As organizations travel the road to data-driven transformation, it is important to connect data to derive accurate business context. Here is exactly where Graph Analytics comes into play. It helps make sense of the underlying data implications and powers analytics to deliver value. Research suggests that the global graph analytics market size is predicted to reach USD 2,522 million by 2024 at a CAGR of 34% during the forecast period.
This blog will cover all the aspects revolving around graph analytics:
- Definition and importance of graph analytics
- Graph analytics vs regular analytics
- Types of graph analytics
- How graph analytics supports higher education
- Business use cases
Keep reading!
What is graph analytics?
Graph analytics, also known as graph algorithms or network analysis, is the analysis of data in a graph format using data points as nodes and relationships as edges. Nodes signify points or entities Every node and edge hold specific properties that define its characteristics. Graph analytics is used to determine the strength and the direction of connections between objects in a graph. Simply put, graph analytics enables you to store, manage, and query data in the form of a graph. It allows you to find the relationships between people, places, things, events, locations, and others.
Educational Institution Example
Node – Bob Age 19 – Course – Robotics
Node – John Age 21 – Course – Business Management
Edge – Chess club
Graph analytics tends to provide a pairwise relationship between the objects and represent the structural characteristics of a graph. Moreover, graphs are visually appealing and make it easier to understand how complex networks behave. The best part about graph analytics is the ease with which you can add information to a graph. It is not necessary to understand everything about your data to start storage and investigation.
Some of the most popular tools for graph analytics include Neo4j, OrientDB, Amazon Neptune, and several others.
Graph analytics vs regular analytics
Regular analytics is largely based on statistics, computer programming, and operations exploration. Graph analytics, on the other hand, is based on graph-specific algorithms to analyze the relationships between entities.
Also, regular analytics uses SQL to retrieve data and find reasons behind data insights. The SQL joins are slow due to multiple joins in tabular tables. The SQL queries might take thousands of milliseconds to show results. On the contrary, graph analytics leverages graph databases. In a graph database, all the nodes are connected by relationships. Hence, it tends to show faster results than SQL queries.
Types of graph analytics
Graph analytics can be categorized into four main types. Let’s have a quick look at them.
1. Path analysis
Path analysis tends to focus on the relationships between two nodes in a graph. It determines the shortest distance between two nodes and analyzes similar shapes and distances from different paths that connect entities within the graph.
2. Connectivity analysis
Connectivity analysis is used to determine the strength of two interconnected nodes. It helps you identify whether the connection between the nodes is strong or weak. Connectivity analysis also determines the number of edges flowing into the node and the ones flowing out of the node.
3. Centrality analysis
It tends to determine the importance of a present node within the graph network and its connectivity to others. Centrality analysis enables you to understand the most influential node and the connection it accesses.
4. Network analysis
Network analysis, also known as, community analysis tends to define the density of relationships between nodes. It helps you learn about the nodes that frequently interact with each other in a graph network. Network analysis also enables you to determine if the connecting edges are transient. It tends to predict whether the created graph network will grow or not.
Types of graph analytics
Here’s a list of a few standard techniques that businesses can utilize to conduct data lineage:
1. Pattern-based lineage
This technique leverages patterns to perform data lineage. Pattern-based lineage does not deal with the code that is used to transform data. It uses metadata for tables, columns, and business reports. This technique only monitors data and not the data processing algorithms.
2. Lineage by parsing
It is one of the most advanced data lineage techniques. It tends to automatically read the logic that is used to process data. Lineage by parsing has a complex deployment. You need to comprehend different tools and programming languages like ETL logic, SQL, Java solutions, and more.
3. Lineage by data tagging
In this technique, every data that moves or transforms gets tagged by a transformation engine. Lineage by data tagging requires a consistent transformation tool to control all the data movement. To produce a lineage presentation, all tags are read from start to finish.
4. Self-contained lineage
This technique can inherently present lineage and does not require any external tools. There are certain data environments that comprise data lakes. The data lake can store all kinds of data in all stages of its lifecycle.
How graph analytics works
Graph analytics tends to use graph specific algorithms to analyze relationships between entities. Let’s have a look at those algorithms one by one:
1. Clustering
It groups objects based on their similar characteristics. Customized categorization of graph data is simpler with clustering.
