The term Big Data describes a massive volume of data that cannot be stored and processed by the traditional data storage / processing systems. Lately, data is generated at a rapid pace and in huge volume. It is being used by businesses toprocessandanalyzeto uncover hidden patterns and discover useful insights which add values to the business.
Big Data is commonly classified into three different categories.
Structured Data
Semi-Structured Data
Unstructured Data
Structured Data is characterized by the well-defined structure or schema. It follows a set of rules and constraints. Structured data usually consists of well-defined columns and stored in databases. The popular storage and processing system is called Database Management System (DBMS) or Relational Database Management System (RDBMS) such as MS SQL Server, Oracle, DB2 etc.
Semi-Structured Data is another form of structure data which follows only few characteristics of structured data and it does not comply with the formal structure of RDBMS data model. But the semi-structured data is also popular and useful in data processing such as Extensible Markup Language (XML), Comma Separated Values (CSV) file etc.
Unstructured data is completely undefined which means it does not follow any schema of formal data models. These type of data does not have any consistent format or fixed format. The commonly used unstructured data is image, audio, and video files.
Metaverse is the next evolution of digital technologies. It includes 3D virtualization and will transform digital technologies in the next 5–10 years. It is a collective virtual space, created by the convergence of virtually enhanced physical and digital reality. In other words, it is device-independent and is not owned by a single vendor. It is an independent virtual economy, enabled by digital currencies and nonfungible tokens (NFTs).
A Metaverse represents a combinatorial innovation, as it requires multiple technologies and trends to function. Contributing tech capabilities include augmented reality (AR), flexible work styles, head-mounted displays (HMDs), an AR cloud, the Internet of Things (IoT), 5G, artificial intelligence (AI) and spatial technologies.
There is a lot of excitement around Metaverse, much of it driven by technology companies preemptively claiming to be Metaverse companies, or creating Metaverses to enhance or augment the digital and physical realities of people. Moreover, activities that currently take place in siloed environments will eventually take place in a single Metaverse, such as:
Purchasing outfits and accessories for online avatars
Buying digital land and constructing virtual homes
Participating in a virtual social experience
Shopping in virtual malls via immersive commerce
Using virtual classrooms to experience immersive learning
Buying digital art, collectibles and assets (NFTs)
Interacting with digital humans for onboarding employees, customer service, sales and other business interactions
Elements of a Metaverse
Gartner is a technology research and consulting company. It described the elements of a Metaverse in the below diagram.
Gartner expects that by 2026, 25% of people will spend at least one hour a day in the Metaverse for work, shopping, education, social media and/or entertainment.
Over the past decade, business intelligence has been revolutionized. Data exploded and became big. And just like that, we all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly advanced analytics wasn’t just for the analysts.
Read on to see our top 10 business intelligence trends in 2022:
Artificial Intelligence
Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. This is a trend that is wildly being covered by Gartner in their latest Strategic Technology Trends report, combining AI with engineering and hyperautomation, and concentrating on the level of security in which AI risks developing vulnerable points of attacks. It is expected that in the coming year’s AI will evolve into a more responsible and scalable technology as organizations will require a lot more from AI-based systems.
Data Security
Data and information security have been on everyone’s lips in 2021, and they continue to buzz the world in 2022. The implementation of privacy regulations such as the GDPR (General Data Protection Regulation) in the EU, the CCPA (California Consumer Privacy Act) in the USA, and the LGPD (General Personal Data Protection Law) in Brazil have set building blocks for data security and management of users’ personal information.
Moreover, the recent overturn by the European Court of Justice of the legal framework called Data Privacy Shield hasn’t made software companies’ life much easier. The Shield was a legal framework that enabled companies to transfer data from the EU to the USA but, with recent legal developments causing the invalidation of the process, companies that have their headquarters in the US don’t have the right to transfer any of the EU data subjects.
Data Discovery/Visualization
Data discovery has increased its impact in the last year. A survey conducted by the Business Application Research Center listed data discovery in the top 4 business intelligence trends by the importance hierarchy for 2022. BI practitioners steadily show that the empowerment of business users is a strong and consistent trend.
Essentially, data discovery is the process of collecting data from various internal and external sources and using advanced analytics and visualizations to consolidate all the information. This allows businesses to keep every relevant stakeholder engaged with the data by empowering them to analyze and manipulate the information in an intuitive way and extract actionable insights. To achieve this, businesses of all sizes turn to modern solutions such as business intelligence tools that offer data integration, interactive visualizations, a user-friendly interface, and the flexibility to work with big amounts of data in an efficient and intuitive way.
