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5 Data Analytics Trends That You Should Know This 2020

It was once said that humans are not only creatures of emotion but also creatures of logic. Because of our intelligence, we can reach logical conclusions without losing our cool. When it comes to running a business, creating strategies, and attracting leads, data analytics helps you make better decisions. According to the 2020 Global State of Enterprise Analytics Report, 94% of business professionals agree that data analytics is essential to their business’ growth. With the tools they use to gather information, 45% of companies concede that they were able to innovate their existing business models. More and more, we are seeing businesses gain enthusiasm and find the means for making better-informed decisions with the rise of digital data analytics.

In the rise of big data, cloud storage, the Internet of Things, and artificial intelligence, experts at Quanthub say that companies big and small today tend to hire those with data engineer skills. As we go through the second quarter of 2020, here are five of the many trends that are taking the analytics world by storm:

1. Tools That Have Augmented Analytics

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This type of analytics uses automation instead of manual configuration. Without regular monitoring needed, automated Customer Relationship Management software or CRMs are able to gather essential insights without needing other software integrations. Through the help of machine learning and artificial intelligence, these tools can observe data and pose relevant queries that may affect the bottom line of the company. By crunching the numbers and recognizing patterns quickly, AI and ML augment our problem-solving skills and helps us solve any potential problems before they even happen.

In line with this, Explainable and commercial AI and ML are starting to fill the market. As open-source platforms have been developing these, businesses are now providing avenues to connect to these platforms and make them available to the general public. By making sure that each model comes with descriptions of their capabilities and behaviors, businesses can help higher institutions regulate these technologies.

2. Data Analytics and blockchain

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In recent years, blockchain has been associated with cybersecurity because of its advanced encryption technology. This has been used to securely track assets and transactions to provide transparency for all the parties involved while making sure third parties are kept out. While it is not yet available to the market, there is a growing need to integrate the data stored on blockchain systems to streamline management and recording.

As blockchain technology continues to evolve, businesses and customers are starting to combine data security and analytics. Today, we are not only looking for clean data but also high-quality and GDPR-compliant ones.

3. Internet of Things and predictive analytics

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Internet of Things or IoT allows different devices to receive and send data to other smart appliances via the Internet. In 2020, IoT solutions providers might be able to create a more holistic approach and combine different channels of IoT analytics into a single stream.

As analytics is also shifting towards future conditions and trends, tools that help businesses forecast these will become more popular. When this IoT and predictive analytics go hand-in-hand with automation, companies will be able to create personalized strategies for their target market based on their current behavior. Not only will this streamline decision-making processes within different organizations but also keep KPIs timely, accurate, and precise.

4. Introduction of persistent memory technology

As opposed to the most common database management systems (DBMS), persistent memory servers allow businesses to export insights from existing data without waiting for the server to load. While it is not yet out to the market, companies are eyeing this type of technology to deal with the disadvantages of in-memory database structures. With more information being stored in servers, the lack of memory and speed can ultimately affect business performance.

By using persistent memory, businesses can access large, complex data sets and environments that are otherwise sensitive to downtimes caused by system crashes or power failures. This means that with persistent memory, there will be no more need to reload data from disks or to cache company data in memory throughout the lifetime of a company’s server. Moreover, by using persistent memory for their servers, businesses will only have to store their data and use them as necessary.

When this is paired with real-time intelligence, companies can improve on decisions and strategies as current trends dictate without their processor failing on them. According to Gartner, it is expected that more than 50% of startups will be using “continuous intelligence” by 2022.

5. Increased Use of Graph Analytics

Source:towardsdatascience.com

As data analytics grows more complex, business owners often find themselves asking more complicated questions that they couldn’t answer with unorganized data. Because organizations use different tools to measure engagement, conversion, and nurturing, there is a growing need to simplify complex information. People are now turning to graph analytics to present correlations and predictions easily. By using different charts, maps, and infographics, data are visually crunched to be more palatable to the eyes and mind. According to Gartner, this type of process will increase a hundredfold every year to aid data science.

Graph analytics are mostly used and applied for marketing, business operations, healthcare, and even national security. Each of these sectors makes use of one of the four main types of graph analytics, namely: centrality analysis, community detection, connectivity analysis, and path analysis.

Such analyses are performed with the help of graph database tools that connect nodes and create relationships in the form of graphs, which can then be easily interpreted by its users. Some of these popular tools are Amazon Neptune, ArangoDB, Cayley, Data Stax, Titan, FlockDB, Neo4j, OrientDB, and many more.

Whether you are a business owner or consumer, it is essential to recognize the technological changes around us. The possibilities seem endless. With the continuous growth and development of technology, we can expect both digital disruption and opportunity. As the speed of innovation increases, businesses of today must meet and keep up with the customers of tomorrow to stay ahead of the curve.


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