Wednesday, October 27, 2021



 DataOps: Enabling Business Agility

The DataOps development methodologies allow companies to build, deploy, and optimize data-powered applications more quickly and more easily.

The agile approach involves identifying a problem to solve and breaking it down into smaller pieces. The work is divided between a team of developers for each piece, with each piece divided into a defined timeframe - a sprint - that should include planning, development, testing, and implementation.

Companies using DataOps are not only doing well but are also outpacing their competition. DataOps aims to optimize how an organization manages its data to make better decisions.

The practice of DataOps can lead to increased collaboration between data scientists, data engineers, data analysts, operations, and product owners across organizations. For DataOps to be successful, each of these roles must be aligned.

Forrester research indicates that companies that integrate analytics and data science into their operating models to bring actionable knowledge into every decision are twice as likely to be in market-leading positions as those that do not.



There is more to DataOps than making existing data pipelines work efficiently, getting reports and AI/ML outputs and inputs to appear as needed, etc. DataOps includes all aspects of data management.


From raw data to insights, DataOps is a journey for data teams. As much as possible, DataOps stages are automated to shorten the time to value. The steps below show the full lifecycle of a data-driven application.

 Plan: Define how data analytics can be used to resolve a business challenge. Identify the data sources, processing steps, and analytics steps necessary to solve the problem. Decide on the right technology and delivery platform, and then specify the budget and performance requirements.

 Create: Design and implement the data pipelines and application programming code necessary for ingesting, transforming, and analysing the data. SQL, Scala, Python, R, or Java are among the programming languages used to develop data applications, depending on the desired outcome.

 Orchestrate: Create an effective system that connects the stages needed to create the desired effect. Code execution should be based on when the results are needed; when the most cost-effective processing is available; and when related jobs (inputs and outputs, or steps in a pipeline) are running simultaneously.

Test & Fix: Simulation of the code running on data sources in a sandbox environment. Find and eliminate data pipeline bottlenecks. Check for accuracy, quality, efficiency, and performance before submitting results.

 Continuous Integration: The revised code should meet established criteria to be promoted into production. Accelerate improvements and reduce risk by incrementally integrating the latest code and data sources.

 Deploy: The best scheduling window for a job should be determined by SLAs and budget. Ascertain whether the changes have improved the process; if not, roll them back, and revise.

 Operate: The code runs against data to resolve the business problem, and stakeholder feedback is solicited. Determining deviations from SLAs and fixing them to assure compliance.

 Monitor: End-to-end process monitoring, including data pipelines and code execution. The data operators and engineers use tools to observe how code runs against data in a busy environment and to troubleshoot any issues that may occur.

 Optimize: To ensure high-quality, cost-effective, and business-focused results for data applications and pipelines. To optimize the application's performance and effectiveness, team members optimize the app's resource usage.

 Feedback: Data team members gather feedback from all stakeholders, including app users and line of business management. During this phase, results are evaluated against success criteria and input is sent to the planning phase.


DataOps has two characteristics that apply to every stage of the lifecycle: end-to-end observability and real-time collaboration.


Observability from beginning to end is essential for the delivery of high-quality data products on time and on budget. Data-driven applications must be able to measure key KPIs, including the data sets they process and the resources they consume. Metrics include application/pipeline latency, SLA score, error rate, result correctness, cost of a run, resource usage, data quality, and data usage.

This visibility is needed horizontally - across every step and service in the pipeline - and vertically to understand whether the problem is with the application code, the service, the container, the data, or the infrastructure. Observability across the entire data lifecycle provides teams with a reliable and precise means of collaborating around data.


Working on short sprints, for example, allows teams to work in a rhythm-based on real-time collaboration. DataOps lifecycles help teams identify the stage in which they're working, and to reach out to other stages to solve problems, both at the time and soon.

Collaboration in real-time requires open discussion of results as they occur. Every discussion in the observability platform is grounded in shared facts derived from a single source of truth. Real-time collaboration is the only way for a relatively small group to deliver high-quality products regularly and over time.




By applying a DataOps approach to their work and paying attention to each step in the DataOps lifecycle, data teams can increase their productivity and the quality of the results they provide to the organization.

In addition to increasing the ability to deliver predictable and reliable business value from data assets, the business as a whole will be able to make more and better use of data in decision-making, product development, and service delivery.

In many cases, advanced technologies, such as artificial intelligence and machine learning, can make organizations more competitive and lead to significant revenue increases.






In recent years, data breaches have increased significantly. New ways of data breaches are discovered every year, and millions of incidents are reported. The best way to protect your data is to stay up to date on the latest data theft techniques.

Business of all sizes have become increasingly dependent on digital data, cloud computing, and workforce mobility, resulting in widespread attention to data breaches. Data from a company is stored on local machines, in enterprise databases, and on cloud servers, so breaching a company's data is as simple - or complex - as gaining access to restricted networks.

