management is one of the most critical data practices for a successful digital strategy in any organization dealing with data. With the rise of distributed architectures, cloud and big data, metadata management is now critical in the management of data in organizations. Why organizations need MDM systems? By having a metadata management system, organizations are able to have its employees add metadata into their repositories quickly and accurately without affecting the access of data in their systems. Metadata management improves creative workflows, thus enabling enhanced business processes.

Managing metadata can be overwhelming when the right tools are not used. Digital asset management platform such as DQLabs.ai comes in handy. Digital asset management systems facilitate metadata management and offer security features that control content access and distribution as well as tools to support creative workflows. Benefits of Metadata Management:

Enhanced data quality

Faster project delivery timelines

Enhanced speed to make insights

Improved productivity & reduced costs

Regulatory compliance

Digital transformation

Data quality metrics are very important in assessing the efforts made to increase the quality of your data. Data quality metrics must be top-notch and must be clearly defined. In the data quality metrics, be sure to look out for; accuracy, consistency, completeness, integrity, and timeliness. Let us discuss different categories of data quality metrics and what they hold in;

Accuracy

Data accuracy refers to the degree to which the said data accurately reflects an event or object that is described.

Completeness

Data is considered to be complete when it fulfills certain expectations of comprehensiveness in an organization. Data completeness indicates if there is enough of it that can draw meaningful conclusions.

Consistency

Data consistency simply specifies that two data values retrieved from multiple and separate data sets should in no way conflict with each other. However, data consistency does not necessarily imply that the data is correct.

Integrity

Also referred to as data validation, data integrity refers to structurally testing data to ensure compliance with an organization’s data procedures. Such data shows that it has no unintended errors, and that it corresponds to its appropriate data types.

Timeliness

When your data isn’t ready when users need it, it fails to fulfill the data quality dimension of timeliness.

Some examples of data metrics that help an organization to measure data quality efforts include;

The ratio of data to errors

This data metric allows tracking of the number of known errors within a data set corresponding to the actual size of the data set.

Number of empty values

This metric counts the number of times there is an empty field within a data set. Empty values usually indicate missing information or information recorded in the wrong field.

Data time-to-value

This metric evaluates how long it takes to gain meaningful insights from a data set.

Data transformation error rate

This metric will track how often a data transformation operation will fail.

Data storage costs

Data management is simply defined as the implementation of tools, processes as well as architectures designed to achieve your organization’s objectives. 

Data preparation

Data preparation is the process of cleaning as well as transforming raw data to enable accurate analysis of the said data. In an organization, the rush for reporting and analysis, this critical first step oftentimes gets missed, leading to bad decision making from bad data.

Data pipelines

These are channels used to automatically transmit data from a system to another.

Data governance

Data governance helps to define policies and procedures so as to maintain data security and data compliance.

Data catalogs

Data catalogs help to create and capture a complete picture of the data by helping in the management of metadata as well as making data easier to find and track.

Data warehouses

These consolidate all data sources to provide a clear route to data analysis.

Data extract, transform, load

Simply abbreviated as Data ETL, this refers to the process of transforming data for it to load in an organization’s data warehouse. Data ETLs are mainly automated processes once they are built.

Data security

Data has grown to become an organization’s most valuable asset. Not every data is valuable, but only data that can be trusted. If organizations work with untrustworthy data, it can easily result in wrong insights, skewed analysis, and incorrect decisions.

Data quality and data integrity are two terms used to describe the condition of data. The two terms are oftentimes used interchangeably but are very distinct. An organization working to maximize the consistency, accuracy, and context of its data to draw insights and make better decisions needs to understand the difference between data integrity and data quality. We start by defining them, before outlining how to ensure them in your organization.

What is Data Quality?

Data quality is defined as the ability of data to serve its intended purpose. It refers to the reliability of data. Data is considered to be quality data if it is; complete, unique, valid, timely, and consistent.

Also read: Why data quality is important?

What is Data Integrity?

Data integrity can be defined as the reliability as well as the trustworthiness of data throughout its lifecycle. Data integrity can be described as the state of your data or the process of ensuring the accuracy and validity of data. One of the methods of ensuring data integrity is checking for its compliance with regulatory standards such as GDPR.

Having understood the difference, how then do we ensure data quality and integrity? We do this by outlining some steps.

Accurate gathering of data requirements

This is an important aspect of having good data quality. It aims at satisfying the requirements and delivering the data to clients and users for the purpose the data is intended. The data requirements should capture the state of the data, all data conditions, and scenarios. Proper documentation of the requirements, coupled with easy access and sharing, must be enforced. Finally, impact analysis is done to make sure that the data produced meets all the requirements expected.

