Blog | Data Intelligence | November 6, 2023 | 5 min read

What is Data Streaming?

By Actian Corporation

#Data Warehouse #Databases #IoT
data streaming

By Actian Corporation

#Data Warehouse #Databases #IoT
In this post:

Data streaming is a transformative approach to managing and processing data in real time, providing businesses with a competitive advantage in today’s complex landscape. Here’s an overview of data streaming, its purpose, and its impact on your business.

Data streaming is centered around the real-time processing, transmission, and analysis of continuous data streams, rather than storing them in traditional databases. This approach involves the continuous and high-speed transmission of data, typically over networks. As a result, data is processed as it arrives, allowing for immediate responsiveness to information. With the ever-increasing volume of data collected and utilized by your organization, embracing real-time data processing becomes increasingly vital, and this is where data streaming comes into play.

Are you working in sectors such as finance, health monitoring, or logistics? Do you need to manage substantial amounts of data while keeping storage requirements to a minimum? If so, data streaming is well-suited for your needs, as it involves temporary data storage. With the expansion of the Internet of Things (IoT), data streaming has become indispensable for processing data generated by sensors and connected devices. Furthermore, it empowers quick and informed decision-making, a critical aspect of staying competitive and addressing evolving customer demands in an increasingly digital and interconnected world.

How Does Data Streaming Work?

Data streaming is a mechanism designed to enable the real-time transfer, processing, and analysis of continuous data streams. It operates differently from traditional databases, where data is typically stored before being processed. The data streaming process can be broken down into six essential steps:

Data Capture

Data is generated in real-time from various sources, such as IoT sensors, online applications, social networks, servers, and more.

Data Ingestion

Raw data is collected using ingestion tools like Apache Kafka, RabbitMQ, or APIs. These tools ensure the reliable routing of data to the streaming platform.

Real-Time Processing

Once ingested, data becomes immediately available for processing. Streaming engines, such as Apache Flink, Apache Spark Streaming, or Kafka Streams, are employed to process this data in real-time. During this stage, data can be filtered, transformed, aggregated, or enriched while it’s in transit.

Temporary Storage

In many cases, data is stored temporarily, allowing for short-term access. This temporary storage facilitates re-examination or additional analyses if necessary.

Dissemination or Real-Time Action

The results of the processing can be disseminated in real-time to downstream applications, such as real-time dashboards, alerts, and automated actions.

Archiving or Long-Term Storage

After real-time processing, data can be archived in long-term storage systems, like databases or data warehouses. This archived data can then be used for future analyses and historical reference.

Batch Processing vs. Data Streaming: What are the Differences?

Batch processing and data streaming represent two distinct approaches to data handling, each serving unique purposes. Their core distinctions lie in how they manage and analyze information.

In batch processing, data is gathered and stored over a period until there is enough for processing, introducing a delay between data capture and analysis. Data is processed at predefined intervals, such as daily or weekly, in designated batches. This method is apt for situations where immediate analysis isn’t imperative, making it suitable for tasks like historical trend analysis and reporting.

On the other hand, data streaming operates in real-time. It processes data as it arrives, eliminating the need for interim storage between capture and analysis. This results in minimal latency, enabling immediate insights and actions based on fresh data. Data streaming is ideal for applications that demand real-time reactivity and rely on the most current data, such as fraud detection, IoT sensor data processing, and real-time analytics.

What are the Advantages of Data Streaming?

Real-time processing is a standout benefit, particularly in today’s fast-paced business environment where rapid decision-making is crucial. This real-time dimension significantly shortens time-to-market.

Another advantage is cost control. Data streaming eliminates the need for extensive long-term data storage, helping organizations save on storage costs. This is because data is processed as it arrives, reducing the need for large-scale data repositories typically associated with traditional batch processing.

Data streaming also excels at handling substantial data flows from various sources, including the Internet of Things (IoT), social networks, and online applications. Furthermore, data streaming promotes automation, enhancing operational efficiency. By enabling real-time data processing and decision-making, it reduces the need for manual interventions and allows systems to respond promptly to data insights.

What are the Use Cases for Data Streaming?

Data streaming is applied across various sectors, with a primary focus on real-time monitoring. Detecting anomalies in information systems, financial systems, and industrial machines, enabling rapid responses to deviations from the norm to prevent issues and optimize operations.

In the realm of cybersecurity, data streaming is crucial for identifying and responding to security threats in real-time, helping to monitor network traffic, detect intrusions, and protect digital assets.
Data streaming is an ideal solution for IoT applications, where sensors continually generate data. It is widely used in industrial contexts to monitor parameters like temperature and pressure for process control and predictive maintenance.

In the financial sector, data streaming is extensively used for real-time market analysis, empowering traders and financial institutions to make informed decisions and react instantly to market fluctuations. It supports various applications, including algorithmic trading, risk management, and fraud detection.