2. Partitioning
Partitioning, also known as cutting, finds the cut with the fewest number of crossing edges.
3. Shortest path
It finds the shortest path between two nodes of interest. It can be best used for social network analysis and solving optimization challenges.
4. Connected components
It enables you to get to every node in the graph from any starting node. The connected components are the maximal sub-regions of a graph.
Implementing graph analytics in higher education
Various higher education institutions are expressing the need for technologies to track students’ end-to-end journeys and enhance experiences. In such scenarios, graph databases come to the rescue. They help educators to analyze and connect different learner data and deliver personalized experiences.
1. Create personalized learning experiences
Academic professionals can create a knowledge graph using a graph database. The knowledge graph will provide a conceptual framework for different courses and help manage students and their progress. Storing students’ information and learning resources as graphs enables academic institutions to facilitate learning more holistically
2. Rank and score applicants
Graph analytics makes it easier to fast-track screening processes for different applicants. Here’s a pictorial representation of the same.
3. Facilitate better student engagement
Let’s understand this with the help of an example. Imagine that a university needs to invite certain students to celebrate the introduction of a new football training facility. On what basis should it choose the students? How should the university data be filtered? It’s simple. The university can filter based on age, affiliation with football, and alumni status. When such data is stored in a graph, identifying accurate information becomes easier and quicker.
Graph analytics works wonders for alumni engagement as well. Think of a university that is looking to track alumni engagement on the basis of time, talent, and resources.
The above graph can easily help derive the following insights:
- John is a generous donor to social welfare events.
- Nick is an active event organizer.
- Tom is an influencer and attracts the crowd with his powerful speeches.
Some more business use cases of graph analytics
Apart from higher education, graph analytics can be applied to various industries and categories such as finance, manufacturing, marketing, data regulation and privacy, and others.
- Graph analytics simplifies fraud detection. All you need to do is create a graph from transactions between entities as well as entities that share information like email addresses, passwords, and more. By running a simple query, you will find all customer accounts with similar information. It will also help discover which accounts are performing cash exchange.
- Social media platforms like Spotify, Instagram, and LinkedIn are connection driven platforms and can leverage graph analytics to generate useful insights. For example, connectivity and community analytics help map out influencers with their followers and subsequently their followers’ connections. It enables you to create word-of-mouth chain effects.
- Graph analytics can also be used for supply chain optimization. With graph analytics algorithms like shortest path and partitioning, you can optimize routes in transportation and supply chain networks.
- Graph analytics helps you trace sensitive data across enterprise systems and also offers a visual picture of the data flowing through various systems. This further enables you to achieve regulatory compliance.
Wrapping up
As businesses continue to push the capabilities of big data, graph analytics should be leveraged to derive insights from complex data networks. Our experts at LumenData can help you adopt graph analytics technologies in a hassle-free manner. Unlock the most relevant nodes and focus on connections that matter. Get in touch with us today.
In today’s modern times, the way businesses handle information is rapidly evolving. With data governance, businesses are able to identify data inconsistencies and break down data silos across the organization.
As per Markets And Markets research, the global data governance market size is expected to reach USD 5.7 billion by 2025 at a CAGR of 22.3%. This blog shall cover all the relevant concepts revolving around data governance and how it can help take your business to the next level. Read on!
Definition of data governance
Data governance is the sum of processes, policies, and standards to facilitate the appropriate use of business information. The data policies should be directed well toward how the data is collected, processed, stored, and disposed of. Businesses also need to comply with external standards defined by stakeholders and government agencies.
Simply put, data governance involves all the actions that you perform to ensure data security, privacy, accuracy, availability, and usability.
Top reasons businesses need to govern data
Absence of centralized enterprise data architecture within an organization results in the development of data silos. Here is exactly where data governance comes to the rescue. Let’s have a look at a few points that highlight how you can benefit from implementing a robust data strategy:
a) Be a master of timely business decisions
Data governance helps you ensure that your data is accurate and consistent. When you conduct effective data governance, it becomes easier to find, access, and leverage data. This further helps enhance your products and services, dig deeper into customer insights, and take timely business decisions to maximize revenues.
b) Meet regulatory compliances like a pro
Bid goodbye to the risks associated with non-compliance and save yourself from poor regulatory compliance. Data governance helps you design new compliances best suited to your business requirements. Data governance policies offer various ways to address the requirements of government regulations concerning data privacy. For example, GDPR, HIPAA, PCI DSS, and more.
