Article | April 13, 2020
THE CORONAVIRUS PANDEMIC has spurred interest in big data to track the spread of the fast-moving pathogen and to plan disease prevention efforts. But the urgent need to contain the outbreak shouldn’t cloud thinking about big data’s potential to do more harm than good.Companies and governments worldwide are tapping the location data of millions of internet and mobile phone users for clues about how the virus spreads and whether social distancing measures are working. Unlike surveillance measures that track the movements of particular individuals, these efforts analyze large data sets to uncover patterns in people’s movements and behavior over the course of the pandemic.
Article | April 13, 2020
The marketing industry keeps changing every year. Businesses and enterprises have the task of keeping up with the changes in marketing trends as they evolve. As consumer demands and behavior changed, brands had to move from traditional marketing channels like print and electronic to digital channels like social media, Google Ads, YouTube, and more. Businesses have begun to consider marketing analytics a crucial component of marketing as they are the primary reason for success.
In uncertain times, marketing analytics tools calculate and evaluate the market status and enhances better planning for enterprises.
As Covid-19 hit the world, organizations that used traditional marketing analytics tools and relied on historical data realized that many of these models became irrelevant. The pandemic rendered a lot of data useless.
With machine learning (ML) and artificial intelligence (AI) in marketers’ arsenal, marketing analytics is turning virtual with a shift in the marketing landscape in 2021. They are also pivoting from relying on just AI technologies but rather combining big data with it.
AI and machine learning help advertisers and marketers to improve their target audience and re-strategize their campaigns through advanced marketing attributes, which in turn increases customer retention and customer loyalty.
While technology is making targeting and measuring possible, marketers have had to reassure their commitment to consumer privacy and data regulations and governance in their initiatives. They are also relying on third-party data.
These data and analytics trends will help organizations deal with radical changes and uncertainties, with opportunities they bring with them over the next few years.
To know why businesses are gravitating towards these trends in marketing analytics, let us look at why it is so important.
Importance of Marketing Analytics
As businesses extended into new marketing categories, new technologies were implemented to support them. This new technology was usually deployed in isolation, which resulted in assorted and disconnected data sets.
Usually, marketers based their decisions on data from individual channels like website metrics, not considering other marketers channels. Website and social media metrics alone are not enough. In contrast, marketing analytics tools look at all marketing done across channels over a period of time that is vital for sound decision-making and effective program execution.
Marketing analytics helps understand how well a campaign is working to achieve business goals or key performance indicators.
Marketing analytics allows you to answer questions like:
• How are your marketing initiatives/ campaigns working? What can be done to improve them?
• How do your marketing campaigns compare with others? What are they spending their time and money on? What marketing analytics software are they using that helps them?
• What should be your next step? How should you allocate the marketing budget according to your current spending?
Now that the advantages of marketing analytics are clear, let us get into the details of the trends in marketing analytics of 2021:
Rise of real-time marketing data analytics
Reciprocation to any action is the biggest trend right now in digital marketing, especially post Covid. Brands and businesses strive to respond to customer queries and provide them with solutions. Running queries in a low-latency customer data platform have allowed marketers to filter the view by the audience and identify underachieving sectors. Once this data is collected, businesses and brands can then readjust their customer targeting and messaging to optimize their performance.
To achieve this on a larger scale, organizations need to invest in marketing analytics software and platforms to balance data loads with processing for business intelligence and analytics. The platform needs to allow different types of jobs to run parallel by adding resources to groups as required. This gives data scientists more flexibility and access to response data at any given time.
Real-time analytics will also aid marketers in identifying underlying threats and problems in their strategies. Marketers will have to conduct a SWOT analysis and continuously optimize their campaigns to suit them better.
Data security, regulatory compliance, and protecting consumer privacy
Protecting market data from a rise in cybercrimes and breaches are crucial problems to be addressed in 2021. This year has seen a surge in data breaches that have damaged businesses and their infrastructures to different levels. As a result, marketers have increased their investments in encryption, access control, network monitoring, and other security measures.
To help comply with the General Data Protection Regulation (GDPR) of the European Union, the California Consumer Privacy Act (CCPA), and other regulatory bodies, organizations have made the shift to platforms where all consumer data is in one place. Advanced encryptions and stateless computing have made it possible to securely store and share governed data that can be kept in a single location. Interacting with a single copy of the same data will help compliance officers tasked with identifying and deleting every piece of information related to a particular customer much easier and the possibility of overseeing something gets canceled.
Protecting consumer privacy is imperative for marketers. They offer consumers the control to opt out, eradicate their data once they have left the platform, and remove information like location, access control to personally identifiable information like email addresses and billing details separated from other marketing data.
Predictive analytics’ analyzes collected data and predicts future outcomes through ML and AI. It maps out a lookalike audience and identifies which strata are most likely to become a high-value customer and which customer strata has the highest likelihood of churn. It also gauges people’s interests based on their browsing history. With better ML models, predictions have become better overtime, leading to increased customer retention and a drop in churn.
According to the research by Zion Market Research, by 2022, the global market for predictive analytics is set to hit $11 billion.
