By data Statista, worldwide data will increase to 94 zettabytes by 2022. At the same time, companies will be able to earn up to $ 264 billion on the use of big data systems and business intelligence.
The data market is growing rapidly, the number of offers is increasing – in these conditions, it is extremely important for a business to choose the most effective analytical system. Pavel Dubinin, Yandex DataLens service development manager of the Yandex.Cloud cloud platform, talks about the main trends in data analysis and visualization.
Analytics and data visualization trends in 2021Evgenia Hrisanfova
How analytics is changing today
A business is a living organism that can carry out several million operations a day every day: selling, buying, producing products, interacting with partners, hiring, launching advertising. To maximize the benefits, each action needs to be analyzed and the data presented in a format that is understandable to the end user.
To effectively use big data, both small businesses and large companies open departments, test various types of analytical technologies, and experiment with new tools. With the pandemic, the growth rate of such experiments has only increased.
Let’s look at several trends in analytics and data visualization from Gartner reports and BARC Data, BI and Analytics Trend Monitor 2021.
Create and Improve New Analytics Methods Using AI
Artificial intelligence and machine learning technologies have formed the basis of many promising analytical tools in 2020. Advanced analytics allows you to optimize existing business processes, as well as generate new insights that are already available not only to big data specialists, but also to heads of companies and related employees. For example, over the past few years, using advanced analytics, banks have identified the propensity of young people under 35 to asset management and wealth planning products.
Predictive analytics (forecasting based on historical data) also remains one of the most promising areas of business intelligence. With its help, companies, for example, calculate the effectiveness of advertising campaigns or predict the issuance of loans.
Not only methods of data analysis continue to develop, but also approaches to collecting and processing information. Only structured data in tabular form becomes insufficient. Analytics of unstructured texts, video and audio materials are of increasing value. To move to the next level of business intelligence within companies, structured data types are effectively enriched with unstructured sources.
For a more effective implementation of X-analytics in organizations, official trading platforms will develop – data exchanges, where companies from different fields will be able to sell and buy data. However, in the future there is still a lot of work to regulate such sites.
Organizations increasingly strive to keep data up to date and updated in real time. Information usually goes a long way to get into a report. The user sees outdated data. A perfect report shows the events happening right now – this gives the business a huge advantage in the speed of reaction to change.
Democratizing Business Intelligence Tools and Moving to the Cloud
Analytical tools will become more personalized and easier to learn. Simplification of implementation and use leads to widespread adoption of self-service analytics tools, or self-service analytics… And thanks to the cloud, access to the most modern technologies for any organization becomes available in just a few clicks.
Why cloud analytics has become one of the main trends
For many companies, security is becoming a barrier to migrating or moving individual IT systems to the cloud. However, in practice, there are cases when strict information security requirements are precisely the reason for the use of cloud technologies.
No company is isolated from the outside world. For effective business development, analytics and decision making, you need to use not only your own, but external data sources – for example, market data, information from trackers of mobile applications and websites, data from partners and external APIs. A large number of data streams from outside, especially highly loaded and real-time, can be a serious risk from the point of view of information security of an organization. Therefore, often, in order not to give access to the inside, to the corporate network, to such a number of sources, it is easier to make data storage and business intelligence in the cloud.
Example: One of the largest Russian banks has come to a compromise solution, combining internal and cloud analytics. An “intermediate mart” was created in the cloud for external data, where preliminary cleaning, data verification, and virus checking are carried out. Then, through secure channels, the company loads only the necessary units into its perimeter.
Example Another large bank showed that for effective product analytics of a mobile bank, it is more convenient to organize external storage and self-service analytics for business users in the cloud than to receive data from a mobile application tracker in corporate BI with a significant delay.
Cloud analytics are also used to accelerate time-to-market, fast hypothesis testing and ad hoc analytics.… You can quickly prototype a solution, develop it iteratively, gradually increasing resources and connecting additional sources.
Example: The team at a small restaurant rental charger startup tested several new hypotheses every week. Data structures and sources were regularly changed, the number of external devices and partners grew. Analytics was required everywhere and everywhere, for example, to determine the optimal locations for business development.
These companies are focused on business outcomes. They need a convenient cloud service for analytics, there is no time and resources to create a self-written solution, to configure and support servers.
Also, cloud analytics is often used to provide reporting to external partners and contractors: suppliers, distributors, manufacturers, and so on. In the cloud, you can configure the required data mart and dashboards so that each partner sees the actual data only for himself.
Example: One large retailer has more than 400 suppliers who regularly need to display inventory balances and marketing analytics. By setting up cloud BI, a company can share analytics with partners in real time, securely and in a controlled manner, without manual distribution of Excel documents and complex licensing of external users.
Despite the fact that we see a lot of specific “cloud” scenarios for analytics, many companies also build classic regulated BI with dashboard reports in the cloud – because of the simplicity, reliability, security and economics of the solution.
Example: One of the Russian manufacturers of meat products has built an automatic data collection and end-to-end analytics system for all business subject areas: from suppliers and production to sales and marketing. A set of dashboards with a wide range of settings and filters has been created for each division of the company. The data obtained is used for both operational meetings and reporting on the results of work for the period.
Things to remember when choosing an analytics platform
- It is important to look not only at the visualization tool, but also at the data storage and processing subsystem. A solution from a single source will ensure a more stable and efficient operation.
- Choose a turnkey platform from a trusted vendor with a broad partner ecosystem. An integrator partner will help with implementation and initial configuration, and subsequent development and support can be done by the customer or another partner.
- Consider who will use the analytics and how. The complexity of the tool often contradicts the massiveness of implementation. Turn to services where ordinary employees without special skills will be able to fully work with reports and visualizations and independently modify them if necessary.
- You shouldn’t chase the exoticism of new tools, marketing promises, the number of possible features and types of charts. Try on a solution for your task, do not strive for maximum functionality. As practice shows, for business intelligence tasks, the most popular and understandable are the usual columnar and linear visualizations, tabular forms. It is better to transfer work with complex infographics to designers.
Source: RB.RU by rb.ru.
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