The new thing of the future is data science, which is guaranteed to be the hottest thing in the next few years. If you wish to become a data scientist, you can enroll in our master’s program in data science. Data Analysis has found a niche in many professional sectors to the point of becoming a fundamental tool in our daily lives. But do we really know how to face all the challenges that Big Data and data analysis entail?
The year 2021 has been the year of the consolidation of the data scientist and data analyst profiles, two profiles that aim to obtain the maximum performance of the data to generate a positive impact in any type of organization. Both profiles share objectives but from different approaches, the data scientist to clean and filter the data, and the data analytics to extract information from the data related to the business. The work of both and the support of the management are essential to become a true Data Driven company. For all this, in this article, we point out the Big Data trends for 2022 so that you do not miss a detail. Do not miss it.
Big Data and Analytics Trends 2022
Before starting, I would like to highlight a phrase to understand the magnitude of value that Big Data possesses: “ Data is ‘the new oil’, it is becoming a key piece of society and the economy”.
This is so because data is the new value to be managed by organizations of all kinds. Companies are looking for capabilities in terms of data capture, storage, and processing, and those that achieve it will have achieved an advantage over their competition, it is what is called Analytical Advantage. Those companies that achieve the desired analytical advantages will be able to say, then, that they are true Data-Driven companies, companies focused on the value of data.
Data Strategy: the data strategy integrated into the growth strategy of companies
Until now the data strategy was formulated independently by the IT or Data teams within the organizations and created to add to the global strategy of the companies, the course seems to have changed, and the companies are already introducing data in their primary strategies as part of the core business.
Therefore, in 2022 and beyond we will see how the strategic plans of many organizations will include clear elements of Data Strategy. In fact, we have to add that the vast majority of companies’ digital transformation strategies are based on a data strategy. One more sign that it is one of the key trends of Big Data.
Evolution of augmented analytics
Many experts agree that 2022 will be the year in which Big Data will achieve considerable technological evolution, but we still have a long way to go to see everything that Big Data can do for us. Without a doubt, it will be a change in the economic and social context.
With augmented analytics, we will see the appearance of more important knowledge or changes that will help businesses to optimize decision-making.
For all this, another of the Big Data trends is augmented analytics. Augmented analytics uses machine learning and artificial intelligence to improve data analysis by finding a new method of creating, developing, and sharing data analysis. Many enterprise customers prefer augmented analytics to traditional analytics to reduce human error and bias.
By 2022, augmented analytics will be a dominant driver of new technology tools that make the data analytics process itself simple and accessible to most profiles in an organization. These tools will try to democratize data science and machine learning to an entire organization.
Artificial Intelligence, the key to decision making
Another keyword when we talk about technological advances in Artificial Intelligence, which is making it possible to be faster and more precise when making strategic decisions in many business sectors. What we will certainly see is how the rise of digital technologies, lower-cost data storage, high-performance hardware, and embedded software will drive change in both large and small businesses.
Companies that adopt AI as part of their business processes will be more and more. It is logical, since the advantages offered by this technology are many at different levels, such as, for example, at the level of processes, the creation of new business models, interaction with the client, and even interaction between the people of a company.
Data as a Service
Data as a service uses cloud technology to give users and applications access to information on demand without depending on where users or applications may be. It is one of the current trends of Big Data.
In-memory computing is data that is stored in a new level of memory that is situated between NAND flash memory and dynamic random access memory. This provides much faster memory that can support high-performance workloads for advanced data analytics in enterprises.
Data Lakehouse: Beyond the Data Lake
We continue with more Big Data Trends. Currently, many of the large companies have one or several Data Lakes and one or several Data warehouses. Probably most also separate the use cases in cases of AI (Artificial Intelligence) or Data Science from the cases of BI. The most common is that Data Lakes are used for the former and Data warehouses are used for the latter.
Unfortunately, the worlds of BI and Big Data are still separate. Mainly, because the way to answer business questions is different:
BI: The questions are known by those of us who model the data to get answers to those questions
Big Data: We don’t know the questions so we analyze the data looking for that golden nugget.
