Advances in data analytics are changing the way business is done. Analytics trends suggest that our reliance on these technologies will only grow in the coming years.
Data analysts and businesses continue to collaborate on making use of data better, easier, and more efficient. As 2020 draws near, these 10 data analytics trends epitomize such a fruitful collaboration. The insights below will help you refine an analytics strategy, whether you’re at the planning stage or you want to pivot midway.
Data has finally taken center stage in this digital economy. It’s now clear how data and analytics continue to transform the business world. In a study, almost half of businesses agree that big data and analytics had altered how things are done in marketing, sales, and other activities.
To survive in today’s highly competitive market, businesses must have three integrated elements. They need disruptive business models, agile product innovation, and actionable customer insights.
Can extract new value and insights from existing data/analytics applications%
Can provide enhanced security and access controls for enterprise systems%
Can deploy analytics with high performance and scalability%
Can easily access and combine data from various external data sources%
Can certify data and create and enforce a single version of the truth%
Can deliver actionable, customized intelligence to staff across the enterprise%
Source: Harvard Business Review Analytic Services Survey 2018Designed by
The world now produces 2.5 quintillion bytes of data each day. Ninety percent of all existing data was made during the last two years.
With so much raw information around, the biggest challenge is separating the signal from the noise. Analytics should help businesses cut through all the data to discover actionable insights and create value.
New developments have emerged, with some old problems taking on new appearances. As innovations succeed to improve analytics, new challenges arise to complicate things. This is why it is essential to look deeper at these business analytics trends that will dominate 2020 and beyond.
Automation continues to transform the world. In business, automation had spurred transformations that result in sustained efficiency. In recent years, the automation of big data analytics offers perhaps one of the biggest of automation’s capabilities.
Data analytics automation is truly a disruptive force. One survey found that 48% of executives consider data analytics as critically important.
The total global information is growing twice every 18 months. This technology will help hasten productivity and increase valuable data usage.
Automation of big data analysis offers numerous benefits to businesses. It will enable executives to effectively predict further ahead. This will help to steer their organizations using the correct analytics to support decision making.
Data analysis automation has added benefits like enhanced scalability of big data technologies and improved self-service modules. Also, it helps improve operational efficiency and reduce operational costs.
One notable feature is that it can search for categorical data to create a set of features with relevant values. In an ecommerce business, it can run as a numerical identifier that flourishes across massive datasets.
Another major trend that’s expected to make a considerable mark in 2020 is in-memory computing (or IMC). Since the cost of memory has decreased lately, IMC is now a major technological solution that offers numerous advantages in analytics.
Data is commonly stored in a centralized database. With IMC, data storage happens in RAM across various computing devices. This innovation results in agile performance and allows real-time data scaling.
Starting in 2020, more businesses will turn to IMC for their computing needs. This analytics can expand the efficiencies of business intelligence solutions.
IMC effectively solves the actual speed and massive scalability needs of businesses. In turn, this helps firms address complex requirements. These include addressing the demands for real-time regulatory compliance, omnichannel marketing, and digital transformation.
It is a proven enabling technology today but is expected to undergo more developments. IMC offers a remarkably robust mass-memory to facilitate high-performance business activities.
IMC is part of the memory-centric architecture. This larger technology initiative aims to support the more efficient use of memory and other storage types.
One of the major predictive analytics trends today is the increasing use of augmented analytics. It uses artificial intelligence and machine learning protocols to transform how analytics data is generated, processed, and shared.
By deploying sophisticated algorithms, this trending analytics tool can automate insights generation. In the process, it can drastically lessen businesses’ long-time reliance on data scientists and analysts.
By 2020, augmented analytics will become a key factor behind the growth of analytics and BI platforms. Also, it will assume a critical role in the advancement of embedded analytics and data science platforms.
The rising volume of business data is one of the major drivers of augmented analytics deployment. Likewise, the increasing demand for obtaining critical insights from customer data is boosting its widespread use.
