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Data governance can be defined as a compilation of various activities involved for utilizing the data for facilitating the organization towards its purpose. These activities include the organization’s practices, dedicated roles for every activity, regulatory policies & terms to be followed, etc. When these activities are kept in – check, the data Governance is said to be exceptional by attaining maximum efficiency for data processing. This process helps in commencing the data management techniques and related tasks so as to guarantee the performance and data security for the data being handled transversely through the organization, which can be applied at any time and in any situation.Components of Data Governance
Hadoop, Data Science, Statistics & others
Below are the categories with respect to the said questions:WHO – People in the Organization
User Group Charter
Decision Making ProfessionalsWHY – Target
Achieve the set Goals
Mission & VisionWHAT – Centre of Attention
Factors that Quantify
Data Management MethodsWHEN – Activities
Implementation of Plan
Data Management Process
Record the Process Progress
When these components work along with one another, there comes efficient Data Governance for the given organization. Under each of these components, there can be a number of people, roles, and actions carried out. Apart from this, Data Governance can be achieved technologically with the help of the Metadata Repository, Data Profiling Tools, Data Cleansing Tools, Data Mining Tools, Data Management activities, etc.
A mixture of processes performed in Data Governance is sticking to the organization’s policies, process monitoring, data cleansing, data profiling, data extraction, data analysis, data processing, etc. These smaller processes make a bigger impact on the organization’s Data Governance rates, as each of the activities is interdependent in most of the cases.Data Governance Process
A well-controlled Data Governance approach is an essential constituent for an institute’s performance, where the data science technologies are put in use. It aids in taking the multiple Data Governance components in –line, which simply means all the minor activities that contribute to any and all the Data Governance components. The people/ roles involved in this process, like Business professionals, who make the important decisions, are responsible for emphasizing the particulars on the data to be handled under data governance.Data Governance Challenges
Constructing and applying a Data Governance flow in an institution is an intimidating and overpowering task. In addition to a proper management team, Data Governance requires a completely workable plan and the ability to follow the plan to the point, without any deviations. A few of the common problems observed in employing the Data Governance technique in an organization are:1. Roles and Responsibility
It is hard to place the respective people’s inappropriate roles for handling the designated responsibilities in the data sector in the said organization. Structuring the data governance procedure involves people handling, placing the respective person in a suitable role, access level to the data, every person’s accountability, etc. On the highest level is the Chief Data Officer of the organization, which has become a regular practice in this technologically evolving world of Data.2. Data Handling
A Major hiccup observed in many cases of Data Governance application is that the data is gathered from multiple data sources, and not all the data sources are going to be clean and processed. It is important to gather junk-free data in order to maintain the efficiency of the Data Governance method produced by the organization.3. Data Store/ Data Mart 4. Organization’s Preparedness
When organizations pick up the benefits of Data Governance and decide to implement the same in their own organization, it is highly likely that they look only at the external factors. It is equally important to look at the internal practices of the organization that are being practiced in the current situation. The work pattern should be modified in a way to accommodate the upcoming changes along with the Data Governance execution. While the Data Governance planning is in progress, the existing tradition of the institute should be rechecked and keep the platforms open to place the Data Governance production in place.Advantages
Taking up the Data Governance method into an organization can improvise the company’s Data Management ability while holding the quality and value of the in–house data.
Data grouping from different areas and sectors can help all future processes.
Imposing people’s positions and responsibilities in data administration.
Allowing peripheral data contribution assignments.
Sustaining rigid consistency agreements.
Modeling the data regulation and consumption of the data operated in Data Governance.
Helps in trimming down the disorganized data and managing the costs for data management.Conclusion
To sum up, Data governance assists in making definite about the places of every role and task that is required for carrying out of Data Governance in a Venture. When designing the Data Governance configuration by embracing the deliberations, the company’s imprudence is fixed once for all in terms of administration and management of data.Recommended Articles
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Introduction to Challenges of Big Data Analytics
Data is a very valuable asset in the world today. The economics of data is based on the idea that data value can be extracted through analytics. Though Big data and analytics are still in their initial growth stage, their importance cannot be undervalued. As big data starts to expand and grow, the Importance of big data analytics will continue to grow in everyday personal and business lives. In addition, the size and volume of data are increasing daily, making it important to address big data daily. Here we will discuss the Challenges of Big Data Analytics.