Fortunately, the industry is moving to more holistic models. Specifically, data architectures are coming together in platforms that can extract the best of both worlds by favoring synergies between BI and Big Data.
Fundamental Characteristics of a Data Lakehouse
“This change has to continue to satisfy the needs for speed, performance, quality, and accuracy that large companies continue to require,”. He tells us that The idea of a Data Lakehouse is as follows:
Could a Datawarehouse be created on top of a cheap distributed storage system, without losing system performance and still covering most enterprise use cases?
The answer is: Yes. And, in essence, a Data Lakehouse is a Datawarehouse that has a Data Lake as data storage. The idea is simple, but it hides a great technical complexity that many companies have been able to translate into reality: Databricks, Snowflake, Microsoft…
The fundamental characteristics of a Data Lakehouse are:
- a) ACID support
- b) Schema and metadata management
- c) Connectivity with BI tools
- d) Storage decoupled from processing
- e) Open Source storage formats (parquet)
- f) Support for structured and unstructured data
- g) Support for different use cases: Machine learning, reporting/dashboarding, ETL
- h) Ability to manage data in real-time/streaming
Lakehouse is a new paradigm that radically simplifies enterprise data infrastructure. Also, it accelerates innovation in an era where Machine Learning is poised to revolutionize all industries. Especially, mixing all kinds of data, including those:
- a) Internal
- b) External
- c) Structured
- d) Unstructured
Big Data and Climate Change
Climate change may not be a new topic for scientists, but harnessing Big Data to combat it may be mainstream in 2022. In fact, it is believed that harnessing Big Data can help us understand the current state of climate change. Not only that, but even data from meteorological research, earth sciences, ocean research, and even nuclear research facilities are stipulated to help us understand climate change and other environmental conditions related to the planet.
Natural language processing (NLP) and conversational analysis.
Just as search interfaces like Google made the Internet accessible to everyday consumers, NLP provides an easier way to ask questions about data and get more accurate information. Conversation analytics takes the concept of NLP a step further by allowing such questions to be asked and answered verbally rather than through text.
Another big data trend for 2022 is reported to be NLP and conversational analytics driving analytics and business intelligence adoption from 35% of employees to over 50%, including new classes of users.
The voice seems that it will be able to impose itself as the main channel of interaction between machines and people, and in a certain way it makes perfect sense. Technology must be transparent to people in its use, and therefore, it must be natural. What is more natural than using the voice to communicate? This technology is in very immature phases but it is only a matter of time.
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Connected data platforms
Companies’ data platforms cannot remain isolated from other organizations within the sector’s own value chain or the industry. “The trend is towards the interconnection of data platforms within an ecosystem that allows greater value to be added to the customer experience as a whole, and not in isolation,”. This trend, which already existed in previous years, is expected to be strengthened by 2022 thanks to the use of Cloud Computing. This will allow developing advanced analytics actions with lower costs. Big tech companies like Amazon, Google, and Microsoft are investing heavily in their cloud platforms to enable this type of advanced computing on data.
Another trend in Big Data is Active Intelligence. The current processes that companies have to migrate data from its different sources to the catalog continue to be a great challenge for organizations. “Companies that aim to be Data-Driven, and that have gone through the first phases of data collection and transformation, must continue their evolution towards Active Intelligence processes.
Metaverse and Data Analytics
The metaverse could not be missing from the list of Big Data trends. The bet of Meta, formerly Facebook, for the creation of a metaverse as a digital space for human interaction creates a new world of opportunities for data analytics. If until now the geolocation of a person can only be carried out under the authorization of a judge, in the metaverse there would be no such restriction. This opens a new line of analysis of geolocated data in metaverses.
The negative part of this new behavioral analysis opportunity will again be who will be able to carry out this type of action. Questions also arise about the objectives and whether the user could refuse to use their navigation data in the metaverse.
More and more external data sources
It is observed how companies use data external to their organizations more frequently to improve their data models and their analytical capacity. It is expected that in 2022 organizations will increase the use of external data due to a greater offer of existing Open Data. Also by private and anonymous data platforms.
These external data are often largely scattered. Typically multi-source and probabilistic. However, when used well, they will be invaluable for companies that know how to use them efficiently.
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