Unleash the power of Business Intelligence for your company

The terms business insight and business intelligence are frequently utilized reciprocally. The term business intelligence means inspecting information to discover patterns in them which leads to arriving at certain facts.  At the point when utilized together, business intelligence and business analytics are two sides of a coin and possess more extensive importance and incorporates each part of collecting, inspecting, and interpreting data and insights which can be even visualization of data. (Oracle, 2021)

BI Techniques for 2021

BI tools perform data analysis and make reports, rundowns, dashboards, guides, diagrams, and graphs to give clients definite knowledge about the idea of the business.

Data Analytics

Data Analytics is an important domain in business intelligence that every small and medium business should use in their operations in 2021, this method includes the investigation of raw data to extract significant patterns and insights.

This is a mainstream BI strategy since it allows organizations to profoundly comprehend their business data and drive extreme worth with information-driven choices, it can greatly help business managers from making wild choices in making important decisions.(IDA Ireland 2021) For example, how DigiX Media is utilizing data analytics to create customized marketing budgets, strategies, improve the client experience, and for quality affirmation purposes.

Data Mining

Data mining is an important subset of data science and an integral part of business intelligence. It is a method for finding insights in very large datasets and frequently consolidates data set frameworks, insights, and AI to discover patterns.

Data Visualization

Visualization is an important part of BI where dashboards of data visualization can be created using popular softwares' like Tableau, Power BI, etc.

SAP, as the most popular BI tool for large businesses. 

Fig. Using SAP BI tool for creating Business Objectives 

It is the most popular application used at the enterprise level for the purpose of client/server systems. SAP facilitates data visualization, data analytics, creating drill-down reports reporting, and many other integrations with Office support.

SAP is created to help all levels to managements, developers, clients, and other departments including sales. The reason for the popularity of SAP is that it provides tons of functionalities.(SAP, 2021)

Tableau, the simplest Business Intelligence software for everyone

Fig. Using Tableau to find customer segments

In simple words, won't be it great to easily find out - 

• How to increase profit
• How customers are behaving
• How competitors are growing
• Is the performance good enough
• Predicting outcomes
• Find out issues and problems

Tableau posses a simple interface for anyone with data analytics knowledge to use it for business intelligence purpose finding the hidden insights from data and using  it for managerial decision making.

The availability of enough features in data is also a critical factor in deciding the dataset is good for usage with a BI software. (Tableau.com, 2021) 

-  Spoorthi Joshi S

REFERENCES

Oracle 2021 | How business intelligence can keep your organization in the know | https://www.oracle.com/ie/what-is-business-intelligence.html (Accessed on 29 March 2021)

IDA Ireland 2021|  As market leader in enterprise application software, SAP (NYSE: SAP) helps companies of all sizes and industries run better |  https://www.idaireland.com/how-we-help/case-studies/sap (Accessed on 29 March 2021)

Tableau 2021 | Powerful analytics anyone can use | https://www.tableau.com/resource/business-intelligence  (Accessed on 29 March 2021)

Emerging Technology: Revolutionizing the Customer Experience with Artificial Intelligence

Artificial Intelligence (AI) is ubiquitous, and it has accelerated with the digitalization of processes and activities among many organizations by increasing their capabilities. In terms of corporate language, chatbots technology is a part of human-robot interactions, which offers various opportunities and support firms to discover better insights and provides a better platform for sound decision making. (Luce, 2018) Nowadays, facing challenges and risks are pretty standard in business, both internally and externally. To compete in a new era, firms create a good relationship between customers by providing an efficient service in terms of verbal or physical depending on the affordable product. Focussing on improving customer experience, continual advancements in technology are incorporated to provide better response time and increased quality of interaction (Delamater, 2017). A chatbot can identify consumer needs quickly once when they started interacting with AI-powered bots and proactively start a conversation with them by providing relevant information throughout the entire chats. It can transfer a caller to a service agent if a human is necessary for the loop.
AI algorithms can search through each customer's buying habits, including what they purchased and when they made the purchase. AI improves the shopping experience by making product recommendations based on what the customers are interested in and most likely to buy. AI-enabled chatbots can provide the customers with consistently pleasant customer service and an immediate response instead of being put into a queue to wait for an available support representative. They were making the process of communication as easy and pleasant as possible leads to satisfy customers while at the same time freeing up organizations employees so they can focus their energy and time on other tasks. Consumers today are looking for convenience when it comes to shopping and making payments. 