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According to surveys, many companies are opening up to using big data analytics in their daily functioning. With the rising popularity of Big data analytics, it is obvious that investing in this medium will secure the future growth of companies and brands.
The key to data value creation is Big Data Analytics, so it is important to focus on that aspect of analytics. Many companies use different methods to employ Big Data analytics, and there is no magic solution to successfully implementing this. While data is important, even more important is the process through which companies can gain insights with their help. Gaining insights from data is the goal of big data analytics, so investing in a system that can deliver those insights is extremely crucial and important. Therefore, successful implementation of big data analytics requires a combination of skills, people, and processes that can work in perfect synchronization with each other.
With great potential and opportunities, however, come great challenges and hurdles. This means that companies must be able to solve all the hurdles to unlock the full potential of big data analytics and its concerned fields. When big data analytics challenges are addressed in a proper manner, the success rate of implementing big data solutions automatically increases. As big data makes its way into companies and brands around the world, addressing these challenges is extremely important.Major Challenges of Big Data Analytics
Some of the major challenges that big data analytics programs are facing today include the following:
Uncertainty of Data Management Landscape: Because big data is continuously expanding, new companies and technologies are developed every day. A big challenge for companies is to find out which technology works bests for them without introducing new risks and problems.
The Big Data Talent Gap: While Big Data is growing, very few experts are available. This is because Big data is a complex field, and people who understand this field’s complexity and intricate nature are far from between. Another major challenge in the field is the talent gap that exists in the industry
Getting data into the big data platform: Data is increasing every single day. This means that companies have to tackle a limitless amount of data on a regular basis. The scale and variety of data available today can overwhelm any data practitioner, which is why it is important to make data accessibility simple and convenient for brand managers and owners.
Need for synchronization across data sources: As data sets become more diverse, they must be incorporated into an analytical platform. It can create gaps and lead to wrong insights and messages if ignored.
Getting important insights through the use of Big data analytics: It is important that companies gain proper insights from big data analytics, and it is important that the correct department has access to this information. A major challenge in big data analytics is bridging this gap in an effective fashion.
This article will look at these challenges in a closer manner and understand how companies can tackle these challenges in an effective fashion. Implementation of Hadoop infrastructure. Learn Hadoop skills like HBase, Hive, Pig, and Mahout.
The challenge of rising uncertainty in data management: In a world of big data, the more data you have, the easier it is to gain insights from them. However, in big data, there are a number of disruptive technology in the world today, and choosing from them might be a tough task. That is why big data systems need to support both the operational and, to a great extent, analytical processing needs of a company. These approaches are generally lumped into the NoSQL framework category, which differs from the conventional relational database management system.
There is a number of different NoSQL approaches available in the company, from using methods like hierarchal object representation to graph databases that can maintain interconnected relationships between different objects. As big data is still in its evolution stage, there are many companies that are developing new techniques and methods in the field of big data analytics.
In fact, new models developed within each NoSQL category help companies reach their goals. These Big analytics tools are suited for different purposes as some provide flexibility while other heal companies reach their goals of scalability or a wider range of functionality. This means that the wide and expanding range of NoSQL tools has made it difficult for brand owners to choose the right solution to help them achieve their goals and be integrated into their objectives.
The gap in experts in big data analytics: An industry completely depends on the resources it has access to, whether human or material. Some tools for big data analytics range from traditional relational database tools with alternative data layouts designed to increase access speed while decreasing the storage footprint, in-memory analytics, NoSQL data management frameworks, and the broad Hadoop ecosystem. With so many systems and frameworks, there is a growing and immediate need for application developers who have knowledge of all these systems. Despite the fact that these technologies are developing at a rapid pace, there is a lack of people who possess the required technical skill.
Another thing to remember is that many experts in the field of big data have gained experience through tool implementation and its use as a programming model instead of data management aspects. This means that many data tool experts lack knowledge about the practical aspects of data modeling, data architecture, and data integration.
This lack of knowledge will result in less than successful data and analytical processes implementations within a company/brand.
According to analyst firm McKinsey & Company, “By 2023, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effective decisions.