Blog | Data Management | November 3, 2023 | 6 min read

9 Ways to Maintain Data Quality

By Actian Corporation

#Data Management #Data Management Strategy #Data Quality
data quality

By Actian Corporation

#Data Management #Data Management Strategy #Data Quality
In this post:

Summary

This blog emphasizes the importance of data quality in informed decision-making and operational efficiency, offering actionable strategies to ensure data integrity across various organizational processes.

  • Implement Data Profiling: Utilize tools to analyze data sources, identifying anomalies, inconsistencies, and errors to enhance data quality before integration. 
  • Establish Data Quality Rules: Define and enforce rules for data validation, cleansing, and enrichment to maintain accuracy and consistency across datasets. 
  • Monitor Data Quality Continuously: Employ real-time monitoring systems to detect and address data quality issues promptly, ensuring ongoing data reliability.

Data quality is essential to informing decisions, predicting and resolving problems, and enabling desired outcomes, but do you know how to maintain and deliver the quality your analysts and other data users need? A data management strategy is one essential component to ensure that data meets your quality standards. Likewise, it’s important to understand and address common factors that reduce data quality.

At Actian, we define data quality management as “the mature processes, tools, and in-depth understanding of data you need to make decisions or solve problems to minimize risk and impact to your organization or customers.” The data must be accurate, current, complete, trusted, and usable by the various teams that need it.

Here are 9 ways to improve and maintain data quality:

1. Determine the Data Quality Standard You Need

You’ll need to define your standard for data quality. This standard should align with your business goals and anticipated uses to ensure the data meets your needs. The standard should also meet your data compliance and data governance requirements. Performing a data quality assessment lets you determine the current state of your data, then you can identify what needs to be improved to reach your data quality standard. When your data is trusted and meets the standard for its intended use, analysts and others will have confidence in the data and the analytics insights.

2. Create a Data Governance Framework

Data governance establishes the protocols and framework for maintaining data quality. It assigns the policies, processes, and roles within your organization to make sure data meets your quality standard for integrity, availability, and security. The framework also ensures your data meets compliance standards for regulated industries and for individuals’ personal data. A robust governance framework delivers quality data to all users, when and where it’s needed.

3. Implement Data Quality Tools  

The right tools give you a modern approach to enabling data quality by automating processes for assessing data and identifying quality issues. The tools also help with essential processes such as profiling, cleansing, and standardizing data. Data management tools vary wildly in capabilities, so look for products that can provide a quick ‘at-a-glance’ view of data quality based on the rules you’ve established. These tools can also be integrated into data pipeline processes to automate data quality checks as data is ingested.

4. Profile Data to Identify Issues

Data profiling is essentially performing an audit to find quality issues. As Gartner notes, “Data profiling is a technology for discovering and investigating data quality issues, such as duplication, lack of consistency, and lack of accuracy and completeness.” Data profiling tools also look at data sources and metadata to uncover data errors. The process allows you to fix quality issues before the data is analyzed or integrated with other data, and it also allows you to solve problems to prevent them from reoccurring.

5. Cleanse Data to Address Inconsistencies

Gaps and inconsistencies can exist in datasets, which impact quality. Data that’s incorrect, incomplete, or has missing fields will not deliver the granular, trusted results users need. Data cleansing is a critical process that lets you find and fix inaccuracies, fill in missing information, and identify inconsistent data. The right approach to cleansing data helps ensure datasets are accurate, reliable, and complete.

6. Standardize Data into the Correct Format

Data standardization can be considered part of data cleansing. This process ensures data is in the required format for data users. It also makes sure you’re using a common format for all of your data for consistency and easier integration. Likewise, standardizing data makes it easier for you to perform data analytics and store the data because it’s in the most optimal format for your organization. Transforming the data into a usable, accessible, and shareable format ensures analysts and others can leverage it for maximum value.

7. Use Deduplication Processes to Eliminate Redundancies

Data redundancy, which results in multiple versions of the same data, is a common problem. Copies of data are made for backups, testing, specific uses, or other reasons. This can lead to data silos, which in turn increases costs by storing the same data several times. Data deduplication is the process that looks for and eliminates duplicate, or redundant, versions of data. The process identifies extra copies and deletes them so only a single instance of the dataset is stored. Deduplication helps with quality by eliminating data copies that can quickly become outdated, and it encourages analysts to use the current, verified data that’s available on a centralized data platform.

8. Train Employees to Recognize Quality Issues

Building a data-driven culture entails more than creating an environment in which everyone has access to and utilizes data. It also involves giving employees the proper tools and training them on best practices for maintaining data quality so they can identify issues and either fix them or report them. Many organizations have employees who focus on data stewardship, a role that’s responsible for the oversight and usage of data assets. Each department can have its own data steward to ensure data meets quality standards and that data governance policies are followed.

9. Monitor Data on an Ongoing Basis

Maintaining data quality is a continuous process. You can streamline much of it by using automated monitoring tools that routinely check and evaluate data quality, and identify any issues. When there is an issue, alerts are sent to notify the proper stakeholders to take corrective action. Continuous monitoring ensures that data maintains your quality standard as it’s shared and reused across the organization.

