Data Governance: Sustainable Data Management

The power of knowledge and data was understood in today's increasingly expanding technical landscape as smartphones, notebook computers, and computers become an inseparable component of human existence. Now, when we live in the 'digital age,' the data volumes explode; in the past two years, more data were produced than in the whole of human existence. Data analysis and data processing have a massive opportunity for the future by applying data mining tools for the sustainable management of data (Carroll et al., 2020).

To find trends within and create interrelationships to solve problems through data analysis, data mining is the method of sorting large-scale data sets (Abraham, Schneider and Vom Brocke, 2019). Data extraction is the detection of vast datasets of important, unexpected or valuable structures.

1. Statistics: data interaction numerical analysis

2. Wisdom artificial: the intelligence of people seen by software and equipment

3. Learning machinery: algorithms to use data to forecast potential patterns

In order to identify the prospective and perspective of the data, data mining gathers vast collections of data. The data market demand is increasingly expanding in the current scenario. We should not neglect the analysis and the transformation of data into significant facts. It is indispensable. Any data set is essential for its research and can predict company dynamics, revenue forecasts, costs, etc (Leonelli, 2019).

Data governance is about enabling strategies, improving results and reducing vulnerability to improved monitoring and data processing. It can be considered to address today's needs without jeopardizing future generations' capacity to meet their own needs. Successful data processing organizations, at all stages, develop operational understanding and appreciation of the meaning, usefulness and importance of their data. Data extraction is a pillar of data analysis that helps create models for uncovering correlations in millions of documents (Janssen et al., 2020).

Today 90% of the modern world is comprised of unstructured data alone. However, additional detail does not always equal more information. Data mining technology is constantly in evolution to keep pace with the unlimited data required.

As a hybrid discipline, data extraction represents a wide array of approaches or strategies utilized in diverse expertise, which answer a wide range of organizational requirements, pose different types of questions, and use different levels of human feedback or rules to reach a judgment. The protocol as follows:

1. Collecting of requirements: the data mining project begins with the collection and interpretation of requirements. The scope of the necessity is specified in terms of industry. Once established, the scope is moved to the next step

2. Exploring data: the data is collected here, analyzed and examined according to the project requirements. Understand and translate issues, obstacles into metadata. Data mining statistics are used to classify data trends and transform them.

3. Preparations of data: Convert the data to helpful modeling details. This move can be used for the ETL procedure — extracting, transforming, and loading. They often create new attributes for the results. Various methods are used here for structural data presentation without modifying the context of data sets.

4. Modeling: for this phase, the right resources are in position as this plays a crucial role in the complete data processing. Modeling and assessment are correlated measures, and the criteria are checked simultaneously. The results can be confirmed quality after the final modeling is completed (Al-Ruithe, Benkhelifa, and Hameed, 2019).

5. Assessment: after good simulation, this is the filtering method. If the result is not met, it is passed back to the model. The criterion is re-examined after the final result, ensuring that no argument is overlooked—experts in mining judge the whole outcome at the end.

6. Deployment: The complete process' final phase Data in the form of tablets or graphs for sellers.

Damanvir Kaushal

Keywords

#Data governance

#Data management

#Data mine 

#Data Development

References

Abraham, R., Schneider, J. and Vom Brocke, J., 2019. Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, pp.424-438.

Al-Ruithe, M., Benkhelifa, E. and Hameed, K., 2019. A systematic literature review of data governance and cloud data governance. Personal and Ubiquitous Computing, 23(5), pp.839-859.

Carroll, S.R., Garba, I., Figueroa-Rodríguez, O.L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R. and Sara, R., 2020. The CARE Principles for Indigenous Data Governance. Data Science Journal, 19(1).

Janssen, M., Brous, P., Estevez, E., Barbosa, L.S. and Janowski, T., 2020. Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), p.101493.

Leonelli, S., 2019. Data governance is key to interpretation: Reconceptualizing data in data science. Harvard Data Science Review, 1(1).

What is GDPR? What are the Seven Principles of GDPR?