Data Quality Management
Data quality management ensures that companies can make the right data-driven decisions by using the correct data for their analytical purpose. This means there is no definitive truth about the way businesses can measure the quality of the data as this solely depends on the context. That said, there are guidelines to follow in order to ensure a successful data management process, some of them include data being accurate, consistent, complete, timely, and compliant. Meaning, no duplicate or missing values, no outdated data that doesn’t represent the required timeline, and no data that is not consistent. A simple example of data consistency would be that the sum of employees in each department does not exceed the total number of employees in that organization.
Predictive & Prescriptive Analytics Tools
Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. It’s an extension of data mining that refers only to past data. Predictive analytics includes estimated future data and therefore always includes the possibility of errors from its definition, although those errors steadily decrease as software that manages large volumes of data today becomes smarter and more efficient. Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessment. Applied to business, predictive analytics is used to analyze current data and historical facts in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company.
Real-time Data & Analytics
The need for real-time data has tremendously evolved this year and will continue to do so as one of the data analytics trends for 2022. We have seen since the pandemic arrived, that the needs for real-time and accurate updates are critical in developing proper strategies to respond to such unfortunate situations. Some countries have used data to make the best possible decisions, and companies followed to ensure survival in these uncertain times. Real-time access to data has become a norm in everyday life, not just for businesses, but the general public as well, where we could see press conferences filled with the most recent information, graphs, and statistics that have defined some of the strategies against the pandemic. But not only; creating ad hoc analysis has enabled businesses to stay on top of changes and adapt to immense challenges that this year has brought.
Collaborative Business Intelligence
BI tools make sharing easier in generating automated reports that can be scheduled at specific times and to specific people. For instance; they enable you to set up business intelligence alerts, share public or embedded dashboards with a flexible level of interactivity. All these possibilities are accessible on all devices which enhances the decision-making and problem-solving processes, critical for today’s ever-changing environment. This is especially necessary now that the pandemic has forced businesses to shift to a home office dynamic in which collaboration needs to be supported by the right tools more than ever.
Collaborative information, information enhancement, and collaborative decision-making are the key focus of new BI solutions. But collaborative BI does not only remain around some documents’ exchanges or updates. It has to track the various progress of meetings, calls, e-mails exchanges, and ideas collection. More recent insights predict that collaborative business intelligence will become more connected to greater systems and larger sets of users. The team’s performance will be affected, and the decision-making process will thrive in this new concept.
Data Literacy
As data becomes the foundation of strategic decisions for businesses of all sizes, the ability to understand this data and use it as a collaborative tool that everyone in the organization can use becomes critical for success. That said, data literacy will be one of the relevant data analytics trends to look out for in 2022.
Data literacy is defined as the ability to understand, read, write, and communicate data in a specific context. This means understanding the techniques and methods used to analyze the data as well as the tools and technologies implemented. According to Gartner, poor data literacy is listed as the second-biggest roadblock to the success of the CDO’s office, and it adds that by 2023 data literacy will become essential in driving business value.
Data Automation
Business intelligence topics wouldn’t be complete without data (analysis) automation. In the last decade, we saw so much data produced, stored, and ready to process that companies and organizations were seriously looking for modern data automation solutions to tackle massive volumes of information that has been collected. A survey by KDNuggets predicts that in the next decade, data science tasks will be automated, hence, this is one of the trends in business intelligence that we need to keep an eye on since we don’t know when it will exactly happen.
Business intelligence has brought many automation possibilities and in 2022, we will see even more. Long-standing barriers between data scientists and business users are being slowly mixed into a one-stop-shop for any data requirement a company might have – from collecting, analyzing, monitoring, reporting, and sharing findings. A scenario might include intelligent reporting – predictive analytics and automated reports increase the business users’ capabilities to automate data on their own, without the help of the IT department. On the other hand, data scientists still will manage complex analysis where manual scripting and coding are necessary.
Embedded Analytics
Whether you need to create a sales report or send multiple dashboards to clients, embedded analytics is becoming a standard in business operations, and in 2022, we will see even more companies adopting it. Departments and company owners are looking for professional solutions to present their data without the need to build their own software. By simply white labeling the chosen application, organizations can achieve a polished presentation and reporting which they can offer to consumers.