All this means that while this sector will have multiple job openings, there will be very few experts who will actually have the knowledge to fill these positions effectively. While data practitioners become more experienced through continuous working in the field, the talent gap will eventually close. At the same time, it is important to remember that when developers cannot address fundamental data architecture and data management challenges, the ability to take a company to the next level of growth is severely affected. This means that companies must always invest in the right resources, be it technology or expertise, to ensure that their goals and objectives are objectively met in a sustained manner.
As companies have a lot of data, understanding that data is very important because, without that basic knowledge, it is difficult to integrate it with the business data analytics program. Communication plays an integral role here as it helps companies and the concerned team educate, inform and explain the various aspects of business development analytics.
Before even going towards implementation, companies must a good amount of time explaining the benefits and features of business analytics to individuals within the organizations, including stakeholders, management, and IT teams. While companies will be skeptical about implementing business analytics and big data within the organization, once they understand its immense potential, they will easily be more open and adaptable to the entire big data analytical process.
The challenge of the need for synchronization across data sources: Once data is integrated into a big platform, data copies are migrated from different sources at different rates. Schedules can sometimes be out of sync within the entire system. There are different types of synchrony. It is important that data is in sync. Otherwise, this can impact the entire process. With so many conventional data marks and data warehouses, sequences of data extractions, transformations, and migrations, there is always a risk of data being unsynchronized.
With exploding data volumes and the rising speed at which updates are created, ensuring that data is synchronized at all levels is difficult but necessary. This is because data is not in sync. It can result in analyses that are wrong and invalid. If inconsistent data is produced at any stage, it can result in inconsistencies at all stages and have disastrous results. Wrong insights can damage a company to a great degree, sometimes even more than not having the required data insights.
The challenge of getting important insights through Big data analytics: Data is valuable only as long as companies can gain insights from them. By augmenting the existing data storage and providing access to end users, big data analytics needs to be comprehensive and insightful. The data tools must help companies not just have access to the required information but also eliminate the need for custom coding. As data grows inside, it is important that companies understand this need and process it in an effective manner. With the increase in data size on time and cycle, ensuring the proper adaptation of data is a critical factor in the success of any company.Conclusion
These are just some of the few challenges that companies are facing in the process of implementing big data analytics solutions. While these challenges might seem big, it is important to address them in an effective manner because everyone knows that business analytics can truly change the fortune of a company. The possibilities with business analytics are endless, from preventing fraud to gaining a competitive edge over competitors to helping retain more customers and anticipating business demands. In the last decade, big data has come a very long way, and overcoming these challenges is going to be one of the major goals of the Big data analytics industry in the coming years.Recommended Articles
This article has been a guide to the Challenges of Big Data analytics. Here we have discussed the basic concept and challenges of Big Data analytics. You may also look at the following article to learn more –
IoT (Internet of Things) is a network of devices, vehicles, buildings or other connected electronic devices. This connection eases the collection and exchange of data. An IoT system has the following parts −
Backend; also known as data centre
IoT is a connection of embedded devices along with the internet infrastructure. It is an era of smart, connected products that communicate and transfer massive amount of data and upload it to cloud.Examples of IoT
Wearable tech − Wearable gadgets like smart watches, Fitbit bands, Apple watches, etc. easily connect with mobile devices in synchronization. They collect necessary information such as health, heart rate monitoring, sleeping activity, etc. These devices display data, notifications from mobile devices on them.
Infrastructure and development − With an application like CitySense, we can easily collect outdoor lightning data. On the basis of these data, the street lights are controlled. There are other applications also to control traffic signals and parking in a cosmopolitan city.
Healthcare − In healthcare sector, IoT is used to monitor the health conditions of the patients. On the basis of the benchmarked data, the dose of medicine at different times in a day is controlled. Applications (e.g., UroSense) track and monitor the fluid levels in the patient, and initiate fluid transfer as per the needs. The data can be simultaneously transferred wirelessly to the stakeholders.IoT Technologies
RFID (radio frequency identification) tags and EPC (electronic product code)
NFC (near field communication) − It is used for 2-way interaction between electronic devices. It is mainly used for smartphones and for contactless payment transactions.