As a result of public concern over privacy, the GDPR was adopted by the European Parliament to protect the privacy and regulate the exportation of personal data of EU Citizens.  Collecting and processing data of European Union countries’ citizens, companies must comply with strict rules that protect customer data (Nadeau, 2020).

 

According to Data Protection Commission Ireland, General Data Protection Regulation (GDPR) is a standardized data protection law applied across European Union that came into effect in 2018. Article 5 of the regulation identifies the seven main principles of GDPR about how personal data should be gathered and processed by organizations. The seven principles can be explained as below (Kulakova, no date):

 

     1. Lawfulness, fairness, and transparency mean all personal data should be processed, referring to these standards. Lawfulness requires a legal basis within the GDPR when a company wants to use personal data; fairness tells how the organization must be fair to data subject during the data processing by not being harmful or tricky. Transparency means the users should be informed clearly by the organizations with straightforward explanations about the purpose of asking for personal information before the collection and processing of data.

2. Purpose Limitation means all personal data should be gathered for significant and legal purposes and should be limited to any future processing which is irrelevant to current goals. On the other hand, personal data could be processed for public interest, such as creating statistics if it is related to the original purpose. This principle aims to let organizations explain their intentions from the beginning about why they will be processing the personal data and for which purposes they will use it for.

 3. Data minimization principle allows organizations to gather only compatible and necessary information related to the specific purpose of the data processing. This principle and Purpose Limitation could be considered as complementary principles in terms of their aims. The nature of data minimization helps organizations collect up-to-date and recent personal data, and the principal protects the secrecy and entirety of data by avoiding any possible hijacking. As a result of this, it is recommended that companies should regularly check personal data by the criteria of compatibility and adequacy and should erase the unnecessary data. 

 4. According to the accuracy principle, all personal data collected and processed by organizations has to be precise, and they should be updated or deleted immediately if anything changes or turns into inaccurate information. In addition to this, companies should also be aware of their responsibilities considering individuals' rights, such as providing correction or completion of inaccurate and missing data. 

5. Storage limitation means organizations are able to keep personal data only within the time limit of the required period, which also has to be related to the purpose of data processing. As mentioned before, personal data may be kept for later for public interest considering and ensuring the regulation rules as a whole. The unnecessary data has to be erased immediately if it doesn’t serve the initial purpose. GDPR leaves it to the organizations how they identify which data is no longer necessary or not and encourage companies to inform their data subject about any changes. Besides, companies may anonymize personal data if the data subject cannot be recognized anymore, considering the compatibility to be considered anonymous. 

6. Integrity and confidentiality principles aim to secure all personal data processed by organizations and keep it confidential to avoid any serious harm or loss. Therefore, it is recommended that companies should benefit from security measures and control them regularly to ensure an entire security approach. 

     7. Lastly, the accountability principle, which is the newest one among others, means that organizations must take their responsibility to fulfill GDPR principles in coherence and prove them with appropriate data processing demonstrations. These demonstrations may include adopting internal policies, privacy policies, reporting any data issues, and updating the security measures for the organizations in the digital environment. 

Buket Bostanci

Keywords: GDPR, GDPR principles, data protection, privacy, personal data

References & Sources

Nadeau, M.,2020. General Data Protection Regulation (GDPR): What You Need to Know to Stay Compliant.[online] CSO. Available at: https://www.csoonline.com/article/3202771/general-data-protection-regulation-gdpr-requirements-deadlines-and-facts.html [Accessed 13 March 2021].

 

Kulakova, G., No date. 7 Principles of the GDPR and What They Mean [online] Amara. Available at: https://www.amara-marketing.com/travel-blog/7-principles-of-the-gdpr-and-what-they-mean [Accessed 13 March 2021]. 

Data structures in digital marketing and making it useful for your business : Segmentation

Data structure is a very common word heard in this big data world, so what is Data Structure? Famous scientists Peter Wegner and Edwin D.Reilly defined data structure better way which is more precise in their publication Encyclopedia of Computer Science as it is a collection of data values and the relationships between those data values and the functions or operations that can be applied to the data.