More than just embedding a dashboard or BI features to an application, embedding analytics allows for collaboration by keeping every single stakeholder involved. By providing clients and employees the possibility to manipulate the data in a well-known environment you facilitate the extraction of insights from every area of your business. This makes it one of the fastest-growing business intelligence trends from this list.
You can read more about Analytics And Business Intelligence Trends For 2022 here.
Teknita has the expert resources to support all your technology initiatives. We are always happy to hear from you.
There are several reasons why software programs fail, and some basic best practices can be employed to minimize the likelihood of that happening. They include the following:
Implementing load balancing. As the number of website users increase and they log on to add their personal data, a crash can impact other features, like access to the bank they hope to draw from when they check out. Think “Black Friday” and what happened when websites were not equipped to handle shopper traffic. On an e-commerce website when the number of users increases sharply to take advantage of an online offer that could potentially cause a crash, that can impact other features, like access to the payment page when they check out. Avoid a single point of failure by load balancing system traffic across multiple server locations.
Applying program scaling. This is the ability of a program’s application nodes to automatically adjust and ramp up to handle increased traffic via machine learning, as it analyzes the metrics on a real time basis. Scheduled scaling can be employed during forecasted peak hours or for special sale events, such as Amazon Prime Day. At off-peak hours, those nodes then can be scaled down. Dynamic scaling involves software changes based on metrics including CPU utilization and memory. Predictive scaling entails understanding current and forecasted future needs, utilizing machine learning modules and system monitoring.
Using continuous load and stress testing to ensure reliability of the code. Build a software program with a high degree of availability in mind, accessible every day of the year with a miniscule period of downtime. Even one hour offline a year can be costly. Employ chaos engineering during the development and beta testing stage, introducing worst-case scenarios when it comes to the load on a system. Then write a program to overcome those issues without resorting to downtime.
Developing a backup plan and program for redundancy. It’s crucial to be able to replicate and recover data in the event of a crash. Instill this type of business ethic within the corporate structure.
Monitoring a system’s performance using metrics and observation. Note any variance from the norm and take immediate action where needed. A word of caution: the most common reason for software failure is the introduction of a change to the operating system in production.
One Step at a Time
The first step in developing a software program is choosing the right type of architecture. Using the wrong type can lead to costly downtime and can discourage end users from returning for a second visit if other sites or apps offer the same products and services.
The second step is to incorporate key features including the ability to scale as demand on the program peaks (perhaps a popular retail site having a sale), redundancy that allows a backup component to takeover in case of a failure, and the need for continuous system testing.
The final step is to establish standards of high availability and high expectations where downtime is not an option. Following these steps creates a template to design better system applications that are reliable in all but the rarest of circumstances.
You can read more about Best Practices for Avoiding Software Failures here.
Teknita has the expert resources to support all your technology initiatives. We are always happy to hear from you.
A test case is a written document that gives detailed step-by-step instructions on how to perform a given test for a software feature.
A test case typically features:
the conditions necessary for the start of the test
one or more inputs, if any, for the test
the action that is to be performed
the results of the test, which may consist of outputs or changes in the conditions of the “world”
A test case resides at the tactical level. It tells the tester what they need to do and in what order, details the outcomes they should expect.
A test scenario is a more high-level description of a given concern that needs to be tested. Rather than being a step-by-step guide, test scenarios describe testing needs in very broad strokes.
Test scenarios typically live at the strategic level, which means they care about the why rather than the how. They’ll typically express the business motivation and rationale behind testing a given feature.
Test scenarios give origin to test cases.
You’ll typically have way more test cases than test scenarios as a logical consequence. The creation of test cases is typically more labor-intensive due to the granularity of details involved. But the creation of test scenarios can oftentimes be harder since it has a degree of ambiguity and uncertainty involved and needs to connect to the business in a way that makes sense ROI-wise.
Test Case
Test Scenario
Tactical level
Strategic level
Cares about the how
Cares about the why
There are many per test scenario.
One test scenario gives origin to many test cases.
It’s executed by a tester, QA professional, or developer.
It can’t be executed but serves as higher-level guidance for decision-making.
It can be automated with the help of a code-based or codeless test automation tool
It can’t be automated because it’s not a set of steps
It can be labor-intensive to elaborate on but is objective and unambiguous
Can have some degree of ambiguity
You can read more about Test Cases and Scenarios here.
Teknita has the expert resources to support all your technology initiatives. We are always happy to hear from you.