Investment in first-party data
Cookies-enabled website tracking led marketers to know who was visiting their website and re-calibrate their ads to these people throughout the web.
However, in 2020, Google announced cookies would be phased out of Chrome within two years while they had already removed them from Safari and Firefox.
Now that adding low-friction tracking to web pages will be tough, marketers will have to gather more limited data. This will then be then integrated with first-party data sets to get a rounded view of the customer. Although a big win for consumer privacy activists, it is difficult for advertisers and agencies to find it more difficult to retarget ads and build audiences in their data management platforms.
In a digital world without cookies, marketers now understand how customer data is collected, introspect on their marketing models, and evaluate their marketing strategy.
Emergence of contextual customer experience
These trends in marketing analytics have become more contextually conscious since the denunciation of cookies. Since marketers are losing their data sets and behavioral data, they have an added motivation to invest in insights.
This means that marketers have to target messaging based on known and inferred customer characteristics like their age, location, income, brand affinity, and where these customers are in their buying journey. For example, marketers should tailor messaging in ads to make up consumers based on the frequency of their visits to the store.
Effective contextual targeting hinges upon marketers using a single platform for their data and creates a holistic customer profile.
Reliance on third-party data
Even though there has been a drop in third-party data collection, marketers will continue to invest in third-party data which have a complete understanding of their customers that augments the first-party data they have.
Historically, third-party data has been difficult to source and maintain for marketers. There are new platforms that counter improvement of data like long time to value, cost of maintaining third-party data pipelines, and data governance problems.
U.S. marketers have spent upwards of $11.9 billion on third-party audience data in 2019, up 6.1% from 2018, and this reported growth curve is going to be even steeper in 2021, according to a study by Interactive Advertising Bureau and Winterberry Group.
Marketing analytics enables more successful marketing as it shows off direct results of the marketing efforts and investments.
These new marketing data analytics trends have made their definite mark and are set to make this year interesting with data and AI-based applications mixed with the changing landscape of marketing channels. Digital marketing will be in demand more than ever as people are purchasing more online.
Frequently Asked Questions
Why is marketing analytics so important?
Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity.
What is the use of marketing analytics?
Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods.
Which industries use marketing analytics?
Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically.
What are the types of marketing analytics tools?
Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc.
"name": "Why is marketing analytics so important?",
"text": "Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity."
"name": "What is the use of marketing analytics?",
"text": "Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods."
"name": "Which industries use marketing analytics?",
"text": "Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically."
"name": "What are the types of marketing analytics tools?",
"text": "Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc."
Article | April 13, 2020
In an era of big data, data health has become a pressing issue when more and more data is being stored and processed. Therefore, preserving the integrity of the collected data is becoming increasingly necessary. Understanding the fundamentals of data integrity and how it works is the first step in safeguarding the data.
Data integrity is essential for the smooth running of a company. If a company’s data is altered, deleted, or changed, and if there is no way of knowing how it can have significant impact on any data-driven business decisions.
Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is the overall accuracy, completeness, and consistency of data. It can be indicated by lack of alteration between two updates of a data record, which means data is unchanged or intact. Data integrity refers to the safety of data regarding regulatory compliance- like GDPR compliance- and security. A collection of processes, rules, and standards implemented during the design phase maintains the safety and security of data.
The information stored in the database will remain secure, complete, and reliable no matter how long it’s been stored; that’s when you know that the integrity of data is safe. A data integrity framework also ensures that no outside forces are harming this data.
This term of data integrity may refer to either the state or a process. As a state, the data integrity framework defines a data set that is valid and accurate. Whereas as a process, it describes measures used to ensure validity and accuracy of data set or all data contained in a database or a construct.
Data integrity can be enforced at both physical and logical levels. Let us understand the fundamentals of data integrity in detail:
Types of Data Integrity
There are two types of data integrity: physical and logical. They are collections of processes and methods that enforce data integrity in both hierarchical and relational databases.
Physical integrity protects the wholeness and accuracy of that data as it’s stored and retrieved. It refers to the process of storage and collection of data most accurately while maintaining the accuracy and reliability of data. The physical level of data integrity includes protecting data against different external forces like power cuts, data breaches, unexpected catastrophes, human-caused damages, and more.
Logical integrity keeps the data unchanged as it’s used in different ways in a relational database. Logical integrity checks data accuracy in a particular context. The logical integrity is compromised when errors from a human operator happen while entering data manually into the database. Other causes for compromised integrity of data include bugs, malware, and transferring data from one site within the database to another in the absence of some fields.
There are four types of logical integrity:
A database has columns, rows, and tables. These elements need to be as numerous as required for the data to be accurate, but no more than necessary. Entity integrity relies on the primary key, the unique values that identify pieces of data, making sure the data is listed just once and not more to avoid a null field in the table. The feature of relational systems that store data in tables can be linked and utilized in different ways.
Referential integrity means a series of processes that ensure storage and uniform use of data. The database structure has rules embedded into them about the usage of foreign keys and ensures only proper changes, additions, or deletions of data occur. These rules can include limitations eliminating duplicate data entry, accurate data guarantee, and disallowance of data entry that doesn’t apply. Foreign keys relate data that can be shared or null. For example, let’s take a data integrity example, employees that share the same work or work in the same department.