Talking about the data structure domain to easily understand we can create 3 categories 

The format: It is mainly the process of arranging data based on its structure or type into different categories, It can be Arrays, Stack, Queues, Linked Lists, Trees, Graphs, Hash Tables, etc.

Storage: It included storing, and the management of data. Moving a business to the cloud takes out the expense of equipment and support. Eliminating these capital consumptions and the related help compensations can convert into huge expense reserve funds. Your cloud storage provider will maintain, manage and support your solution. This frees up employees who would otherwise cover the tasks necessary for keeping your data safe and your server(s) up and running.

Efficiency: Efficiency is a pivotal necessity in light of the fact that effective information structures defeat their own purpose totally.

Segmentation can be treated as another side of the coin when taken in the case of digital marketing with data structures. The ordinary utilization of marketing segmentation is to partition data into several groups with similarities and differentiate marketing strategies for them. Each organization has a set of Ideal customers, the customers who have an affection for the service a company offers. If we envision the entire clients are that way. Productive, exceptional, the best client we could ever want. Regardless of whether we could get a couple of a greater amount of these, it could have a significant effect.

                                               Pillars-of-segmentation

Data structures are fundamental for most developers since they're the foundation of how all products work. Data is everywhere, and the capacity to coordinate the data in an effective way is definitely essential for building solutions and quality programming.

Collecting and using client information is an exceptionally significant step for any company. Most advertisers will concur that we are exactly toward the start of what is actually possible. The innovation is to utilize client information and predictive analytics considerably with a proper understanding of data structures and knowledge of segmentation.

The most grounded sign of the future of structured data comes from Google itself. where they developed their own structured data testing tools. An example is that how google's algorithm gets updated frequently to filter most suited search results for a keyword. 

- Spoorthi Joshi S

REFERENCES

[1] Smart Insights 2019 Get your data structure in shape for your next step in customer segmentation. Available at https://www.smartinsights.com/digital-marketing-strategy/customer-segmentation-targeting/get-your-data-structure-in-shape-for-your-next-step-in-customer-segmentation/ [Accessed 3rd March 2021]

[2] The Coder Pedia 2020 Type of Data Structures (Complete Overview) Available at https://www.thecoderpedia.com/blog/type-of-data-structures/ [Accessed 3rd March 2021]

[3] Digital Marketing Institute The Importance of Structured Data in SEO 2017 Available at https://digitalmarketinginstitute.com/blog/the-importance-of-structured-data-in-seo [Accessed 3rd March 2021]

VALUE-KNOWLEDGE MANAGEMENT. HOW IT CAN WORK AND ITS BENEFITS IN AN ORGANIZATION.

Knowledge management is the deliberate method of identifying, structuring, maintaining, and exchanging an organization's employees' knowledge and experience. Knowledge management is necessary because it increases the productivity of a company's decision-making ability. In making sure that all employees have access to the overall expertise held within the organization, a smarter workforce is built who are more able to make quick, informed decisions that benefit the company.

Knowledge management may have a positive effect on individual divisions within an organization, such as the IT support team and the customer service department. Many organizations have incorporated digital applications, such as chatbots, into their information management systems. In this scenario, knowledge management is mainly used to help the IT support team. Knowledge management data will aid some AI ventures in the reverse direction and assists the customer service staff in addressing customer concerns. They compile details on the problems that customers report. They can classify specific problems as well as problems that involve a broad organization by evaluating this collection of data. As a result, they would need to develop new content to aid consumers with addressing their challenges on their own.

Companies can offer more content to their consumers by using knowledge management. This method, regardless of how you use new automation services and chatbots, is even more effective if it happens earlier. If you use these artificial intelligence techniques to create content, it will be better. Automation for content is recommended for businesses that choose to have more human contact. Chatbots may be used for those that are dealing with big problems or issues that concern a large number of customers. Agents will provide content for certain chatbots based on the issues they're dealing with.

 

Benefits and examples of Value-Knowledge management in organizations.