Companies did not start experiencing data breaches when they began storing their protected data in digital form. Data breaches have existed for as long as individual and corporate records have been kept and private information stored. In the days before computing became prevalent, a data breach could be something as simple as viewing an individual's medical records without authorization or discovering discarded sensitive documents. Despite this, there were more publicly-disclosed data breaches in the 1980s, and in the 1990s and early 2000s, public awareness of the potential for breaches increased.

Companies and organizations handling sensitive consumer information are provided with guidelines through laws and regulations such as HIPAA and the PCI Data Security Standard. Although these regulations set up the standards for safeguarding, storing, and using sensitive information, they don't apply to all industries, nor can they prevent data breaches in all cases.

The majority of information about data breaches is from 2005 to the present. The reason for this is due to the rapid advancement of technology and proliferation of electronic data throughout the world, giving both businesses and consumers a concern about data breaches. Almost all data breaches today affect hundreds of thousands - if not millions - of individuals and even more records, all from one attack on a single company.

Data loss or leaks in large organizations occur most often as a result of hacking, negligence, or a combination of both. However, there are a few other types of data loss and/or corruption that would be considered breaches. Let's take a look at four additional types of breaches.


Ransomware is malicious software that steals access to vital data (e.g., files, systems) and locks down those access points. Businesses are the most common targets of these attacks. Locked down files and/or systems are demanded with the use of cryptocurrencies (most often Bitcoin).

Malware is software that damages computer files or systems. Often, malicious code masquerades as a warning against malicious programs in an effort to convince users to download the very program types mentioned in the "warning" message.

Phishing is when someone or something poses as a trustworthy, reputable entity in an attempt to collect sensitive data (typically banking or highly personal details). It is not only the Internet that is subject to these attacks. Typical phishing scams use the following methods:

  • Browser pop-ups
  • An email attached to a link
  • Pretending to be a representative of a reputable company

A denial-of-service (DoS) attack prevents users from accessing websites and webpages. It's known as a distributed denial-of-service (DDoS) when it happens at large scale. Certain large-scale attacks can cause the disruption of many online services in certain regions. Among the largest DDoS attacks on record is the 2016 attack on Dyn, which rendered a significant portion of Eastern U.S. Internet access virtually unusable for several hours. GitHub was the victim of the largest and most recent DDoS attack in February of 2018.


There have been attempts by experts and other media outlets to identify the largest data breaches in history. The number of cyber attacks is on the rise, according to Statista, which measures US data breaches and records exposed since 2005. 157 data breaches were reported in the U.S. in 2005, with 66.9 million records exposed. Almost 85.61 million records were exposed in 2014, a four-fold increase from 2005. The number of reported breaches more than doubled in three years to 1,579 in 2017. These are Statista's numbers, which are somewhat conservative in comparison with Verizon's data breach report or other industry standards.

Although the trend is not constant, it was down from 656 breaches in 2008 to 498 in 2009. However, the number of records exposed has increased sharply since 2008, rising from 35.7 million to 222.5 million in 2009. The figures indicate that despite fewer breaches, the breaches themselves were larger, exposing more records per breach even though fewer breaches occurred overall.

The number of data breaches reported between 2010 and 2011 also decreased by 662 in 2010, and by 419 in 2011. However, the number of data breaches reported in the United States has steadily increased since 2011:

  • 614 data breaches reported in 2013 and 783 in 2014 while 1,093 data breaches reported in 2016 followed by 1,579 data breaches reported in 2017.

Forbes reports that there have been over 300 data breaches over the past decade, resulting in 100,000 or more records being stolen. These are only a few of the data breaches that were publicly reported.



One of the three major credit reporting agencies, Experian, was indirectly involved in the largest data breach in history. The company acquired Court Ventures, which aggregates public records and gathers information, in March 2012. As part of the acquisition, Court Ventures acquired a company called U.S.A Info Search. Customers of U.S.A Info Search were able to access the data of the company to find addresses that they could use to determine which court records they needed to review.

In addition, Court Ventures sold information to a Vietnamese fraudster service, which then provided its own customers with access to American personal information, including financial information and Social Security numbers, which was then used to commit identity theft in many cases.

Following the acquisition of Court Ventures by Experian, the U.S.A Secret Service notified us that Court Ventures had been and was continuing to resell data from a U.S.A Info Search database to a third party, possibly engaged in illegal activity. Court Ventures facilitated the access to U.S.A Info Search's databases, which were obtained before Experian acquired the company." Experian maintains that no Experian databases were breached; U.S.A Info Search's databases contained the consumer information.

It is reported that 200 million records have been compromised in this breach, which lasted for more than 10 months after Experian acquired Court Ventures. However, reports that 200 million records represent the number of records that were initially exposed, not the actual number of records exposed.

Thursday, October 21, 2021



How Artificial Intelligence Can Benefit Supply Chain Management

        For months now, the COVID-19 pandemic has wreaked havoc on global supply chain management, slowing economic growth. The rise of Artificial Intelligence and Machine Learning (AI/ML) is changing the face of the supply chain industry with their phenomenal speed, accuracy, and efficiencies. Businesses today must run at maximum productivity with fewer uncertainties and errors in an increasingly competitive environment. Therefore, Artificial Intelligence (AI) in Supply Chain Management & Logistics is an essential component of enhancing productivity, quality, lowering costs, and boosting output. By leveraging AI/ML, enterprises are gaining access to all aspects of the supply chain beyond what their own capabilities can provide. COVID-19 has been causing economic growth to slacken for months now due to its effects on supply chain management. According to the Institute for Supply Chain Management, approximately 75% of the companies reported having experienced some sort of supply disruption as a result of Coronavirus. For months, transportation restrictions have constrained the flow of supplies. 