Domain Integrity can be defined as a collection of processes ensuring the accuracy of each piece of data in a domain. A domain is a set of acceptable values a column is allowed to contain. It includes constraints that limit the format, type, and amount of data entered. In domain integrity, all values and categories are set. All categories and values in a database are set, including the nulls.
This type of logical integrity involves the user's constraints and rules to fit their specific requirements. The data isn’t always secure with entity, referential, or domain integrity. For example, if an employer creates a column to input corrective actions of the employees, this data would fall under user-defined integrity.
Difference between Data Integrity and Data Security
Often, the terms data security and data integrity get muddled and are used interchangeably. As a result, the term is incorrectly substituted for data integrity, but each term has a significant meaning.
Data integrity and data security play an essential role in the success of each other. Data security means protecting data against unauthorized access or breach and is necessary to ensure data integrity.
Data integrity is the result of successful data security. However, the term only refers to the validity and accuracy of data rather than the actual act of protecting data. Data security is one of the many ways to maintain data integrity. Data security focuses on reducing the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some facets of data security tactics include permissions management, data classification, identity, access management, threat detection, and security analytics.
For modern enterprises, data integrity is necessary for accurate and efficient business processes and to make well-intentioned decisions. Data integrity is critical yet manageable for organizations today by backup and replication processes, database integrity constraints, validation processes, and other system protocols through varied data protection methods.
Threats to Data Integrity
Data integrity can be compromised by human error or any malicious acts. Accidental data alteration during the transfer from one device to another can be compromised. There is an assortment of factors that can affect the integrity of the data stored in databases. Following are a few of the examples:
Data integrity is put in jeopardy when individuals enter information incorrectly, duplicate, or delete data, don’t follow the correct protocols, or make mistakes in implementing procedures to protect data.
A transfer error occurs when data is incorrectly transferred from one location in a database to another. This error also happens when a piece of data is present in the destination table but not in the source table in a relational database.
Bugs and Viruses
Data can be stolen, altered, or deleted by spyware, malware, or any viruses.
Hardware gets compromised when a computer crashes, a server gets down, or problems with any computer malfunctions. Data can be rendered incorrectly or incompletely, limit, or eliminate data access when hardware gets compromised.
Preserving Data Integrity
Companies make decisions based on data. If that data is compromised or incorrect, it could harm that company to a great extent. They routinely make data-driven business decisions, and without data integrity, those decisions can have a significant impact on the company’s goals.
The threats mentioned above highlight a part of data security that can help preserve data integrity. Minimize the risk to your organization by using the following checklist:
Require an input validation when your data set is supplied by a known or an unknown source (an end-user, another application, a malicious user, or any number of other sources). The data should be validated and verified to ensure the correct input.
Verifying data processes haven’t been corrupted is highly critical. Identify key specifications and attributes that are necessary for your organization before you validate the data.
Eliminate Duplicate Data
Sensitive data from a secure database can easily be found on a document, spreadsheet, email, or shared folders where employees can see it without proper access. Therefore, it is sensible to clean up stray data and remove duplicates.
Data backups are a critical process in addition to removing duplicates and ensuring data security. Permanent loss of data can be avoided by backing up all necessary information, and it goes a long way. Back up the data as much as possible as it is critical as organizations may get attacked by ransomware.
Another vital data security practice is access control. Individuals in an organization with any wrong intent can harm the data. Implement a model where users who need access can get access is also a successful form of access control. Sensitive servers should be isolated and bolted to the floor, with individuals with an access key are allowed to use them.
Keep an Audit Trail
In case of a data breach, an audit trail will help you track down your source. In addition, it serves as breadcrumbs to locate and pinpoint the individual and origin of the breach.
Data collection was difficult not too long ago. It is no longer an issue these days. With the amount of data being collected these days, we must maintain the integrity of the data. Organizations can thus make data-driven decisions confidently and take the company ahead in a proper direction.
Frequently Asked Questions
What are integrity rules?
Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution.
What is a data integrity example?
Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are:
• When a user tries to enter a date outside an acceptable range
• When a user tries to enter a phone number in the wrong format
• When a bug in an application attempts to delete the wrong record
What are the principles of data integrity?
The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives.
"name": "What are integrity rules?",
"text": "Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution."
"name": "What is a data integrity example?",
"text": "Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are:
When a user tries to enter a date outside an acceptable range
When a user tries to enter a phone number in the wrong format
When a bug in an application attempts to delete the wrong record"
"name": "What are the principles of data integrity?",
"text": "The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives."
Article | April 13, 2020
The upsurge in data generation and its computing has raised the need for more power, storage and speed. What we call as big data is extremely memory-hungry and power-sapping and to fetch this requirement, engineers have put forward an innovative method. Recently, electrical engineers at Northwestern University and the University of Messina in Italy have developed a new magnetic memory device that could potentially support the surge of data-centric computing, which requires ever-increasing power, storage, and speed. Based on antiferromagnetic (AFM) materials, the device is the smallest of its kind ever demonstrated and operates with record-low electrical current to write data.