·   Cross Training programs- Mentoring, shadowing, and other cross-training activities allow workers to acquire the company expertise by studying others at work. New employees at Toyota, for example, spend months shadowing seasoned employees, and new plants are originally staffed by both experienced personnel from existing factories and new hires. 

·   Document management system -Organizations can store business records in the cloud, exchange them, and monitor access permissions at a granular level through database management systems like Google Drive and Package. These tools typically have mechanisms for tagging files and incorporating metadata to make it easier to locate content.

·   Content management systems (CMSs) - Teams and individuals can publish, upgrade, and view information on a business intranet using content management systems including SharePoint and Bloomfire.

·   Social networking tools – Teams can connect and interact in a public environment using private social networking platforms like Workplace through Facebook and Slack. However, since these resources preserve all past discussions, they can be used as a knowledge management programs, allowing workers to check for previously shared material.

·   Chatbots- We agree that chatbots are the next logical phase in knowledge management. AI and deep learning are used by chatbots like Banks and airline websites to respond to employee questions and requests for information. Employees don't have to worry about who to ask or where to go for information as they use chatbots. They don't have to search into a CMS, record system, or chat history to discover what they're searching for. They can ask natural-language questions, such as "How do I add a new baby to my insurance?", and the chatbot will reply with the most appropriate information from its knowledge base, regardless of where that information is stored inside the company.

DAMANVIR KAUSHAL

Keywords

#value Knowledge management

#Knowledge management

#Chatbots

#Management knowledge

#Business objectives

References

https://www.atspoke.com/blog/knowledge-management/knowledge-management-importance/

 https://ecommercenewsforyou.com/the-importance-of-knowledge-management-in-marketing/

 https://www.emerald.com/insight/content/doi/10.1108/09544780610659970/full/htm 

 https://www.researchgate.net/publication/228471641_What_is_the_Value_of_Knowledge_Management_Practices 

How Trustful Is Your Data? | Veracity & Data Cleansing and Transformation

The term veracity can be defined one of the 5Vs of big data which signifies the trustfulness, quality and the credibility of data that an organisation has collected to gain meaningful insights for the best decision-making processes (Understanding The 5Vs Of Big Data, Blog - Acuvate, 2021). In general, veracity focuses on nature of the data source, considering how accurate and reliable is the information and how it is relevant to business objectives of an organisation. Moreover, having records with high veracity may bring good opportunities to businesses to achieve their goals while processing big data such as helping them to find out a relationship between a purchased product and a training course related to the product directly (Smallcombe, 2021).

Why Veracity is Vital for Businesses?

In today’s accelerated digital world, businesses more likely to rely on real-time decision-making processes regarding the high volume and velocity of data they gather to analyse, by the help of machine learning rather than human brain. As a result, this trend brings the question for accuracy and trustfulness issues of the data and highlights the term veracity. For example, deprivation of veracity can cause a snowball effect and lead to unplanned results for the organisations such as cost loss after being exposed to manipulated data. Imagine a brand who spreads wrong information which is related to human health, could cause a serious damage in terms of its consumers. Besides, the brand could lose its reputation easily (How Data Veracity Will Determine Our Future, Accenture Insi, 2021). In addition, by ensuring an accurate and trustful data, veracity provides a goal-oriented customer engagement which may lead to a better and personalised customer experience for businesses. It also encourages the self-assurance of digital marketers by ensuring them a trustful, quality and consistent data (Why Data Veracity is the Foundation for a Personalized Customer Experience, 2021).

Data Cleansing and the Difference Between Cleansing and Transformation

Data cleansing can be expressed as a process which errors are detected and eliminated, incompatibilities are fixed, and data is transformed into a similar format to improve the quality of data. Therefore, data cleansing and data quality are connected to each other as data cleansing is the only way to create a quality data as long as enormous amount of data collected from different sources might have accuracy and inconsistency issues that affect the quality of data (Ridzuan and Wan Zainon, 2019).