Bluetooth − This technology is used in cases where short-range communication is well enough to solve the problem. Bluetooth is mostly used in wearable technologies.
Z-Wave − This low power radio frequency communication technology is used in home automation, light controlling, and so on.
WiFi − This technology is the most common choice for Internet of Things. WiFi along with a LAN (Local Area Network) helps transfer files, data and messages easily.IoT Testing
IoT testing is a sub-category of testing to check IoT devices. We now need to provide better and faster services. There is a huge global demand to access, create, use and transfer data. The aim is to provide insight and control, of various interconnected devices. That is why IoT testing framework is so important.Types of IoT Testing
IoT testing generally revolves around security, analytics, devices, networks, processors, operating systems, platforms and standards.Usability Testing
Users use many devices of varying shape and form factors. Also, the perception varies from user to user. This is why investigating the usability of the system is very important in IoT testing. The usability of each device used in IoT must be determined. In healthcare. The tracking devices used must be portable so that they can be moved to different divisions. The equipment used should be smart enough to push notifications, error messages, warnings, etc. The system must log all the events occurring to provide clarity to the end users.Compatibility Testing
A number of devices can be connected through the IoT system. Such devices have varying software configurations and hardware configurations. Therefore, there is a huge number of possible combinations, thereby making the compatibility of an IoT system important.
Compatibility testing is also important due to the complex architecture of the IoT system. Testing items like OS versions, browser types, devices’ generation, communication modes is vital for compatibility testing.Reliability and Scalability Testing
Reliability and scalability of any IoT system is important for setting up the IoT testing environment that involves simulation by using virtualization tools and technologies.Data Integrity Testing
Data integrity testing of an IoT system is important as it includes massive amount of data and its applications.Security Testing
In an IoT environment, a large number of users try to access a massive amount of data. This is why it becomes important to determine user validation through authentication, possess data privacy controls as in security testing.
IoT is data-centric, i.e., all the devices, equipment, system, etc. operate based on the available data. While the data is getting transferred between devices, it can always be read or accessed. The data must be checked to determine if the data is protected/encrypted while it is getting transferred between devices.Performance Testing
Performance testing is essential for developing a strategic approach to develop and implement the IoT testing plan. The chart below is the applications of the different types of testing for various IoT components.
IoT Testing TypesSensorApplicationNetworkBackendFunctional testingTrueTrueFalseFalseUsability testingTrueTrueFalseFalseSecurity testingTrueTrueTrueTruePerformance testingFalseTrueTrueTrueCompatibility testingTrueTrueFalseFalseServices testingFalseTrueTrueTrueOperational testingTrueTrueTrueTrueIoT Testing Process
Test CategoriesSample Test ConditionsComponents Validation
Data format testing
Basic device testing
Testing of IoT devices
Data transit frequency
Multiple request handling
Security and Data Validation
Validation of data packets
Verification of data losses or corrupt packets
Data encryption or decryption
User roles and responsibilities and utility pattern.
Cloud interface testing
Device-to-cloud protocol testing
Sensor data analytics checking
IoT system operational analytics
System filter analytics
Device to Device
ProtocolChallenges faced in IoT testing
Both, the network and internal communication, needs to be checked.
One of the biggest concerns in IoT testing is security and privacy because the tasks are done via Internet.
The software complexity as well as the system itself may conceal the bugs or defects found in the IoT technology.
There are limitations on memory, processing power, bandwidth, battery life, etc.Suggestions to make IoT testing effective
Gray box testing and IoT testing should be performed simultaneously as it enables the designing of effective test cases. This helps us understand the operating system, architecture, third-party hardware, new connectivity, and hardware restrictions.
Real-time OS is vital to provide scalability, modularity, connectivity, and security, all of which are essential to IoT.
To make it effective, IoT testing can be automated.Tools for IoT testing
Shodan − This tool can be used to determine which device/s is/are connected to the Internet. It helps track all the computers which can be directly accessed from the Internet. Shodan is also used in connectivity testing. It helps in the verification of the devices connected to the IoT hub. It provides the connected devices, their locations, user information, etc. It tracks and records all the computers connected to the network.
Thingful − This is a Search Engine for IoT. It helps keep interoperability between millions of objects through the Internet, secured. Thingful is used to control how tha data is used. It also helps take more decisive and valuable decisions.