In this article, we list the key benefits of artificial intelligence in supply chain management. 

Management of Accurate Inventory: Accurate inventory management with AI-driven tools can help to analyze and interpret huge datasets quickly by forecasting supply and demand in real-time. It can also be crucial in limiting overstocking, inadequate stock, and unexpected stock-outs.

Efficiency in Warehouses: AI systems have the capability to solve a wide variety of warehouse issues, more quickly and accurately. Therefore, the involvement of humans is eliminated to simplify compound procedures, thus speeding up the process. It thus saves time and lowers warehouse staffing costs. 

Improved Safety: AI-based automated data analysis makes workplace safety easier to understand and informs manufacturers of possible risks. Automated data systems for tracking stocking parameters help in keeping warehouses safe and make the company compliant with safety standards since they provide the necessary information to update operations.

Reduced Operations Costs: In every business organization, maximization of utility from minimal investment is the basic premise. One of the biggest benefits AI can offer is this. Customer service desk to the warehouse, its automated intelligent operations deliver error-free work execution with reduced workplace incidents. The installation of AI-enabled robots in warehouses can enhance speed and accuracy while increasing productivity. 

On-Time Delivery. Supply Chain Management can reduce the occurrence of human errors by using Artificial Intelligence. This reduces turnaround time by facilitating on-time delivery of goods to customers. Automating systems can enable organizations to reduce the effort of meeting delivery targets, removing operational bottlenecks. 

Thursday, October 7, 2021



With today's competitive market scenario, business intelligence is a true partner for modern enterprises. The benefits of business intelligence solutions range from creating valuable business insights to quickly and accurately rendering reports. 

  An organization will benefit from adopting BI or business intelligence strategies in order to get insights from its data. Business intelligence tools enable organizations to make decisions based not only on a small data accumulation, but also on a comprehensive picture of the data. BI or business intelligence is a type of software that allows a company to harness the strength of the data within it. This article will explain the definition, how it can direct a business, and what features to look for in BI products. Organizations can use it to sort, compare, and review data and to make smart decisions. Implementing business intelligence services can give organizations insight into business data and help them take plausible action. Insights like these are helpful in enhancing productivity, enhancing growth, and increasing revenue for organizations.

BI is all it's cracked up to be: BI solutions are adopted by organizations for various reasons. You may wonder if investing the time, effort, and cost to add BI software to an existing software suite is worth the effort, time, and expense. A number of BI tools sought to find out whether BI software lived up to its smooth marketing and whether organizations felt that they benefited from it to their fullest extent. Business intelligence solutions can assist in cementing your decision to integrate them into your software suite.

Plan and analyze better- Organizations believed that BI systems facilitated faster reporting, planning, and analysis. Following the adoption of a BI suite, 64% of organizations rated their ability to report, plan, and scrutinize data as 'good.'

Accuracy optimized - Over 56% of the businesses surveyed accepted that using BI data would improve their analysis and planning.

Aided in the forecasting of sales - The area where organizations believed BI data helped most was sales forecasting and planning, which ranked 57% among the various tasks they considered BI data could help them with. The other areas, where they think business intelligence data is most important, are consolidated customer views (32%), and consumer behavior analysis (40%).

Enhanced pricing and offers-As a result of BI system adoption, prices and offers improved somewhat. According to 27% of respondents, the additional data they gathered from their business intelligence system assisted them in optimizing their pricing structure to become more competitive, and improving the attractiveness of their offers.

How can BI aid your company?

  • Optimized planning and analysis can be utilized to optimize processes, highlight business operations, and use data to steer decision making.
  • Accurate data leads to better decisions. A high degree of accuracy also improves forecasting confidence. Having reliable data will allow you to make better predictions about what your business needs in the future.
  • The optimization of the sales forecasting process helps organizations with budgeting, marketing, and more. You can schedule your overall organizational budget more accurately the better your sales forecast is.
  • Optimizing your product offerings and pricing in line with the market can, in turn, increase sales that will scale up your revenues.

BI software has a positive ripple effect that affects every section of your organization, as you can see from the above. Optimizing access to a firm's data is not all that it's about. In other words, it is about maximizing profits with that data.

Business intelligence is information that is gathered and analyzed within a company. Data gathering and analysis, as well as how findings are communicated, form the backbone of business intelligence, which encompasses a wide array of tools and technologies. Business intelligence enables businesses to understand how they have performed historically and why. The execution of a successful BI strategy requires a good understanding of how data is handled from start to finish.

Datagridz' business intelligence consultation can be of assistance if you need cloud-based BI software that delivers exceptional data quality, advanced reporting features, and easy data management.