Using smart speakers such as Echo and Alexa, consumers can make purchases and pay their bills with voice-enabled AI, interacting with any business at a time that's best for them. Sometimes customers want their questions answered after business hours, or they may want to update an order before the business opens the next day. For a small business, hiring employees to be available at all hours is unrealistic and costly. Using AI chatbots can give marketers customers the service they want regardless of what time it is. AI creates a shopping experience that's hassle-free and simple to interact with customers. Consumers are looking for fast and easy interactions when making purchases. AI makes buying smooth and easy, from deciding what product to buy to complete the transaction with fast shipping and delivery.

In the past, phone bots have been automated with limited functionality, usually providing one-word answers and simple directions to consumers. This type of simplicity in business communication can quickly lead to customer frustration. Today's AI phone bots are intelligent and helpful, solving problems and answering questions efficiently and precisely. This means that when customers call the business, they are getting accurate responses to their questions with valuable and intelligent voice interaction (Muhammadian, 2020).

The chatbot is a conversational agent that reproduces the conversation with customers, usually over the internet. They communicate with human users, especially the end-users, through messages via texts and reply accordingly. Even though the technological concept of interacting with the customers is not new, the design of chatbots was made by considering various assumptions. The artificial intelligence developed in the past tends to answer one-liner questions and could not answer any other conversation with the users. Firstly, the chatbot was designed to have limited domain knowledge as one can talk about any topic with the customers (Luce, 2018). Chatbots are designed to focus on specific issues. By the end of every conversation, the customers want to achieve a particular result. This will have an impact on the flow of the conversation. The designers of chatbots can exploit this as some behavioral patterns will arise as a result. 

By 2010, various assistants like Apple, Siri, Cortana, and Alexa came into the market that dealt with a dual conversation with a goal-oriented dialogue. The release of messenger for Facebook is another breakthrough and allowed the agents to discuss non-AI-related companies (Campione, 2021). Chatbots include working with multiple components. When chatbots are receiving a new message, the language identification module will first process the message. The new message and its language, along with any previous conversation, will be retrieved and fed into the classifier module, which will imply the message the user is trying to convey. This will then be used to decide the action sequence. For example, the chatbot reply will be in the form of whether the message is not clear. The proper execution of the situation happens with the module known as an action handler.

Damanvir Kaushal

Keywords

#Emerging Technology

#Customer Service

#Artificial Intelligence

#Conversational Service Automation

References

Campione, R., 2021. HOW DIGITALIZATION IS REVOLUTIONIZING RELATIONSHIPS BETWEEN CUSTOMERS AND SERVICE PROVIDERS IN THE HOSPITALITY INDUSTRY: PERSONALIZATION AND GAMIFICATION. International Journal of Information, Business and Management, 13(1), pp.35-52.

Delamater, N., 2017. A brief history of artificial intelligence and how it’s revolutionizing customer service today. Available in https://images. g2crowd. com/uploads/attachment/file/73099/expirable-direct-uploads_2F469f2619-a917-446d-b2b8-14cf8f8 d2c4e_2FChatBotWhitePaper2017. pdf Disponible el, 17.

Luce, L., 2018. Artificial intelligence for fashion: How AI is revolutionizing the fashion industry. Apress.

Muhammadian, R., 2020. Artificial intelligence in marketing. How AI is Revolutionizing Digital Marketing.

How the Loss of Third-Party Cookies Will Affect the Future of Digital Marketing?

The Background: What Has Happened Recently?

As the privacy concerns and controls about the protection of personal data have been raising since few years, many tech giants such as Facebook and Twitter started to take action to improve their security systems and be clearer about how they keep and use the data of the users. Moreover, the recent legislations like General Data Protection Regulation (GDPR) aims to protect and encourage customers to have more control on their personal data, and to ensure transparency on the usage of customer information by companies. As a result, these protective regulations have also affected the cookie policies and lead to a crumbling of third-party cookies. This trend began with disapproval of third-party cookies by well-known browsers Firefox and Safari and continued with Apple’s decision on the apps that they won’t be holding a device identity unless the related customer consent to that (Remekie, 2021). Lastly, third-party cookies which are also identified as tracking cookies, have been blocked by Chrome, Google’s leading browser. Besides, Google has declared that the company will not publish a substitute for third-party cookies, and in March 2021, it has announced the first-party data will be the new substitution for third-party cookies (Tracking Cookies are Dead: What Marketers Can Do About It, 2021).