Wireshark − This open-source tool is used to monitor the traffic in interfaces, source/destination host addresses, and so on.
Tcpdump − This tool is quite similar to Wireshark, but for the absence of GUI (Graphical User Interface). This tool is based on command line. It helps users display packets such as TCP/IP that are transmitted over a network.
JTAG Dongle − This tool is quite like a debugger in desktop applications. It is used in debugging the target platform or device code, and display variables step by step.
Digital Storage Oscilloscope − This tool is used to investigate the different events with time stamps, glitches in power supply, and signal integration.
Software Defined Radio − This tool is used to mimic receiver and transmitter for a wide range of wireless gateways.
MQTT Spy − If the device supports MQTT protocol, then this tool is the most useful. MQTT Spy is an efficient open-source tool for IoT testing. It is particularly useful for day-to-day usage.Prerequisites of IoT Testing
Setting up IoT device − The IoT device must be turned on, and can be accessed and used in real life. E.g., while testing a smart watch, make sure to wear it on wrist. Placing it on a table would not be regarded as a real user case.
Setting up of IoT Hub − IoT hub is a server that can connect with IoT devices and gather information from them. An IoT hub may be an application in a mobile device or a web server on a cloud. The IoT hub must be set up properly.
Setting up network − We need a strong wireless connection to connect IoT hub and the IoT device together. This can be possible with a Wi-Fi, Bluetooth, satellite signals, NFC (near field communication), etc. While connecting wearable device with a mobile app, ensure the following −
The Bluetooth of both the devices is turned on.
Both the devices are paired together.
Both the devices are in range of each other.
Cloud computing governance and compliance is critically important for a key reason: cloud computing impacts so many aspects of our business and personal lives. As consumers, we think nothing of connecting to Dropbox or using an online graphics program. As business people, we use cloud computing applications like Salesforce for CRMs, MS Office 365 for productivity, and Box for file sharing.
So here is the $64,000 question: does your business know how to orchestrate multiple cloud computing services for cost, workflow, and compliance? Chances are it does not. Adopting a few cloud applications on a limited scale is one thing. But when companies decide to invest heavily in cloud computing, then IT and their counterparts in governance and risk management must adapt to a complex new reality. This reality is called cloud governance.
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The simplest definition of cloud computing is delivering cloud-based services to end-users. Computing clouds may be private, public, or a hybrid combination of the two. The major cloud computing service models are Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a service (PaaS). But whether your business uses public, private or hybrid cloud computing, proper governance is essential to harvesting maximum gain from the cloud, and to monitor an array of critical security issues. Before making a decision on cloud types or vendors to use in your business, read our comprehensive guide to cloud computing.
Cloud governance manages IT processes to receive maximum value from cloud computing investments. Although establishing cloud governance takes time and resources at the beginning, it should deliver significant cost savings wicth management processes and frameworks for cloud computing IT spend.
Cloud governance is a business-wide initiative because it involves compliance officers, risk managers, and senior executives as well as IT. However, cloud governance is closely related to IT, who is responsible for cloud computing.
Let’s look at the COBIT model, which publishes five essential process areas both business-wide and for specific stakeholders including IT. They list IT’s five process areas as strategic alignment, delivering value, managing resources, managing risk, and measuring performance.
Strategic Alignment: Link cloud computing services with business and IT strategy planning. Cloud computing should the value of IT as a strategic asset. The governance framework encourages IT and the business to detail strategic business objectives for cloud computing. IT aligns to business goals by establishing metrics and specific responsibilities, and tracks concrete objectives for each cloud computing service.
Delivering Value: Moving to cloud computing and a governance framework can disrupt the status quo. IT and executives need to clearly state the value proposition and measure results against the strategy. The emphasis is on lowering cost and communicating clear user benefits.
Managing Resources: Carefully plan resources at the beginning of the cloud governance project. Include people, applications, business information, and on-premise computing infrastructure.
Manage Risk: Clarify and manage risk to compliance, profitability, and employee satisfaction. Ongoing risk management will minimize negative impacts and maximize benefits.
Measure Performance: Set up tracking mechanisms and monitor metrics around project management and completion, resources, new processes, and delivery.