The quality data has 5 components which are defined as validity, accuracy, completeness, consistency and uniformity that can be ordered according to business objectives of an organisation.

Finally, while data cleansing focuses on removal of the unrelated data from the dataset, data transformation can be explained as a process of changing the structure or format of the data into another one to be analysed. Data transformation is also called as data wrangling, or data munging (Data cleaning: The benefits and steps to creating and using clean data, 2021).

Recommended Tasks Before Data Cleansing and Transformation

Here are some tasks recommended by software company Import.io that organisations should consider to be done before data cleansing and transformation (What is Data Cleansing and Transformation/ Wrangling?, Import.io, 2021): 

• Clarify the business objectives including general strategy, current customer issues, estimated return on investment etc.

• Examine sources of data to develop a data model related to business objectives as well as decide for what purpose the data will be used for.

• Data profiling is required before transforming the data to identify the structure of the data and detect quality problems to understand whether the data is valuable enough for transformation or not.

Keywords: Veracity, Quality Data, Trustfulness, Decision-making, Accuracy, Data Cleansing, Data Transformation, Business Objectives

Ilgin Damla Omay 

References and Sources

Acuvate. 2021. Understanding The 5Vs Of Big Data, Blog - Acuvate. [online] Available at: https://acuvate.com/blog/understanding-the-5vs-of-big-data/ [Accessed 23 February 2021].

Import.io. 2021. What is Data Cleansing and Transformation/ Wrangling?, Import.io. [online] Available at: https://www.import.io/post/what-is-data-cleansing-and-transformation-wrangling/ [Accessed 23 February 2021].

Redpoint Global. 2021. Why Data Veracity is the Foundation for a Personalized Customer Experience. [online] Available at: https://www.redpointglobal.com/blog/why-data-veracity-is-the-foundation-for-a-personalized-customer-experience/ [Accessed 23 February 2021].

Ridzuan, F. and Wan Zainon, W., 2019. A Review on Data Cleansing Methods for Big Data. Procedia Computer Science, 161, pp.731-738.

Smallcombe, M., 2021. The 7 Vs of Big Data. [online] Xplenty. Available at: https://www.xplenty.com/blog/7-vs-big-data/#veracity [Accessed 23 February 2021].

Tableau. 2021. Data cleaning: The benefits and steps to creating and using clean data. [online] Available at: https://www.tableau.com/learn/articles/what-is-data-cleaning [Accessed 23 February 2021].

WordPressBlog. 2021. How Data Veracity Will Determine Our Future, Accenture Insi. [online] Available at: https://www.accenture.com/nl-en/blogs/insights/data-veracity-and-the-future-of-the-digital-economy [Accessed 23 February 2021].

What are the Benefits of Volume-Data Storage and How it is Beneficial to Digital Marketers?

Data storage is a medium to store a huge amount of Information. Any electronic document can be store in less space than any paper document. This information converts into bits and can be stored in the electric circuit. The storage volume can be removable hard disk but it doesn’t remove physically from computer or storage system. This Volume data storage is flexible and it can contract or expand. In it, files and documents can record digitally and saved in a storage system for future use. If we talk about a business point of view data is the most important asset. So in today’s digital world, the best method of volume data storage is websites, blogs, social media sites. Making a high-quality website for storing data is a good option so that it can include information about things that customers find relevant and interesting. With the help of data storage, it’s easy to get the contact information of the customers. Digital marketers have to deal with a large amount of data on different sources like social media, websites and that collected data helps them to do marketing digitally in the digital world. Not only this there are many other data management or data storage platforms where digital marketing experts can store their data and use it whenever they needed. Here if we talk about different marketing which is called content marketing, in content marketing data storage is very much important because if we don’t have any accurate data or we are not able to make proper and right content and in content marketing strategy data plays a vital role especially when we don’t have any idea about data analytics. By capturing or recording data digital marketers are better to understand what content or marketing strategies and methods are required for getting a better result. The things which come in mind for storing huge amount of data for a long time is optical media like DVD, and external USB - SSD drive for storing last 20 years as well as Network Attached Storage (NAS) with RAID: These have high performance magnetic hard disks and are your best option. Because of RAID, there is failure protection. I am not sure that original disks can last 20 years, but you can replace them (NAS allows hot-swapping of disks). In volume data storage it helps to keep a record of all past and activities of digital transactions. It also helps in data analysis so that any marketer can forecast the trends of the future market. It also provides flexibility and mobility and storage is easy and can move from one place to another, not only this it’s easy to forecast the better strategy formation. With the help of volume data storage decision-making is easy for the management which benefits the company’s business. 