After mentioning the terms “first-party” and “third-party”, let’s take a deep dive into the purpose of third-party cookies in general, and then go deep into the future impacts of their loss in terms of digital marketing.

What Does the Loss of Third-Party Cookie Mean?

Unlike the functional first-party cookies which help website owners to collect information; third-party cookies, -also known as marketing cookies- are formed by domains, not by the website. These cookies are used for marketing purposes, mostly for online advertising, by allowing advertisers to track the user every time they visit a website. For example, when a user visits a news website, a first-party cookie is created and held by the website. Then, when the website benefits from ads made via different websites, third-party cookies will be created and these will be held in the computer of a user rather than a website itself (1 et al., 2021). If we have a look at the pros and cons of third-party cookies, it is obvious that they can be beneficial for advertisers and practicable for users by ensuring them targeted ads in the light of “personalisation”. But, on the other hand, visitors have also struggled with privacy concerns as they can’t control which companies are gathering and using their personal data. Even being protected by GDPR’s cookie consent, users wouldn’t able to see for which organisation their data will be using after they have consented to related website (2021).

As cookie-based data is considered as an important input for digital marketing campaigns, basically, demise of third-party cookies means less data to be processed. Therefore, the new conditions lead to marketers and advertisers to find less costly solutions, and to be more creative when it’s come to design a digital marketing campaign. Agencies and brands may be dealing with offering non-targeted ads to their customers by having less clear consumer behaviours. Consumers will be affected positively in terms of privacy issues, but they might be exposed to more irrelevant ads (Juneau, 2021). Publishers will also be affected by the loss the third-party cookies as they rely on cookie-based data. The study conducted by Google among top global 500 publishers, shows that disabling access to third-party cookies decreased the average revenue of publishers by 52% (2021). According to Cookiepedia, The Daily Mail holds 19,136 third-party cookies on its site (Know your cookies: A guide to internet ad trackers - Digiday, 2021).

Source: https://digiday.com/media/know-cookies-guide-internet-ad-trackers/

What Happens Next? What Marketers Need to Do?

• Marketers will be relying on first-party data, high quality data or real-time data.

• Personalisation is still possible considering the value of first-party data.

• Contextual advertising/targeting will be replacing the cookies.

• People-based marketing/advertising will be targeting customer’s real-time behavioural data.

• Advertising might be more human engaged rather than automated systems (Tracking Cookies are Dead: What Marketers Can Do About It, 2021).

Keywords: Third-party cookies, First-party cookies, First-party data, Advertising, Privacy, GDPR

Ilgin Damla Omay

References and Sources

1, C., 2, C., Scanner, F., Function, T., Base, K., Subscription, M., Edition, F., Now, B., Subscription, M., Us, A., Us, C., Demo, R., Quote, R., Consent, C., Rights, D., Consent, M., Management, D., Support, Q., Tools, F., Scanner, W., Button, C., CMP, I., Developers, F., Publishers, F., Agencies, F., Calculator, R., Directory, P., Partners, R., Partners, M., TCF, I., DPC, I., 220, N., PDPA, T., Library, R., Base, K., Plugins, I., Hub, D., Us, A., Us, C., Demo, R., Quote, R., Plans, C. and Edition, F., 2021. What is a Third-Party Cookie? | Knowledge | CookiePro. [online] CookiePro. Available at: https://www.cookiepro.com/knowledge/what-is-a-third-party-cookie/ [Accessed 21 March 2021].

Digiday. 2021. Know your cookies: A guide to internet ad trackers - Digiday. [online] Available at: https://digiday.com/media/know-cookies-guide-internet-ad-trackers/ [Accessed 21 March 2021].

Invoca.com. 2021. Tracking Cookies are Dead: What Marketers Can Do About It. [online] Available at: https://www.invoca.com/blog/tracking-cookies-are-dead-what-marketers-can-do-about-it [Accessed 21 March 2021].

Juneau, T., 2021. Council Post: Digital Marketing In A Cookie-Less Internet. [online] Forbes. Available at: https://www.forbes.com/sites/forbesagencycouncil/2020/05/18/digital-marketing-in-a-cookie-less-internet/?sh=13d751121e2d [Accessed 21 March 2021].

Remekie, S., 2021. What Happens to Marketing When the Cookie Disappears?. [online] CMSWire.com. Available at: https://www.cmswire.com/digital-marketing/the-demise-of-the-cookie-and-the-rise-of-first-party-data/ [Accessed 21 March 2021].