IT’s cloud computing responsibility also includes a simple governance question: Does it work? Each cloud computing application needs to meet SLAs around three primary technical domains: quality of service, quality of service, application integration, and the biggest challenge of them all: security.
Cloud computing services operate from the providers’ remote data centers. This means that providers and businesses must maximize efficient throughput for performance and latency, and sign meaningful service level agreements (SLAs) around availability and durability.
Acceptable performance and low latency depend on efficient application code, sufficient bandwidth, geo-location, and fast server and storage throughput in the cloud and on-premise. Application availability and data durability are also major issues. Durability is not particularly difficult for cloud providers, who practice data redundancy across multiple devices and sites. (All three public clouds offer 11 nines or 0% data loss guarantees.) Availability is a different issue. Be sure to look at a cloud provider’s average application uptime, and understand how they remediate any service outages, particularly similar outages that have occurred more than once.
The cloud and on-premise applications may or may not have internal integration points. (Oracle Cloud Adapter does in fact integrate Salesforce.) A cloud computing governance platform encourages IT to discover existing integration points, track integration dependencies, and optimize less than ideal integrations.
Corporate and cloud security are in the news: hackers and malware attempts are more common than ever, and can affect thousands of employees and millions of users with a single hack. A cloud provider’s data center is not magically immune to these types of attack. In fact, the cloud computing model has vulnerabilities of its own.
First, cloud computing aggregates much of their customers ‘data into single files and stores massive data sets in a single location. The cloud provider almost certainly builds in data redundancy against data loss, but a hacking attempt can expose huge volumes of data for download and sales. A single company can experience a disaster when a single malware penetration occurs on employee workstation. Should the same malware penetrate a cloud data center, it could compromise multiple tenants’ data.
Companies must do careful due diligence on cloud provider security. Understand how they protect their data centers against physical disasters, energy loss, and both physical and digital intrusion. Encryption is a critical security measure, and don’t leave key protection solely with the provider. Strongly consider using multi-factor authentication tools to protect against unauthorized user access. Also, ask how the cloud provider protects customer data against staff error or deliberate malfeasance.
Cloud governance has more to do with to do with process management than legal and regulatory issues. However, cloud compliance is an extremely important challenge whenever you store regulated or sensitive data in the cloud. Ask your cloud provider how they comply with government and industry regulations, and look for certified data centers and expert provider InfoSec teams. Find out how your cloud provider supports cross-border investigations. Here are some questions to ask:
How compliant are you with government and industry regulations? When you store regulated or sensitive data in the cloud, you need to know your provider’s level of compliance with regulations like HIPAA and PCI DSS. Remember that you still have primary responsibility for compliance, but your provider should have some responsibility for data storage and privacy regulations.
How can I be sure that my data is present and recoverable? Recovery assurance is important with any data on the cloud, especially with online production data. SLAs should cover data availability and durability as well as correctly observing data retention requirements.
How do you keep my data safe? Most compliance standards include physical and digital data security. Verify your cloud data center’s physical security and digital information security. Ask for reports on yearly audits and compliant storage practices, and ask about security ratings like SSAE-16. Ask about segmentation policies in multi-tenant environments including intrusion security and noisy neighbor management. Encryption and user access control are also critical security measures.
Do you support cross-border investigations? When you’re pursuing cross-border investigations, you need to comply with differing national and regional data privacy laws. For example, several European countries require sensitive data to stay within their borders, or at the least within the European Union’s geographical borders. The EU’s new General Data Protection Regulation (GDRP) will be even stricter around data security and privacy. And however much China courts foreign business, it’s all too easy for investigators to run afoul of state secret laws. When you research cloud computing cloud providers, be certain that they have the knowledge and capacity to store your data in regional data centers. Ask if they will work with you to migrate culled data sets between countries.
Most companies already have some cloud computing services, and adding more may not seem to be much of a challenge. But diving into cloud computing can have a big impact on your infrastructure, employees, and strategic goals. It’s simply good business to adopt cloud governance for integrating and optimizing cloud computing for your own business.
A common data type that is used to train computer vision (CV) is video data. As the demand for autonomous vehicles and other computer vision-enabled technologies rises, so does the need for video data since it is considered the fuel that makes these technologies work.