Because of its sophisticated application portfolio, the demand for augmented analytics continues to rise in some sectors. These include the aerospace, defense, and transportation industries.
Conversational data offers information on how people interact with a chatbot or device. Conversational analytics helps in processing these valuable datasets. With an AI-based analytics tool, you can track and analyze these data in real-time and deliver the correct response.
By 2020, voice, natural language processing, and text search will comprise half of all analytical queries. By 2021, NLP-conversational analytics will support BI and analytics utilization from 35% to over 50% of employees.
Most BI and analytics platforms can process inquiries posted on a page and provide visual analysis. But NLP-conversational analytics elevates this convenience a step further. Users can post questions to be as simple as a discussion with a digital assistant or a Google-like search.
Any person can search or make inquiries using voice or text with more intricate queries and responses. To expand their use and development, these analytics tools must be more accessible and user-friendly.
This trend allows employees to quickly analyze complex data combinations with an easy-to-use analytics solution. With an NLP-conversational analytics tool, you only need to enter a basic search query, and results will be provided.
What’s more, business users can initiate conversations with virtual assistants to retrieve data. With every interaction, the capabilities of the NLP-conversational analytics platform will grow exponentially.
There will be over 30.7 billion Internet-of-Things (IoT) connected devices by 2020. IoT’s growth is expected to have a significant impact on numerous business activities. And one of the most affected will be data analytics.
As the number of IoT sensors that get connected to devices increases, a continuously-growing volume of data is created. However, these data can only be useful if handled and processed correctly. This is why data analytics will be vital to exploring the vast possibilities these new massive datasets will bring.
Businesses are expected to increasingly turn to advanced IoT-analytics solutions. These sophisticated tools can provide relevant data and the needed data transparency to the general public.
Combining data analytics and IoT offers a wealth of benefits and possibilities for businesses. For instance, IoT devices will constantly generate large volumes of data and diverse data structures. In turn, data analytics software will enable companies to analyze the information, no matter what structure or size.
Likewise, the combined IoT-analytics use offers a powerful source for gaining actionable market insights. This will help a lot in designing a seamless customer experience, which translates to better profits. Deploying the I0T-analytics tandem can provide a competitive edge in the long run.
This innovation will likely be one of the customer analytics trends for years to come.
Source: Statista 2019Designed by
By 2020, up to 90% of large enterprises are expected to generate some revenue from data-as-a-service (DaaS). This is a cloud-based technology that enables subscribers to access and use digital files through the internet.
DaaS is a data stream that subscribers can access on demand. Almost every modern business has embraced data as a decision-making tool. However, only a few companies have the internal resources to completely leverage the power of their collected data.
As most users can now easily access high-speed internet, DaaS will have a broader reach. This trend will likely be one of today’s top data and analytics advancements.
Before, working with massive data is very challenging. You need an extensive amount of computing resources for data processing and storage. Since this involves the use of enterprise-grade data centers, it’s very demanding financially and resource-wise.
Thanks to the cloud-based option that DaaS introduced, most of these data processing and storage are now more affordable and less resource-intensive.
Its growth will help units of large companies to better collaborate without any added expense. This innovation will streamline data sharing. Ultimately, DaaS will help enhance business productivity.
People have always been cautious about the possibility of machine invasion. It’s not a sci-fi fear anymore. More than 70% of Americans are concerned about robots taking over their very livelihood today.
This is why there’s always the demand for a complete understanding of how AI systems reach the decisions they make. When trust and explainability are lacking, our capability to completely trust AI systems is hampered.
We want advanced computer systems to work as we expect them to. We want them to provide transparency in how they reach decisions. This is the existential cause of the emergence of Explainable AI or XAI.
A developing field in machine learning, Explainable AI (XAI)seeks to determine how AI systems make black box decisions. It examines and aims to appreciate the various decision-making models and processes involved.
When did the AI system fail, and when did it succeed? Why did it fail, and why did it succeed? Why didn’t the AI system do something else? These are among the key questions that guide this emerging analytics field.