In conclusion, it is not easy to manage and recognize the huge amount of data for the human brain so that with the help of data storage it is easy to keep a record of the day to day activities which increase the efficiency of the employee in many ways as a result easy to analysis the market scenario that helps business winning out in today’s competition.

Damanvir Kaushal

Keywords

#Volume Data storage

#Digital marketing

#Data storage

#Digital Transformation

#Social media

References and Sources :

• https://searchstorage.techtarget.com/definition/volume
• https://techterms.com/definition/volume
• https://docs.oracle.com/cloud-machine/latest/stcomputecs/ELUSE/GUID-6ACE7B13-442B-459C-8868-566F1CB65E3F.htm#ELUSE-GUID-6ACE7B13-442B-459C-8868-566F1CB65E3F
• https://docs.microsoft.com/en-us/windows-hardware/drivers/ifs/storage-device-stacks--storage-volumes--and-file-system-stacks
• https://kt.cern/competences/high-volume-data-management-storage

Velocity – Data Processing

What is Big Data?

Big Data is a modern subfield of data science that requires the use of several tools, methodologies and techniques to explore, analyse and process complex data sets in order to be provided insights and information systematically. 

The Five V’s of Big Data 

Big Data had broken into four dimensions by IBM data scientists. Volume, Velocity, Variety, Veracity and Value are known as the five V’s which can be referred as the characteristics or the key elements that define Big Data. These characteristics are crucial and must be considered by companies that want to run operations successfully. Volume refers to the size of big data while velocity can be defined as the pace in which the data is getting accumulated. Variety stands for the complexity of the data while Veracity can be considered to assure the accuracy of the data. Lastly, the term Value was added, representing the usefulness of accumulated data.
 
 
Velocity – Analysis of Streaming Data 
 
The speed at which data from IoT, mobile data and social media is generated, accumulated, distributed and collected can be defined as Velocity. The rate of velocity parallels with the speed in with the data is acquired and processed. Velocity-oriented databases provide users real-time analytics, information and insights which enable companies to make valuable business decisions at the right time. In addition; acquiring, analysing and processing data in real-time allows companies to solve upcoming problems before facing complex issues, making real-time decisions and catching frauds as well. Besides, Processing information quickly into data allows users to have flexibility in their queries and reports.
 
On the other hand, the systems which analyse the data should be compatible with the task which means high velocity data may require distributed processing techniques due to the speed it is being generated. Some organizations came up with several solutions for streaming data such as; Apache’s Kafka and Spark, Amazon’s Kinesis, Google’s Cloud Functions and other streaming applications that process information in almost real-time to handle the high velocity of data (Reca, 2020).
 

Every day 2.5 quintillion bytes of data is created to be managed, secured (Marr, 2018). Processing that streaming data instantaneously requires strong data handling processes. High velocity allows users to react high volume of information within a short time. Twitter or Facebook messages, social media posts, Facebook status updates, online transactions, fraud checking, credit card swipes, compliance mechanisms, GPS signals, purchase transaction records and live transportation data can be shown as high velocity data examples. For instance, Facebook needs to ingest, process and file all of the photos uploaded by the users in a short time (Gewirtz, 2018). Also, one TB of trade information can be captured by The New York Stock Exchange during each trading session. In addition, velocity is essential for packet analysis for cybersecurity since the massive flow of data should be analysed and investigated quickly in order to detect anomalies and prevent risks. Thus, data should be gathered, processed and presented near real-time to achieve successful business results (Tudor, 2020). 
 