Services.google.com. 2021. [online] Available at: https://services.google.com/fh/files/misc/disabling_third-party_cookies_publisher_revenue.pdf [Accessed 21 March 2021].

www2.deloitte.com. 2021. [online] Available at: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/consultancy/deloitte-uk-cookie-less-marketing.pdf [Accessed 21 March 2021].

Using Cookies to assess digital marketing performance

 What is a cookie?

Before we start it is important to know what exactly the cookies are and how it helps in the field of digital marketing. So, cookies are a piece of code that lives on your web browser and stores information in the user’s web browser to identify and enhance your experience online. This technology is utilized to work with different capacities which include keeping track of stateful data such as items added into the shopping cart, keeping data from applications that have already been filled out (for autocomplete function), Account- protected servers receive the user’s account information and log-in status through authentication cookies. (Kaspersky 2021)

With an increasing understanding of privacy concerns and laws like the EU's General Data Protection Regulation (GDPR) and e-privacy, there is a greater need to educate consumers about what cookie files are, what information they should hold, and what types of cookies are available. (Clearcode 2021)

Now we will see the types of cookies. Essentially cookies are divided into first-party cookies and third-party cookies from a technological standpoint. However, all store the same data and can execute the same task. But what makes them unique is how websites build and use them.

 First party cookies

They are specifically stored by the domain (website) you are visiting. They enable website owners to collect analytics data, remember language preferences, and perform other valuable functions that aid in the delivery of a positive user experience.

Third-party cookies 

As mentioned even they perform the same function as cookies do but third-party cookies offer information that lets advertisers identify their audiences' taste and expectations beyond their interactions with the brand.  Exposing trends and other user data can be used to help influence potential marketing strategies. To view related advertisements, they store information across web domains and across interactions, hopping from browsers to social media applications and beyond. They are created and saved in a user’s web browser to monitor their online activity through various websites. (Adroll 2021)

Cookies in Digital marketing 

Now let's see how cookies are used in facilitating your digital marketing function. Considering Targeted digital marketing, web cookies are used in delivering many forms of personalized digital ads. They save user data and behavior records, allowing advertisement providers to reach a specific customer audience based on factors such as gender, age, interests, location, actions on websites, and actions on search engines or social media.

Cookies may be a tiny aspect but it plays a crucial role in promoting targeted ads on social media platforms. Without cookies, accounts could not be generated, downloaded, or maintained; without the user profile, consumer data would be unavailable; and without the data, targeted ads would be impossible. To begin thinking about social media accounts in this way – as freely surrendered caches of user data – is to obtain an understanding of how Facebook has created one of the world's most powerful targeting empires. It's even a little unsettling.(Targetinternet 2021)

 Using third-party cookies to assess marketing performance.

 Well, you have already seen third-party cookies in motion if you've spent some time on the internet. It is quite often that you have been to an online retailer and looked at a few items only to find advertisements for the same items on entirely different websites? Third-party cookies operate exactly in the same way. they observe your actions outside of the brand's ecosystem and then use it to provide personalized marketing to help you move from surfing to a paying client. In the long run, the information gathered from third-party cookies is used to create accurate consumer profiles.

Consumers are becoming more aware of how advertisers use their personal information as they do more of their business online. As a result, there is a growing trend for an online marketing future that is more privacy-friendly. Google's decision this year isn't the first time browser creators have restricted the use of third-party cookies; Safari did so in 2014, and Mozilla's Firefox in 2019.( Targetinternet 2021)

 

 Viewing the web cookies can be an interesting experience. Each one is intended to support a specific purpose while also adding a subtle layer of color to your online experience. We get the feeling that, while cookies aren't great, we can't imagine life without them.

- Spoorthi Joshi S

 References

 AdRoll. 2021 |MEGAN PRATT |Third- party cookies and digital marketing: what’s next? https://www.adroll.com/blog/marketing/third-party-cookies-and-digital-marketing-whats-next ( Accessed on 21^st march 2021)

2.      Targetinternet 2021 | The digital marketing guide to web cookies | target market| https://www.targetinternet.com/digital-marketing-guide-to-cookies (accessed on 21st march 2021)

3.      Clearcode 2021| Michal Wlosik, Michael Sweeney| First party and third- party cookies: what’s the difference? https://clearcode.cc/blog/difference-between-first-party-third-party-cookies/#cookie-types ( Accessed on 20^th march 2021)

4.      Kaspersky 2021| what are cookies? https://www.kaspersky.com/resource-center/definitions/cookies ( Accessed on 20^th march 2021)

 

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]