CIO Research ranked analytics as the top priority after surveying 255+ executives globally in July 2020. This means the pandemic has affected how organizations are treating data, analytics, and decision-making. Of course, positively.
Being the top choice for 36% of the respondents, analytics is seeing many reforming trends. Many new and a few old—with a revamp, like graphs—have emerged as the most coveted analytics implementations, use-cases, and trends.
Source: CIO Research
Let’s take a look at the most talked-about and many of Gartner’s favorite analytics trends.
Analytics Trends that will Shape 2021 and Beyond
1. Rise of Stories
Dashboards are going out of fashion fast. Stories have replaced them now. Yes, data-driven stories—or data storytelling—give a focused insight into one aspect of data.
Dashboards are complex to understand and difficult to analyze for users who aren’t tech-savvy. Data stories generate personalized insights for people of all skills-sets every day. Such stories turn data into information anyone can read, ruling out the need for people asking questions to get their answers. Because what if people don’t know what to ask for?
Data stories bring answers you have been looking for without you knowing your questions.
Imagine it as a news feed when you think of data stories. Gartner predicts that data stories would capture the analytics arena by 2025, and ADM (read further) would generate 75% of these stories.
2. Augmented Data Management
Although augmented data management (ADM) is related more to the backend side of the data world, we can’t discuss it here.
Data prep tasks like analysis, cleansing, profiling, and integration of data are tiring tasks that consume specialists’ time. But not when you bring in AI and ML models into the backend.
ADM enables people without expertise in data science and engineering—citizen data scientists—to handle data management and preparation tasks.
According to AlliedMarketResearch, ADM or augmented analytics will grow with a CAGR of 28.4% from 2018 to 2025.
The role of the cloud in analytics needs no explanation. With companies moving data to the cloud for flexibility, scalability, cost-saving, and ease in recovery, the cloud is strengthening its position in the analytics world every passing year.
Not only are the pay-as-you-go models beneficial for testing new products and innovations for startups and enterprises equally, but the cloud also offers the ease of leveraging AI/ML in analytics.
By 2022, 90% of analytics and data-based solutions would need the public cloud. With the availability of data lakes and data fabric (read next), the cloud makes managing voluminous data easy and affordable.
4. Data Fabric
Data fabric, simply put, is an architecture that unifies data from disparate sources on hybrid and multi-cloud systems. Consider data fabric as a layer above your data lake, which leverages data modelling, governance, and infrastructure.
With data lakes mostly serving as a swamp for dumping data, transactional data for various departments/domains get a hit in the self-service model. Data fabric helps in solving this problem and targets business goals in solving issues with siloed data.
Also, data fabric enables the implementation of artificial intelligence and automation smoothly. Data fabric reduces the time in integration and deployment by 1/3 and maintenance by 2/3 approximately.
5. Autonomous Databases
An autonomous database is a self-healing and self-repairing data platform with AI-led capabilities that can monitor databases, discover issues and self-heal (or repair) a data management system.
AIOps or AI-enabled operations are at the core of an autonomous database. To detect anomalies and remedy them with corrective steps, the AI algorithm keeps monitoring logs, various parameters like threshold values, and other metadata to determine the corrective actions.
For example, ML programs hit the correct log files and analyze them to handle anomalies. Checking the regular health of a database also avoids its collapse. Standard routine maintenance like data compression, archiving, and backing up data with AI and ML manages data storage efficiently.
DevOps is creating much stir in the IT world by revolutionizing development and operation processes and bringing them together. DataOps, based on similar guidelines, is an agile-based development and delivery methodology.
For data-driven organizations, DataOps brings the analytics world and its experts—data scientists and engineers—with DevOps specialists. DataOps facilitate rapid innovation and continuous delivery while offering insights to the users at velocity. Meanwhile, the methodology reduces data errors and improves quality.
Continuous analytics and data processing brings in fresher data faster and offers immediate insights.
7. Graph Analytics
Graph analytics analyzes relationships among people, places, and things. In crux, this technology focuses on relationships between data points.
Although not the latest trend in analytics, data experts and organizations are exploring various innovative and new use cases in this area. Problems related to ride-sharing companies seek a lot of solutions in this technology. Other applications like customer buying behaviours and location-hoping by consumers use this tech nowadays.
Graph technologies are predicted to contribute and influence decisions in 30% of organizations by 2023.
8. Decision Intelligence
Decision intelligence encapsulates various methods and technologies that augment decision-making processes. In simple words, decision intelligence helps you to make better decisions by knowing the impact of those choices in the future. This stream of analytics emerged in 2012 as a part of Qunatellia’s research.
Decision intelligence proves fruitful for an organization. More importantly, it has the power to assess the impact of decisions about more global issues.
9. Data Security, Privacy, and Governance
Data breaches and privacy concerns are not new in the data world. So is not data governance. But these are complex tasks and need much manual intervention.
AI and ML models automate data classification and tagging under various categories of privacy and sensitivity like personal information and business information. This helps in affirming regulations like GDPR and HIPPA.
AI works 24x7 on data and prevents security breaches and threats to a great length. Automating two-step authentication and user access and security management on applications help strengthen security features.
Microsoft is an excellent example to use machine learning to comply with their SOX requirement by detecting anomalies and reducing approval hassles in their royalty statements by 50%.
10. Data-driven culture
Democratizing data at each level of their org-structure has become a motto for companies after the pandemic started and work shifted to WFH culture. And to make this motto a success, data literacy is equally important to achieve data-driven culture.
To facilitate and establish a data-driven culture, a CDO—chief data officer—role needs to be created in the organizations, if not already. And a CDO should nurture the thought of data as an asset and not a by-product, quotes Gartner.
A CDO should analyze the 3 areas to inculcate a data-driven culture: business value, cultural changes needs and impacts, and ethical implications of data and analytics
Which Trends Are You Rooting for?
Whether you are keen on leveraging graphs analytics or interested in decision intelligence, some tools and technologies that would impact your analytics strategy are AI, ML, cloud, business intelligence, and data analytics tools.
We at Logesys take pride in having the expertise of and dealing in these technologies and tools. Please contact us here should you seek any assistance for weeding out the hurdles in your analytics journey.