c) Win greater customer trust
Right governance of internal and external data policies ensures the safety of sensitive information shared by your customers and partners. It enables you to build better business trust and optimize customer satisfaction.
d) Avoid security breaches
Data governance helps you keep a tab on where the data is coming from, where it is located, how it is leveraged, and who all have access to it. It prevents data exposure to unauthorized individuals and systems and alleviates data-related risks within your organization.
e) Win greater customer trust
Data governance helps eliminate data duplication that comes with data silos. It saves you from incurring unnecessary costs on maintaining expensive hardware.
Critical concepts around data governance
Data masking
Data masking is creating structurally similar data to your organization’s original data. The values are altered while the data format remains the same. Data masking protects sensitive business information and is a viable solution against data loss and data breaches. Moreover, it enables businesses to share data with authorized users like developers and testers without the fear of data exposure.
Organizations can leverage data masking to protect the following types of data:
- Personally Identifiable Information (PII)
- Intellectual Property
- Protected Health Information (PHI)
- Payment Card Industry Data Security Standard (PCI-DSS)
Let’s take a quick look at some of the most popular data masking techniques:
a) Scrambling
It reorders alphanumeric characters to hide the original data. Scrambling tends to jumble the numbers and characters into a random order. Example: When a customer complaint ticket with ID 12678 in a production environment undergoes scrambling, it could be presented as ID 71862 in some other environment.
b) Substitution
Considered one of the most effective data masking techniques, substitution involves the masking of data with another value while preserving the original look of the data. For example, you can use a random lookup file to hide customer names. The substitution technique can be applied to different types of data.
c) Shuffling
It means the reordering of values within a column. Shuffling masks the data in a column with some other data from the same column. This technique enables you to mask date, numeric, and string data types.
d) Nullification
It tends to replace the real values in a column with a null value. You can use the nullification technique to mask binary, date, numeric, and string data. However, the nullification technique makes testing and development environments more complex.
e) Encryption
This technique makes it impossible for unauthorized users to access data without decryption data. It enables you to use an encryption algorithm that masks the data. An encryption key is required to decrypt such data.
Data governance policies
A data governance policy is a documented set of standards that ensure consistent data management across the organization. The data standards are generally directed towards individual guidelines on data quality, security, access, usage, and roles to meet regulatory compliance.
Here are some data governance rules that a data policy should have:
a) Data access
All the guidelines that ensure appropriate data access to businesses come under the data access policy. This means that all individuals within an organization are granted data access according to their specific roles.
b) Data usage
This guideline mainly revolves around data privacy and compliance. It ensures that all data is leveraged ethically as per applicable data privacy laws. It also mentions the disciplinary steps that would be taken against individuals who violate the data rules.
c) Data integrity
The data integrity policy ensures data consistency within an organization. All essential data elements are integrated into various units and systems to ensure data accuracy at all business levels. Common data definitions are created to avoid data silos.
Best practices for data governance
Here are some pointers that you need to keep in mind:
- Data governance consists of components like people, processes, and technology. Look at one component at a time while keeping the big picture in mind. Select the right people to design processes and leverage the appropriate technology.
- Map your data governance goals tailored to your unique business requirements.
- Make sure to identify all data domains and measure progress. Keep a track of important metrics concerning data scope and sources.
- Ensure to establish a monitoring system to avoid data quality errors.
Advantages of collaborating with LumenData
With a special focus on addressing the processes involving technical implementation, LumenData approaches data governance in a pragmatic manner. We have a team of seasoned data management professionals with deep expertise across several data domains. LumenData provides you with industry-specific solutions. We help you leverage the power of MDM to develop the right data governance strategy for your business.
Here’s an overview of our data governance program:
Wrapping up
There is no denying that data is ruling the business world. It propels digital transformation and helps you stand out from the competition. Efficient data governance ensures that data is readily available, secured, compliant, of high-quality, and relevant to your business. Conduct data governance for your business and take the right step towards transformation and growth.
Our experts, at LumenData, can assist you in crafting the right strategy to achieve industry standard data governance. Get in touch with us without delay!
This post first appeared on Vice-President Global Delivery Jobs, please read the originial post: here
