According to scientific estimates, the brain of a human being makes over 35 thousand decisions daily. These are actions that need instant decisions to help an individual function normally. They are decisions such as waking up, keeping radio volume high or low, going to work, clothes to wear, etc.
In the same way, businesses need to make decisions based on big data collected from multiple sources. They need to make real-time decisions such as whether to call off a deal, make purchases, launch a marketing campaign, or change products. Businesses rely on real-time data analytics to make these decisions.
Storing real-time data for real-time analytics
For companies to make real-time decisions, the data they rely on must be available in real-time. One of the technologies that make this possible is the operational data store which stores operational data for immediate access.
The technology is used to collect transactional data from multiple sources and consolidate it into one place for a short time. That means data stored on ODS is updated all the time such that what is available is only the latest data. This data is very important when a company needs to make fast decisions because it is what can be called green data or the latest data.
Real-time analytics means a business can receive fresh data and use it to process queries on the spot. The purpose of the immediate processing of fresh data is to help a business make fast decisions on matters that might affect it positively or negatively.
In its application, real-time defines a time within which the data is received, processed, and a decision made, which should be in a matter of seconds or very few minutes from the time the fresh data arrives.
Why data lakes are important in the real-time data processing
Every fast-growing business requires advanced document management methods to prevent document loss or mishandling which can translate into huge losses. One of the solutions required is larger storage, also known as a data lake.
It is a central repository that allows businesses to store their data in either a structured or unstructured form. A data lake can accommodate data of any quantity without changing its original form. In other words, a data lake stores data in its row status that can be accessed as it is and processed for analytics in real-time.
Businesses need data lakes in real-time decision-making processes because the data is available as it is and can be used in machine learning analytics where raw data from log files, social media, various internet-enabled devices, and clickstreams are needed to help a business make quick decisions.
This kind of data is important when a business needs to act fast when a business opportunity presents itself. Such an opportunity can help a business make a profit, retain clients, increase productivity, and make informed decisions.
Realtime data driving real-time strategic decisions
Before big data technology was developed, businesses relied on internal data stored in their systems. As business trends change, a newer need to include external data in a business decision-making process evolved too.
To make this need a reality, different technologies were developed to help companies harvest data from both internal and external sources. Some of the technologies developed were machine learning, AI, and streaming analytics. Through these technologies, businesses can process and act on analytics generated as data flows in. Even with these available technologies, businesses still need a storage solution that can hold real-time data for processing. The solution behind this need is real-time data lakes. It centralizes data gathered from multiple sources into cloud storage.
As the data increases, it creates a cloud data lake containing a large volume of raw data. Data extraction from a data lake can involve any type of data. It can be data from social media, logs from servers, sensors, etc. This can be slightly different from a data warehouse that uses ODS to extract transactional data. Using this technology, some 500 fortune companies are processing data to the tune of over one million events every second. These companies rely on big real-time data to understand fast-changing trends in the market.
An oil and petroleum company needs to study and understand the market globally as oil demand fluctuates. They can quickly adjust prices, extract more crude oil, and know which countries to target.
Using data lakes as a solution to real-time data analytics
Primarily, most companies choose to store data in data warehouses. When there is a need to store bigger volumes of data, a data lake becomes a solution. In a physical sense, a data lake can be likened to a real lake. It is bigger than a dam, pond, or river.
Because it’s big, it can accommodate bigger volumes of data, but a company must provide advanced technologies to retrieve it, process, and analyze it for real-time business decisions. Data warehouses can hold big data from ODS, but a data lake requires more than an ODS because its data is from various sources. To realize its full potential, its data needs to be accelerated, extracted without affecting sources, and create data pipelines in seconds.
In a business environment where self-service has become important in saving processing costs, businesses are integrating several technologies to help manage and govern data more effectively.
How real-time data lakes are making it possible for fast decision-making processes
The secret behind real-time data is the execution speed during collection and analysis. Data must be collected super-fast because if the process is too slow, a business will lose many business opportunities. The technology used in data lakes data processing makes it possible to process data at a speed of a million events or more per second.
Customer engagement is another important point. Using machine learning, big data is helping predict customer engagement in various business fields. A business can know if a customer will likely engage them again and create opportunities for them to do so. Finally, intelligent data offers newer data experiences. Instead of a business processing terabytes of data, some of which it might never need, technology is helping them process only the data they need at the moment.