However, studies show that in the entire development stage of a CV system, the data collection stage often gets neglected.
This article aims to remedy this issue by exploring what video data collection is, what the challenges are in gathering video data and what are some best practices to consider.What is video data collection for AI/ML?
Video data collection for AI/ML training is the process of gathering video-object-detection systems, a specific type of video data, to train and deploy a CV system.
A video dataset can include clips of people, animals, objects, environments, etc. For instance, a video dataset to train a self-driving car might include clips of:
Different vehicles driving on the road,
People crossing the road or walking on the sidewalk,
Animals or pets crossing the road or on the sidewalk
Other objects on the road or sidewalk (such as street signs, barriers, etc.)What are the challenges in collecting video data?
Data collectors who collect video data might face the following challenges:1. Cost of collection
Collecting video data can be expensive, especially when the dataset is supposed to be large. Even though smartphones are easily available now to record videos, the recordings can be low-resolution. So data collectors have to use expensive cameras to capture high-quality recordings.
In addition, recording videos on large scales requires extra labor, which can be an expensive process for diverse datasets.2. Time-consuming
Gathering video data can be time-consuming since they take longer to record as compared to image data.
For instance, if a CV-enabled security surveillance system requires data to be collected at a specific time of the day (at dawn, for example), then such data will take significantly longer to collect as compared to data collected during the daytime. This is because the data collector will have a limited time window to record such videos. This issue might arise for image data collection as well; however, taking photos takes significantly less time than recording videos.3. Unbiased/diverse data collection
A study by Georgia tech identified that computer vision systems are surprisingly good at detecting pedestrians with light skin color. With autonomous vehicles, this kind of discrimination can be fatal if the technology doesn’t detect people of different skin colors. For instance, Tesla’s system did not recognize horse carriages on the road since the system was never trained with horse carriage video data.
Therefore, collecting diverse video data to avoid such biases and errors can become a challenge if done in-house, even for big companies such as Tesla.What are some best practices for video data collection?
While collecting video data, you can consider the following best practices:1. Automate video data collection
Video data collection can be automated by using web scraping tools. The user can set parameters for the required data that each video should have, which allows the scraper bot to be specific about gathering the relevant data from the internet.2. Leverage crowdsourcing
Another effective method of gathering diverse and large datasets is through crowdsourcing.
Through a crowdsourcing model, contributors around the world can be hired through a platform to complete mini video data collection tasks. There are third-party crowdsourcing data collection specialists for companies to reach out to avoid the hassle of developing a crowdsourcing platform in-house.
To learn more about crowdsourcing data collection, check out this quick read.3. Consider ethical and legal factors
Like every other type of data, gathering video data can also have some legal and ethical baggage. For instance, collecting videos of people for a face detection system can be subjected to some rules and policies that are important to consider in some countries such as the US.
To learn more about why data collection ethics is important and to achieve it check out this quick read.4. Ensure data quality
While collecting video data, maintaining the level of quality is very important for the overall performance of the CV system.
The video data should be:
Recorded with consistency – i.e., with similar resolution, light variations, angles, etc.
Recorded with diversity in mind. The data should be all-inclusive and comprehensive vis-a-vis the subject for which the data is being collected for.
The video data should be authentic and should not have been physically or digitally modified.
To learn more about data collection quality and how to main it, check out this quick read.
For more in-depth knowledge on data collection, feel free to download our whitepaper:
You can also check our data-driven list of data collection/harvesting services to find the option that best suits your project needs.Further reading
If you need help finding a vendor or have any questions, feel free to contact us:
Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.
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Data story telling is an important part of the report development process. It changes the way the audience views reports and allows them to look at the data being presented in a clearer manner.
To truly understand the importance of data story telling, we need to think about the primary goal of creating data visualization and reports.
When we create reports, our primary goal is to communicate information clearly and effectively.
Storytelling is about transmission of information. Our brains are wired to retain information when hearing compelling stories. Stories have always been the most effective way to share information with others.
Stories that incorporate data provide a higher impact and are more convincing than reports without a story.
It limits the cognitive load, sparks the audience curiosity, and delivers a clear message that makes the audience focus on what is essential.