Businesses have been gradually deploying AI systems to help make better decisions. But there are situations when they must legitimize how these systems were able to reach such decisions. XAI fills this gap for the needed interpretation and reasoning.
In due time, we can have both accuracy and capability with the needed explainability and transparency.
Traditional strategies for data security can’t cope with how sophisticated cybercriminals have become. Their advanced methods are likely to advance further. With malicious insiders involved in recent major security breaches, the stakes will increase more.
Today’s security situation requires more efficient detection strategies. Fortunately, big data analytics offers that needed capability.
By 2020, the largest concern of 70% of businesses involves the privacy risk of archived personal data. This is why security experts have designed the appropriate detection approach. This is the reason behind the emergence of a new class of big data security analytics.
To be effective against privacy cybercrimes, detection must be capable of determining the shifting use patterns. Likewise, it should be able to perform complex, intricate analysis quickly, close to real-time.
Big data security analytics are capable of collecting, storing, and analyzing large security data in near real-time. It can easily run complicated correlations involving large chunks of data. It can manage extensive sources of data, from user activities and network events to application logs.
This analytics-based security protocol is further augmented by external threat intelligence and supplementary context data. This is different from conventional IT security software: these tools create a few security alerts ranked per severity.
Enhanced with added forensic details, they simplify the entire security analysis. These tools also allow users to quickly detect and mitigate cyberattacks.
Relevant data not collected%
Inadequate analytical know-how in the company%
Lack of resources/investment%
Lack of awareness in the organization%
Source: Kuppingercole & BARCDesigned by
Recently, GDPR compliance helped bring order to the handling of data across the world. It had compelled businesses and organizations to prioritize data governance. Its May 2018 implementation was done so quickly that even up to now, many are still yet to fully comply.
This groundbreaking law has vastly improved consumer data protection. By 2020, another major internet-related law will be unveiled: the California Consumer Privacy Act (CCPA).
Along with GPDR, this new policy will further elevate business compliance on data security, consumer profiling, and data handling. The pressure will not only focus on the compliance aspect. It will also be on how this new law will affect business operations.
By January 2020, CCPA provides consumers with comprehensive control over their personal data. It is the most sweeping new privacy law in the US by far.
Once passed, CCPA will give California consumers broader rights to protecting their personal information. These include the legal prerogative to:
Further, this law gives them the right against discrimination when using such rights. Businesses will be given up to 45 days to act on such requests from consumers.
It’s best to prepare now for this upcoming law. Non-compliance in this age of social media brings businesses too much negative impact. Before it gets enacted, deploying compliance management software is a good first step.
Everything is connected. Each element of the physical and virtual worlds is related to the other elements.
These connections are complicated, inexplicit, and challenging to identify. Thanks to graph analytics, we can now create a clear structure that can represent the relationship using lines and dots.
This type of innovative analytics makes something highly complicated, easier to understand, and use. It makes typically complex activities like analyzing big data to result in visually engaging outcomes.
Also called graph database, this tool leverages graphs for analyzing, codifying, and visualizing data or devices. Graph database emerges as a critical trend to replace the old relational database.
Companies using graph analytics have managed to considerably improve business decision making. They’re able to draw insights from the interrelationship of data. In the process, they’re able to achieve cost and time savings.
Graph analytics usage will grow 100% every year until 2022. You need robust data visualization software to take advantage of this trend.
Data has become the new oil that powers today’s digital economy. It propels the engines that run businesses and industries. This ongoing shift to data will expectedly bring in some growing pains for many organizations.
Investments in analytics will continue to increase. Businesses will have to make considerable adjustments to experience the returns they’re expecting. They will encounter challenges as they scale their analytics use across the organization.
The above analytics trends indicate that the business world is quickly evolving to become data-centric. Be it automation, AI, IoT, or new privacy regulations, knowing these trends is crucial.
To help you sustain your success in the coming years, it’s also great to know today’s relevant analytics statistics.
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