Monitoring and cleansing data

Monitoring and cleansing data involves verifying data against standard statistical measures. It involves validating data against matching defined descriptions and uncovering relationships within the data. This step also checks for the uniqueness of data and analyzes it for reusability.

Access control

Access control goes hand-in-hand with audit trails. People within an organization without proper access may have malicious intent and can do grave harm to vital data. Systems should ensure audit trails are clear and tamper-proof. These are not only a safety measure, but also help to trace a problem, when it occurs.

Validate data input

A good system should require input validation from data from all sources – known and unknown. Data sources could be; users, other applications, and external sources. To enhance accuracy, all data should be verified and validated.

Remove duplicate data

Sensitive data from a repository in an organization can find its place in a document, spreadsheet, email, or in shared folders where users without proper access can tamper with it, and introduce duplicates. Cleaning up stray data and removing duplicates ensures data quality and integrity.

Back up

Inasmuch as removing duplicates to ensure data security is important, backing up the data, is equally a critical part of ensuring integrity. Backing up is vital and goes a long way in preventing permanent loss of data. Backing up data should take place as often as possible. Be sure to encrypt your data for maximum security. When there is a breach of security, say an attack, backups come in handy.

Good data quality control teams

There are two types of teams that play a vital role in ensuring high data quality – the Quality Assurance and the Business Analysts teams. The quality assurance team checks for the quality of software and programs installed at the beginning or during the data lifecycle. It is the team that oversees change management to ensure data quality in an organization undergoing fast transformations as well as changes with applications that are data-intensive. The business analysts team, on the other hand,  has a good grasp of the business rules and requirements. It is the team whose tasks involve; detecting data abnormalities, any outliers, any broken trends, or any unusual events occurring at the production of data.

Conclusion

Data ingestion involves transporting raw data from different sources into a storage medium so that it can be accessed, analyzed by data analysts and scientists.  The storage medium can be a data warehouse, data mart, or simply a database, while its sources can be from applications, databases, spreadsheets, or raw data scraped from the web. In any data analytics architecture, the data ingestion layer is a backbone. Why data Ingestion?

Organizations needs data ingestion to make better decisions in their operations and deliver better customer service. Through data ingestions, businesses can understand the needs of their stakeholders, customers, and partners. Data ingestion is the ultimate way for businesses to deal with tons of inaccurate and unreliable data. 

How is data ingestion done?

Data ingestion is performed in various ways:

Real-time

Batches

Lambda architecture.

Data Ingestion best practices:

Self-service data ingestion:

Organizations have several sources of data. All the data needs to be ingested before storage and onward processing. If the process of the ingestion is through self-service such as automation, it becomes very easy and will require minimum to no intervention from technical personnel.

Automating the Process:

Automating the ingestion process offers additional benefits including architectural consistency, error management, consolidated management, and safety. These benefits come in handy to reduce the time taken to process data.

Anticipate challenges and planning appropriately:

Data ingestion process helps you anticipate the challenges and plan accordingly in advance, and work on them efficiently as they come, without necessarily having to incur any loss of time and output.  

Use Artificial Intelligence:

AI concepts such as statistical algorithms and machine learning eliminates the need for manual interventions in the data ingestion process. AI not only eliminates the errors but also makes the whole process faster and increases the accuracy level. 

Data governance is the process of managing the data’s usability, security, availability, and quality within an organization using internally set and enforced rules and policies.

Why does data governance matter?

Data governance is a must for any organization that seeks to use their data for analysis. Data governance creates an environment where data can thrive as a source of useful insight that enables the organization to prosper. Without it, data may fail to meet the quality standards necessary for usable insight extraction or be exposed to security threats that compromise its integrity thereby putting the organization at the risk of being sued.

Benefits of data governance:

Provides consistency in compliance: Data protection regulations such as EU General Data Protection Regulation (GDPR), PCI DSS (Payment Card Industry Data Security Standards) and US HIPAA (Health Insurance Portability and Accountability Act) are very strict on how data should be managed. Failure to comply with these laws can lead to the organization incurring hefty fines and damaging their reputation. Data governance takes into consideration all the requirements of the applying laws early on thereby protecting the organizations’ data.

Improved quality of data: strong data governance ensures that all points of data creation function with data quality as a priority. This leads to an overall improvement of data quality within the organization.

Readily available and accurate data map: by defining where the data is and how it can be accessed, data governance works like an address book for all the data in the organization. This ensures there is no data that is isolated by errors of commission or omission from the overall organization’s rules and policies.

Data profiling is where to start when data quality is a high priority. This is the step that ensures that the data you have access to is legitimate and has high quality. Data profiling focuses on examining and analyzing data, followed by creating insights of data. Effective data profiling falls into three categories:

Structural discovery - Validates data’s consistency and correct formatting

Content discovery - Focuses on individual records to check for error

Relationship discovery - Understand the relationship between parts of the data