Effective data story telling consists of telling a story with data. It is a way to communicate with the audience through data not just by showing graphs, but also by thinking thoughtfully about how you can take these graphs and transform information into a compelling story.
Through the use of storytelling, we target three main goals. Get the audience’s attention, build credibility, and move the audience towards action.
The problem with storytelling is that it takes time to process because it needs creative thinking. Most analysts spend a large majority of their time exporting and doing repetitive tasks and less time analyzing in order to meet deadlines.
Good analysts also aren’t necessarily good storytellers or designers. But the skills required to tell a good story can be learned and improved through practice. We must all think from the perspective of the audience. Who wants to hear dull presentation with no particular focus, no call to action or conclusion? No one.
This is why whenever we create our reports, we need to ask the right questions that will allow us to tell an engaging story.
The first important step in telling a good data story is familiarizing yourself with the technical environment. We need to check the boundaries of what we can or cannot do. In doing this, we need to look at three critical areas – the enterprise, the data involved, and the business.
Let’s start with the enterprise. One of the first questions we should ask is, what specific platforms are in use? Is it Power BI Pro? Does the user have premium capacity? We need to think about how the report will be consumed by the end user.
If you’re using Power BI Premium, for example, you can simply share your report to 100 users without any problem. But if you’re using Power BI Pro, those 100 users should be using a pro license as well before they can view and interact with your report.
The person you’re making the report for might also have different needs. If they need the data to be refreshed a few times each day, then using Power BI Pro might have some limitations knowing that it only allows up to 8 refreshes a day. If you use Power BI Premium, you’re given up to 48 refreshes daily.
Think about the external tools as well. DAX Studio will allow you to create queries effortlessly. You can monitor each query’s performance and adjust your formulas as needed. If you’re using Tabular Editor, you can build calculation groups and create an analytical experience that you couldn’t create if you rely solely on the capabilities of Power BI Desktop.
The use of custom visuals is another consideration. The visualization menu within the Power BI Desktop will show you what you’re capable of when you go into the visual library.
This custom visual library contains many type of visuals that are not native and can be useful in creating more effective reports. This is where you can find Power Automate in case you need to create workflows and work on other kinds of use cases.
You can also access Charticulator in case you want to create your own custom visuals.
Make sure you also check any graphics standards that need to be followed. Is there a specific color theme to be used? Which icons and fonts are needed? This will impact the branding and overall look of your report.
To create a seamless story, you need to understand first where your data is coming from. How many data sources do you have? Are you using Excel, SQL, or other data sources? Knowing where your data is coming from gives you better control of the way the data should be consumed.
Data quality is another thing to consider. Is there a need for you to use DirectQuery? Are you supposed to use import mode?
Knowing the ins and outs of your data allows you to prepare everything that you need before jumping into Power BI. Some aspects of the datasets, for example, might have to be tweaked before being imported into Power BI. Importing the wrong type of data into Power BI does not just impact overall performance; it could result in wrong analysis and results as well.
Checking on volumetry also gives you a better idea on how to deal with the data provided. If you’re working in Direct Query, for instance, this means that you’re dealing with a huge amount of data. Find out right away if you’ll be working with 100 million rows of data, because you’d have to make a lot of adjustments to your approach.
The last set of considerations would be about the business requirements. This is critical because this dictates what kind of information the end user needs to see and how they want to see it.
Are you working on marketing data? Financial data? Human resources data?
Check on how sensitive the data is as well. Is there a need for anonymity?
This is also where the type of collaboration required should be considered. Are you working with data engineers who will require some SQL integration? Are you working with a data science team? Understanding the kind of people you’re working with allows you to set boundaries and understand what your scope should be.
It would be helpful to understand whether the expected report should be live, where the data is refreshed every few seconds, or if it’s okay to keep the data stagnant.
Data story telling truly makes an impact especially if you want to effectively engage your audience. Although data professionals and enthusiasts like us might consume data in specific ways, remember that the audience might not think the same way. This is why it’s important to deliver the data in a way that will pique their interest.
Note that data story telling isn’t just about aesthetics, either. It’s finding the balance between analysis and creative presentation. The goal is to deliver the right type of data to the right type of people in the right way.
All the best,
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