Buket Bostanci

Keywords: big data, data velocity, velocity, data flow, data volume

References & Sources

Gewirtz, D., 2018. Volume, Velocity, and Variety: Understanding the three V’s of Big Data.  [online] ZDNet. Available at: https://www.zdnet.com/article/volume-velocity-and-variety-understanding-the-three-vs-of-big-data/[Accessed 21 February 2021].
 
Reca, M., 2020. The 5 V’s of Big Data. [online] FlyData. Available at: https://www.flydata.com/blog/5-vs-of-big-data [Accessed 21 February 2021].
 
Marr, B., 2018. How Much Data do we Create Every Day? The Mind-Blowing Stats Everyone Should Read. [online] Forbes. Available at: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/?sh=6da11b3560ba [Accessed 21 February 2021].
 
Tudor, N., 2020. Understanding the 5Vs of Big Data: Volume, Velocity, Variety, Veracity & Value. [online] BornFight. Available at: https://bornfight.com/blog/understanding-the-5-vs-of-big-data-volume-velocity-variety-veracity-value/ [Accessed 21 February 2021].

COVID19 and Big data | Big data helping businesses in 2021

Big data is the focus domain in the data science domain after covid19 and recent times. This can be simply explained by the example that recently to renew the license people used to visit the office before but now it's all done online. The amount of data collected starting from 2020 is enormous. It is same time true that big data analytics helped in tracking and preventing the spread of coronavirus. JHU released a dashboard for tracking and reporting real-time data on COVID-19 for most of the countries. (JHU, 2021)

COVID19 tracking dashboard by John Hopkins

Arogya Setu, the covid19 tracking app of India is downloaded 127.6 million till now on App Store and Play Store which makes it the world's most downloaded covid tracking app, the data collected from users including location, their test details helped in tracking and isolating the covid positive patients. Similiar applications developed are greatly supporting the segregation of population and location based on the amount of risk and also supports government and organizations to take up timely actions. (Yourstory, 2020)

Since the virus is novel, there require researchers to analyze a huge volume of information – out of scope for the human brain to comprehend on its own. The area called predictive analytics helps governments to manage patients who are more vulnerable most at risk from the virus.

Machine learning along with big data analytics aids in treatments like finding antibodies required for vaccine preparation.

Big Data in 2021 comes to action in similar scenarios as discussed where it helped in tracking spread to planning activities regarding the virus, the same time big data during this scenario came into wider discussion due to the fact that these applications collect a large amount of user data to fuel its functionalities. (TCD, 2021)

The future of business in 2021 can be improved like how Big Data supports covid19 tracking and prevention from 2019. Businesses can make smarter decisions using Big Data analytics where strategies are made using facts derived from data and not from assumptions or feelings.

Adoption of strategies: There is a wide range of tools available for data analysts where the centralized data is available to the different departments of the company not just the IT departments with controlled access s required. This enables departments to form strategies that aid in their varied operations. This avoids any miscalculation by the IT department since the data is available across all the departments to cross-check analysts and reports.

Serving the right customer: Likewise, how Facebook and Google know about us it is crucial for companies to know their customers well using the data they provided with content to better serve them and increase conversions by providing customized solutions.

Products and services: As discussed above knowing customer's likes and behavior also helps in customizing and offering unique products and services. Big Data Analytics will also assist in promoting and improving products and services that need attention leading to improved innovation. 

Boosting income: Using data it is possible it conduct competitor analysis to understand what works better for them in comparison. This helps in identifying the trends in their market, capture them at early stages, and adopting them. 

Big Data opens door to AI to automate decision making leading to increased revenue and happy customers. 