Making High-Quality Data Easy to Use and Analyze

Analysts, decision-makers, and others throughout the company must be able to trust the data in order to have confidence in the insights. Providing quality data is one way to establish that trust. Actian can help. We offer tools and expertise to help you identify and correct data anomalies to give you high-quality data that improves the effectiveness of your data-driven initiatives. We also make data easy. The Actian Data Platform simplifies how you connect, manage, and analyze data. This makes trusted data readily and easily available to everyone in your organization to accelerate your growth.

Additional Resources:

Blog | Data Analytics | November 2, 2023 | 5 min read

Analytics Program and Project Justification

By William McKnight

#Business Data #Data Analytics #Data Management #Enterprise Data Management
data analytics tools

By William McKnight

#Business Data #Data Analytics #Data Management #Enterprise Data Management
In this post:

The economy is currently in a state of flux based on analytics, and there are both positive and negative signals regarding its future. As a result of factors, such as the low unemployment rate, growing wages, and rising prices, businesses find themselves in a spectrum of states. 

Recent pullbacks appear to be driven primarily by macro factors. I have a positive outlook on IT budgets in 2024 because I anticipate a loosening of IT expenditures, which have been limited by fears of a recession, since 2022. This will allow pent-up demand, which was cultivated in 2023, to be released. Because data is the key to success for these new endeavors, the demand for data cleansing and governance technologies has increased to address broad data quality issues in preparation for AI-based endeavors. 

Taking a broader perspective, despite the instability of the macro environment, the data and analytics sector is experiencing growth that is both consistent and steady. However, there is a greater likelihood of acceptance for business programs that concentrate more on optimization than on change. As a means of cutting costs, restructuring and modernizing applications as well as practicing sound foundational engineering are garnering an increasing amount of interest. For instance, businesses are looking at the possibility of containerizing their applications because the operation costs of containerized applications are lower. 

At this point, in this environment, project approval is taking place; nonetheless, the conditions for approval are rather stringent. Businesses are becoming increasingly aware of the importance of maximizing the return on their investments. There has been a resurgence of interest in return on investment (ROI), and those who want their projects to advance to the next stage would do well to bring their A-game by integrating ROI into the structure of their projects. 

Program and Project Justification

First, it is important to comprehend the position that you are attempting to justify: 

  • A program for analytics that will supply analytics for a number of different projects.
  • A project that will make use of analytics.
  • Analytics pertaining to a project.
  • The integration of newly completed projects into an already established analytics program.

Find your way out of the muddle by figuring out what exactly needs to be justified and then getting to work on that justification. When justifying a business initiative with ROI, it is possible to limit the project to its projected bottom-line cash flows to the corporation in order to generate the data layer ROI (which is perhaps more accurately referred to as a misnomer in this context). In order for the project to be a catalyst for an effective data program, it is necessary for the initiative to deliver returns. 

The question that needs to be answered to justify the starting of an existing data program or the extension of an existing data program is as follows: Why architect the new business project(s) into the data program/architecture rather than employing an independent data solution?  These projects require data and perhaps a data store, if the application doesn’t already come with one, then synergy should be established with what has previously been constructed.   

In this context, there is optimization, a reduction back to the bare essentials, and everything in between. The bare essentials approach can happen in an organization in a variety of different ways. All of these are indications of an excessive reach and expanded data debt: 

  1. Deciding against utilizing leverageable platforms like data warehouses, data lakes, and master data management in favor of “one-off”, and apparently (deceptively) less expensive, unshared databases tight fit to a project. 
  2. Putting a halt to the recruiting of data scientists. Enterprises that take themselves seriously need to take themselves seriously when it comes to employing the elusive genuine data scientist. If you fall behind in this race, it will be quite difficult for you to catch up to the other competitors. Even if they have to wrangle the data first before using data science, data scientists are able to work in almost any environment. 
  3. Ignoring the fact that the data platforms and architecture are significantly more important to the success of a data program than the data access layer, and as a result, concentrating all of one’s efforts on the business intelligence layer. You should be able to drop numerous BI solutions on top of a robust data architecture and still reach where you need to go. 
  4. Not approaching data architecture from the perspective of data domains. This leads to duplicate and inconsistent data, which leads to data debt through additional work that needs to be done during the data construction process, as well as a post-access reconciliation process (with other similar-looking data). Helping to prevent this is master data management and a data mesh approach that builds domains and assigns ownership of data.   

Cutting Costs

If your enterprise climate is cautious spending, target the business deliverables of your data project and use a repeatable, consistent process using governance for project justification. Use the lowering of expenses to justify data programs. Also, avoid slashing costs to the extreme by going overboard with your data cuts, since this can cause you to lose the future.  

Although it should be at all times, it’s times like these when efficiencies develop in organizations, and they become hyper-attracted to value. You may have to search beyond the headlines to bring this value to your organization. People in data circles know about Actian. I know firsthand how it outperforms and is less costly than the data warehouses getting most of the press, yet is also fully functional. 

All organizations need to do R&D to cut through the clutter and have a read on the technologies that will empower them through the next decade. I compel you to try the Actian Data Platform.

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