- Spoorthi Joshi S

References

YourStory 2020 | Arogya Setu remains among top 10 downloaded apps globally in May: NITI Aayog CEO Kant | https://yourstory.com/2020/06/arogya-setu-top-10-downloaded-apps-globally-niti-aayog  (accessed on 14th February 2021) 

JHU 2021 | The Johns Hopkins Coronavirus Resource Center (CRC) | https://coronavirus.jhu.edu/about 

(accessed on 14th February 2021) 

TCD 2021 | COVID-19 and the Perils and Promise of Big Data | https://www.tcd.ie/business/news-events/covid-19-big-data.php (accessed on 15th February 2021) 

What Is Big Data and How It Affects the Way of Decision Making?

In the era of digital transformation and emerging technologies, one cannot deny the importance of Big Data analytics which directly affects and enhances the decision-making process of an organisation. Here comes the question. What is Big Data and why effective usage of Big Data is crucial for companies who wants to achieve their digital marketing goals in the long term?

Components of Big Data

Big Data can be defined as large amount of data which is also fast and complex that makes the analysing process unmanageable with conventional methods. According to Laney, the components of Big Data are the three Vs which forms the definition of the term as high volume, velocity and variety of data to be processed. Volume means the various different sources that organisations can benefit for data collection such as connected IoT devices and social media platforms. Velocity describes the high-speed streaming of data including near real-time actions. Variety expresses the different formats of data which can be occurred both structured or unstructured such as email, video or financial transactions (Big Data: What it is and why it matters, 2021). Two more Vs which are entitled value and veracity have been added to the three Vs by some sources to emphasise the importance of the value and truthfulness of the data (What Is Big Data? | Oracle Ireland, 2021).

Why Organisations Analysing Big Data?

A survey was conducted in 2017 indicated that the number of companies which have adapted Big Data analytics has been dramatically increasing in the recent years. Moreover, the survey reveals the most common purpose of using Big Data as data warehouse optimisation which requires an integration of Big Data platforms such as Hadoop and Spark into an existing data infrastructure of an organisation. The main reason of using Big Data is followed by the purpose of analysing social media and streaming data coming from IoT devices which considered as data lake sources to define the unprocessed data, and exposing fraud detection (Watson, 2019). Once the organisations know the sources of Big Data, they will be able to manage and store the data by the help of Big Data platforms or cloud spaces, and then analyse it in terms of relevancy for the business purposes to have better, evident-based and data driven decisions in the marketplace that they compete (Big Data, what it is and why it matters, no date). It could be said organisations that are aware of the significance of the Big Data and have ability to process and analyse it properly, are more likely to have meaningful insights from their customers and make successful decisions while applying their digital marketing strategy.

Big Data Applications

There have been many applications of Big Data that have emerged as Natural Language Processing including Digital Assistants such as Amazon's Alexa and Apple's Siri, chatbots which are used for customer support, and Visualisation Systems including Drones and Facial Actions Coding Systems (FACS). For example, the company P&G used a coding software to analyse facial expressions of people for testing the scents of its new product which ended up with more precise and successful predictions of the software rather than what participants said (Hugh J., 2019). Another example of using Big Data shows how Amazon has been collecting detailed purchase data from its customers since then and how it lets other companies to access its ad portal to make them reach to the specific target audience (17 Big Data Examples & Applications, 2021).

Finally, using of Big Data by many companies brings governance concerns and security issues as well. To protect the privacy of personal data, regular controls of service providers need to be reviewed and the compliance with laws and regulations should be considered.

Keywords: Analytics, Big Data, Decision Making, Digital Marketing, IoT, Social Media

Ilgin Damla Omay

References and Sources

Built In. 2021. 17 Big Data Examples & Applications. [online] Available at: https://builtin.com/big-data/big-data-examples-applications [Accessed 16 February 2021].

Hugh J., W., 2019. Update Tutorial: Big Data Analytics: Concepts, Technology, and Applications. Communications of the Association for Information Systems, pp.364-379.

Oracle.com. 2021. What Is Big Data? | Oracle Ireland. [online] Available at: https://www.oracle.com/ie/big-data/what-is-big-data/ [Accessed 16 February 2021].

Sas.com. 2021. Big Data: What it is and why it matters. [online] Available at: https://www.sas.com/en_us/insights/big-data/what-is-big-data.html [Accessed 16 February 2021].